From b11d99f24fe94ee446f6bb72179897789cad134b Mon Sep 17 00:00:00 2001 From: Rijin Date: Wed, 8 Nov 2023 03:43:43 +0000 Subject: [PATCH] initial implementation framework; model prediction added to node main --- .../perception/camera_segmentation.Dockerfile | 42 +- profiles/docker-compose.perception.yaml | 22 +- .../camera_segmentation/__init__.py | 9 + .../camera_segmentation_node.py | 111 +- .../camera_segmentation/config.py | 239 + .../Base-ADE20K-UnifiedSegmentation.yaml | 68 + .../ade20k/oneformer_R50_bs16_160k.yaml | 58 + ...ormer_dinat_large_IN21k_384_bs16_160k.yaml | 42 + ...former_swin_large_IN21k_384_bs16_160k.yaml | 40 + .../Base-Cityscapes-UnifiedSegmentation.yaml | 68 + .../cityscapes/oneformer_R50_bs16_90k.yaml | 59 + .../oneformer_dinat_large_bs16_90k.yaml | 22 + ...eformer_swin_large_IN21k_384_bs16_90k.yaml | 20 + .../coco/Base-COCO-UnifiedSegmentation.yaml | 54 + .../configs/coco/oneformer_R50_bs16_50ep.yaml | 59 + .../oneformer_dinat_large_bs16_100ep.yaml | 22 + 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src/perception/camera_segmentation/requirements.txt diff --git a/docker/perception/camera_segmentation.Dockerfile b/docker/perception/camera_segmentation.Dockerfile index 7c59a6ab..0ebe0eb6 100644 --- a/docker/perception/camera_segmentation.Dockerfile +++ b/docker/perception/camera_segmentation.Dockerfile @@ -1,11 +1,34 @@ # ================= Dependencies =================== -FROM ros:humble AS base +FROM leungjch/cuda118-tensorrt-base:latest as base -# ADD DEPENDENCIES HERE +# Segmentation Dependencies +# Install essential packages and dependencies +RUN sudo apt-get update && sudo apt-get install -y \ + git \ + make build-essential libssl-dev zlib1g-dev \ + libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \ + libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev \ + ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx \ + && sudo rm -rf /var/lib/apt/lists/* + +RUN curl https://pyenv.run | bash + +ENV PATH="/root/.pyenv/bin:$PATH" +ENV PYENV_ROOT="/root/.pyenv" + +SHELL ["/bin/bash", "-c"] +# RUN source "$HOME/.bashrc" && pyenv install 3.8.15 && pyenv global 3.8.15 +SHELL ["/bin/sh", "-c"] + +RUN sudo apt-get install -y python3-pip + +RUN pip3 install --no-cache-dir --upgrade pip setuptools wheel +RUN pip3 install ninja +RUN pip install --no-cache-dir --upgrade requests urllib3 # fix user permissions when deving in container -COPY docker/fixuid_setup.sh /project/fixuid_setup.sh -RUN /project/fixuid_setup.sh +# COPY docker/fixuid_setup.sh /project/fixuid_setup.sh +# RUN /project/fixuid_setup.sh USER docker:docker ENV DEBIAN_FRONTEND noninteractive @@ -21,12 +44,17 @@ WORKDIR /home/docker/ament_ws/src COPY src/perception/camera_segmentation/ camera_segmentation COPY src/wato_msgs/sample_msgs sample_msgs +COPY src/perception/camera_segmentation/requirements.txt camera_segmentation/requirements.txt +RUN pip3 install --no-cache-dir --upgrade -r camera_segmentation/requirements.txt + WORKDIR /home/docker/ament_ws -RUN . /opt/ros/$ROS_DISTRO/setup.sh && \ +RUN . /opt/ros/$ROS_DISTRO/setup.bash && \ + sudo rosdep init && \ rosdep update && \ - rosdep install -i --from-path src --rosdistro $ROS_DISTRO -y && \ + rosdep install -i --from-path src \ + --rosdistro $ROS_DISTRO -y && \ colcon build \ - --cmake-args -DCMAKE_BUILD_TYPE=Release + --cmake-args -DCMAKE_BUILD_TYPE=Release -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda # Entrypoint will run before any CMD on launch. Sources ~/opt//setup.bash and ~/ament_ws/install/setup.bash COPY docker/wato_ros_entrypoint.sh /home/docker/wato_ros_entrypoint.sh diff --git a/profiles/docker-compose.perception.yaml b/profiles/docker-compose.perception.yaml index 6c247762..7aea39fb 100644 --- a/profiles/docker-compose.perception.yaml +++ b/profiles/docker-compose.perception.yaml @@ -1,16 +1,16 @@ version: "3.8" services: - radar_object_detection: - build: - context: .. - dockerfile: docker/perception/radar_object_detection.Dockerfile - cache_from: - - "${RADAR_OBJECT_DETECTION_IMAGE:?}:${TAG}" - - "${RADAR_OBJECT_DETECTION_IMAGE:?}:main" - image: "${RADAR_OBJECT_DETECTION_IMAGE:?}:${TAG}" - user: ${FIXUID:?}:${FIXGID:?} - volumes: - - ../src/perception/radar_object_detection:/home/docker/ament_ws/src/radar_object_detection + # radar_object_detection: + # build: + # context: .. + # dockerfile: docker/perception/radar_object_detection.Dockerfile + # cache_from: + # - "${RADAR_OBJECT_DETECTION_IMAGE:?}:${TAG}" + # - "${RADAR_OBJECT_DETECTION_IMAGE:?}:main" + # image: "${RADAR_OBJECT_DETECTION_IMAGE:?}:${TAG}" + # user: ${FIXUID:?}:${FIXGID:?} + # volumes: + # - ../src/perception/radar_object_detection:/home/docker/ament_ws/src/radar_object_detection camera_segmentation: build: context: .. diff --git a/src/perception/camera_segmentation/camera_segmentation/__init__.py b/src/perception/camera_segmentation/camera_segmentation/__init__.py index e69de29b..39ebcd38 100755 --- a/src/perception/camera_segmentation/camera_segmentation/__init__.py +++ b/src/perception/camera_segmentation/camera_segmentation/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import data # register all new datasets +from . import modeling + +# config +from .config import * + +# models +from .oneformer_model import OneFormer \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/camera_segmentation_node.py b/src/perception/camera_segmentation/camera_segmentation/camera_segmentation_node.py index b5dc7dfe..736ab77e 100755 --- a/src/perception/camera_segmentation/camera_segmentation/camera_segmentation_node.py +++ b/src/perception/camera_segmentation/camera_segmentation/camera_segmentation_node.py @@ -27,15 +27,122 @@ def image_callback(self): def main(args=None): - rclpy.init(args=args) + print("Hello World") - node = CameraSegmentationNode() + import detectron2 + from detectron2.utils.logger import setup_logger + setup_logger() + setup_logger(name="oneformer") + + # Import libraries + import numpy as np + import cv2 + import torch + import imutils + import os + import subprocess + from huggingface_hub import hf_hub_download + + # Import detectron2 utilities + from detectron2.config import get_cfg + from detectron2.projects.deeplab import add_deeplab_config + from detectron2.data import MetadataCatalog + from .demo.defaults import DefaultPredictor + from .demo.visualizer import Visualizer, ColorMode + + from .config import ( + add_oneformer_config, + add_common_config, + add_swin_config, + add_dinat_config, + add_convnext_config, + ) + + torch.zeros(1).cuda() + cpu_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + SWIN_CFG_DICT = {"cityscapes": "/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml",} + + DINAT_CFG_DICT = {"cityscapes": "/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml",} + + def setup_cfg(dataset, model_path, use_swin): + # load config from file and command-line arguments + cfg = get_cfg() + add_deeplab_config(cfg) + add_common_config(cfg) + add_swin_config(cfg) + add_dinat_config(cfg) + add_convnext_config(cfg) + add_oneformer_config(cfg) + if use_swin: + cfg_path = SWIN_CFG_DICT[dataset] + else: + cfg_path = DINAT_CFG_DICT[dataset] + cfg.merge_from_file(cfg_path) + cfg.MODEL.DEVICE = 'cuda' + cfg.MODEL.WEIGHTS = model_path + cfg.freeze() + return cfg + + def setup_modules(dataset, model_path, use_swin): + cfg = setup_cfg(dataset, model_path, use_swin) + predictor = DefaultPredictor(cfg) + metadata = MetadataCatalog.get( + cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused" + ) + if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]: + from cityscapesscripts.helpers.labels import labels + stuff_colors = [k.color for k in labels if k.trainId != 255] + metadata = metadata.set(stuff_colors=stuff_colors) + + return predictor, metadata + + def panoptic_run(img, predictor, metadata): + visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) + predictions = predictor(img, "panoptic") + panoptic_seg, segments_info = predictions["panoptic_seg"] + out = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(cpu_device), segments_info, alpha=0.5 + ) + return out + + def instance_run(img, predictor, metadata): + visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) + predictions = predictor(img, "instance") + instances = predictions["instances"].to(cpu_device) + out = visualizer.draw_instance_predictions(predictions=instances, alpha=0.5) + return out + def semantic_run(img, predictor, metadata): + visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE) + predictions = predictor(img, "semantic") + out = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=0.5 + ) + return out + + TASK_INFER = {"panoptic": panoptic_run, + "instance": instance_run, + "semantic": semantic_run} + + predictor, metadata = setup_modules("cityscapes", "250_16_dinat_l_oneformer_cityscapes_90k.pth", False) + + img = cv2.imread("/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/samples/test.png") + img = imutils.resize(img, width=512) + task = "panoptic" + out = TASK_INFER[task](img, predictor, metadata).get_image() + cv2.imwrite("/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/samples/test_results.png", out) + + print("At the end") + + rclpy.init(args=args) + node = CameraSegmentationNode() rclpy.spin(node) # Destroy the node explicitly # (optional - otherwise it will be done automatically # when the garbage collector destroys the node object) + node.destroy_node() rclpy.shutdown() diff --git a/src/perception/camera_segmentation/camera_segmentation/config.py b/src/perception/camera_segmentation/camera_segmentation/config.py new file mode 100755 index 00000000..78bc13fd --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/config.py @@ -0,0 +1,239 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.config import CfgNode as CN + +__all__ = ["add_common_config", "add_oneformer_config", "add_swin_config", + "add_dinat_config", "add_beit_adapter_config", "add_convnext_config"] + +def add_common_config(cfg): + """ + Add config for common configuration + """ + # data config + # select the dataset mapper + cfg.INPUT.DATASET_MAPPER_NAME = "oneformer_unified" + # Color augmentation + cfg.INPUT.COLOR_AUG_SSD = False + # We retry random cropping until no single category in semantic segmentation GT occupies more + # than `SINGLE_CATEGORY_MAX_AREA` part of the crop. + cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0 + # Pad image and segmentation GT in dataset mapper. + cfg.INPUT.SIZE_DIVISIBILITY = -1 + + cfg.INPUT.TASK_SEQ_LEN = 77 + cfg.INPUT.MAX_SEQ_LEN = 77 + + cfg.INPUT.TASK_PROB = CN() + cfg.INPUT.TASK_PROB.SEMANTIC = 0.33 + cfg.INPUT.TASK_PROB.INSTANCE = 0.66 + + # test dataset + cfg.DATASETS.TEST_PANOPTIC = ("",) + cfg.DATASETS.TEST_INSTANCE = ("",) + cfg.DATASETS.TEST_SEMANTIC = ("",) + + # solver config + # weight decay on embedding + cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0 + # optimizer + cfg.SOLVER.OPTIMIZER = "ADAMW" + cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1 + + # wandb + cfg.WANDB = CN() + cfg.WANDB.PROJECT = "unified_dense_recognition" + cfg.WANDB.NAME = None + + cfg.MODEL.IS_TRAIN = False + cfg.MODEL.IS_DEMO = True + + # text encoder config + cfg.MODEL.TEXT_ENCODER = CN() + + cfg.MODEL.TEXT_ENCODER.WIDTH = 256 + cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77 + cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12 + cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408 + cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2 + cfg.MODEL.TEXT_ENCODER.N_CTX = 16 + + # mask_former inference config + cfg.MODEL.TEST = CN() + cfg.MODEL.TEST.SEMANTIC_ON = True + cfg.MODEL.TEST.INSTANCE_ON = False + cfg.MODEL.TEST.PANOPTIC_ON = False + cfg.MODEL.TEST.DETECTION_ON = False + cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0 + cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0 + cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False + cfg.MODEL.TEST.TASK = "panoptic" + + # TEST AUG Slide + cfg.TEST.AUG.IS_SLIDE = False + cfg.TEST.AUG.CROP_SIZE = (640, 640) + cfg.TEST.AUG.STRIDE = (426, 426) + cfg.TEST.AUG.SCALE = (2048, 640) + cfg.TEST.AUG.SETR_MULTI_SCALE = True + cfg.TEST.AUG.KEEP_RATIO = True + cfg.TEST.AUG.SIZE_DIVISOR = 32 + + # pixel decoder config + cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256 + # adding transformer in pixel decoder + cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0 + # pixel decoder + cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder" + cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256 + cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256 + + # LSJ aug + cfg.INPUT.IMAGE_SIZE = 1024 + cfg.INPUT.MIN_SCALE = 0.1 + cfg.INPUT.MAX_SCALE = 2.0 + + # MSDeformAttn encoder configs + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"] + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4 + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8 + +def add_oneformer_config(cfg): + """ + Add config for ONE_FORMER. + """ + + # mask_former model config + cfg.MODEL.ONE_FORMER = CN() + + # loss + cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True + cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1 + cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0 + cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0 + cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0 + cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5 + cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07 + + # transformer config + cfg.MODEL.ONE_FORMER.NHEADS = 8 + cfg.MODEL.ONE_FORMER.DROPOUT = 0.1 + cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048 + cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0 + cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2 + cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6 + cfg.MODEL.ONE_FORMER.PRE_NORM = False + + cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256 + cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120 + cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16 + cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True + + cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = "res5" + cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False + + # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet) + # you can use this config to override + cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32 + + # transformer module + cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = "ContrastiveMultiScaleMaskedTransformerDecoder" + + # point loss configs + # Number of points sampled during training for a mask point head. + cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112 + # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the + # original paper. + cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0 + # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in + # the original paper. + cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75 + +def add_swin_config(cfg): + """ + Add config forSWIN Backbone. + """ + + # swin transformer backbone + cfg.MODEL.SWIN = CN() + cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224 + cfg.MODEL.SWIN.PATCH_SIZE = 4 + cfg.MODEL.SWIN.EMBED_DIM = 96 + cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2] + cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24] + cfg.MODEL.SWIN.WINDOW_SIZE = 7 + cfg.MODEL.SWIN.MLP_RATIO = 4.0 + cfg.MODEL.SWIN.QKV_BIAS = True + cfg.MODEL.SWIN.QK_SCALE = None + cfg.MODEL.SWIN.DROP_RATE = 0.0 + cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0 + cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3 + cfg.MODEL.SWIN.APE = False + cfg.MODEL.SWIN.PATCH_NORM = True + cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"] + cfg.MODEL.SWIN.USE_CHECKPOINT = False + ## Semask additions + cfg.MODEL.SWIN.SEM_WINDOW_SIZE = 7 + cfg.MODEL.SWIN.NUM_SEM_BLOCKS = 1 + +def add_dinat_config(cfg): + """ + Add config for NAT Backbone. + """ + + # DINAT transformer backbone + cfg.MODEL.DiNAT = CN() + cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5] + cfg.MODEL.DiNAT.OUT_FEATURES = ["res2", "res3", "res4", "res5"] + cfg.MODEL.DiNAT.EMBED_DIM = 64 + cfg.MODEL.DiNAT.MLP_RATIO = 3.0 + cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16] + cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2 + cfg.MODEL.DiNAT.KERNEL_SIZE = 7 + cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]] + cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3) + cfg.MODEL.DiNAT.QKV_BIAS = True + cfg.MODEL.DiNAT.QK_SCALE = None + cfg.MODEL.DiNAT.DROP_RATE = 0 + cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0. + cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4 + +def add_convnext_config(cfg): + """ + Add config for ConvNeXt Backbone. + """ + + # swin transformer backbone + cfg.MODEL.CONVNEXT = CN() + cfg.MODEL.CONVNEXT.IN_CHANNELS = 3 + cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3] + cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536] + cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4 + cfg.MODEL.CONVNEXT.LSIT = 1.0 + cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3] + cfg.MODEL.CONVNEXT.OUT_FEATURES = ["res2", "res3", "res4", "res5"] + +def add_beit_adapter_config(cfg): + """ + Add config for BEiT Adapter Backbone. + """ + + # beit adapter backbone + cfg.MODEL.BEiTAdapter = CN() + cfg.MODEL.BEiTAdapter.IMG_SIZE = 640 + cfg.MODEL.BEiTAdapter.PATCH_SIZE = 16 + cfg.MODEL.BEiTAdapter.EMBED_DIM = 1024 + cfg.MODEL.BEiTAdapter.DEPTH = 24 + cfg.MODEL.BEiTAdapter.NUM_HEADS = 16 + cfg.MODEL.BEiTAdapter.MLP_RATIO = 4 + cfg.MODEL.BEiTAdapter.QKV_BIAS = True + cfg.MODEL.BEiTAdapter.USE_ABS_POS_EMB = False + cfg.MODEL.BEiTAdapter.USE_REL_POS_BIAS = True + cfg.MODEL.BEiTAdapter.INIT_VALUES = 1e-6 + cfg.MODEL.BEiTAdapter.DROP_PATH_RATE = 0.3 + cfg.MODEL.BEiTAdapter.CONV_INPLANE = 64 + cfg.MODEL.BEiTAdapter.N_POINTS = 4 + cfg.MODEL.BEiTAdapter.DEFORM_NUM_HEADS = 16 + cfg.MODEL.BEiTAdapter.CFFN_RATIO = 0.25 + cfg.MODEL.BEiTAdapter.DEFORM_RATIO = 0.5 + cfg.MODEL.BEiTAdapter.WITH_CP = True + cfg.MODEL.BEiTAdapter.INTERACTION_INDEXES=[[0, 5], [6, 11], [12, 17], [18, 23]] + cfg.MODEL.BEiTAdapter.OUT_FEATURES = ["res2", "res3", "res4", "res5"] \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml new file mode 100755 index 00000000..31eab45b --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml @@ -0,0 +1,68 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ade20k_panoptic_train",) + TEST_PANOPTIC: ("ade20k_panoptic_val",) + TEST_INSTANCE: ("ade20k_instance_val",) + TEST_SEMANTIC: ("ade20k_sem_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 512 + MAX_SIZE_TRAIN: 2048 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 512) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 512 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "oneformer_unified" + MAX_SEQ_LEN: 77 + TASK_SEQ_LEN: 77 + TASK_PROB: + SEMANTIC: 0.33 + INSTANCE: 0.66 +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [256, 384, 512, 640, 768, 896] + MAX_SIZE: 3584 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_R50_bs16_160k.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_R50_bs16_160k.yaml new file mode 100755 index 00000000..770ffc81 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_R50_bs16_160k.yaml @@ -0,0 +1,58 @@ +_BASE_: Base-ADE20K-UnifiedSegmentation.yaml +MODEL: + META_ARCHITECTURE: "OneFormer" + SEM_SEG_HEAD: + NAME: "OneFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 150 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + ONE_FORMER: + TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + CONTRASTIVE_WEIGHT: 0.5 + CONTRASTIVE_TEMPERATURE: 0.07 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 150 + USE_TASK_NORM: True + NHEADS: 8 + DROPOUT: 0.1 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + CLASS_DEC_LAYERS: 2 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEXT_ENCODER: + WIDTH: 256 + CONTEXT_LENGTH: 77 + NUM_LAYERS: 6 + VOCAB_SIZE: 49408 + PROJ_NUM_LAYERS: 2 + N_CTX: 16 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 + TASK: "panoptic" +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml new file mode 100755 index 00000000..d5fa95be --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml @@ -0,0 +1,42 @@ +_BASE_: oneformer_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2DiNAT" + DiNAT: + EMBED_DIM: 192 + MLP_RATIO: 2.0 + DEPTHS: [3, 4, 18, 5] + NUM_HEADS: [6, 12, 24, 48] + KERNEL_SIZE: 11 + DROP_PATH_RATE: 0.3 + DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]] + WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 250 +SOLVER: + AMP: + ENABLED: False +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" +TEST: + DETECTIONS_PER_IMAGE: 250 + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml new file mode 100755 index 00000000..69c44ade --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml @@ -0,0 +1,40 @@ +_BASE_: oneformer_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 250 +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" +TEST: + DETECTIONS_PER_IMAGE: 250 + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml new file mode 100755 index 00000000..88ea243c --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml @@ -0,0 +1,68 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" # use syncbn for cityscapes dataset + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("cityscapes_fine_panoptic_train",) + TEST_PANOPTIC: ("cityscapes_fine_panoptic_val",) + TEST_INSTANCE: ("cityscapes_fine_instance_seg_val",) + TEST_SEMANTIC: ("cityscapes_fine_sem_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 90000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 1024 + MAX_SIZE_TRAIN: 4096 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 1024) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: -1 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "oneformer_unified" + MAX_SEQ_LEN: 77 + TASK_SEQ_LEN: 77 + TASK_PROB: + SEMANTIC: 0.33 + INSTANCE: 0.66 +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792] + MAX_SIZE: 4096 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_R50_bs16_90k.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_R50_bs16_90k.yaml new file mode 100755 index 00000000..a4746e47 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_R50_bs16_90k.yaml @@ -0,0 +1,59 @@ +_BASE_: Base-Cityscapes-UnifiedSegmentation.yaml +MODEL: + META_ARCHITECTURE: "OneFormer" + SEM_SEG_HEAD: + NAME: "OneFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 19 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + ONE_FORMER: + TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + CONTRASTIVE_WEIGHT: 0.5 + CONTRASTIVE_TEMPERATURE: 0.07 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 150 + USE_TASK_NORM: True + NHEADS: 8 + DROPOUT: 0.1 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + ENC_LAYERS: 0 + CLASS_DEC_LAYERS: 2 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEXT_ENCODER: + WIDTH: 256 + CONTEXT_LENGTH: 77 + NUM_LAYERS: 6 + VOCAB_SIZE: 49408 + PROJ_NUM_LAYERS: 2 + N_CTX: 16 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 + TASK: "panoptic" +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml new file mode 100755 index 00000000..fe3f64cb --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml @@ -0,0 +1,22 @@ +_BASE_: oneformer_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2DiNAT" + DiNAT: + EMBED_DIM: 192 + MLP_RATIO: 2.0 + DEPTHS: [3, 4, 18, 5] + NUM_HEADS: [6, 12, 24, 48] + KERNEL_SIZE: 7 + DROP_PATH_RATE: 0.3 + DILATIONS: [[1, 18, 1], [1, 5, 1, 9], [1, 2, 1, 3, 1, 4, 1, 2, 1, 3, 1, 4, 1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]] + WEIGHTS: "dinat_large_in22k_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 250 +SOLVER: + AMP: + ENABLED: False +TEST: + DETECTIONS_PER_IMAGE: 250 \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml new file mode 100755 index 00000000..30b0a7da --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml @@ -0,0 +1,20 @@ +_BASE_: oneformer_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 250 +TEST: + DETECTIONS_PER_IMAGE: 250 diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/coco/Base-COCO-UnifiedSegmentation.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/coco/Base-COCO-UnifiedSegmentation.yaml new file mode 100755 index 00000000..ccd24f34 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/coco/Base-COCO-UnifiedSegmentation.yaml @@ -0,0 +1,54 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("coco_2017_train_panoptic_with_sem_seg",) + TEST_PANOPTIC: ("coco_2017_val_panoptic_with_sem_seg",) # to evaluate instance and semantic performance as well + TEST_INSTANCE: ("coco_2017_val",) + TEST_SEMANTIC: ("coco_2017_val_panoptic_with_sem_seg",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (327778, 355092) + MAX_ITER: 368750 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + IMAGE_SIZE: 1024 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_unified_lsj" + MAX_SEQ_LEN: 77 + TASK_SEQ_LEN: 77 + TASK_PROB: + SEMANTIC: 0.33 + INSTANCE: 0.66 +TEST: + EVAL_PERIOD: 5000 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_R50_bs16_50ep.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_R50_bs16_50ep.yaml new file mode 100755 index 00000000..f768c8fa --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_R50_bs16_50ep.yaml @@ -0,0 +1,59 @@ +_BASE_: Base-COCO-UnifiedSegmentation.yaml +MODEL: + META_ARCHITECTURE: "OneFormer" + SEM_SEG_HEAD: + NAME: "OneFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 133 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + ONE_FORMER: + TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + CONTRASTIVE_WEIGHT: 0.5 + CONTRASTIVE_TEMPERATURE: 0.07 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 150 + USE_TASK_NORM: True + NHEADS: 8 + DROPOUT: 0.1 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + CLASS_DEC_LAYERS: 2 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEXT_ENCODER: + WIDTH: 256 + CONTEXT_LENGTH: 77 + NUM_LAYERS: 6 + VOCAB_SIZE: 49408 + PROJ_NUM_LAYERS: 2 + N_CTX: 16 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + DETECTION_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 + TASK: "panoptic" +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_dinat_large_bs16_100ep.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_dinat_large_bs16_100ep.yaml new file mode 100755 index 00000000..1b46b63a --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_dinat_large_bs16_100ep.yaml @@ -0,0 +1,22 @@ +_BASE_: oneformer_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2DiNAT" + DiNAT: + EMBED_DIM: 192 + MLP_RATIO: 2.0 + DEPTHS: [3, 4, 18, 5] + NUM_HEADS: [6, 12, 24, 48] + KERNEL_SIZE: 11 + DROP_PATH_RATE: 0.3 + DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]] + WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 150 +SOLVER: + STEPS: (655556, 710184) + MAX_ITER: 737500 +TEST: + DETECTIONS_PER_IMAGE: 150 \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml b/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml new file mode 100755 index 00000000..faae6553 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml @@ -0,0 +1,25 @@ +_BASE_: oneformer_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + ONE_FORMER: + NUM_OBJECT_QUERIES: 150 +SOLVER: + STEPS: (655556, 735184) + MAX_ITER: 737500 + AMP: + ENABLED: False +TEST: + DETECTIONS_PER_IMAGE: 150 diff --git a/src/perception/camera_segmentation/camera_segmentation/data/__init__.py b/src/perception/camera_segmentation/camera_segmentation/data/__init__.py new file mode 100755 index 00000000..63ba265b --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import datasets diff --git a/src/perception/camera_segmentation/camera_segmentation/data/bpe_simple_vocab_16e6.txt b/src/perception/camera_segmentation/camera_segmentation/data/bpe_simple_vocab_16e6.txt new file mode 100755 index 00000000..aff3ec8f --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/bpe_simple_vocab_16e6.txt @@ -0,0 +1,262145 @@ +"bpe_simple_vocab_16e6.txt#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e +v er +a r +f ro +a u +p o +c e +gh t +ar e +s s +fro m +c h +t r +ou n +on e +b y +d o +t h +w or +er e +k e +p ro +f or +d s +b o +t a +w e +g o +h e +t er +in g +d e +b e +ati on +m or +a y +e x +il l +p e +k s +s c +l u +f u +q u +v er +ðŁ ĺ +j u +m u +at e +an d +v e +k ing +m ar +o p +h i +.. . +p re +a d +r u +th at +j o +o f +c e +ne w +a m +a p +g re +s s +d u +no w +y e +t ing +y our +it y +n i +c i +p ar +g u +f i +a f +p er +t er +u p +s o +g i +on s +g r +g e +b r +p l +' t +m i +in e +we e +b i +u s +sh o +ha ve +to day +a v +m an +en t +ac k +ur e +ou r +â Ģ +c u +l d +lo o +i m +ic e +s om +f in +re d +re n +oo d +w as +ti on +p i +i r +th er +t y +p h +ar d +e c +! ! +m on +mor e +w ill 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?? +! âĿ¤ï¸ıâĿ¤ï¸ıâĿ¤ï¸ı +ðŁĴĻ ðŁĴļðŁĴĽ +ðŁĩ´ ðŁĩ² +ìĨ Ķë +미ë Ĥĺ +é İ +åį ģ +âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ +z abu +ye young +ye ji +y ma +wild boyz +weald stonefc +vote ariana +ven ne +v bn +ut zon +uise du +ufo table +ufo sighting +uff ington +tu le +tt itz +troph ication +travel awesome +transcendent alism +tra pper +tik va +ti sed +thisis england +teenwolf season +tech support +sundar c +suare zg +su sy +stron gheart +street food +story bots +sto c +st gile +srsg vac +southeast ward +soccer saturday +so zone +smid t +sm city +sli mey +sin claire +sd reader +scare d diff --git a/src/perception/camera_segmentation/camera_segmentation/data/build.py b/src/perception/camera_segmentation/camera_segmentation/data/build.py new file mode 100755 index 00000000..fb775313 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/build.py @@ -0,0 +1,117 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Any, Callable, Dict, List, Optional, Union +import torch.utils.data as torchdata + +from detectron2.config import configurable + + +from detectron2.data.common import DatasetFromList, MapDataset +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.samplers import ( + InferenceSampler, +) +from detectron2.data.build import ( + get_detection_dataset_dicts, + trivial_batch_collator +) +""" +This file contains the default logic to build a dataloader for training or testing. +""" + +__all__ = [ + "build_detection_test_loader", +] + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + if isinstance(dataset_name, str): + dataset_name = [dataset_name] + + dataset = get_detection_dataset_dicts( + dataset_name, + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return { + "dataset": dataset, + "mapper": mapper, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + "sampler": InferenceSampler(len(dataset)) + if not isinstance(dataset, torchdata.IterableDataset) + else None, + } + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader( + dataset: Union[List[Any], torchdata.Dataset], + *, + mapper: Callable[[Dict[str, Any]], Any], + sampler: Optional[torchdata.Sampler] = None, + batch_size: int = 1, + num_workers: int = 0, + collate_fn: Optional[Callable[[List[Any]], Any]] = None, +) -> torchdata.DataLoader: + """ + Similar to `build_detection_train_loader`, with default batch size = 1, + and sampler = :class:`InferenceSampler`. This sampler coordinates all workers + to produce the exact set of all samples. + + Args: + dataset: a list of dataset dicts, + or a pytorch dataset (either map-style or iterable). They can be obtained + by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper: a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + sampler: a sampler that produces + indices to be applied on ``dataset``. Default to :class:`InferenceSampler`, + which splits the dataset across all workers. Sampler must be None + if `dataset` is iterable. + batch_size: the batch size of the data loader to be created. + Default to 1 image per worker since this is the standard when reporting + inference time in papers. + num_workers: number of parallel data loading workers + collate_fn: same as the argument of `torch.utils.data.DataLoader`. + Defaults to do no collation and return a list of data. + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if isinstance(dataset, torchdata.IterableDataset): + assert sampler is None, "sampler must be None if dataset is IterableDataset" + else: + if sampler is None: + sampler = InferenceSampler(len(dataset)) + return torchdata.DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + drop_last=False, + num_workers=num_workers, + collate_fn=trivial_batch_collator if collate_fn is None else collate_fn, + ) \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/__init__.py b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/__init__.py new file mode 100755 index 00000000..8b137891 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/__init__.py @@ -0,0 +1 @@ + diff --git a/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py new file mode 100755 index 00000000..677b9eaf --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py @@ -0,0 +1,341 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import copy +import logging + +import numpy as np +import torch + +from detectron2.data import MetadataCatalog +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.structures import BitMasks, Instances +from oneformer.utils.box_ops import masks_to_boxes +from oneformer.data.tokenizer import SimpleTokenizer, Tokenize + +__all__ = ["COCOUnifiedNewBaselineDatasetMapper"] + + +def build_transform_gen(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + Returns: + list[Augmentation] + """ + assert is_train, "Only support training augmentation" + image_size = cfg.INPUT.IMAGE_SIZE + min_scale = cfg.INPUT.MIN_SCALE + max_scale = cfg.INPUT.MAX_SCALE + + augmentation = [] + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + + augmentation.extend([ + T.ResizeScale( + min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size + ), + T.FixedSizeCrop(crop_size=(image_size, image_size)), + ]) + + return augmentation + + +# This is specifically designed for the COCO dataset. +class COCOUnifiedNewBaselineDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by OneFormer. + + This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + num_queries, + tfm_gens, + meta, + image_format, + max_seq_len, + task_seq_len, + semantic_prob, + instance_prob, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + crop_gen: crop augmentation + tfm_gens: data augmentation + image_format: an image format supported by :func:`detection_utils.read_image`. + """ + self.tfm_gens = tfm_gens + logging.getLogger(__name__).info( + "[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}".format( + str(self.tfm_gens) + ) + ) + + self.img_format = image_format + self.is_train = is_train + self.meta = meta + self.ignore_label = self.meta.ignore_label + self.num_queries = num_queries + + self.things = [] + for k,v in self.meta.thing_dataset_id_to_contiguous_id.items(): + self.things.append(v) + self.class_names = self.meta.stuff_classes + self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len) + self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len) + self.semantic_prob = semantic_prob + self.instance_prob = instance_prob + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + tfm_gens = build_transform_gen(cfg, is_train) + dataset_names = cfg.DATASETS.TRAIN + meta = MetadataCatalog.get(dataset_names[0]) + + ret = { + "is_train": is_train, + "meta": meta, + "tfm_gens": tfm_gens, + "image_format": cfg.INPUT.FORMAT, + "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX, + "task_seq_len": cfg.INPUT.TASK_SEQ_LEN, + "max_seq_len": cfg.INPUT.MAX_SEQ_LEN, + "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC, + "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE, + } + return ret + + def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj): + instances = Instances(image_shape) + + classes = [] + texts = ["a semantic photo"] * self.num_queries + masks = [] + label = np.ones_like(pan_seg_gt) * self.ignore_label + + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + mask = pan_seg_gt == segment_info["id"] + if not np.all(mask == False): + if class_id not in classes: + cls_name = self.class_names[class_id] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + else: + idx = classes.index(class_id) + masks[idx] += mask + masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool) + label[mask] = class_id + + num = 0 + for i, cls_name in enumerate(self.class_names): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_bboxes = torch.zeros((0, 4)) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + # Placeholder bounding boxes for stuff regions. Note that these are not used during training. + instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0]) + return instances, texts, label + + def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj): + instances = Instances(image_shape) + + classes = [] + texts = ["an instance photo"] * self.num_queries + masks = [] + label = np.ones_like(pan_seg_gt) * self.ignore_label + + for segment_info in segments_info: + class_id = segment_info["category_id"] + if class_id in self.things: + if not segment_info["iscrowd"]: + mask = pan_seg_gt == segment_info["id"] + if not np.all(mask == False): + cls_name = self.class_names[class_id] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + label[mask] = class_id + + num = 0 + for i, cls_name in enumerate(self.class_names): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_bboxes = torch.zeros((0, 4)) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_bboxes = masks_to_boxes(instances.gt_masks) + return instances, texts, label + + def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj): + instances = Instances(image_shape) + + classes = [] + texts = ["a panoptic photo"] * self.num_queries + masks = [] + label = np.ones_like(pan_seg_gt) * self.ignore_label + + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + mask = pan_seg_gt == segment_info["id"] + if not np.all(mask == False): + cls_name = self.class_names[class_id] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + label[mask] = class_id + + num = 0 + for i, cls_name in enumerate(self.class_names): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_bboxes = torch.zeros((0, 4)) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_bboxes = masks_to_boxes(instances.gt_masks) + for i in range(instances.gt_classes.shape[0]): + # Placeholder bounding boxes for stuff regions. Note that these are not used during training. + if instances.gt_classes[i].item() not in self.things: + instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.]) + return instances, texts, label + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + image_shape = image.shape[:2] # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + return dataset_dict + + # semantic segmentation + if "sem_seg_file_name" in dataset_dict: + # PyTorch transformation not implemented for uint16, so converting it to double first + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + sem_seg_gt = transforms.apply_segmentation(sem_seg_gt) + else: + sem_seg_gt = None + + if "pan_seg_file_name" in dataset_dict: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + pan_seg_gt = rgb2id(pan_seg_gt) + + prob_task = np.random.uniform(0,1.) + + num_class_obj = {} + + for name in self.class_names: + num_class_obj[name] = 0 + + if prob_task < self.semantic_prob: + task = "The task is semantic" + instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj) + elif prob_task < self.instance_prob: + task = "The task is instance" + instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj) + else: + task = "The task is panoptic" + instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj) + + + dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long() + dataset_dict["instances"] = instances + dataset_dict["orig_shape"] = image_shape + dataset_dict["task"] = task + dataset_dict["text"] = text + dataset_dict["thing_ids"] = self.things + + return dataset_dict diff --git a/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/dataset_mapper.py b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/dataset_mapper.py new file mode 100755 index 00000000..b8617e8d --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/dataset_mapper.py @@ -0,0 +1,203 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import copy +import logging +import numpy as np +from typing import List, Optional, Union +import torch + +from detectron2.config import configurable + +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from oneformer.data.tokenizer import SimpleTokenizer, Tokenize + +__all__ = ["DatasetMapper"] + + +class DatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by the model. + + This is the default callable to be used to map your dataset dict into training data. + You may need to follow it to implement your own one for customized logic, + such as a different way to read or transform images. + See :doc:`/tutorials/data_loading` for details. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies cropping/geometric transforms to the image and annotations + 3. Prepare data and annotations to Tensor and :class:`Instances` + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + task_seq_len: int, + task: str = "panoptic", + use_instance_mask: bool = False, + use_keypoint: bool = False, + instance_mask_format: str = "polygon", + keypoint_hflip_indices: Optional[np.ndarray] = None, + precomputed_proposal_topk: Optional[int] = None, + recompute_boxes: bool = False, + ): + """ + NOTE: this interface is experimental. + + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + use_keypoint: whether to process keypoint annotations if available + instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation + masks into this format. + keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` + precomputed_proposal_topk: if given, will load pre-computed + proposals from dataset_dict and keep the top k proposals for each image. + recompute_boxes: whether to overwrite bounding box annotations + by computing tight bounding boxes from instance mask annotations. + """ + if recompute_boxes: + assert use_instance_mask, "recompute_boxes requires instance masks" + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.instance_mask_format = instance_mask_format + self.use_keypoint = use_keypoint + self.keypoint_hflip_indices = keypoint_hflip_indices + self.proposal_topk = precomputed_proposal_topk + self.recompute_boxes = recompute_boxes + self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len) + self.task = task + assert self.task in ["panoptic", "semantic", "instance"] + + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = utils.build_augmentation(cfg, is_train) + if cfg.INPUT.CROP.ENABLED and is_train: + augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + recompute_boxes = cfg.MODEL.MASK_ON + else: + recompute_boxes = False + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "instance_mask_format": cfg.INPUT.MASK_FORMAT, + "use_keypoint": cfg.MODEL.KEYPOINT_ON, + "task_seq_len": cfg.INPUT.TASK_SEQ_LEN, + "recompute_boxes": recompute_boxes, + "task": cfg.MODEL.TEST.TASK, + } + + if cfg.MODEL.KEYPOINT_ON: + ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) + + if cfg.MODEL.LOAD_PROPOSALS: + ret["precomputed_proposal_topk"] = ( + cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN + if is_train + else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST + ) + return ret + + def _transform_annotations(self, dataset_dict, transforms, image_shape): + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + if not self.use_instance_mask: + anno.pop("segmentation", None) + if not self.use_keypoint: + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations( + obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices + ) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + instances = utils.annotations_to_instances( + annos, image_shape, mask_format=self.instance_mask_format + ) + + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + if self.recompute_boxes: + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + dataset_dict["instances"] = utils.filter_empty_instances(instances) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + # USER: Write your own image loading if it's not from a file + image = utils.read_image(dataset_dict["file_name"], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + task = f"The task is {self.task}" + dataset_dict["task"] = task + + # USER: Remove if you don't do semantic/panoptic segmentation. + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + else: + sem_seg_gt = None + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + transforms = self.augmentations(aug_input) + image, sem_seg_gt = aug_input.image, aug_input.sem_seg + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + # USER: Remove if you don't use pre-computed proposals. + # Most users would not need this feature. + if self.proposal_topk is not None: + utils.transform_proposals( + dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk + ) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + self._transform_annotations(dataset_dict, transforms, image_shape) + + return dataset_dict \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/oneformer_unified_dataset_mapper.py b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/oneformer_unified_dataset_mapper.py new file mode 100755 index 00000000..cf156766 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/dataset_mappers/oneformer_unified_dataset_mapper.py @@ -0,0 +1,375 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import copy +import logging +import os + +import numpy as np +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.structures import BitMasks, Instances +from detectron2.data import MetadataCatalog +from detectron2.projects.point_rend import ColorAugSSDTransform +from oneformer.utils.box_ops import masks_to_boxes +from oneformer.data.tokenizer import SimpleTokenizer, Tokenize + +__all__ = ["OneFormerUnifiedDatasetMapper"] + + +class OneFormerUnifiedDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by OneFormer for universal segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + name, + num_queries, + meta, + augmentations, + image_format, + ignore_label, + size_divisibility, + task_seq_len, + max_seq_len, + semantic_prob, + instance_prob, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + ignore_label: the label that is ignored to evaluation + size_divisibility: pad image size to be divisible by this value + """ + self.is_train = is_train + self.meta = meta + self.name = name + self.tfm_gens = augmentations + self.img_format = image_format + self.ignore_label = ignore_label + self.size_divisibility = size_divisibility + self.num_queries = num_queries + + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}") + + self.things = [] + for k,v in self.meta.thing_dataset_id_to_contiguous_id.items(): + self.things.append(v) + self.class_names = self.meta.stuff_classes + self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len) + self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len) + self.semantic_prob = semantic_prob + self.instance_prob = instance_prob + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + augs = [ + T.ResizeShortestEdge( + cfg.INPUT.MIN_SIZE_TRAIN, + cfg.INPUT.MAX_SIZE_TRAIN, + cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING, + ) + ] + if cfg.INPUT.CROP.ENABLED: + augs.append( + T.RandomCrop_CategoryAreaConstraint( + cfg.INPUT.CROP.TYPE, + cfg.INPUT.CROP.SIZE, + cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA, + cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + ) + ) + if cfg.INPUT.COLOR_AUG_SSD: + augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT)) + augs.append(T.RandomFlip()) + + # Assume always applies to the training set. + dataset_names = cfg.DATASETS.TRAIN + meta = MetadataCatalog.get(dataset_names[0]) + ignore_label = meta.ignore_label + + ret = { + "is_train": is_train, + "meta": meta, + "name": dataset_names[0], + "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX, + "task_seq_len": cfg.INPUT.TASK_SEQ_LEN, + "max_seq_len": cfg.INPUT.MAX_SEQ_LEN, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "ignore_label": ignore_label, + "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY, + "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC, + "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE, + } + return ret + + def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj): + pan_seg_gt = pan_seg_gt.numpy() + instances = Instances(image_shape) + + classes = [] + texts = ["a semantic photo"] * self.num_queries + masks = [] + label = np.ones_like(pan_seg_gt) * self.ignore_label + + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + mask = pan_seg_gt == segment_info["id"] + if not np.all(mask == False): + if class_id not in classes: + cls_name = self.class_names[class_id] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + else: + idx = classes.index(class_id) + masks[idx] += mask + masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool) + label[mask] = class_id + + num = 0 + for i, cls_name in enumerate(self.class_names): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_bboxes = torch.zeros((0, 4)) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + # Placeholder bounding boxes for stuff regions. Note that these are not used during training. + instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0]) + return instances, texts, label + + def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj): + pan_seg_gt = pan_seg_gt.numpy() + instances = Instances(image_shape) + + classes = [] + texts = ["an instance photo"] * self.num_queries + masks = [] + label = np.ones_like(pan_seg_gt) * self.ignore_label + + for segment_info in segments_info: + class_id = segment_info["category_id"] + if class_id in self.things: + if not segment_info["iscrowd"]: + mask = pan_seg_gt == segment_info["id"] + if not np.all(mask == False): + cls_name = self.class_names[class_id] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + label[mask] = class_id + + num = 0 + for i, cls_name in enumerate(self.class_names): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_bboxes = torch.zeros((0, 4)) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_bboxes = masks_to_boxes(instances.gt_masks) + return instances, texts, label + + def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj): + pan_seg_gt = pan_seg_gt.numpy() + instances = Instances(image_shape) + + classes = [] + texts = ["a panoptic photo"] * self.num_queries + masks = [] + label = np.ones_like(pan_seg_gt) * self.ignore_label + + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + mask = pan_seg_gt == segment_info["id"] + if not np.all(mask == False): + cls_name = self.class_names[class_id] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + label[mask] = class_id + + num = 0 + for i, cls_name in enumerate(self.class_names): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_bboxes = torch.zeros((0, 4)) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_bboxes = masks_to_boxes(instances.gt_masks) + for i in range(instances.gt_classes.shape[0]): + # Placeholder bounding boxes for stuff regions. Note that these are not used during training. + if instances.gt_classes[i].item() not in self.things: + instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.]) + return instances, texts, label + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "OneFormerUnifiedDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + # semantic segmentation + if "sem_seg_file_name" in dataset_dict: + # PyTorch transformation not implemented for uint16, so converting it to double first + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + else: + sem_seg_gt = None + + # panoptic segmentation + if "pan_seg_file_name" in dataset_dict: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + else: + pan_seg_gt = None + segments_info = None + + if pan_seg_gt is None: + raise ValueError( + "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format( + dataset_dict["file_name"] + ) + ) + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + if sem_seg_gt is not None: + sem_seg_gt = aug_input.sem_seg + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) + pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long")) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + image = F.pad(image, padding_size, value=128).contiguous() + if sem_seg_gt is not None: + sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous() + pan_seg_gt = F.pad( + pan_seg_gt, padding_size, value=0 + ).contiguous() # 0 is the VOID panoptic label + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + + if "annotations" in dataset_dict: + raise ValueError("Pemantic segmentation dataset should not have 'annotations'.") + + prob_task = np.random.uniform(0,1.) + + num_class_obj = {} + + for name in self.class_names: + num_class_obj[name] = 0 + + if prob_task < self.semantic_prob: + task = "The task is semantic" + instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj) + elif prob_task < self.instance_prob: + task = "The task is instance" + instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj) + else: + task = "The task is panoptic" + instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj) + + dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long() + dataset_dict["instances"] = instances + dataset_dict["orig_shape"] = image_shape + dataset_dict["task"] = task + dataset_dict["text"] = text + dataset_dict["thing_ids"] = self.things + + return dataset_dict diff --git a/src/perception/camera_segmentation/camera_segmentation/data/datasets/__init__.py b/src/perception/camera_segmentation/camera_segmentation/data/datasets/__init__.py new file mode 100755 index 00000000..59ce3071 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/datasets/__init__.py @@ -0,0 +1,7 @@ +from . import ( + register_ade20k_panoptic, + register_cityscapes_panoptic, + register_coco_panoptic_annos_semseg, + register_ade20k_instance, + register_coco_panoptic2instance, +) diff --git a/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_ade20k_instance.py b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_ade20k_instance.py new file mode 100755 index 00000000..0cf5466e --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_ade20k_instance.py @@ -0,0 +1,56 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_instance.py +# ------------------------------------------------------------------------------ + +import json +import logging +import numpy as np +import os +from PIL import Image + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.coco import load_coco_json, register_coco_instances +from detectron2.utils.file_io import PathManager + +ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}] + + +_PREDEFINED_SPLITS = { + # point annotations without masks + "ade20k_instance_train": ( + "ADEChallengeData2016/images/training", + "ADEChallengeData2016/ade20k_instance_train.json", + ), + "ade20k_instance_val": ( + "ADEChallengeData2016/images/validation", + "ADEChallengeData2016/ade20k_instance_val.json", + ), +} + + +def _get_ade_instances_meta(): + thing_ids = [k["id"] for k in ADE_CATEGORIES] + assert len(thing_ids) == 100, len(thing_ids) + # Mapping from the incontiguous ADE category id to an id in [0, 99] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in ADE_CATEGORIES] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + return ret + + +def register_all_ade20k_instance(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_ade_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_instance(_root) diff --git a/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_ade20k_panoptic.py b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_ade20k_panoptic.py new file mode 100755 index 00000000..527910f1 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_ade20k_panoptic.py @@ -0,0 +1,394 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_panoptic.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +ADE20K_150_CATEGORIES = [ + {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"}, + {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"}, + {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"}, + {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"}, + {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"}, + {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"}, + {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"}, + {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"}, + {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "}, + {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"}, + {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"}, + {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"}, + {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"}, + {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"}, + {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"}, + {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"}, + {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"}, + {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"}, + {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"}, + {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"}, + {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"}, + {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"}, + {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"}, + {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"}, + {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"}, + {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"}, + {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"}, + {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"}, + {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"}, + {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"}, + {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"}, + {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"}, + {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"}, + {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"}, + {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"}, + {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"}, + {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"}, + {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"}, + {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"}, + {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"}, + {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"}, + {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"}, + {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"}, + {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"}, + { + "color": [6, 51, 255], + "id": 44, + "isthing": 1, + "name": "chest of drawers, chest, bureau, dresser", + }, + {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"}, + {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"}, + {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"}, + {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"}, + {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"}, + {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"}, + {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"}, + {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"}, + {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"}, + {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"}, + {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"}, + { + "color": [255, 71, 0], + "id": 56, + "isthing": 1, + "name": "pool table, billiard table, snooker table", + }, + {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"}, + {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"}, + {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"}, + {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"}, + {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"}, + {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"}, + {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"}, + {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"}, + { + "color": [0, 255, 133], + "id": 65, + "isthing": 1, + "name": "toilet, can, commode, crapper, pot, potty, stool, throne", + }, + {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"}, + {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"}, + {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"}, + {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"}, + {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"}, + {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"}, + {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"}, + {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"}, + {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"}, + {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"}, + {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"}, + {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"}, + {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"}, + {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"}, + {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"}, + {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"}, + {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"}, + {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"}, + {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"}, + {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"}, + {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"}, + {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"}, + {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"}, + {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"}, + {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"}, + {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"}, + {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"}, + {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"}, + {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"}, + { + "color": [0, 122, 255], + "id": 95, + "isthing": 1, + "name": "bannister, banister, balustrade, balusters, handrail", + }, + { + "color": [0, 255, 163], + "id": 96, + "isthing": 0, + "name": "escalator, moving staircase, moving stairway", + }, + { + "color": [255, 153, 0], + "id": 97, + "isthing": 1, + "name": "ottoman, pouf, pouffe, puff, hassock", + }, + {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"}, + {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"}, + { + "color": [143, 255, 0], + "id": 100, + "isthing": 0, + "name": "poster, posting, placard, notice, bill, card", + }, + {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"}, + {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"}, + {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"}, + {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"}, + { + "color": [133, 0, 255], + "id": 105, + "isthing": 0, + "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter", + }, + {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"}, + { + "color": [184, 0, 255], + "id": 107, + "isthing": 1, + "name": "washer, automatic washer, washing machine", + }, + {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"}, + {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"}, + {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"}, + {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"}, + {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"}, + {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"}, + {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"}, + {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"}, + {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"}, + {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"}, + {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"}, + {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"}, + {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"}, + {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"}, + {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"}, + {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"}, + {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"}, + {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"}, + {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"}, + {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"}, + {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"}, + {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"}, + {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"}, + {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"}, + {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"}, + {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"}, + {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"}, + {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"}, + {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"}, + {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"}, + {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"}, + {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"}, + {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"}, + {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"}, + {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"}, + {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"}, + {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"}, + {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"}, + {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"}, + {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"}, + {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"}, + {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"}, +] + +ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES] + +MetadataCatalog.get("ade20k_sem_seg_train").set( + stuff_colors=ADE20k_COLORS[:], +) + +MetadataCatalog.get("ade20k_sem_seg_val").set( + stuff_colors=ADE20k_COLORS[:], +) + + +def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = ann["image_id"] + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_ade20k_panoptic( + name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None, +): + """ + Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + Args: + name (str): the name that identifies a dataset, + e.g. "ade20k_panoptic_train" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_ade20k_panoptic_json( + panoptic_json, image_root, panoptic_root, semantic_root, metadata + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="ade20k_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +_PREDEFINED_SPLITS_ADE20K_PANOPTIC = { + "ade20k_panoptic_train": ( + "ADEChallengeData2016/images/training", + "ADEChallengeData2016/ade20k_panoptic_train", + "ADEChallengeData2016/ade20k_panoptic_train.json", + "ADEChallengeData2016/annotations_detectron2/training", + "ADEChallengeData2016/ade20k_instance_train.json", + ), + "ade20k_panoptic_val": ( + "ADEChallengeData2016/images/validation", + "ADEChallengeData2016/ade20k_panoptic_val", + "ADEChallengeData2016/ade20k_panoptic_val.json", + "ADEChallengeData2016/annotations_detectron2/validation", + "ADEChallengeData2016/ade20k_instance_val.json", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] + stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES] + stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(ADE20K_150_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def register_all_ade20k_panoptic(root): + metadata = get_metadata() + for ( + prefix, + (image_root, panoptic_root, panoptic_json, semantic_root, instance_json), + ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_ade20k_panoptic( + prefix, + metadata, + os.path.join(root, image_root), + os.path.join(root, panoptic_root), + os.path.join(root, semantic_root), + os.path.join(root, panoptic_json), + os.path.join(root, instance_json), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_panoptic(_root) diff --git a/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_cityscapes_panoptic.py b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_cityscapes_panoptic.py new file mode 100755 index 00000000..07ecb23b --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_cityscapes_panoptic.py @@ -0,0 +1,199 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import json +import logging +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES +from detectron2.utils.file_io import PathManager + +""" +This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog. +""" + + +logger = logging.getLogger(__name__) + + +def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info): + files = [] + # scan through the directory + cities = PathManager.ls(image_dir) + logger.info(f"{len(cities)} cities found in '{image_dir}'.") + image_dict = {} + for city in cities: + city_img_dir = os.path.join(image_dir, city) + for basename in PathManager.ls(city_img_dir): + image_file = os.path.join(city_img_dir, basename) + + suffix = "_leftImg8bit.png" + assert basename.endswith(suffix), basename + basename = os.path.basename(basename)[: -len(suffix)] + + image_dict[basename] = image_file + + for ann in json_info["annotations"]: + image_file = image_dict.get(ann["image_id"], None) + assert image_file is not None, "No image {} found for annotation {}".format( + ann["image_id"], ann["file_name"] + ) + label_file = os.path.join(gt_dir, ann["file_name"]) + segments_info = ann["segments_info"] + files.append((image_file, label_file, segments_info)) + + assert len(files), "No images found in {}".format(image_dir) + assert PathManager.isfile(files[0][0]), files[0][0] + assert PathManager.isfile(files[0][1]), files[0][1] + return files + + +def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". + gt_dir (str): path to the raw annotations. e.g., + "~/cityscapes/gtFine/cityscapes_panoptic_train". + gt_json (str): path to the json file. e.g., + "~/cityscapes/gtFine/cityscapes_panoptic_train.json". + meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id" + and "stuff_dataset_id_to_contiguous_id" to map category ids to + contiguous ids for training. + + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + return segment_info + + assert os.path.exists( + gt_json + ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files." # noqa + + + with open(gt_json) as f: + json_info = json.load(f) + + files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info) + ret = [] + for image_file, label_file, segments_info in files: + sem_label_file = ( + image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png" + ) + segments_info = [_convert_category_id(x, meta) for x in segments_info] + ret.append( + { + "file_name": image_file, + "image_id": "_".join( + os.path.splitext(os.path.basename(image_file))[0].split("_")[:3] + ), + "sem_seg_file_name": sem_label_file, + "pan_seg_file_name": label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile( + ret[0]["sem_seg_file_name"] + ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa + assert PathManager.isfile( + ret[0]["pan_seg_file_name"] + ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py" # noqa + return ret + + +_RAW_CITYSCAPES_PANOPTIC_SPLITS = { + "cityscapes_fine_panoptic_train": ( + "cityscapes/leftImg8bit/train", + "cityscapes/gtFine/cityscapes_panoptic_train", + "cityscapes/gtFine/cityscapes_panoptic_train.json", + ), + "cityscapes_fine_panoptic_val": ( + "cityscapes/leftImg8bit/val", + "cityscapes/gtFine/cityscapes_panoptic_val", + "cityscapes/gtFine/cityscapes_panoptic_val.json", + ), + # "cityscapes_fine_panoptic_test": not supported yet +} + + +def register_all_cityscapes_panoptic(root): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES] + thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES] + stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES] + stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # There are three types of ids in cityscapes panoptic segmentation: + # (1) category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the classifier + # (2) instance id: this id is used to differentiate different instances from + # the same category. For "stuff" classes, the instance id is always 0; for + # "thing" classes, the instance id starts from 1 and 0 is reserved for + # ignored instances (e.g. crowd annotation). + # (3) panoptic id: this is the compact id that encode both category and + # instance id by: category_id * 1000 + instance_id. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for k in CITYSCAPES_CATEGORIES: + if k["isthing"] == 1: + thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"] + else: + stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"] + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items(): + image_dir = os.path.join(root, image_dir) + gt_dir = os.path.join(root, gt_dir) + gt_json = os.path.join(root, gt_json) + + if key in DatasetCatalog.list(): + DatasetCatalog.remove(key) + + DatasetCatalog.register( + key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta) + ) + MetadataCatalog.get(key).set( + panoptic_root=gt_dir, + image_root=image_dir, + panoptic_json=gt_json, + gt_dir=gt_dir.replace("cityscapes_panoptic_", ""), + evaluator_type="cityscapes_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **meta, + ) + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_cityscapes_panoptic(_root) \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_coco_panoptic2instance.py b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_coco_panoptic2instance.py new file mode 100755 index 00000000..79e1a95c --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_coco_panoptic2instance.py @@ -0,0 +1,44 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + + +""" +This file registers pre-defined datasets at hard-coded paths, and their metadata. + +We hard-code metadata for common datasets. This will enable: +1. Consistency check when loading the datasets +2. Use models on these standard datasets directly and run demos, + without having to download the dataset annotations + +We hard-code some paths to the dataset that's assumed to +exist in "./datasets/". + +Users SHOULD NOT use this file to create new dataset / metadata for new dataset. +To add new dataset, refer to the tutorial "docs/DATASETS.md". +""" + +import os +from detectron2.data.datasets.builtin_meta import _get_builtin_metadata +from detectron2.data.datasets.coco import register_coco_instances + + +_PREDEFINED_SPLITS_COCO = { + "coco_2017_val_panoptic2instance": ("coco/val2017", "coco/annotations/panoptic2instances_val2017.json"), +} + + +def register_panoptic2instances_coco(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_builtin_metadata("coco"), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +_root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) +register_panoptic2instances_coco(_root) \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_coco_panoptic_annos_semseg.py b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_coco_panoptic_annos_semseg.py new file mode 100755 index 00000000..ac1118bc --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/datasets/register_coco_panoptic_annos_semseg.py @@ -0,0 +1,367 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES +from detectron2.utils.file_io import PathManager +import contextlib +import logging +import io +from fvcore.common.timer import Timer +import pycocotools.mask as mask_util +from detectron2.structures import BoxMode + + +logger = logging.getLogger(__name__) + + +_PREDEFINED_SPLITS_COCO_PANOPTIC = { + "coco_2017_train_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_semseg_train2017", + ), + "coco_2017_val_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_semseg_val2017", + ), +} + +def load_coco_instance_json(json_file, image_root, dataset_name=None): + from pycocotools.coco import COCO + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + coco_api = COCO(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(coco_api.getCatIds()) + cats = coco_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + img_ids = sorted(coco_api.imgs.keys()) + # imgs is a list of dicts, each looks something like: + # {'license': 4, + # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', + # 'file_name': 'COCO_val2014_000000001268.jpg', + # 'height': 427, + # 'width': 640, + # 'date_captured': '2013-11-17 05:57:24', + # 'id': 1268} + imgs = coco_api.loadImgs(img_ids) + # anns is a list[list[dict]], where each dict is an annotation + # record for an object. The inner list enumerates the objects in an image + # and the outer list enumerates over images. Example of anns[0]: + # [{'segmentation': [[192.81, + # 247.09, + # ... + # 219.03, + # 249.06]], + # 'area': 1035.749, + # 'iscrowd': 0, + # 'image_id': 1268, + # 'bbox': [192.81, 224.8, 74.73, 33.43], + # 'category_id': 16, + # 'id': 42986}, + # ...] + anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(coco_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + if "minival" not in json_file: + # The popular valminusminival & minival annotations for COCO2014 contain this bug. + # However the ratio of buggy annotations there is tiny and does not affect accuracy. + # Therefore we explicitly white-list them. + ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] + assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( + json_file + ) + + imgs_anns = list(zip(imgs, anns)) + logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) + + dataset_dicts = {} + + ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + + num_instances_without_valid_segmentation = 0 + + for (img_dict, anno_dict_list) in imgs_anns: + record = {} + record["file_name"] = os.path.join(image_root, img_dict["file_name"]) + record["height"] = img_dict["height"] + record["width"] = img_dict["width"] + image_id = record["image_id"] = img_dict["id"] + + objs = [] + for anno in anno_dict_list: + # Check that the image_id in this annotation is the same as + # the image_id we're looking at. + # This fails only when the data parsing logic or the annotation file is buggy. + + # The original COCO valminusminival2014 & minival2014 annotation files + # actually contains bugs that, together with certain ways of using COCO API, + # can trigger this assertion. + assert anno["image_id"] == image_id + + assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' + + obj = {key: anno[key] for key in ann_keys if key in anno} + if "bbox" in obj and len(obj["bbox"]) == 0: + raise ValueError( + f"One annotation of image {image_id} contains empty 'bbox' value! " + "This json does not have valid COCO format." + ) + + segm = anno.get("segmentation", None) + if segm: # either list[list[float]] or dict(RLE) + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + else: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + keypts = anno.get("keypoints", None) + if keypts: # list[int] + for idx, v in enumerate(keypts): + if idx % 3 != 2: + # COCO's segmentation coordinates are floating points in [0, H or W], + # but keypoint coordinates are integers in [0, H-1 or W-1] + # Therefore we assume the coordinates are "pixel indices" and + # add 0.5 to convert to floating point coordinates. + keypts[idx] = v + 0.5 + obj["keypoints"] = keypts + + obj["bbox_mode"] = BoxMode.XYWH_ABS + if id_map: + annotation_category_id = obj["category_id"] + try: + obj["category_id"] = id_map[annotation_category_id] + except KeyError as e: + raise KeyError( + f"Encountered category_id={annotation_category_id} " + "but this id does not exist in 'categories' of the json file." + ) from e + objs.append(obj) + record["annotations"] = objs + dataset_dicts[image_id] = record + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. Please " + "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" + ) + return dataset_dicts + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + stuff_colors = [k["color"] for k in COCO_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(COCO_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace("panoptic_", ""), instances_name) + + ret = [] + for ann in json_info["annotations"]: + image_id = int(ann["image_id"]) + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + "annotations": instance_data_dicts[image_id]["annotations"], + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_coco_panoptic_annos_sem_seg( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name, +): + panoptic_name = name + delattr(MetadataCatalog.get(panoptic_name), "thing_classes") + delattr(MetadataCatalog.get(panoptic_name), "thing_colors") + MetadataCatalog.get(panoptic_name).set( + thing_classes=metadata["thing_classes"], + thing_colors=metadata["thing_colors"], + # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"], + ) + + # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg" + semantic_name = name + "_with_sem_seg" + DatasetCatalog.register( + semantic_name, + lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata), + ) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="coco_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +def register_all_coco_panoptic_annos_sem_seg(root): + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): + + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + + if 'val' in instances_json: + instances_json = instances_json.replace('instances_', 'panoptic2instances_') + + register_coco_panoptic_annos_sem_seg( + prefix, + get_metadata(), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + prefix_instances, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_coco_panoptic_annos_sem_seg(_root) diff --git a/src/perception/camera_segmentation/camera_segmentation/data/tokenizer.py b/src/perception/camera_segmentation/camera_segmentation/data/tokenizer.py new file mode 100755 index 00000000..09db70f1 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/data/tokenizer.py @@ -0,0 +1,200 @@ +# ------------------------------------------------------------------------- +# MIT License +# +# Copyright (c) 2021 OpenAI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# Modified by Jiarui Xu +# ------------------------------------------------------------------------- + +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re +import torch + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt') + +@lru_cache() +def bytes_to_unicode(): + """Returns list of utf-8 byte and a corresponding list of unicode strings. + + The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab + if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent + coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables + between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + +class Tokenize: + + def __init__(self, tokenizer, max_seq_len=77, truncate=True): + self.tokenizer = tokenizer + self.max_seq_len = max_seq_len + self.truncate = truncate + + def __call__(self, texts): + expanded_dim = False + if isinstance(texts, str): + texts = [texts] + expanded_dim = True + + sot_token = self.tokenizer.encoder['<|startoftext|>'] + eot_token = self.tokenizer.encoder['<|endoftext|>'] + all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > self.max_seq_len: + if self.truncate: + tokens = tokens[:self.max_seq_len] + tokens[-1] = eot_token + else: + raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}') + result[i, :len(tokens)] = torch.tensor(tokens) + + if expanded_dim: + return result[0] + + return result + + +class SimpleTokenizer(object): + + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + + with open(bpe_path) as f: + contents = f.readlines() + merges = [] + for cnt in contents: + merges.append(cnt.split('\n')[0]) + merges.append("") + + # merges = gzip.open(bpe_path).read().decode('utf-8').split('\n') + merges = merges[1:49152 - 256 - 2 + 1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v + '' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} + self.pat = re.compile( + r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", + re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + (token[-1] + '', ) + pairs = get_pairs(word) + + if not pairs: + return token + '' + + while True: + bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: # noqa: E722 + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word) - 1 and word[i + 1] == second: + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ') + return text \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/demo/__init__.py b/src/perception/camera_segmentation/camera_segmentation/demo/__init__.py new file mode 100755 index 00000000..e69de29b diff --git a/src/perception/camera_segmentation/camera_segmentation/demo/colormap.py b/src/perception/camera_segmentation/camera_segmentation/demo/colormap.py new file mode 100755 index 00000000..3eff9a46 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/demo/colormap.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +""" +An awesome colormap for really neat visualizations. +Copied from Detectron, and removed gray colors. +""" + +import numpy as np +import random +random.seed(0) + +__all__ = ["colormap", "random_color", "random_colors"] + +# fmt: off +# RGB: +# _COLORS = np.array( +# [ +# 0.000, 0.447, 0.741, +# 0.850, 0.325, 0.098, +# 0.929, 0.694, 0.125, +# 0.494, 0.184, 0.556, +# 0.466, 0.674, 0.188, +# 0.301, 0.745, 0.933, +# 0.635, 0.078, 0.184, +# 0.300, 0.300, 0.300, +# 0.600, 0.600, 0.600, +# 1.000, 0.000, 0.000, +# 1.000, 0.500, 0.000, +# 0.749, 0.749, 0.000, +# 0.000, 1.000, 0.000, +# 0.000, 0.000, 1.000, +# 0.667, 0.000, 1.000, +# 0.333, 0.333, 0.000, +# 0.333, 0.667, 0.000, +# 0.333, 1.000, 0.000, +# 0.667, 0.333, 0.000, +# 0.667, 0.667, 0.000, +# 0.667, 1.000, 0.000, +# 1.000, 0.333, 0.000, +# 1.000, 0.667, 0.000, +# 1.000, 1.000, 0.000, +# 0.000, 0.333, 0.500, +# 0.000, 0.667, 0.500, +# 0.000, 1.000, 0.500, +# 0.333, 0.000, 0.500, +# 0.333, 0.333, 0.500, +# 0.333, 0.667, 0.500, +# 0.333, 1.000, 0.500, +# 0.667, 0.000, 0.500, +# 0.667, 0.333, 0.500, +# 0.667, 0.667, 0.500, +# 0.667, 1.000, 0.500, +# 1.000, 0.000, 0.500, +# 1.000, 0.333, 0.500, +# 1.000, 0.667, 0.500, +# 1.000, 1.000, 0.500, +# 0.000, 0.333, 1.000, +# 0.000, 0.667, 1.000, +# 0.000, 1.000, 1.000, +# 0.333, 0.000, 1.000, +# 0.333, 0.333, 1.000, +# 0.333, 0.667, 1.000, +# 0.333, 1.000, 1.000, +# 0.667, 0.000, 1.000, +# 0.667, 0.333, 1.000, +# 0.667, 0.667, 1.000, +# 0.667, 1.000, 1.000, +# 1.000, 0.000, 1.000, +# 1.000, 0.333, 1.000, +# 1.000, 0.667, 1.000, +# 0.333, 0.000, 0.000, +# 0.500, 0.000, 0.000, +# 0.667, 0.000, 0.000, +# 0.833, 0.000, 0.000, +# 1.000, 0.000, 0.000, +# 0.000, 0.167, 0.000, +# 0.000, 0.333, 0.000, +# 0.000, 0.500, 0.000, +# 0.000, 0.667, 0.000, +# 0.000, 0.833, 0.000, +# 0.000, 1.000, 0.000, +# 0.000, 0.000, 0.167, +# 0.000, 0.000, 0.333, +# 0.000, 0.000, 0.500, +# 0.000, 0.000, 0.667, +# 0.000, 0.000, 0.833, +# 0.000, 0.000, 1.000, +# 0.000, 0.000, 0.000, +# 0.143, 0.143, 0.143, +# 0.857, 0.857, 0.857, +# 1.000, 1.000, 1.000 +# ] +# ).astype(np.float32).reshape(-1, 3) +# fmt: on + +_COLORS = [] + + +def gen_color(): + color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3)) + if color not in _COLORS and np.mean(color) != 0.0: + _COLORS.append(color) + else: + gen_color() + + +for _ in range(300): + gen_color() + + +def colormap(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1] + """ + assert maximum in [255, 1], maximum + c = _COLORS * maximum + if not rgb: + c = c[:, ::-1] + return c + + +def random_color(rgb=False, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a vector of 3 numbers + """ + idx = np.random.randint(0, len(_COLORS)) + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + + +def random_colors(N, rgb=False, maximum=255): + """ + Args: + N (int): number of unique colors needed + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a list of random_color + """ + indices = random.sample(range(len(_COLORS)), N) + ret = [_COLORS[i] * maximum for i in indices] + if not rgb: + ret = [x[::-1] for x in ret] + return ret + + +if __name__ == "__main__": + import cv2 + + size = 100 + H, W = 10, 10 + canvas = np.random.rand(H * size, W * size, 3).astype("float32") + for h in range(H): + for w in range(W): + idx = h * W + w + if idx >= len(_COLORS): + break + canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx] + cv2.imshow("a", canvas) + cv2.waitKey(0) \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/demo/defaults.py b/src/perception/camera_segmentation/camera_segmentation/demo/defaults.py new file mode 100755 index 00000000..2abcab7a --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/demo/defaults.py @@ -0,0 +1,77 @@ +import torch +import detectron2.data.transforms as T +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.data import ( + MetadataCatalog, +) +from detectron2.modeling import build_model + + +__all__ = [ + "DefaultPredictor", +] + + +class DefaultPredictor: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + Compared to using the model directly, this class does the following additions: + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + This is meant for simple demo purposes, so it does the above steps automatically. + This is not meant for benchmarks or running complicated inference logic. + If you'd like to do anything more complicated, please refer to its source code as + examples to build and use the model manually. + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image, task): + """ + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + task = f"The task is {task}" + + inputs = {"image": image, "height": height, "width": width, "task": task} + predictions = self.model([inputs])[0] + return predictions \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/demo/predictor.py b/src/perception/camera_segmentation/camera_segmentation/demo/predictor.py new file mode 100755 index 00000000..2d2d5c47 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/demo/predictor.py @@ -0,0 +1,190 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Copied from: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py +import atexit +import bisect +import multiprocessing as mp +from collections import deque + +import cv2 +import torch + +from detectron2.data import MetadataCatalog +from defaults import DefaultPredictor +from detectron2.utils.video_visualizer import VideoVisualizer +from visualizer import ColorMode, Visualizer + + +class VisualizationDemo(object): + def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False): + """ + Args: + cfg (CfgNode): + instance_mode (ColorMode): + parallel (bool): whether to run the model in different processes from visualization. + Useful since the visualization logic can be slow. + """ + self.metadata = MetadataCatalog.get( + cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused" + ) + if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST[0]: + from cityscapesscripts.helpers.labels import labels + stuff_colors = [k.color for k in labels if k.trainId != 255] + self.metadata = self.metadata.set(stuff_colors=stuff_colors) + self.cpu_device = torch.device("cpu") + self.instance_mode = instance_mode + + self.parallel = parallel + if parallel: + num_gpu = torch.cuda.device_count() + self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu) + else: + self.predictor = DefaultPredictor(cfg) + + def run_on_image(self, image, task, sem_gt, pan_gt, ins_gt, box_gt): + """ + Args: + image (np.ndarray): an image of shape (H, W, C) (in BGR order). + This is the format used by OpenCV. + Returns: + predictions (dict): the output of the model. + vis_output (VisImage): the visualized image output. + """ + vis_output = None + # Convert image from OpenCV BGR format to Matplotlib RGB format. + image = image[:, :, ::-1] + vis_output = {} + + if task == 'panoptic': + visualizer = Visualizer(image, metadata=self.metadata, instance_mode=0) + predictions = self.predictor(image, "panoptic") + panoptic_seg, segments_info = predictions["panoptic_seg"] + vis_output['panoptic'] = visualizer.draw_panoptic_seg_predictions( + panoptic_seg.to(self.cpu_device), segments_info, alpha=1 + ) + + # visualizer = Visualizer(image, metadata=self.metadata, instance_mode=0) + # vis_output['pan_gt'] = visualizer.draw_panoptic_seg( + # pan_gt[0].to(self.cpu_device), pan_gt[1], alpha=1 + # ) + + if task == 'panoptic' or task == 'semantic': + visualizer = Visualizer(image, metadata=self.metadata, instance_mode=1) + predictions = self.predictor(image, "semantic") + vis_output['semantic'] = visualizer.draw_sem_seg( + predictions["sem_seg"].argmax(dim=0).to(self.cpu_device), alpha=1 + ) + + # visualizer = Visualizer(image, metadata=self.metadata, instance_mode=1) + # vis_output['gt_sem'] = visualizer.draw_sem_seg( + # sem_gt.to(self.cpu_device), alpha=1 + # ) + + if task == 'panoptic' or task == 'instance': + visualizer = Visualizer(image, metadata=self.metadata, instance_mode=2) + predictions = self.predictor(image, "instance") + instances = predictions["instances"].to(self.cpu_device) + vis_output['instance'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1) + + if 'boxes' in predictions: + boxes, labels, scores = predictions["boxes"] + visualizer = Visualizer(image, False, metadata=self.metadata, instance_mode=0) + vis_output['boxes'] = visualizer.draw_box_predictions( + boxes.to(self.cpu_device), labels.to(self.cpu_device), scores.to(self.cpu_device)) + + + # visualizer = Visualizer(image, metadata=self.metadata, instance_mode=2) + # vis_output['ins_gt'] = visualizer.draw_instance_predictions(predictions=ins_gt.to(self.cpu_device), alpha=1) + # vis_output['input'] = visualizer.get_image(image) + + return predictions, vis_output + + +class AsyncPredictor: + """ + A predictor that runs the model asynchronously, possibly on >1 GPUs. + Because rendering the visualization takes considerably amount of time, + this helps improve throughput a little bit when rendering videos. + """ + + class _StopToken: + pass + + class _PredictWorker(mp.Process): + def __init__(self, cfg, task_queue, result_queue): + self.cfg = cfg + self.task_queue = task_queue + self.result_queue = result_queue + super().__init__() + + def run(self): + predictor = DefaultPredictor(self.cfg) + + while True: + task = self.task_queue.get() + if isinstance(task, AsyncPredictor._StopToken): + break + idx, data = task + result = predictor(data) + self.result_queue.put((idx, result)) + + def __init__(self, cfg, num_gpus: int = 1): + """ + Args: + cfg (CfgNode): + num_gpus (int): if 0, will run on CPU + """ + num_workers = max(num_gpus, 1) + self.task_queue = mp.Queue(maxsize=num_workers * 3) + self.result_queue = mp.Queue(maxsize=num_workers * 3) + self.procs = [] + for gpuid in range(max(num_gpus, 1)): + cfg = cfg.clone() + cfg.defrost() + cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu" + self.procs.append( + AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue) + ) + + self.put_idx = 0 + self.get_idx = 0 + self.result_rank = [] + self.result_data = [] + + for p in self.procs: + p.start() + atexit.register(self.shutdown) + + def put(self, image): + self.put_idx += 1 + self.task_queue.put((self.put_idx, image)) + + def get(self): + self.get_idx += 1 # the index needed for this request + if len(self.result_rank) and self.result_rank[0] == self.get_idx: + res = self.result_data[0] + del self.result_data[0], self.result_rank[0] + return res + + while True: + # make sure the results are returned in the correct order + idx, res = self.result_queue.get() + if idx == self.get_idx: + return res + insert = bisect.bisect(self.result_rank, idx) + self.result_rank.insert(insert, idx) + self.result_data.insert(insert, res) + + def __len__(self): + return self.put_idx - self.get_idx + + def __call__(self, image): + self.put(image) + return self.get() + + def shutdown(self): + for _ in self.procs: + self.task_queue.put(AsyncPredictor._StopToken()) + + @property + def default_buffer_size(self): + return len(self.procs) * 5 diff --git a/src/perception/camera_segmentation/camera_segmentation/demo/visualizer.py b/src/perception/camera_segmentation/camera_segmentation/demo/visualizer.py new file mode 100755 index 00000000..183e160d --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/demo/visualizer.py @@ -0,0 +1,1350 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import colorsys +import logging +import math +import numpy as np +from enum import Enum, unique +import cv2 +import matplotlib as mpl +import matplotlib.colors as mplc +import matplotlib.figure as mplfigure +import pycocotools.mask as mask_util +import torch +from matplotlib.backends.backend_agg import FigureCanvasAgg +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes +from detectron2.utils.file_io import PathManager +import random +random.seed(0) +from .colormap import random_color, _COLORS +logger = logging.getLogger(__name__) + +__all__ = ["ColorMode", "VisImage", "Visualizer"] + + +_SMALL_OBJECT_AREA_THRESH = 1000 +_LARGE_MASK_AREA_THRESH = 120000 +_OFF_WHITE = (1.0, 1.0, 240.0 / 255) +_BLACK = (0, 0, 0) +_RED = (1.0, 0, 0) + +_KEYPOINT_THRESHOLD = 0.05 + + +def instance_color(rgb=False, idx=1, maximum=255): + """ + Args: + rgb (bool): whether to return RGB colors or BGR colors. + maximum (int): either 255 or 1 + Returns: + ndarray: a vector of 3 numbers + """ + ret = _COLORS[idx] * maximum + if not rgb: + ret = ret[::-1] + return ret + +@unique +class ColorMode(Enum): + """ + Enum of different color modes to use for instance visualizations. + """ + + IMAGE = 0 + """ + Picks a random color for every instance and overlay segmentations with low opacity. + """ + SEGMENTATION = 1 + """ + Let instances of the same category have similar colors + (from metadata.thing_colors), and overlay them with + high opacity. This provides more attention on the quality of segmentation. + """ + IMAGE_BW = 2 + """ + Same as IMAGE, but convert all areas without masks to gray-scale. + Only available for drawing per-instance mask predictions. + """ + + +class GenericMask: + """ + Attribute: + polygons (list[ndarray]): list[ndarray]: polygons for this mask. + Each ndarray has format [x, y, x, y, ...] + mask (ndarray): a binary mask + """ + + def __init__(self, mask_or_polygons, height, width): + self._mask = self._polygons = self._has_holes = None + self.height = height + self.width = width + + m = mask_or_polygons + if isinstance(m, dict): + # RLEs + assert "counts" in m and "size" in m + if isinstance(m["counts"], list): # uncompressed RLEs + h, w = m["size"] + assert h == height and w == width + m = mask_util.frPyObjects(m, h, w) + self._mask = mask_util.decode(m)[:, :] + return + + if isinstance(m, list): # list[ndarray] + self._polygons = [np.asarray(x).reshape(-1) for x in m] + return + + if isinstance(m, np.ndarray): # assumed to be a binary mask + assert m.shape[1] != 2, m.shape + assert m.shape == ( + height, + width, + ), f"mask shape: {m.shape}, target dims: {height}, {width}" + self._mask = m.astype("uint8") + return + + raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m))) + + @property + def mask(self): + if self._mask is None: + self._mask = self.polygons_to_mask(self._polygons) + return self._mask + + @property + def polygons(self): + if self._polygons is None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + return self._polygons + + @property + def has_holes(self): + if self._has_holes is None: + if self._mask is not None: + self._polygons, self._has_holes = self.mask_to_polygons(self._mask) + else: + self._has_holes = False # if original format is polygon, does not have holes + return self._has_holes + + def mask_to_polygons(self, mask): + # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level + # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. + # Internal contours (holes) are placed in hierarchy-2. + # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours. + mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr + res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) + hierarchy = res[-1] + if hierarchy is None: # empty mask + return [], False + has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 + res = res[-2] + res = [x.flatten() for x in res] + # These coordinates from OpenCV are integers in range [0, W-1 or H-1]. + # We add 0.5 to turn them into real-value coordinate space. A better solution + # would be to first +0.5 and then dilate the returned polygon by 0.5. + res = [x + 0.5 for x in res if len(x) >= 6] + return res, has_holes + + def polygons_to_mask(self, polygons): + rle = mask_util.frPyObjects(polygons, self.height, self.width) + rle = mask_util.merge(rle) + return mask_util.decode(rle)[:, :] + + def area(self): + return self.mask.sum() + + def bbox(self): + p = mask_util.frPyObjects(self.polygons, self.height, self.width) + p = mask_util.merge(p) + bbox = mask_util.toBbox(p) + bbox[2] += bbox[0] + bbox[3] += bbox[1] + return bbox + + +class _PanopticPrediction: + """ + Unify different panoptic annotation/prediction formats + """ + + def __init__(self, panoptic_seg, segments_info, metadata=None): + if segments_info is None: + assert metadata is not None + # If "segments_info" is None, we assume "panoptic_img" is a + # H*W int32 image storing the panoptic_id in the format of + # category_id * label_divisor + instance_id. We reserve -1 for + # VOID label. + label_divisor = metadata.label_divisor + segments_info = [] + for panoptic_label in np.unique(panoptic_seg.numpy()): + if panoptic_label == -1: + # VOID region. + continue + pred_class = panoptic_label // label_divisor + isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() + segments_info.append( + { + "id": int(panoptic_label), + "category_id": int(pred_class), + "isthing": bool(isthing), + } + ) + del metadata + + self._seg = panoptic_seg + + self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info + segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True) + areas = areas.cpu().numpy() + sorted_idxs = np.argsort(-areas) + self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs] + self._seg_ids = self._seg_ids.tolist() + for sid, area in zip(self._seg_ids, self._seg_areas): + if sid in self._sinfo: + self._sinfo[sid]["area"] = float(area) + + def non_empty_mask(self): + """ + Returns: + (H, W) array, a mask for all pixels that have a prediction + """ + empty_ids = [] + for id in self._seg_ids: + if id not in self._sinfo: + empty_ids.append(id) + if len(empty_ids) == 0: + return np.zeros(self._seg.shape, dtype=np.uint8) + assert ( + len(empty_ids) == 1 + ), ">1 ids corresponds to no labels. This is currently not supported" + return (self._seg != empty_ids[0]).numpy().astype(np.bool) + + def semantic_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or sinfo["isthing"]: + # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions. + continue + yield (self._seg == sid).cpu().numpy().astype(np.bool), sinfo + + def instance_masks(self): + for sid in self._seg_ids: + sinfo = self._sinfo.get(sid) + if sinfo is None or not sinfo["isthing"]: + continue + mask = (self._seg == sid).cpu().numpy().astype(np.bool) + if mask.sum() > 0: + yield mask, sinfo + + +def _create_text_labels(classes, scores, class_names, is_crowd=None): + """ + Args: + classes (list[int] or None): + scores (list[float] or None): + class_names (list[str] or None): + is_crowd (list[bool] or None): + Returns: + list[str] or None + """ + labels = None + if classes is not None: + if class_names is not None and len(class_names) > 0: + labels = [class_names[i] for i in classes] + else: + labels = [str(i) for i in classes] + if scores is not None: + if labels is None: + labels = ["{:.0f}%".format(s * 100) for s in scores] + else: + labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)] + if labels is not None and is_crowd is not None: + labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)] + return labels + + +class VisImage: + def __init__(self, img, scale=1.0): + """ + Args: + img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255]. + scale (float): scale the input image + """ + self.img = img + self.scale = scale + self.width, self.height = img.shape[1], img.shape[0] + self._setup_figure(img) + + def _setup_figure(self, img): + """ + Args: + Same as in :meth:`__init__()`. + Returns: + fig (matplotlib.pyplot.figure): top level container for all the image plot elements. + ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system. + """ + fig = mplfigure.Figure(frameon=False) + self.dpi = fig.get_dpi() + # add a small 1e-2 to avoid precision lost due to matplotlib's truncation + # (https://github.com/matplotlib/matplotlib/issues/15363) + fig.set_size_inches( + (self.width * self.scale + 1e-2) / self.dpi, + (self.height * self.scale + 1e-2) / self.dpi, + ) + self.canvas = FigureCanvasAgg(fig) + # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig) + ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) + ax.axis("off") + self.fig = fig + self.ax = ax + self.reset_image(img) + + def reset_image(self, img): + """ + Args: + img: same as in __init__ + """ + img = img.astype("uint8") + self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest") + + def save(self, filepath): + """ + Args: + filepath (str): a string that contains the absolute path, including the file name, where + the visualized image will be saved. + """ + self.fig.savefig(filepath) + + def get_image(self): + """ + Returns: + ndarray: + the visualized image of shape (H, W, 3) (RGB) in uint8 type. + The shape is scaled w.r.t the input image using the given `scale` argument. + """ + canvas = self.canvas + s, (width, height) = canvas.print_to_buffer() + # buf = io.BytesIO() # works for cairo backend + # canvas.print_rgba(buf) + # width, height = self.width, self.height + # s = buf.getvalue() + + buffer = np.frombuffer(s, dtype="uint8") + + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + return rgb.astype("uint8") + + +class Visualizer: + """ + Visualizer that draws data about detection/segmentation on images. + It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}` + that draw primitive objects to images, as well as high-level wrappers like + `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}` + that draw composite data in some pre-defined style. + Note that the exact visualization style for the high-level wrappers are subject to change. + Style such as color, opacity, label contents, visibility of labels, or even the visibility + of objects themselves (e.g. when the object is too small) may change according + to different heuristics, as long as the results still look visually reasonable. + To obtain a consistent style, you can implement custom drawing functions with the + abovementioned primitive methods instead. If you need more customized visualization + styles, you can process the data yourself following their format documented in + tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not + intend to satisfy everyone's preference on drawing styles. + This visualizer focuses on high rendering quality rather than performance. It is not + designed to be used for real-time applications. + """ + + # TODO implement a fast, rasterized version using OpenCV + + def __init__(self, img_rgb, is_img=True, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE): + """ + Args: + img_rgb: a numpy array of shape (H, W, C), where H and W correspond to + the height and width of the image respectively. C is the number of + color channels. The image is required to be in RGB format since that + is a requirement of the Matplotlib library. The image is also expected + to be in the range [0, 255]. + metadata (Metadata): dataset metadata (e.g. class names and colors) + instance_mode (ColorMode): defines one of the pre-defined style for drawing + instances on an image. + """ + if is_img: + self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8) + else: + self.img = np.zeros_like(img_rgb).clip(0, 255).astype(np.uint8) + if metadata is None: + metadata = MetadataCatalog.get("__nonexist__") + self.metadata = metadata + self.output = VisImage(self.img, scale=scale) + self.cpu_device = torch.device("cpu") + + # too small texts are useless, therefore clamp to 9 + self._default_font_size = max( + np.sqrt(self.output.height * self.output.width) // 90, 10 // scale + ) + self._instance_mode = instance_mode + self.keypoint_threshold = _KEYPOINT_THRESHOLD + + def get_image(self, img): + img = np.asarray(img).clip(0, 255).astype(np.uint8) + return VisImage(img, scale=1.0) + + def draw_box_predictions( + self, + boxes=None, + labels=None, + scores=None, + assigned_colors=None + ): + """ + Args: + boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, + or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image, + or a :class:`RotatedBoxes`, + or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image, + labels (list[str]): the text to be displayed for each instance. + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = 0 + boxes = self._convert_boxes(boxes) + classes = labels.tolist() + scores = scores.tolist() + labels = _create_text_labels(classes, scores, self.metadata.get("stuff_classes", None)) + num_instances = len(boxes) + assert len(labels) == num_instances + if assigned_colors is None: + # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)] + if num_instances == 0: + return self.output + + # Display in largest to smallest order to reduce occlusion. + areas = None + areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1) + + if areas is not None: + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] if boxes is not None else None + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + assigned_colors = [assigned_colors[idx] for idx in sorted_idxs] + + for i in range(num_instances): + color = assigned_colors[i] + if boxes is not None: + self.draw_box(boxes[i], edge_color=color) + + if labels is not None: + # first get a box + if boxes is not None: + x0, y0, x1, y1 = boxes[i] + text_pos = (x0, y0) # if drawing boxes, put text on the box corner. + horiz_align = "left" + else: + continue # drawing the box confidence for keypoints isn't very useful. + # for small objects, draw text at the side to avoid occlusion + instance_area = (y1 - y0) * (x1 - x0) + if ( + instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale + or y1 - y0 < 40 * self.output.scale + ): + if y1 >= self.output.height - 5: + text_pos = (x1, y0) + else: + text_pos = (x0, y1) + + height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width) + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) + * 0.5 + * self._default_font_size + ) + self.draw_text( + labels[i], + text_pos, + color=lighter_color, + horizontal_alignment=horiz_align, + font_size=font_size, + ) + + return self.output + + + def draw_instance_predictions(self, predictions, alpha=0.8, is_text=True): + """ + Draw instance-level prediction results on an image. + Args: + predictions (Instances): the output of an instance detection/segmentation + model. Following fields will be used to draw: + "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). + Returns: + output (VisImage): image object with visualizations. + """ + boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None + scores = predictions.scores if predictions.has("scores") else None + classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None + labels = _create_text_labels(classes, scores, self.metadata.get("stuff_classes", None)) + keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None + + if predictions.has("pred_masks"): + masks = np.asarray(predictions.pred_masks) + masks = [GenericMask(x, self.output.height, self.output.width) for x in masks] + else: + masks = None + + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"): + # colors = [ + # self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes + # ] + colors = [ + instance_color(rgb=True, idx=c, maximum=1) for c in classes + ] + else: + colors = None + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image( + self._create_grayscale_image( + (predictions.pred_masks.any(dim=0) > 0).numpy() + if predictions.has("pred_masks") + else None + ) + ) + + self.overlay_instances( + masks=masks, + boxes=boxes, + labels=labels, + keypoints=keypoints, + assigned_colors=colors, + alpha=alpha, + is_text=is_text, + ) + return self.output + + def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8, is_text=True): + """ + Draw semantic segmentation predictions/labels. + Args: + sem_seg (Tensor or ndarray): the segmentation of shape (H, W). + Each value is the integer label of the pixel. + area_threshold (int): segments with less than `area_threshold` are not drawn. + alpha (float): the larger it is, the more opaque the segmentations are. + Returns: + output (VisImage): image object with visualizations. + """ + if isinstance(sem_seg, torch.Tensor): + sem_seg = sem_seg.numpy() + labels, areas = np.unique(sem_seg, return_counts=True) + sorted_idxs = np.argsort(-areas).tolist() + labels = labels[sorted_idxs] + for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels): + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[label]] + except (AttributeError, IndexError): + mask_color = None + + binary_mask = (sem_seg == label).astype(np.uint8) + text = self.metadata.stuff_classes[label] + self.draw_binary_mask( + binary_mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + is_text=is_text, + ) + return self.output + + def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7, is_text=True,): + """ + Draw panoptic prediction annotations or results. + Args: + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each + segment. + segments_info (list[dict] or None): Describe each segment in `panoptic_seg`. + If it is a ``list[dict]``, each dict contains keys "id", "category_id". + If None, category id of each pixel is computed by + ``pixel // metadata.label_divisor``. + area_threshold (int): stuff segments with less than `area_threshold` are not drawn. + Returns: + output (VisImage): image object with visualizations. + """ + pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) + + if self._instance_mode == ColorMode.IMAGE_BW: + self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask())) + + # draw mask for all semantic segments first i.e. "stuff" + for mask, sinfo in pred.semantic_masks(): + category_idx = sinfo["category_id"] + try: + mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] + except AttributeError: + mask_color = None + + text = self.metadata.stuff_classes[category_idx] + self.draw_binary_mask( + mask, + color=mask_color, + edge_color=_OFF_WHITE, + text=text, + alpha=alpha, + area_threshold=area_threshold, + is_text=is_text, + ) + + # draw mask for all instances second + all_instances = list(pred.instance_masks()) + if len(all_instances) == 0: + return self.output + masks, sinfo = list(zip(*all_instances)) + category_ids = [x["category_id"] for x in sinfo] + + try: + scores = [x["score"] for x in sinfo] + except KeyError: + scores = None + labels = _create_text_labels( + category_ids, scores, self.metadata.stuff_classes, [x.get("iscrowd", 0) for x in sinfo] + ) + + try: + colors = [ + self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) for c in category_ids + ] + except AttributeError: + colors = None + self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha, is_text=is_text) + + return self.output + + draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility + + def draw_dataset_dict(self, dic): + """ + Draw annotations/segmentaions in Detectron2 Dataset format. + Args: + dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format. + Returns: + output (VisImage): image object with visualizations. + """ + annos = dic.get("annotations", None) + if annos: + if "segmentation" in annos[0]: + masks = [x["segmentation"] for x in annos] + else: + masks = None + if "keypoints" in annos[0]: + keypts = [x["keypoints"] for x in annos] + keypts = np.array(keypts).reshape(len(annos), -1, 3) + else: + keypts = None + + boxes = [ + BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS) + if len(x["bbox"]) == 4 + else x["bbox"] + for x in annos + ] + + colors = None + category_ids = [x["category_id"] for x in annos] + if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"): + colors = [ + self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) + for c in category_ids + ] + names = self.metadata.get("stuff_classes", None) + labels = _create_text_labels( + category_ids, + scores=None, + class_names=names, + is_crowd=[x.get("iscrowd", 0) for x in annos], + ) + self.overlay_instances( + labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors + ) + + sem_seg = dic.get("sem_seg", None) + if sem_seg is None and "sem_seg_file_name" in dic: + with PathManager.open(dic["sem_seg_file_name"], "rb") as f: + sem_seg = Image.open(f) + sem_seg = np.asarray(sem_seg, dtype="uint8") + if sem_seg is not None: + self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5) + + pan_seg = dic.get("pan_seg", None) + if pan_seg is None and "pan_seg_file_name" in dic: + with PathManager.open(dic["pan_seg_file_name"], "rb") as f: + pan_seg = Image.open(f) + pan_seg = np.asarray(pan_seg) + from panopticapi.utils import rgb2id + + pan_seg = rgb2id(pan_seg) + if pan_seg is not None: + segments_info = dic["segments_info"] + pan_seg = torch.tensor(pan_seg) + self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5) + return self.output + + def overlay_instances( + self, + *, + boxes=None, + labels=None, + masks=None, + keypoints=None, + assigned_colors=None, + alpha=0.5, + is_text=True, + ): + """ + Args: + boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, + or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image, + or a :class:`RotatedBoxes`, + or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image, + labels (list[str]): the text to be displayed for each instance. + masks (masks-like object): Supported types are: + * :class:`detectron2.structures.PolygonMasks`, + :class:`detectron2.structures.BitMasks`. + * list[list[ndarray]]: contains the segmentation masks for all objects in one image. + The first level of the list corresponds to individual instances. The second + level to all the polygon that compose the instance, and the third level + to the polygon coordinates. The third level should have the format of + [x0, y0, x1, y1, ..., xn, yn] (n >= 3). + * list[ndarray]: each ndarray is a binary mask of shape (H, W). + * list[dict]: each dict is a COCO-style RLE. + keypoints (Keypoint or array like): an array-like object of shape (N, K, 3), + where the N is the number of instances and K is the number of keypoints. + The last dimension corresponds to (x, y, visibility or score). + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = 0 + if boxes is not None: + boxes = self._convert_boxes(boxes) + num_instances = len(boxes) + if masks is not None: + masks = self._convert_masks(masks) + if num_instances: + assert len(masks) == num_instances + else: + num_instances = len(masks) + if keypoints is not None: + if num_instances: + assert len(keypoints) == num_instances + else: + num_instances = len(keypoints) + keypoints = self._convert_keypoints(keypoints) + if labels is not None: + assert len(labels) == num_instances + if assigned_colors is None: + # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)] + if num_instances == 0: + return self.output + if boxes is not None and boxes.shape[1] == 5: + return self.overlay_rotated_instances( + boxes=boxes, labels=labels, assigned_colors=assigned_colors + ) + + # Display in largest to smallest order to reduce occlusion. + areas = None + if boxes is not None: + areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1) + elif masks is not None: + areas = np.asarray([x.area() for x in masks]) + + if areas is not None: + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] if boxes is not None else None + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None + assigned_colors = [assigned_colors[idx] for idx in sorted_idxs] + keypoints = keypoints[sorted_idxs] if keypoints is not None else None + + for i in range(num_instances): + color = assigned_colors[i] + if boxes is not None: + self.draw_box(boxes[i], edge_color=color) + + if masks is not None: + for segment in masks[i].polygons: + self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha) + + if labels is not None: + # first get a box + if boxes is not None: + x0, y0, x1, y1 = boxes[i] + text_pos = (x0, y0) # if drawing boxes, put text on the box corner. + horiz_align = "left" + elif masks is not None: + # skip small mask without polygon + if len(masks[i].polygons) == 0: + continue + + x0, y0, x1, y1 = masks[i].bbox() + + # draw text in the center (defined by median) when box is not drawn + # median is less sensitive to outliers. + text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1] + horiz_align = "center" + else: + continue # drawing the box confidence for keypoints isn't very useful. + # for small objects, draw text at the side to avoid occlusion + instance_area = (y1 - y0) * (x1 - x0) + if ( + instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale + or y1 - y0 < 40 * self.output.scale + ): + if y1 >= self.output.height - 5: + text_pos = (x1, y0) + else: + text_pos = (x0, y1) + + height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width) + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) + * 0.5 + * self._default_font_size + ) + if is_text: + self.draw_text( + labels[i], + text_pos, + color=lighter_color, + horizontal_alignment=horiz_align, + font_size=font_size, + ) + + # draw keypoints + if keypoints is not None: + for keypoints_per_instance in keypoints: + self.draw_and_connect_keypoints(keypoints_per_instance) + + return self.output + + def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None): + """ + Args: + boxes (ndarray): an Nx5 numpy array of + (x_center, y_center, width, height, angle_degrees) format + for the N objects in a single image. + labels (list[str]): the text to be displayed for each instance. + assigned_colors (list[matplotlib.colors]): a list of colors, where each color + corresponds to each mask or box in the image. Refer to 'matplotlib.colors' + for full list of formats that the colors are accepted in. + Returns: + output (VisImage): image object with visualizations. + """ + num_instances = len(boxes) + + if assigned_colors is None: + # assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] + assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)] + if num_instances == 0: + return self.output + + # Display in largest to smallest order to reduce occlusion. + if boxes is not None: + areas = boxes[:, 2] * boxes[:, 3] + + sorted_idxs = np.argsort(-areas).tolist() + # Re-order overlapped instances in descending order. + boxes = boxes[sorted_idxs] + labels = [labels[k] for k in sorted_idxs] if labels is not None else None + colors = [assigned_colors[idx] for idx in sorted_idxs] + + for i in range(num_instances): + self.draw_rotated_box_with_label( + boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None + ) + + return self.output + + def draw_and_connect_keypoints(self, keypoints): + """ + Draws keypoints of an instance and follows the rules for keypoint connections + to draw lines between appropriate keypoints. This follows color heuristics for + line color. + Args: + keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints + and the last dimension corresponds to (x, y, probability). + Returns: + output (VisImage): image object with visualizations. + """ + visible = {} + keypoint_names = self.metadata.get("keypoint_names") + for idx, keypoint in enumerate(keypoints): + + # draw keypoint + x, y, prob = keypoint + if prob > self.keypoint_threshold: + self.draw_circle((x, y), color=_RED) + if keypoint_names: + keypoint_name = keypoint_names[idx] + visible[keypoint_name] = (x, y) + + if self.metadata.get("keypoint_connection_rules"): + for kp0, kp1, color in self.metadata.keypoint_connection_rules: + if kp0 in visible and kp1 in visible: + x0, y0 = visible[kp0] + x1, y1 = visible[kp1] + color = tuple(x / 255.0 for x in color) + self.draw_line([x0, x1], [y0, y1], color=color) + + # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip + # Note that this strategy is specific to person keypoints. + # For other keypoints, it should just do nothing + try: + ls_x, ls_y = visible["left_shoulder"] + rs_x, rs_y = visible["right_shoulder"] + mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2 + except KeyError: + pass + else: + # draw line from nose to mid-shoulder + nose_x, nose_y = visible.get("nose", (None, None)) + if nose_x is not None: + self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED) + + try: + # draw line from mid-shoulder to mid-hip + lh_x, lh_y = visible["left_hip"] + rh_x, rh_y = visible["right_hip"] + except KeyError: + pass + else: + mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2 + self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED) + return self.output + + """ + Primitive drawing functions: + """ + + def draw_text( + self, + text, + position, + *, + font_size=None, + color="g", + horizontal_alignment="center", + rotation=0, + ): + """ + Args: + text (str): class label + position (tuple): a tuple of the x and y coordinates to place text on image. + font_size (int, optional): font of the text. If not provided, a font size + proportional to the image width is calculated and used. + color: color of the text. Refer to `matplotlib.colors` for full list + of formats that are accepted. + horizontal_alignment (str): see `matplotlib.text.Text` + rotation: rotation angle in degrees CCW + Returns: + output (VisImage): image object with text drawn. + """ + if not font_size: + font_size = self._default_font_size + + # since the text background is dark, we don't want the text to be dark + color = np.maximum(list(mplc.to_rgb(color)), 0.2) + color[np.argmax(color)] = max(0.8, np.max(color)) + + x, y = position + self.output.ax.text( + x, + y, + text, + size=font_size * self.output.scale, + family="sans-serif", + bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"}, + verticalalignment="top", + horizontalalignment=horizontal_alignment, + color=color, + zorder=10, + rotation=rotation, + ) + return self.output + + def draw_box(self, box_coord, alpha=1.0, edge_color="g", line_style="-"): + """ + Args: + box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 + are the coordinates of the image's top left corner. x1 and y1 are the + coordinates of the image's bottom right corner. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + Returns: + output (VisImage): image object with box drawn. + """ + x0, y0, x1, y1 = box_coord + width = x1 - x0 + height = y1 - y0 + + linewidth = 2 + + self.output.ax.add_patch( + mpl.patches.Rectangle( + (x0, y0), + width, + height, + fill=False, + edgecolor=edge_color, + linewidth=linewidth * self.output.scale, + alpha=alpha, + linestyle=line_style, + ) + ) + return self.output + + def draw_rotated_box_with_label( + self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None + ): + """ + Draw a rotated box with label on its top-left corner. + Args: + rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle), + where cnt_x and cnt_y are the center coordinates of the box. + w and h are the width and height of the box. angle represents how + many degrees the box is rotated CCW with regard to the 0-degree box. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + edge_color: color of the outline of the box. Refer to `matplotlib.colors` + for full list of formats that are accepted. + line_style (string): the string to use to create the outline of the boxes. + label (string): label for rotated box. It will not be rendered when set to None. + Returns: + output (VisImage): image object with box drawn. + """ + cnt_x, cnt_y, w, h, angle = rotated_box + area = w * h + # use thinner lines when the box is small + linewidth = self._default_font_size / ( + 6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3 + ) + + theta = angle * math.pi / 180.0 + c = math.cos(theta) + s = math.sin(theta) + rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)] + # x: left->right ; y: top->down + rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect] + for k in range(4): + j = (k + 1) % 4 + self.draw_line( + [rotated_rect[k][0], rotated_rect[j][0]], + [rotated_rect[k][1], rotated_rect[j][1]], + color=edge_color, + linestyle="--" if k == 1 else line_style, + linewidth=linewidth, + ) + + if label is not None: + text_pos = rotated_rect[1] # topleft corner + + height_ratio = h / np.sqrt(self.output.height * self.output.width) + label_color = self._change_color_brightness(edge_color, brightness_factor=0.7) + font_size = ( + np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size + ) + self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle) + + return self.output + + def draw_circle(self, circle_coord, color, radius=3): + """ + Args: + circle_coord (list(int) or tuple(int)): contains the x and y coordinates + of the center of the circle. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + radius (int): radius of the circle. + Returns: + output (VisImage): image object with box drawn. + """ + x, y = circle_coord + self.output.ax.add_patch( + mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color) + ) + return self.output + + def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None): + """ + Args: + x_data (list[int]): a list containing x values of all the points being drawn. + Length of list should match the length of y_data. + y_data (list[int]): a list containing y values of all the points being drawn. + Length of list should match the length of x_data. + color: color of the line. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + linestyle: style of the line. Refer to `matplotlib.lines.Line2D` + for a full list of formats that are accepted. + linewidth (float or None): width of the line. When it's None, + a default value will be computed and used. + Returns: + output (VisImage): image object with line drawn. + """ + if linewidth is None: + linewidth = self._default_font_size / 3 + linewidth = max(linewidth, 1) + self.output.ax.add_line( + mpl.lines.Line2D( + x_data, + y_data, + linewidth=linewidth * self.output.scale, + color=color, + linestyle=linestyle, + ) + ) + return self.output + + def draw_binary_mask( + self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10, is_text=True, + ): + """ + Args: + binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and + W is the image width. Each value in the array is either a 0 or 1 value of uint8 + type. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. + text (str): if None, will be drawn on the object + alpha (float): blending efficient. Smaller values lead to more transparent masks. + area_threshold (float): a connected component smaller than this area will not be shown. + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + color = mplc.to_rgb(color) + + has_valid_segment = False + binary_mask = binary_mask.astype("uint8") # opencv needs uint8 + mask = GenericMask(binary_mask, self.output.height, self.output.width) + shape2d = (binary_mask.shape[0], binary_mask.shape[1]) + + if not mask.has_holes: + # draw polygons for regular masks + for segment in mask.polygons: + area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1])) + if area < (area_threshold or 0): + continue + has_valid_segment = True + segment = segment.reshape(-1, 2) + self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha) + else: + # TODO: Use Path/PathPatch to draw vector graphics: + # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha + has_valid_segment = True + self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) + + if is_text: + if text is not None and has_valid_segment: + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + self._draw_text_in_mask(binary_mask, text, lighter_color) + return self.output + + def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5): + """ + Args: + soft_mask (ndarray): float array of shape (H, W), each value in [0, 1]. + color: color of the mask. Refer to `matplotlib.colors` for a full list of + formats that are accepted. If None, will pick a random color. + text (str): if None, will be drawn on the object + alpha (float): blending efficient. Smaller values lead to more transparent masks. + Returns: + output (VisImage): image object with mask drawn. + """ + if color is None: + color = random_color(rgb=True, maximum=1) + color = mplc.to_rgb(color) + + shape2d = (soft_mask.shape[0], soft_mask.shape[1]) + rgba = np.zeros(shape2d + (4,), dtype="float32") + rgba[:, :, :3] = color + rgba[:, :, 3] = soft_mask * alpha + self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) + + if text is not None: + lighter_color = self._change_color_brightness(color, brightness_factor=0.7) + binary_mask = (soft_mask > 0.5).astype("uint8") + # self._draw_text_in_mask(binary_mask, text, lighter_color) + return self.output + + def draw_polygon(self, segment, color, edge_color=None, alpha=0.5): + """ + Args: + segment: numpy array of shape Nx2, containing all the points in the polygon. + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a + full list of formats that are accepted. If not provided, a darker shade + of the polygon color will be used instead. + alpha (float): blending efficient. Smaller values lead to more transparent masks. + Returns: + output (VisImage): image object with polygon drawn. + """ + if edge_color is None: + # make edge color darker than the polygon color + if alpha > 0.8: + edge_color = self._change_color_brightness(color, brightness_factor=-0.7) + else: + edge_color = color + edge_color = mplc.to_rgb(edge_color) + (1,) + + polygon = mpl.patches.Polygon( + segment, + fill=True, + facecolor=mplc.to_rgb(color) + (alpha,), + edgecolor=edge_color, + linewidth=max(self._default_font_size // 15 * self.output.scale, 1), + ) + self.output.ax.add_patch(polygon) + return self.output + + """ + Internal methods: + """ + + def _jitter(self, color): + """ + Randomly modifies given color to produce a slightly different color than the color given. + Args: + color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color + picked. The values in the list are in the [0.0, 1.0] range. + Returns: + jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the + color after being jittered. The values in the list are in the [0.0, 1.0] range. + """ + color = mplc.to_rgb(color) + vec = np.random.rand(3) + # better to do it in another color space + vec = vec / np.linalg.norm(vec) * 0.5 + res = np.clip(vec + color, 0, 1) + return tuple(res) + + def _create_grayscale_image(self, mask=None): + """ + Create a grayscale version of the original image. + The colors in masked area, if given, will be kept. + """ + img_bw = self.img.astype("f4").mean(axis=2) + img_bw = np.stack([img_bw] * 3, axis=2) + if mask is not None: + img_bw[mask] = self.img[mask] + return img_bw + + def _change_color_brightness(self, color, brightness_factor): + """ + Depending on the brightness_factor, gives a lighter or darker color i.e. a color with + less or more saturation than the original color. + Args: + color: color of the polygon. Refer to `matplotlib.colors` for a full list of + formats that are accepted. + brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of + 0 will correspond to no change, a factor in [-1.0, 0) range will result in + a darker color and a factor in (0, 1.0] range will result in a lighter color. + Returns: + modified_color (tuple[double]): a tuple containing the RGB values of the + modified color. Each value in the tuple is in the [0.0, 1.0] range. + """ + assert brightness_factor >= -1.0 and brightness_factor <= 1.0 + color = mplc.to_rgb(color) + polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color)) + modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1]) + modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness + modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness + modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2]) + return modified_color + + def _convert_boxes(self, boxes): + """ + Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension. + """ + if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes): + return boxes.tensor.detach().numpy() + else: + return np.asarray(boxes) + + def _convert_masks(self, masks_or_polygons): + """ + Convert different format of masks or polygons to a tuple of masks and polygons. + Returns: + list[GenericMask]: + """ + + m = masks_or_polygons + if isinstance(m, PolygonMasks): + m = m.polygons + if isinstance(m, BitMasks): + m = m.tensor.numpy() + if isinstance(m, torch.Tensor): + m = m.numpy() + ret = [] + for x in m: + if isinstance(x, GenericMask): + ret.append(x) + else: + ret.append(GenericMask(x, self.output.height, self.output.width)) + return ret + + def _draw_text_in_mask(self, binary_mask, text, color): + """ + Find proper places to draw text given a binary mask. + """ + # TODO sometimes drawn on wrong objects. the heuristics here can improve. + _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8) + if stats[1:, -1].size == 0: + return + largest_component_id = np.argmax(stats[1:, -1]) + 1 + + # draw text on the largest component, as well as other very large components. + for cid in range(1, _num_cc): + if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH: + # median is more stable than centroid + # center = centroids[largest_component_id] + center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1] + self.draw_text(text, center, color=color) + + def _convert_keypoints(self, keypoints): + if isinstance(keypoints, Keypoints): + keypoints = keypoints.tensor + keypoints = np.asarray(keypoints) + return keypoints + + def get_output(self): + """ + Returns: + output (VisImage): the image output containing the visualizations added + to the image. + """ + return self.output \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/evaluation/__init__.py b/src/perception/camera_segmentation/camera_segmentation/evaluation/__init__.py new file mode 100755 index 00000000..49f62369 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/evaluation/__init__.py @@ -0,0 +1,3 @@ +from .detection_coco_evaluator import * +from .coco_evaluator import * +from .cityscapes_evaluation import CityscapesInstanceEvaluator \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/evaluation/cityscapes_evaluation.py b/src/perception/camera_segmentation/camera_segmentation/evaluation/cityscapes_evaluation.py new file mode 100755 index 00000000..4e06ab8c --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/evaluation/cityscapes_evaluation.py @@ -0,0 +1,201 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import glob +import logging +import numpy as np +import os +import tempfile +from collections import OrderedDict +import torch +from PIL import Image + +from detectron2.data import MetadataCatalog +from detectron2.utils import comm +from detectron2.utils.file_io import PathManager + +from .evaluator import DatasetEvaluator + + +class CityscapesEvaluator(DatasetEvaluator): + """ + Base class for evaluation using cityscapes API. + """ + + def __init__(self, dataset_name): + """ + Args: + dataset_name (str): the name of the dataset. + It must have the following metadata associated with it: + "thing_classes", "gt_dir". + """ + self._metadata = MetadataCatalog.get(dataset_name) + self._cpu_device = torch.device("cpu") + self._logger = logging.getLogger(__name__) + + def reset(self): + self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_") + self._temp_dir = self._working_dir.name + # All workers will write to the same results directory + # TODO this does not work in distributed training + assert ( + comm.get_local_size() == comm.get_world_size() + ), "CityscapesEvaluator currently do not work with multiple machines." + self._temp_dir = comm.all_gather(self._temp_dir)[0] + if self._temp_dir != self._working_dir.name: + self._working_dir.cleanup() + self._logger.info( + "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir) + ) + + +class CityscapesInstanceEvaluator(CityscapesEvaluator): + """ + Evaluate instance segmentation results on cityscapes dataset using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + * Only the main process runs evaluation. + """ + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import name2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt") + + if "instances" in output: + output = output["instances"].to(self._cpu_device) + num_instances = len(output) + with open(pred_txt, "w") as fout: + for i in range(num_instances): + pred_class = output.pred_classes[i] + classes = self._metadata.stuff_classes[pred_class] + class_id = name2label[classes].id + score = output.scores[i] + mask = output.pred_masks[i].numpy().astype("uint8") + png_filename = os.path.join( + self._temp_dir, basename + "_{}_{}.png".format(i, classes) + ) + + Image.fromarray(mask * 255).save(png_filename) + fout.write( + "{} {} {}\n".format(os.path.basename(png_filename), class_id, score) + ) + else: + # Cityscapes requires a prediction file for every ground truth image. + with open(pred_txt, "w") as fout: + pass + + def evaluate(self): + """ + Returns: + dict: has a key "segm", whose value is a dict of "AP" and "AP50". + """ + comm.synchronize() + if comm.get_rank() > 0: + return + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json") + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + gt_dir = PathManager.get_local_path(self._metadata.gt_dir) + groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png")) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + )["averages"] + + ret = OrderedDict() + ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100} + self._working_dir.cleanup() + return ret + + +class CityscapesSemSegEvaluator(CityscapesEvaluator): + """ + Evaluate semantic segmentation results on cityscapes dataset using cityscapes API. + + Note: + * It does not work in multi-machine distributed training. + * It contains a synchronization, therefore has to be used on all ranks. + * Only the main process runs evaluation. + """ + + def process(self, inputs, outputs): + from cityscapesscripts.helpers.labels import trainId2label + + for input, output in zip(inputs, outputs): + file_name = input["file_name"] + basename = os.path.splitext(os.path.basename(file_name))[0] + pred_filename = os.path.join(self._temp_dir, basename + "_pred.png") + + output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy() + pred = 255 * np.ones(output.shape, dtype=np.uint8) + for train_id, label in trainId2label.items(): + if label.ignoreInEval: + continue + pred[output == train_id] = label.id + Image.fromarray(pred).save(pred_filename) + + def evaluate(self): + comm.synchronize() + if comm.get_rank() > 0: + return + # Load the Cityscapes eval script *after* setting the required env var, + # since the script reads CITYSCAPES_DATASET into global variables at load time. + import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval + + self._logger.info("Evaluating results under {} ...".format(self._temp_dir)) + + # set some global states in cityscapes evaluation API, before evaluating + cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir) + cityscapes_eval.args.predictionWalk = None + cityscapes_eval.args.JSONOutput = False + cityscapes_eval.args.colorized = False + + # These lines are adopted from + # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa + gt_dir = PathManager.get_local_path(self._metadata.gt_dir) + groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png")) + assert len( + groundTruthImgList + ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format( + cityscapes_eval.args.groundTruthSearch + ) + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt)) + results = cityscapes_eval.evaluateImgLists( + predictionImgList, groundTruthImgList, cityscapes_eval.args + ) + ret = OrderedDict() + ret["sem_seg"] = { + "IoU": 100.0 * results["averageScoreClasses"], + "iIoU": 100.0 * results["averageScoreInstClasses"], + "IoU_sup": 100.0 * results["averageScoreCategories"], + "iIoU_sup": 100.0 * results["averageScoreInstCategories"], + } + self._working_dir.cleanup() + return ret diff --git a/src/perception/camera_segmentation/camera_segmentation/evaluation/coco_evaluator.py b/src/perception/camera_segmentation/camera_segmentation/evaluation/coco_evaluator.py new file mode 100755 index 00000000..dd855785 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/evaluation/coco_evaluator.py @@ -0,0 +1,563 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from .evaluator import DatasetEvaluator + +try: + from detectron2.evaluation.fast_eval_api import COCOeval_opt +except ImportError: + COCOeval_opt = COCOeval + + +class COCOEvaluator(DatasetEvaluator): + """ + Evaluate AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + use_fast_impl=True, + kpt_oks_sigmas=(), + allow_cached_coco=True, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + allow_cached_coco (bool): Whether to use cached coco json from previous validation + runs. You should set this to False if you need to use different validation data. + Defaults to True. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + + if use_fast_impl and (COCOeval_opt is COCOeval): + self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.") + use_fast_impl = False + self._use_fast_impl = use_fast_impl + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = cache_path + convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "instances" in output: + instances = output["instances"].to(self._cpu_device) + prediction["instances"] = instances_to_coco_json(instances, input["image_id"]) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + for pred in predictions: + if "segmentation" in pred: + tasks = {"segm"} + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + use_fast_impl=True, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/evaluation/detection_coco_evaluator.py b/src/perception/camera_segmentation/camera_segmentation/evaluation/detection_coco_evaluator.py new file mode 100755 index 00000000..b3834ecb --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/evaluation/detection_coco_evaluator.py @@ -0,0 +1,723 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + +from .evaluator import DatasetEvaluator + +try: + from detectron2.evaluation.fast_eval_api import COCOeval_opt +except ImportError: + COCOeval_opt = COCOeval + + +class DetectionCOCOEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + max_dets_per_image=None, + use_fast_impl=True, + kpt_oks_sigmas=(), + allow_cached_coco=True, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and + contains all the results in the format they are produced by the model. + 2. "coco_instances_results.json" a json file in COCO's result format. + max_dets_per_image (int): limit on the maximum number of detections per image. + By default in COCO, this limit is to 100, but this can be customized + to be greater, as is needed in evaluation metrics AP fixed and AP pool + (see https://arxiv.org/pdf/2102.01066.pdf) + This doesn't affect keypoint evaluation. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS. + See http://cocodataset.org/#keypoints-eval + When empty, it will use the defaults in COCO. + Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS. + allow_cached_coco (bool): Whether to use cached coco json from previous validation + runs. You should set this to False if you need to use different validation data. + Defaults to True. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + + if use_fast_impl and (COCOeval_opt is COCOeval): + self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.") + use_fast_impl = False + self._use_fast_impl = use_fast_impl + + # COCOeval requires the limit on the number of detections per image (maxDets) to be a list + # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the + # 3rd element (100) is used as the limit on the number of detections per image when + # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval, + # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] + else: + max_dets_per_image = [1, 10, max_dets_per_image] + self._max_dets_per_image = max_dets_per_image + + if tasks is not None and isinstance(tasks, CfgNode): + kpt_oks_sigmas = ( + tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas + ) + self._logger.warn( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + if not hasattr(self._metadata, "json_file"): + if output_dir is None: + raise ValueError( + "output_dir must be provided to COCOEvaluator " + "for datasets not in COCO format." + ) + self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...") + + cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json") + self._metadata.json_file = cache_path + convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._coco_api = COCO(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._coco_api.dataset + if self._do_evaluation: + self._kpt_oks_sigmas = kpt_oks_sigmas + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "box_instances" that contains :class:`Instances`. + """ + for input, output in zip(inputs, outputs): + prediction = {"image_id": input["image_id"]} + + if "box_instances" in output: + instances = output["box_instances"].to(self._cpu_device) + prediction["box_instances"] = instances_to_coco_json(instances, input["image_id"]) + if "proposals" in output: + prediction["proposals"] = output["proposals"].to(self._cpu_device) + if len(prediction) > 1: + self._predictions.append(prediction) + + def evaluate(self, img_ids=None): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + if "proposals" in predictions[0]: + self._eval_box_proposals(predictions) + if "box_instances" in predictions[0]: + self._eval_predictions(predictions, img_ids=img_ids) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _tasks_from_predictions(self, predictions): + """ + Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions. + """ + tasks = {"bbox"} + for pred in predictions: + if "keypoints" in pred: + tasks.add("keypoints") + return sorted(tasks) + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["box_instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res + + def _eval_box_proposals(self, predictions): + """ + Evaluate the box proposals in predictions. + Fill self._results with the metrics for "box_proposals" task. + """ + if self._output_dir: + # Saving generated box proposals to file. + # Predicted box_proposals are in XYXY_ABS mode. + bbox_mode = BoxMode.XYXY_ABS.value + ids, boxes, objectness_logits = [], [], [] + for prediction in predictions: + ids.append(prediction["image_id"]) + boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy()) + objectness_logits.append(prediction["proposals"].objectness_logits.numpy()) + + proposal_data = { + "boxes": boxes, + "objectness_logits": objectness_logits, + "ids": ids, + "bbox_mode": bbox_mode, + } + with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f: + pickle.dump(proposal_data, f) + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info("Evaluating bbox proposals ...") + res = {} + areas = {"all": "", "small": "s", "medium": "m", "large": "l"} + for limit in [100, 1000]: + for area, suffix in areas.items(): + stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit) + key = "AR{}@{:d}".format(suffix, limit) + res[key] = float(stats["ar"].item() * 100) + self._logger.info("Proposal metrics: \n" + create_small_table(res)) + self._results["box_proposals"] = res + + def _derive_coco_results(self, coco_eval, iou_type, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + + Returns: + a dict of {metric name: score} + """ + + metrics = { + "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"], + "keypoints": ["AP", "AP50", "AP75", "APm", "APl"], + }[iou_type] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format(iou_type) + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format(iou_type) + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json(instances, img_id): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + img_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + num_instance = len(instances) + if num_instance == 0: + return [] + + boxes = instances.pred_boxes.tensor.numpy() + boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) + boxes = boxes.tolist() + scores = instances.scores.tolist() + classes = instances.pred_classes.tolist() + + has_mask = instances.has("pred_masks") + if has_mask: + # use RLE to encode the masks, because they are too large and takes memory + # since this evaluator stores outputs of the entire dataset + rles = [ + mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + for mask in instances.pred_masks + ] + for rle in rles: + # "counts" is an array encoded by mask_util as a byte-stream. Python3's + # json writer which always produces strings cannot serialize a bytestream + # unless you decode it. Thankfully, utf-8 works out (which is also what + # the pycocotools/_mask.pyx does). + rle["counts"] = rle["counts"].decode("utf-8") + + has_keypoints = instances.has("pred_keypoints") + if has_keypoints: + keypoints = instances.pred_keypoints + + results = [] + for k in range(num_instance): + result = { + "image_id": img_id, + "category_id": classes[k], + "bbox": boxes[k], + "score": scores[k], + } + if has_mask: + result["segmentation"] = rles[k] + if has_keypoints: + # In COCO annotations, + # keypoints coordinates are pixel indices. + # However our predictions are floating point coordinates. + # Therefore we subtract 0.5 to be consistent with the annotation format. + # This is the inverse of data loading logic in `datasets/coco.py`. + keypoints[k][:, :2] -= 0.5 + result["keypoints"] = keypoints[k].flatten().tolist() + results.append(result) + return results + + +# inspired from Detectron: +# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa +def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None): + """ + Evaluate detection proposal recall metrics. This function is a much + faster alternative to the official COCO API recall evaluation code. However, + it produces slightly different results. + """ + # Record max overlap value for each gt box + # Return vector of overlap values + areas = { + "all": 0, + "small": 1, + "medium": 2, + "large": 3, + "96-128": 4, + "128-256": 5, + "256-512": 6, + "512-inf": 7, + } + area_ranges = [ + [0**2, 1e5**2], # all + [0**2, 32**2], # small + [32**2, 96**2], # medium + [96**2, 1e5**2], # large + [96**2, 128**2], # 96-128 + [128**2, 256**2], # 128-256 + [256**2, 512**2], # 256-512 + [512**2, 1e5**2], + ] # 512-inf + assert area in areas, "Unknown area range: {}".format(area) + area_range = area_ranges[areas[area]] + gt_overlaps = [] + num_pos = 0 + + for prediction_dict in dataset_predictions: + predictions = prediction_dict["proposals"] + + # sort predictions in descending order + # TODO maybe remove this and make it explicit in the documentation + inds = predictions.objectness_logits.sort(descending=True)[1] + predictions = predictions[inds] + + ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"]) + anno = coco_api.loadAnns(ann_ids) + gt_boxes = [ + BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + for obj in anno + if obj["iscrowd"] == 0 + ] + gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes + gt_boxes = Boxes(gt_boxes) + gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0]) + + if len(gt_boxes) == 0 or len(predictions) == 0: + continue + + valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1]) + gt_boxes = gt_boxes[valid_gt_inds] + + num_pos += len(gt_boxes) + + if len(gt_boxes) == 0: + continue + + if limit is not None and len(predictions) > limit: + predictions = predictions[:limit] + + overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes) + + _gt_overlaps = torch.zeros(len(gt_boxes)) + for j in range(min(len(predictions), len(gt_boxes))): + # find which proposal box maximally covers each gt box + # and get the iou amount of coverage for each gt box + max_overlaps, argmax_overlaps = overlaps.max(dim=0) + + # find which gt box is 'best' covered (i.e. 'best' = most iou) + gt_ovr, gt_ind = max_overlaps.max(dim=0) + assert gt_ovr >= 0 + # find the proposal box that covers the best covered gt box + box_ind = argmax_overlaps[gt_ind] + # record the iou coverage of this gt box + _gt_overlaps[j] = overlaps[box_ind, gt_ind] + assert _gt_overlaps[j] == gt_ovr + # mark the proposal box and the gt box as used + overlaps[box_ind, :] = -1 + overlaps[:, gt_ind] = -1 + + # append recorded iou coverage level + gt_overlaps.append(_gt_overlaps) + gt_overlaps = ( + torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32) + ) + gt_overlaps, _ = torch.sort(gt_overlaps) + + if thresholds is None: + step = 0.05 + thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32) + recalls = torch.zeros_like(thresholds) + # compute recall for each iou threshold + for i, t in enumerate(thresholds): + recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos) + # ar = 2 * np.trapz(recalls, thresholds) + ar = recalls.mean() + return { + "ar": ar, + "recalls": recalls, + "thresholds": thresholds, + "gt_overlaps": gt_overlaps, + "num_pos": num_pos, + } + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + iou_type, + kpt_oks_sigmas=None, + use_fast_impl=True, + img_ids=None, + max_dets_per_image=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + if iou_type == "segm": + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + if max_dets_per_image is None: + max_dets_per_image = [1, 10, 100] # Default from COCOEval + else: + assert ( + len(max_dets_per_image) >= 3 + ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3" + # In the case that user supplies a custom input for max_dets_per_image, + # apply COCOevalMaxDets to evaluate AP with the custom input. + if max_dets_per_image[2] != 100: + coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type) + if iou_type != "keypoints": + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + if iou_type == "keypoints": + # Use the COCO default keypoint OKS sigmas unless overrides are specified + if kpt_oks_sigmas: + assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!" + coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas) + # COCOAPI requires every detection and every gt to have keypoints, so + # we just take the first entry from both + num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3 + num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3 + num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas) + assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, ( + f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. " + f"Ground truth contains {num_keypoints_gt} keypoints. " + f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. " + "They have to agree with each other. For meaning of OKS, please refer to " + "http://cocodataset.org/#keypoints-eval." + ) + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval + + +class COCOevalMaxDets(COCOeval): + """ + Modified version of COCOeval for evaluating AP with a custom + maxDets (by default for COCO, maxDets is 100) + """ + + def summarize(self): + """ + Compute and display summary metrics for evaluation results given + a custom value for max_dets_per_image + """ + + def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100): + p = self.params + iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}" + titleStr = "Average Precision" if ap == 1 else "Average Recall" + typeStr = "(AP)" if ap == 1 else "(AR)" + iouStr = ( + "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1]) + if iouThr is None + else "{:0.2f}".format(iouThr) + ) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval["precision"] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, :, aind, mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval["recall"] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:, :, aind, mind] + if len(s[s > -1]) == 0: + mean_s = -1 + else: + mean_s = np.mean(s[s > -1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + + def _summarizeDets(): + stats = np.zeros((12,)) + # Evaluate AP using the custom limit on maximum detections per image + stats[0] = _summarize(1, maxDets=self.params.maxDets[2]) + stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2]) + return stats + + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=0.5) + stats[2] = _summarize(1, maxDets=20, iouThr=0.75) + stats[3] = _summarize(1, maxDets=20, areaRng="medium") + stats[4] = _summarize(1, maxDets=20, areaRng="large") + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=0.5) + stats[7] = _summarize(0, maxDets=20, iouThr=0.75) + stats[8] = _summarize(0, maxDets=20, areaRng="medium") + stats[9] = _summarize(0, maxDets=20, areaRng="large") + return stats + + if not self.eval: + raise Exception("Please run accumulate() first") + iouType = self.params.iouType + if iouType == "segm" or iouType == "bbox": + summarize = _summarizeDets + elif iouType == "keypoints": + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/evaluation/evaluator.py b/src/perception/camera_segmentation/camera_segmentation/evaluation/evaluator.py new file mode 100755 index 00000000..7d0848c7 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/evaluation/evaluator.py @@ -0,0 +1,228 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import datetime +import logging +import time +from collections import OrderedDict, abc +from contextlib import ExitStack, contextmanager +from typing import List, Union +import torch +from torch import nn + +from detectron2.utils.comm import get_world_size, is_main_process +from detectron2.utils.logger import log_every_n_seconds + + +class DatasetEvaluator: + """ + Base class for a dataset evaluator. + + The function :func:`inference_on_dataset` runs the model over + all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs. + + This class will accumulate information of the inputs/outputs (by :meth:`process`), + and produce evaluation results in the end (by :meth:`evaluate`). + """ + + def reset(self): + """ + Preparation for a new round of evaluation. + Should be called before starting a round of evaluation. + """ + pass + + def process(self, inputs, outputs): + """ + Process the pair of inputs and outputs. + If they contain batches, the pairs can be consumed one-by-one using `zip`: + + .. code-block:: python + + for input_, output in zip(inputs, outputs): + # do evaluation on single input/output pair + ... + + Args: + inputs (list): the inputs that's used to call the model. + outputs (list): the return value of `model(inputs)` + """ + pass + + def evaluate(self): + """ + Evaluate/summarize the performance, after processing all input/output pairs. + + Returns: + dict: + A new evaluator class can return a dict of arbitrary format + as long as the user can process the results. + In our train_net.py, we expect the following format: + + * key: the name of the task (e.g., bbox) + * value: a dict of {metric name: score}, e.g.: {"AP50": 80} + """ + pass + + +class DatasetEvaluators(DatasetEvaluator): + """ + Wrapper class to combine multiple :class:`DatasetEvaluator` instances. + + This class dispatches every evaluation call to + all of its :class:`DatasetEvaluator`. + """ + + def __init__(self, evaluators): + """ + Args: + evaluators (list): the evaluators to combine. + """ + super().__init__() + self._evaluators = evaluators + + def reset(self): + for evaluator in self._evaluators: + evaluator.reset() + + def process(self, inputs, outputs): + for evaluator in self._evaluators: + evaluator.process(inputs, outputs) + + def evaluate(self): + results = OrderedDict() + for evaluator in self._evaluators: + result = evaluator.evaluate() + if is_main_process() and result is not None: + for k, v in result.items(): + assert ( + k not in results + ), "Different evaluators produce results with the same key {}".format(k) + results[k] = v + return results + + +def inference_on_dataset( + model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None] +): + """ + Run model on the data_loader and evaluate the metrics with evaluator. + Also benchmark the inference speed of `model.__call__` accurately. + The model will be used in eval mode. + + Args: + model (callable): a callable which takes an object from + `data_loader` and returns some outputs. + + If it's an nn.Module, it will be temporarily set to `eval` mode. + If you wish to evaluate a model in `training` mode instead, you can + wrap the given model and override its behavior of `.eval()` and `.train()`. + data_loader: an iterable object with a length. + The elements it generates will be the inputs to the model. + evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark, + but don't want to do any evaluation. + + Returns: + The return value of `evaluator.evaluate()` + """ + num_devices = get_world_size() + logger = logging.getLogger(__name__) + logger.info("Start inference on {} batches".format(len(data_loader))) + + total = len(data_loader) # inference data loader must have a fixed length + if evaluator is None: + # create a no-op evaluator + evaluator = DatasetEvaluators([]) + if isinstance(evaluator, abc.MutableSequence): + evaluator = DatasetEvaluators(evaluator) + evaluator.reset() + + num_warmup = min(5, total - 1) + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + with ExitStack() as stack: + if isinstance(model, nn.Module): + stack.enter_context(inference_context(model)) + stack.enter_context(torch.no_grad()) + + start_data_time = time.perf_counter() + for idx, inputs in enumerate(data_loader): + total_data_time += time.perf_counter() - start_data_time + if idx == num_warmup: + start_time = time.perf_counter() + total_data_time = 0 + total_compute_time = 0 + total_eval_time = 0 + + start_compute_time = time.perf_counter() + outputs = model(inputs) + if torch.cuda.is_available(): + torch.cuda.synchronize() + total_compute_time += time.perf_counter() - start_compute_time + + start_eval_time = time.perf_counter() + evaluator.process(inputs, outputs) + total_eval_time += time.perf_counter() - start_eval_time + + iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) + data_seconds_per_iter = total_data_time / iters_after_start + compute_seconds_per_iter = total_compute_time / iters_after_start + eval_seconds_per_iter = total_eval_time / iters_after_start + total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start + if idx >= num_warmup * 2 or compute_seconds_per_iter > 5: + eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1))) + log_every_n_seconds( + logging.INFO, + ( + f"Inference done {idx + 1}/{total}. " + f"Dataloading: {data_seconds_per_iter:.4f} s/iter. " + f"Inference: {compute_seconds_per_iter:.4f} s/iter. " + f"Eval: {eval_seconds_per_iter:.4f} s/iter. " + f"Total: {total_seconds_per_iter:.4f} s/iter. " + f"ETA={eta}" + ), + n=5, + ) + start_data_time = time.perf_counter() + + # Measure the time only for this worker (before the synchronization barrier) + total_time = time.perf_counter() - start_time + total_time_str = str(datetime.timedelta(seconds=total_time)) + # NOTE this format is parsed by grep + logger.info( + "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_time_str, total_time / (total - num_warmup), num_devices + ) + ) + total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time))) + logger.info( + "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format( + total_compute_time_str, total_compute_time / (total - num_warmup), num_devices + ) + ) + + results = evaluator.evaluate() + # An evaluator may return None when not in main process. + # Replace it by an empty dict instead to make it easier for downstream code to handle + if results is None: + results = {} + return results + + +@contextmanager +def inference_context(model): + """ + A context where the model is temporarily changed to eval mode, + and restored to previous mode afterwards. + + Args: + model: a torch Module + """ + training_mode = model.training + model.eval() + yield + model.train(training_mode) diff --git a/src/perception/camera_segmentation/camera_segmentation/evaluation/instance_evaluation.py b/src/perception/camera_segmentation/camera_segmentation/evaluation/instance_evaluation.py new file mode 100755 index 00000000..7c5e429f --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/evaluation/instance_evaluation.py @@ -0,0 +1,110 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py +# ------------------------------------------------------------------------------ + +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco +from detectron2.evaluation.fast_eval_api import COCOeval_opt +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + + +# modified from COCOEvaluator for instance segmetnat +class InstanceSegEvaluator(COCOEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + # all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + # num_classes = len(all_contiguous_ids) + # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + # assert category_id < num_classes, ( + # f"A prediction has class={category_id}, " + # f"but the dataset only has {num_classes} classes and " + # f"predicted class id should be in [0, {num_classes - 1}]." + # ) + assert category_id in reverse_id_mapping, ( + f"A prediction has class={category_id}, " + f"but the dataset only has class ids in {dataset_id_to_contiguous_id}." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/__init__.py new file mode 100755 index 00000000..4e133836 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/__init__.py @@ -0,0 +1,5 @@ +from .backbone.swin import D2SwinTransformer +from .backbone.dinat import D2DiNAT +from .pixel_decoder.fpn import BasePixelDecoder +from .pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder +from .meta_arch.oneformer_head import OneFormerHead diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/__init__.py new file mode 100755 index 00000000..9020c2df --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/dinat.py b/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/dinat.py new file mode 100755 index 00000000..ba0af107 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/dinat.py @@ -0,0 +1,296 @@ +# -------------------------------------------------------- +# Neighborhood Attention Transformer +# Licensed under The MIT License +# Written by Ali Hassani +# -------------------------------------------------------- + +# Modified by Jitesh Jain + +import torch +import torch.nn as nn +from timm.models.layers import DropPath +from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec + +from natten import NeighborhoodAttention2D as NeighborhoodAttention + + +class ConvTokenizer(nn.Module): + def __init__(self, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + self.proj = nn.Sequential( + nn.Conv2d(in_chans, embed_dim // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), + nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), + ) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = self.proj(x).permute(0, 2, 3, 1) + if self.norm is not None: + x = self.norm(x) + return x + + +class ConvDownsampler(nn.Module): + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + x = self.reduction(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + x = self.norm(x) + return x + + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class NATLayer(nn.Module): + def __init__(self, dim, num_heads, kernel_size=7, dilation=None, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=None): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.mlp_ratio = mlp_ratio + + self.norm1 = norm_layer(dim) + self.attn = NeighborhoodAttention( + dim, kernel_size=kernel_size, dilation=dilation, num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop) + self.layer_scale = False + if layer_scale is not None and type(layer_scale) in [int, float]: + self.layer_scale = True + self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True) + self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True) + + def forward(self, x): + if not self.layer_scale: + shortcut = x + x = self.norm1(x) + x = self.attn(x) + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + shortcut = x + x = self.norm1(x) + x = self.attn(x) + x = shortcut + self.drop_path(self.gamma1 * x) + x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x))) + return x + + + +class NATBlock(nn.Module): + def __init__(self, dim, depth, num_heads, kernel_size, dilations=None, + downsample=True, + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, layer_scale=None): + super().__init__() + self.dim = dim + self.depth = depth + + self.blocks = nn.ModuleList([ + NATLayer(dim=dim, + num_heads=num_heads, + kernel_size=kernel_size, + dilation=None if dilations is None else dilations[i], + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + layer_scale=layer_scale) + for i in range(depth)]) + + self.downsample = None if not downsample else ConvDownsampler(dim=dim, norm_layer=norm_layer) + + def forward(self, x): + for blk in self.blocks: + x = blk(x) + if self.downsample is None: + return x, x + return self.downsample(x), x + + +class DiNAT(nn.Module): + def __init__(self, + embed_dim, + mlp_ratio, + depths, + num_heads, + drop_path_rate=0.2, + in_chans=3, + kernel_size=7, + dilations=None, + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + norm_layer=nn.LayerNorm, + frozen_stages=-1, + layer_scale=None, + **kwargs): + super().__init__() + self.num_levels = len(depths) + self.embed_dim = embed_dim + self.num_features = [int(embed_dim * 2 ** i) for i in range(self.num_levels)] + self.mlp_ratio = mlp_ratio + + self.patch_embed = ConvTokenizer(in_chans=in_chans, embed_dim=embed_dim, norm_layer=norm_layer) + + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] + self.levels = nn.ModuleList() + for i in range(self.num_levels): + level = NATBlock(dim=int(embed_dim * 2 ** i), + depth=depths[i], + num_heads=num_heads[i], + kernel_size=kernel_size, + dilations=None if dilations is None else dilations[i], + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])], + norm_layer=norm_layer, + downsample=(i < self.num_levels - 1), + layer_scale=layer_scale) + self.levels.append(level) + + # add a norm layer for each output + self.out_indices = out_indices + for i_layer in self.out_indices: + layer = norm_layer(self.num_features[i_layer]) + layer_name = f'norm{i_layer}' + self.add_module(layer_name, layer) + + self.frozen_stages = frozen_stages + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 2: + for i in range(0, self.frozen_stages - 1): + m = self.network[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(DiNAT, self).train(mode) + self._freeze_stages() + + def forward_embeddings(self, x): + x = self.patch_embed(x) + return x + + def forward_tokens(self, x): + outs = {} + for idx, level in enumerate(self.levels): + x, xo = level(x) + if idx in self.out_indices: + norm_layer = getattr(self, f'norm{idx}') + x_out = norm_layer(xo) + outs["res{}".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous() + return outs + + def forward(self, x): + x = self.forward_embeddings(x) + return self.forward_tokens(x) + + +@BACKBONE_REGISTRY.register() +class D2DiNAT(DiNAT, Backbone): + def __init__(self, cfg, input_shape): + + embed_dim = cfg.MODEL.DiNAT.EMBED_DIM + mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO + depths = cfg.MODEL.DiNAT.DEPTHS + num_heads = cfg.MODEL.DiNAT.NUM_HEADS + drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE + kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE + out_indices = cfg.MODEL.DiNAT.OUT_INDICES + dilations = cfg.MODEL.DiNAT.DILATIONS + + super().__init__( + embed_dim=embed_dim, + mlp_ratio=mlp_ratio, + depths=depths, + num_heads=num_heads, + drop_path_rate=drop_path_rate, + kernel_size=kernel_size, + out_indices=out_indices, + dilations=dilations, + ) + + self._out_features = cfg.MODEL.DiNAT.OUT_FEATURES + + self._out_feature_strides = { + "res2": 4, + "res3": 8, + "res4": 16, + "res5": 32, + } + self._out_feature_channels = { + "res2": self.num_features[0], + "res3": self.num_features[1], + "res4": self.num_features[2], + "res5": self.num_features[3], + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert ( + x.dim() == 4 + ), f"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + y = super().forward(x) + for k in y.keys(): + if k in self._out_features: + outputs[k] = y[k] + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + @property + def size_divisibility(self): + return 32 diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/swin.py b/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/swin.py new file mode 100755 index 00000000..1ae976ec --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/backbone/swin.py @@ -0,0 +1,771 @@ +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu, Yutong Lin, Yixuan Wei +# -------------------------------------------------------- + +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former +# ------------------------------------------------------------------------------ + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + +from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__( + self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop + ) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + use_checkpoint=False, + ): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + pretrain_img_size[0] // patch_size[0], + pretrain_img_size[1] // patch_size[1], + ] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2 ** i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint, + ) + self.layers.append(layer) + + num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate( + self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" + ) + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = {} + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + if i in self.out_indices: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs["res{}".format(i + 2)] = out + + return outs + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +@BACKBONE_REGISTRY.register() +class D2SwinTransformer(SwinTransformer, Backbone): + def __init__(self, cfg, input_shape): + + pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE + patch_size = cfg.MODEL.SWIN.PATCH_SIZE + in_chans = 3 + embed_dim = cfg.MODEL.SWIN.EMBED_DIM + depths = cfg.MODEL.SWIN.DEPTHS + num_heads = cfg.MODEL.SWIN.NUM_HEADS + window_size = cfg.MODEL.SWIN.WINDOW_SIZE + mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO + qkv_bias = cfg.MODEL.SWIN.QKV_BIAS + qk_scale = cfg.MODEL.SWIN.QK_SCALE + drop_rate = cfg.MODEL.SWIN.DROP_RATE + attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE + drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE + norm_layer = nn.LayerNorm + ape = cfg.MODEL.SWIN.APE + patch_norm = cfg.MODEL.SWIN.PATCH_NORM + use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT + + super().__init__( + pretrain_img_size, + patch_size, + in_chans, + embed_dim, + depths, + num_heads, + window_size, + mlp_ratio, + qkv_bias, + qk_scale, + drop_rate, + attn_drop_rate, + drop_path_rate, + norm_layer, + ape, + patch_norm, + use_checkpoint=use_checkpoint, + ) + + self._out_features = cfg.MODEL.SWIN.OUT_FEATURES + + self._out_feature_strides = { + "res2": 4, + "res3": 8, + "res4": 16, + "res5": 32, + } + self._out_feature_channels = { + "res2": self.num_features[0], + "res3": self.num_features[1], + "res4": self.num_features[2], + "res5": self.num_features[3], + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert ( + x.dim() == 4 + ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + y = super().forward(x) + for k in y.keys(): + if k in self._out_features: + outputs[k] = y[k] + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + @property + def size_divisibility(self): + return 32 diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/criterion.py b/src/perception/camera_segmentation/camera_segmentation/modeling/criterion.py new file mode 100755 index 00000000..dc78a93b --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/criterion.py @@ -0,0 +1,323 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +""" +OneFormer criterion. +""" +import logging + +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.utils.comm import get_world_size +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) + +from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list +from ..utils import box_ops +import torch.distributed as dist +import diffdist.functional as diff_dist +import numpy as np + +def dist_collect(x): + """ collect all tensor from all GPUs + args: + x: shape (mini_batch, ...) + returns: + shape (mini_batch * num_gpu, ...) + """ + x = x.contiguous() + out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())] + out_list = diff_dist.all_gather(out_list, x) + return torch.cat(out_list, dim=0).contiguous() + +def dice_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(-1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_masks + + +dice_loss_jit = torch.jit.script( + dice_loss +) # type: torch.jit.ScriptModule + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + loss = loss.mean(1) + return loss.sum() / num_masks + + +sigmoid_ce_loss_jit = torch.jit.script( + sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class SetCriterion(nn.Module): + """This class computes the loss for DETR. + The process happens in two steps: + 1) we compute hungarian assignment between ground truth boxes and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) + """ + + def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, + num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None): + """Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + self.cross_entropy = nn.CrossEntropyLoss() + + # pointwise mask loss parameters + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + self.contrast_temperature = contrast_temperature + if self.contrast_temperature is not None: + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature)) + + + def loss_contrastive(self, outputs, targets, indices, num_masks): + assert "contrastive_logits" in outputs + assert "texts" in outputs + image_x = outputs["contrastive_logits"].float() + + batch_size = image_x.shape[0] + # get label globally + labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank() + + text_x = outputs["texts"] + + # [B, C] + image_x = F.normalize(image_x.flatten(1), dim=-1) + text_x = F.normalize(text_x.flatten(1), dim=-1) + + logits_per_img = image_x @ dist_collect(text_x).t() + logits_per_text = text_x @ dist_collect(image_x).t() + + logit_scale = torch.clamp(self.logit_scale.exp(), max=100) + loss_img = self.cross_entropy(logits_per_img * logit_scale, labels) + loss_text = self.cross_entropy(logits_per_text * logit_scale, labels) + + loss_contrastive = loss_img + loss_text + + losses = {"loss_contrastive": loss_contrastive} + return losses + + def loss_labels(self, outputs, targets, indices, num_masks): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"].float() + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + + ce_weight = torch.full( + src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device + ) + ce_weight[idx] = torch.tensor(1.).to(target_classes.device) + + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction="none") + loss_ce = loss_ce.sum(1) / ce_weight.sum() + loss_ce = loss_ce.sum() + losses = {"loss_ce": loss_ce} + return losses + + def loss_masks(self, outputs, targets, indices, num_masks): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + # TODO use valid to mask invalid areas due to padding in loss + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + # No need to upsample predictions as we are using normalized coordinates :) + # N x 1 x H x W + src_masks = src_masks[:, None] + target_masks = target_masks[:, None] + + with torch.no_grad(): + # sample point_coords + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + # get gt labels + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), + "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks), + } + + del src_masks + del target_masks + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_masks): + loss_map = { + 'labels': self.loss_labels, + 'masks': self.loss_masks, + 'contrastive': self.loss_contrastive, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_masks) + + def forward(self, outputs, targets): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_masks = sum(len(t["labels"]) for t in targets) + num_masks = torch.as_tensor( + [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_masks) + num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_masks)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + if loss == "contrastive": + continue + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "losses: {}".format(self.losses), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "num_points: {}".format(self.num_points), + "oversample_ratio: {}".format(self.oversample_ratio), + "importance_sample_ratio: {}".format(self.importance_sample_ratio), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/matcher.py b/src/perception/camera_segmentation/camera_segmentation/modeling/matcher.py new file mode 100755 index 00000000..27f99c19 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/matcher.py @@ -0,0 +1,212 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +""" +Modules to compute the matching cost and solve the corresponding LSAP. +""" +import torch +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment +from torch import nn +from torch.cuda.amp import autocast +import numpy as np + +from detectron2.projects.point_rend.point_features import point_sample + + +def linear_sum_assignment_with_nan(cost_matrix): + cost_matrix = np.asarray(cost_matrix) + nan = np.isnan(cost_matrix).any() + nan_all = np.isnan(cost_matrix).all() + empty = cost_matrix.size == 0 + + if not empty: + if nan_all: + print('Matrix contains all NaN values!') + elif nan: + print('Matrix contains NaN values!') + + if nan_all: + cost_matrix = np.empty(shape=(0, 0)) + elif nan: + cost_matrix[np.isnan(cost_matrix)] = 100 + + return linear_sum_assignment(cost_matrix) + +def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets) + denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :] + loss = 1 - (numerator + 1) / (denominator + 1) + return loss + + +batch_dice_loss_jit = torch.jit.script( + batch_dice_loss +) # type: torch.jit.ScriptModule + + +def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + hw = inputs.shape[1] + + pos = F.binary_cross_entropy_with_logits( + inputs, torch.ones_like(inputs), reduction="none" + ) + neg = F.binary_cross_entropy_with_logits( + inputs, torch.zeros_like(inputs), reduction="none" + ) + + loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum( + "nc,mc->nm", neg, (1 - targets) + ) + + return loss / hw + + +batch_sigmoid_ce_loss_jit = torch.jit.script( + batch_sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +class HungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__(self, cost_class: float = 1, cost_mask: float = 1, + cost_dice: float = 1, num_points: int = 0): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost + cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_mask = cost_mask + self.cost_dice = cost_dice + + assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0" + + self.num_points = num_points + + @torch.no_grad() + def memory_efficient_forward(self, outputs, targets): + """More memory-friendly matching""" + bs, num_queries = outputs["pred_logits"].shape[:2] + + indices = [] + + # Iterate through batch size + for b in range(bs): + out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes] + tgt_ids = targets[b]["labels"] + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + cost_class = -out_prob[:, tgt_ids] + + out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred] + # gt masks are already padded when preparing target + tgt_mask = targets[b]["masks"].to(out_mask) + + out_mask = out_mask[:, None] + tgt_mask = tgt_mask[:, None] + # all masks share the same set of points for efficient matching! + point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device) + # get gt labels + tgt_mask = point_sample( + tgt_mask, + point_coords.repeat(tgt_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + out_mask = point_sample( + out_mask, + point_coords.repeat(out_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + with autocast(enabled=False): + out_mask = out_mask.float() + tgt_mask = tgt_mask.float() + # Compute the focal loss between masks + cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask) + # Compute the dice loss betwen masks + cost_dice = batch_dice_loss(out_mask, tgt_mask) + + # Final cost matrix + C = ( + self.cost_mask * cost_mask + + self.cost_class * cost_class + + self.cost_dice * cost_dice + ) + C = C.reshape(num_queries, -1).cpu() + + indices.append(linear_sum_assignment_with_nan(C)) + + return [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices + ] + + @torch.no_grad() + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + + return self.memory_efficient_forward(outputs, targets) + + def __repr__(self, _repr_indent=4): + head = "Matcher " + self.__class__.__name__ + body = [ + "cost_class: {}".format(self.cost_class), + "cost_mask: {}".format(self.cost_mask), + "cost_dice: {}".format(self.cost_dice), + ] + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/meta_arch/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/meta_arch/__init__.py new file mode 100755 index 00000000..8b137891 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/meta_arch/__init__.py @@ -0,0 +1 @@ + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/meta_arch/oneformer_head.py b/src/perception/camera_segmentation/camera_segmentation/modeling/meta_arch/oneformer_head.py new file mode 100755 index 00000000..cf6dbd9f --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/meta_arch/oneformer_head.py @@ -0,0 +1,135 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import logging +from copy import deepcopy +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY +from ..pixel_decoder.fpn import build_pixel_decoder +from ..transformer_decoder.oneformer_transformer_decoder import build_transformer_decoder + +@SEM_SEG_HEADS_REGISTRY.register() +class OneFormerHead(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + newk = k.replace(prefix, prefix + "pixel_decoder.") + # logger.debug(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + # extra parameters + transformer_predictor: nn.Module, + transformer_in_feature: str, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + transformer_predictor: the transformer decoder that makes prediction + transformer_in_feature: input feature name to the transformer_predictor + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = 4 + self.loss_weight = loss_weight + + self.pixel_decoder = pixel_decoder + self.predictor = transformer_predictor + self.transformer_in_feature = transformer_in_feature + + self.num_classes = num_classes + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + # figure out in_channels to transformer predictor + if cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == "pixel_embedding": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + else: + transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels + + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "pixel_decoder": build_pixel_decoder(cfg, input_shape), + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + "transformer_in_feature": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE, + "transformer_predictor": build_transformer_decoder( + cfg, + transformer_predictor_in_channels, + mask_classification=True, + ), + } + + def forward(self, features, tasks, mask=None): + return self.layers(features, tasks, mask) + + def layers(self, features, tasks, mask=None): + mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features) + + if self.transformer_in_feature == "multi_scale_pixel_decoder": + predictions = self.predictor(multi_scale_features, mask_features, tasks, mask) + else: + if self.transformer_in_feature == "transformer_encoder": + assert ( + transformer_encoder_features is not None + ), "Please use the TransformerEncoderPixelDecoder." + predictions = self.predictor(transformer_encoder_features, mask_features, mask) + elif self.transformer_in_feature == "pixel_embedding": + predictions = self.predictor(mask_features, mask_features, mask) + else: + predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask) + return predictions diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/__init__.py new file mode 100755 index 00000000..9020c2df --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/fpn.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/fpn.py new file mode 100755 index 00000000..a56b89c5 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/fpn.py @@ -0,0 +1,312 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ +from torch.cuda.amp import autocast + +from detectron2.config import configurable +from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.position_encoding import PositionEmbeddingSine +from ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn + + +def build_pixel_decoder(cfg, input_shape): + """ + Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`. + """ + name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME + model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) + forward_features = getattr(model, "forward_features", None) + if not callable(forward_features): + raise ValueError( + "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. " + f"Please implement forward_features for {name} to only return mask features." + ) + return model + + +# This is a modified FPN decoder. +@SEM_SEG_HEADS_REGISTRY.register() +class BasePixelDecoder(nn.Module): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__() + + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + feature_channels = [v.channels for k, v in input_shape] + + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(feature_channels): + if idx == len(self.in_features) - 1: + output_norm = get_norm(norm, conv_dim) + output_conv = Conv2d( + in_channels, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(output_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(None) + output_convs.append(output_conv) + else: + lateral_norm = get_norm(norm, conv_dim) + output_norm = get_norm(norm, conv_dim) + + lateral_conv = Conv2d( + in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + self.add_module("adapter_{}".format(idx + 1), lateral_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + self.mask_dim = mask_dim + self.mask_features = Conv2d( + conv_dim, + mask_dim, + kernel_size=3, + stride=1, + padding=1, + ) + weight_init.c2_xavier_fill(self.mask_features) + + self.oneformer_num_feature_levels = 3 # always use 3 scales + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = {} + ret["input_shape"] = { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + } + ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM + return ret + + def forward_features(self, features): + multi_scale_features = [] + num_cur_levels = 0 + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[::-1]): + x = features[f] + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + if lateral_conv is None: + y = output_conv(x) + else: + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest") + y = output_conv(y) + if num_cur_levels < self.oneformer_num_feature_levels: + multi_scale_features.append(y) + num_cur_levels += 1 + return self.mask_features(y), None, multi_scale_features + + def forward(self, features, targets=None): + logger = logging.getLogger(__name__) + logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.") + return self.forward_features(features) + + +class TransformerEncoderOnly(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_encoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, pos_embed): + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + if mask is not None: + mask = mask.flatten(1) + + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + return memory.permute(1, 2, 0).view(bs, c, h, w) + + +# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map. +@SEM_SEG_HEADS_REGISTRY.register() +class TransformerEncoderPixelDecoder(BasePixelDecoder): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + transformer_dropout: float, + transformer_nheads: int, + transformer_dim_feedforward: int, + transformer_enc_layers: int, + transformer_pre_norm: bool, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_dropout: dropout probability in transformer + transformer_nheads: number of heads in transformer + transformer_dim_feedforward: dimension of feedforward network + transformer_enc_layers: number of transformer encoder layers + transformer_pre_norm: whether to use pre-layernorm or not + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm) + + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + in_channels = feature_channels[len(self.in_features) - 1] + self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1) + weight_init.c2_xavier_fill(self.input_proj) + self.transformer = TransformerEncoderOnly( + d_model=conv_dim, + dropout=transformer_dropout, + nhead=transformer_nheads, + dim_feedforward=transformer_dim_feedforward, + num_encoder_layers=transformer_enc_layers, + normalize_before=transformer_pre_norm, + ) + N_steps = conv_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + # update layer + use_bias = norm == "" + output_norm = get_norm(norm, conv_dim) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(output_conv) + delattr(self, "layer_{}".format(len(self.in_features))) + self.add_module("layer_{}".format(len(self.in_features)), output_conv) + self.output_convs[0] = output_conv + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret[ + "transformer_enc_layers" + ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config + ret["transformer_pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + return ret + + def forward_features(self, features): + multi_scale_features = [] + num_cur_levels = 0 + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[::-1]): + x = features[f] + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + if lateral_conv is None: + transformer = self.input_proj(x) + pos = self.pe_layer(x) + transformer = self.transformer(transformer, None, pos) + y = output_conv(transformer) + # save intermediate feature as input to Transformer decoder + transformer_encoder_features = transformer + else: + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest") + y = output_conv(y) + if num_cur_levels < self.oneformer_num_feature_levels: + multi_scale_features.append(y) + num_cur_levels += 1 + return self.mask_features(y), transformer_encoder_features, multi_scale_features + + def forward(self, features, targets=None): + logger = logging.getLogger(__name__) + logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.") + return self.forward_features(features) diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/msdeformattn.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/msdeformattn.py new file mode 100755 index 00000000..867fec22 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/msdeformattn.py @@ -0,0 +1,357 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ +from torch.cuda.amp import autocast + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.position_encoding import PositionEmbeddingSine +from ..transformer_decoder.transformer import _get_clones, _get_activation_fn +from ..pixel_decoder.ops.modules import MSDeformAttn + +# MSDeformAttn Transformer encoder in deformable detr +class MSDeformAttnTransformerEncoderOnly(nn.Module): + def __init__(self, d_model=256, nhead=8, + num_encoder_layers=6, dim_feedforward=1024, dropout=0.1, + activation="relu", + num_feature_levels=4, enc_n_points=4, + ): + super().__init__() + + self.d_model = d_model + self.nhead = nhead + + encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, enc_n_points) + self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers) + + self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + normal_(self.level_embed) + + def get_valid_ratio(self, mask): + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def forward(self, srcs, pos_embeds): + masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs] + # prepare input for encoder + src_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): + bs, c, h, w = src.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + src = src.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + src_flatten.append(src) + mask_flatten.append(mask) + src_flatten = torch.cat(src_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) + + # encoder + memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten) + + return memory, spatial_shapes, level_start_index, valid_ratios + + +class MSDeformAttnTransformerEncoderLayer(nn.Module): + def __init__(self, + d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4): + super().__init__() + + # self attention + self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation) + self.dropout2 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout3 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, src): + src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) + src = src + self.dropout3(src2) + src = self.norm2(src) + return src + + def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None): + # self attention + src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask) + src = src + self.dropout1(src2) + src = self.norm1(src) + + # ffn + src = self.forward_ffn(src) + + return src + + +class MSDeformAttnTransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + + ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device)) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None): + output = src + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device) + for _, layer in enumerate(self.layers): + output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask) + + return output + + +@SEM_SEG_HEADS_REGISTRY.register() +class MSDeformAttnPixelDecoder(nn.Module): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + transformer_dropout: float, + transformer_nheads: int, + transformer_dim_feedforward: int, + transformer_enc_layers: int, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + # deformable transformer encoder args + transformer_in_features: List[str], + common_stride: int, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_dropout: dropout probability in transformer + transformer_nheads: number of heads in transformer + transformer_dim_feedforward: dimension of feedforward network + transformer_enc_layers: number of transformer encoder layers + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__() + transformer_input_shape = { + k: v for k, v in input_shape.items() if k in transformer_in_features + } + + # this is the input shape of pixel decoder + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + self.feature_strides = [v.stride for k, v in input_shape] + self.feature_channels = [v.channels for k, v in input_shape] + + # this is the input shape of transformer encoder (could use less features than pixel decoder + transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride) + self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5" + transformer_in_channels = [v.channels for k, v in transformer_input_shape] + self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers + + self.transformer_num_feature_levels = len(self.transformer_in_features) + if self.transformer_num_feature_levels > 1: + input_proj_list = [] + # from low resolution to high resolution (res5 -> res2) + for in_channels in transformer_in_channels[::-1]: + input_proj_list.append(nn.Sequential( + nn.Conv2d(in_channels, conv_dim, kernel_size=1), + nn.GroupNorm(32, conv_dim), + )) + self.input_proj = nn.ModuleList(input_proj_list) + else: + self.input_proj = nn.ModuleList([ + nn.Sequential( + nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1), + nn.GroupNorm(32, conv_dim), + )]) + + for proj in self.input_proj: + nn.init.xavier_uniform_(proj[0].weight, gain=1) + nn.init.constant_(proj[0].bias, 0) + + self.transformer = MSDeformAttnTransformerEncoderOnly( + d_model=conv_dim, + dropout=transformer_dropout, + nhead=transformer_nheads, + dim_feedforward=transformer_dim_feedforward, + num_encoder_layers=transformer_enc_layers, + num_feature_levels=self.transformer_num_feature_levels, + ) + N_steps = conv_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + self.mask_dim = mask_dim + # use 1x1 conv instead + self.mask_features = Conv2d( + conv_dim, + mask_dim, + kernel_size=1, + stride=1, + padding=0, + ) + weight_init.c2_xavier_fill(self.mask_features) + + self.oneformer_num_feature_levels = 3 # always use 3 scales + self.common_stride = common_stride + + # extra fpn levels + stride = min(self.transformer_feature_strides) + self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride)) + + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]): + lateral_norm = get_norm(norm, conv_dim) + output_norm = get_norm(norm, conv_dim) + + lateral_conv = Conv2d( + in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + self.add_module("adapter_{}".format(idx + 1), lateral_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = {} + ret["input_shape"] = { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + } + ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM + ret["transformer_dropout"] = cfg.MODEL.ONE_FORMER.DROPOUT + ret["transformer_nheads"] = cfg.MODEL.ONE_FORMER.NHEADS + # ret["transformer_dim_feedforward"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD + ret["transformer_dim_feedforward"] = 1024 # use 1024 for deformable transformer encoder + ret[ + "transformer_enc_layers" + ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config + ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES + ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE + return ret + + @autocast(enabled=False) + def forward_features(self, features): + srcs = [] + pos = [] + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.transformer_in_features[::-1]): + x = features[f].float() # deformable detr does not support half precision + srcs.append(self.input_proj[idx](x)) + pos.append(self.pe_layer(x)) + + y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos) + bs = y.shape[0] + + split_size_or_sections = [None] * self.transformer_num_feature_levels + for i in range(self.transformer_num_feature_levels): + if i < self.transformer_num_feature_levels - 1: + split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i] + else: + split_size_or_sections[i] = y.shape[1] - level_start_index[i] + y = torch.split(y, split_size_or_sections, dim=1) + + out = [] + multi_scale_features = [] + num_cur_levels = 0 + for i, z in enumerate(y): + out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1])) + + # append `out` with extra FPN levels + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]): + x = features[f].float() + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False) + y = output_conv(y) + out.append(y) + + for o in out: + if num_cur_levels < self.oneformer_num_feature_levels: + multi_scale_features.append(o) + num_cur_levels += 1 + + return self.mask_features(out[-1]), out[0], multi_scale_features, spatial_shapes, level_start_index diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO new file mode 100644 index 00000000..18ccefb1 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO @@ -0,0 +1,11 @@ +Metadata-Version: 2.1 +Name: MultiScaleDeformableAttention +Version: 1.0 +Summary: PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention +Home-page: https://github.com/fundamentalvision/Deformable-DETR +Author: Weijie Su +License: UNKNOWN +Platform: UNKNOWN + +UNKNOWN + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt new file mode 100644 index 00000000..74920543 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt @@ -0,0 +1,12 @@ +setup.py +/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/vision.cpp +/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp +/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu +MultiScaleDeformableAttention.egg-info/PKG-INFO +MultiScaleDeformableAttention.egg-info/SOURCES.txt +MultiScaleDeformableAttention.egg-info/dependency_links.txt +MultiScaleDeformableAttention.egg-info/top_level.txt +functions/__init__.py +functions/ms_deform_attn_func.py +modules/__init__.py +modules/ms_deform_attn.py \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/top_level.txt b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/top_level.txt new file mode 100644 index 00000000..25d8f779 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/top_level.txt @@ -0,0 +1,3 @@ +MultiScaleDeformableAttention +functions +modules diff --git 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zng*`9N&64->}~Yz(fKu6r}lqB|83F#&XPB){B2HhT?O96`nRdcZNTlRz%_ur N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, + mode='bilinear', padding_mode='zeros', align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) + return output.transpose(1, 2).contiguous() diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/make.sh b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/make.sh new file mode 100755 index 00000000..38658bc9 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/make.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +FORCE_CUDA=1 python setup.py build install --user diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/modules/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/modules/__init__.py new file mode 100755 index 00000000..6fdbf033 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/modules/__init__.py @@ -0,0 +1,12 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn import MSDeformAttn diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/modules/ms_deform_attn.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/modules/ms_deform_attn.py new file mode 100755 index 00000000..55436f35 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/modules/ms_deform_attn.py @@ -0,0 +1,129 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +if torch.cuda.is_available(): + from ..functions import MSDeformAttnFunction +else: + MSDeformAttnFunction = None +from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 128 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + # try: + if torch.cuda.is_available(): + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + # except: + else: + # CPU + output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + # # For FLOPs calculation only + # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + output = self.output_proj(output) + return output \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/setup.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/setup.py new file mode 100755 index 00000000..3b57ad31 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/setup.py @@ -0,0 +1,78 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +import os +import glob + +import torch + +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +from setuptools import find_packages +from setuptools import setup + +requirements = ["torch", "torchvision"] + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "src") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + sources = main_file + source_cpu + extension = CppExtension + extra_compile_args = {"cxx": []} + define_macros = [] + + # Force cuda since torch ask for a device, not if cuda is in fact available. + if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + if CUDA_HOME is None: + raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.') + else: + raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().') + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + ext_modules = [ + extension( + "MultiScaleDeformableAttention", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + return ext_modules + +setup( + name="MultiScaleDeformableAttention", + version="1.0", + author="Weijie Su", + url="https://github.com/fundamentalvision/Deformable-DETR", + description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention", + packages=find_packages(exclude=("configs", "tests",)), + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp new file mode 100755 index 00000000..48757e2b --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp @@ -0,0 +1,46 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include + +#include +#include + + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h new file mode 100755 index 00000000..51bb27e9 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h @@ -0,0 +1,38 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once +#include + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu new file mode 100755 index 00000000..0c465dab --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu @@ -0,0 +1,158 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include +#include "cuda/ms_deform_im2col_cuda.cuh" + +#include +#include +#include +#include + + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); + + const int batch_n = im2col_step_; + auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto columns = output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { + ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + columns.data()); + + })); + } + + output = output.view({batch, num_query, num_heads*channels}); + + return output; +} + + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto grad_value = at::zeros_like(value); + auto grad_sampling_loc = at::zeros_like(sampling_loc); + auto grad_attn_weight = at::zeros_like(attn_weight); + + const int batch_n = im2col_step_; + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto grad_output_g = grad_output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { + ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), + grad_output_g.data(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + grad_value.data() + n * im2col_step_ * per_value_size, + grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); + + })); + } + + return { + grad_value, grad_sampling_loc, grad_attn_weight + }; +} \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h new file mode 100755 index 00000000..4f0658e8 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h @@ -0,0 +1,35 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once +#include + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh new file mode 100755 index 00000000..c04e0d4a --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh @@ -0,0 +1,1332 @@ +/*! +************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************** +* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) +* Copyright (c) 2018 Microsoft +************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include +#include +#include + +#include +#include + +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N, const int num_threads) +{ + return (N + num_threads - 1) / num_threads; +} + + +template +__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + } + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + *grad_attn_weight = top_grad * val; + *grad_sampling_loc = width * grad_w_weight * top_grad_value; + *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + atomicAdd(grad_attn_weight, top_grad * val); + atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); + atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); +} + + +template +__global__ void ms_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + scalar_t *data_col_ptr = data_col + index; + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + scalar_t col = 0; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; + } + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + } + } + *data_col_ptr = col; + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockSize; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockSize/2; s>0; s>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockDim.x; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); + atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); + atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear_gm( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + grad_sampling_loc, grad_attn_weight); + } + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +void ms_deformable_im2col_cuda(cudaStream_t stream, + const scalar_t* data_value, + const int64_t* data_spatial_shapes, + const int64_t* data_level_start_index, + const scalar_t* data_sampling_loc, + const scalar_t* data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* data_col) +{ + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + const int num_threads = CUDA_NUM_THREADS; + ms_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, + batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +template +void ms_deformable_col2im_cuda(cudaStream_t stream, + const scalar_t* grad_col, + const scalar_t* data_value, + const int64_t * data_spatial_shapes, + const int64_t * data_level_start_index, + const scalar_t * data_sampling_loc, + const scalar_t * data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + if (channels > 1024) + { + if ((channels & 1023) == 0) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_gm + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + else{ + switch(channels) + { + case 1: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 2: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 4: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 8: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 16: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 32: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 64: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 128: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 256: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 512: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 1024: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + default: + if (channels < 64) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + } + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/ms_deform_attn.h new file mode 100755 index 00000000..2f80a1b2 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/ms_deform_attn.h @@ -0,0 +1,67 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once + +#include "cpu/ms_deform_attn_cpu.h" + +#ifdef WITH_CUDA +#include "cuda/ms_deform_attn_cuda.h" +#endif + + +at::Tensor +ms_deform_attn_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_forward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +std::vector +ms_deform_attn_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_backward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/vision.cpp b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/vision.cpp new file mode 100755 index 00000000..4a08821e --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/vision.cpp @@ -0,0 +1,21 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include "ms_deform_attn.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); + m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); +} diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/test.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/test.py new file mode 100755 index 00000000..6e1b5454 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/test.py @@ -0,0 +1,92 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch + + +N, M, D = 1, 2, 2 +Lq, L, P = 2, 2, 2 +shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda() +level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1])) +S = sum([(H*W).item() for H, W in shapes]) + + +torch.manual_seed(3) + + +@torch.no_grad() +def check_forward_equal_with_pytorch_double(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +@torch.no_grad() +def check_forward_equal_with_pytorch_float(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True): + + value = torch.rand(N, S, M, channels).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + func = MSDeformAttnFunction.apply + + value.requires_grad = grad_value + sampling_locations.requires_grad = grad_sampling_loc + attention_weights.requires_grad = grad_attn_weight + + gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step)) + + print(f'* {gradok} check_gradient_numerical(D={channels})') + + +if __name__ == '__main__': + check_forward_equal_with_pytorch_double() + check_forward_equal_with_pytorch_float() + + for channels in [30, 32, 64, 71, 1025, 2048, 3096]: + check_gradient_numerical(channels, True, True, True) + + + diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/__init__.py new file mode 100755 index 00000000..b84bd4ec --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .oneformer_transformer_decoder import ContrastiveMultiScaleMaskedTransformerDecoder \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/oneformer_transformer_decoder.py b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/oneformer_transformer_decoder.py new file mode 100755 index 00000000..a06d586f --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/oneformer_transformer_decoder.py @@ -0,0 +1,528 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +import logging +import fvcore.nn.weight_init as weight_init +from typing import Optional +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d + +from .position_encoding import PositionEmbeddingSine +from .transformer import Transformer + +from detectron2.utils.registry import Registry + + +TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE") +TRANSFORMER_DECODER_REGISTRY.__doc__ = """ +Registry for transformer module in OneFormer. +""" + + +def build_transformer_decoder(cfg, in_channels, mask_classification=True): + """ + Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`. + """ + name = cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME + return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification) + + +class SelfAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + return self.forward_post(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + + +class CrossAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + + +class FFNLayer(nn.Module): + + def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm = nn.LayerNorm(d_model) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt): + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + return tgt + + def forward_pre(self, tgt): + tgt2 = self.norm(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout(tgt2) + return tgt + + def forward(self, tgt): + if self.normalize_before: + return self.forward_pre(tgt) + return self.forward_post(tgt) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@TRANSFORMER_DECODER_REGISTRY.register() +class ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "static_query" in k: + newk = k.replace("static_query", "query_feat") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dropout: float, + dim_feedforward: int, + enc_layers: int, + is_train: bool, + dec_layers: int, + class_dec_layers: int, + pre_norm: bool, + mask_dim: int, + enforce_input_project: bool, + use_task_norm: bool, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + assert mask_classification, "Only support mask classification model" + self.mask_classification = mask_classification + self.is_train = is_train + self.use_task_norm = use_task_norm + + # positional encoding + N_steps = hidden_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + self.class_transformer = Transformer( + d_model=hidden_dim, + dropout=dropout, + nhead=nheads, + dim_feedforward=dim_feedforward, + num_encoder_layers=enc_layers, + num_decoder_layers=class_dec_layers, + normalize_before=pre_norm, + return_intermediate_dec=False, + ) + + # define Transformer decoder here + self.num_heads = nheads + self.num_layers = dec_layers + self.transformer_self_attention_layers = nn.ModuleList() + self.transformer_cross_attention_layers = nn.ModuleList() + self.transformer_ffn_layers = nn.ModuleList() + + for _ in range(self.num_layers): + self.transformer_self_attention_layers.append( + SelfAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_cross_attention_layers.append( + CrossAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_ffn_layers.append( + FFNLayer( + d_model=hidden_dim, + dim_feedforward=dim_feedforward, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.decoder_norm = nn.LayerNorm(hidden_dim) + + self.num_queries = num_queries + # learnable query p.e. + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + # level embedding (we always use 3 scales) + self.num_feature_levels = 3 + self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) + self.input_proj = nn.ModuleList() + for _ in range(self.num_feature_levels): + if in_channels != hidden_dim or enforce_input_project: + self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) + weight_init.c2_xavier_fill(self.input_proj[-1]) + else: + self.input_proj.append(nn.Sequential()) + + self.class_input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1) + weight_init.c2_xavier_fill(self.class_input_proj) + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.ONE_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.ONE_FORMER.NHEADS + ret["dim_feedforward"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD + + # NOTE: because we add learnable query features which requires supervision, + # we add minus 1 to decoder layers to be consistent with our loss + # implementation: that is, number of auxiliary losses is always + # equal to number of decoder layers. With learnable query features, the number of + # auxiliary losses equals number of decoders plus 1. + assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1 + ret["dec_layers"] = cfg.MODEL.ONE_FORMER.DEC_LAYERS - 1 + ret["class_dec_layers"] = cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS + ret["enc_layers"] = cfg.MODEL.ONE_FORMER.ENC_LAYERS + ret["dropout"] = cfg.MODEL.ONE_FORMER.DROPOUT + ret["pre_norm"] = cfg.MODEL.ONE_FORMER.PRE_NORM + ret["enforce_input_project"] = cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ + ret["is_train"] = cfg.MODEL.IS_TRAIN + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["use_task_norm"] = cfg.MODEL.ONE_FORMER.USE_TASK_NORM + + return ret + + def forward(self, x, mask_features, tasks, mask = None): + # x is a list of multi-scale feature + assert len(x) == self.num_feature_levels + src = [] + pos = [] + size_list = [] + + # disable mask, it does not affect performance + del mask + + for i in range(self.num_feature_levels): + size_list.append(x[i].shape[-2:]) + pos.append(self.pe_layer(x[i], None).flatten(2)) + src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) + + # flatten NxCxHxW to HWxNxC + pos[-1] = pos[-1].permute(2, 0, 1) + src[-1] = src[-1].permute(2, 0, 1) + + _, bs, _ = src[0].shape + + # QxNxC + query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) + tasks = tasks.unsqueeze(0) + if self.use_task_norm: + tasks = self.decoder_norm(tasks) + + feats = self.pe_layer(mask_features, None) + + out_t, _ = self.class_transformer(feats, None, + self.query_embed.weight[:-1], + self.class_input_proj(mask_features), + tasks if self.use_task_norm else None) + out_t = out_t[0].permute(1, 0, 2) + + out = torch.cat([out_t, tasks], dim=0) + + output = out.clone() + + predictions_class = [] + predictions_mask = [] + + # prediction heads on learnable query features + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], i=0) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + for i in range(self.num_layers): + level_index = i % self.num_feature_levels + attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False + # attention: cross-attention first + output = self.transformer_cross_attention_layers[i]( + output, src[level_index], + memory_mask=attn_mask, + memory_key_padding_mask=None, # here we do not apply masking on padded region + pos=pos[level_index], query_pos=query_embed + ) + + output = self.transformer_self_attention_layers[i]( + output, tgt_mask=None, + tgt_key_padding_mask=None, + query_pos=query_embed + ) + + # FFN + output = self.transformer_ffn_layers[i]( + output + ) + + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], i=i+1) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + assert len(predictions_class) == self.num_layers + 1 + if self.is_train: + query_class = out.permute(1, 0, 2) + else: + query_class = None + out = { + 'contrastive_logits': query_class, + 'pred_logits': predictions_class[-1], + 'pred_masks': predictions_mask[-1], + 'aux_outputs': self._set_aux_loss( + predictions_class if self.mask_classification else None, + predictions_mask, + ) + } + + return out + + def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, i): + decoder_output = self.decoder_norm(output) + decoder_output = decoder_output.transpose(0, 1) + outputs_class = self.class_embed(decoder_output) + mask_embed = self.mask_embed(decoder_output) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) + + # NOTE: prediction is of higher-resolution + # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW] + attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False) + + # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool') + + # must use bool type + # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. + attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool() + attn_mask = attn_mask.detach() + + return outputs_class, outputs_mask, attn_mask + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + aux_list = [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + aux_list = [{"pred_masks": b} for b, in outputs_seg_masks[:-1]] + + return aux_list \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/position_encoding.py b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/position_encoding.py new file mode 100755 index 00000000..051984d9 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/position_encoding.py @@ -0,0 +1,67 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/position_encoding.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +""" +Various positional encodings for the transformer. +""" +import math + +import torch +from torch import nn + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self, _repr_indent=4): + head = "Positional encoding " + self.__class__.__name__ + body = [ + "num_pos_feats: {}".format(self.num_pos_feats), + "temperature: {}".format(self.temperature), + "normalize: {}".format(self.normalize), + "scale: {}".format(self.scale), + ] + # _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/text_transformer.py b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/text_transformer.py new file mode 100755 index 00000000..d0b72920 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/text_transformer.py @@ -0,0 +1,257 @@ +# ------------------------------------------------------------------------- +# MIT License +# +# Copyright (c) 2021 OpenAI +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +# +# ------------------------------------------------------------------------- + +import torch +import torch.utils.checkpoint as checkpoint +from torch import nn +from collections import OrderedDict +from timm.models.layers import trunc_normal_ + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.q_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.k_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.v_proj = nn.Linear(dim, dim, bias=qkv_bias) + + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, q, k, v): + B, N, C = q.shape + assert k.shape == v.shape + B, M, C = k.shape + q = self.q_proj(q).reshape(B, N, self.num_heads, C // self.num_heads) + k = self.k_proj(k).reshape(B, M, self.num_heads, C // self.num_heads) + v = self.v_proj(v).reshape(B, M, self.num_heads, C // self.num_heads) + + attn = torch.einsum('bnkc,bmkc->bknm', q, k) * self.scale + + attn = attn.softmax(dim=-1) + + x = torch.einsum('bknm,bmkc->bnkc', attn, v).reshape(B, N, C) + + x = self.proj(x) + x = self.proj_drop(x) + return x + +class TransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dropout=0.1, + ): + super().__init__() + self.self_attn = Attention(d_model, nhead, proj_drop=dropout) + self.cross_attn = Attention(d_model, nhead, proj_drop=dropout) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.mlp = nn.Sequential( + nn.Linear(d_model, d_model * 4), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(d_model * 4, d_model) + ) + + def forward(self, x, mem): + q = k = v = self.norm1(x) + x = x + self.self_attn(q, k, v) + q = self.norm2(x) + x = x + self.cross_attn(q, mem, mem) + x = x + self.dropout(self.mlp(self.norm3(x))) + return x + + +class ContextDecoder(nn.Module): + def __init__(self, + transformer_width=256, + transformer_heads=4, + transformer_layers=6, + visual_dim=1024, + dropout=0.1, + **kwargs): + super().__init__() + + self.memory_proj = nn.Sequential( + nn.LayerNorm(visual_dim), + nn.Linear(visual_dim, transformer_width), + nn.LayerNorm(transformer_width), + ) + + self.text_proj = nn.Sequential( + nn.LayerNorm(visual_dim), + nn.Linear(visual_dim, transformer_width), + ) + + self.decoder = nn.ModuleList([ + TransformerDecoderLayer(transformer_width, transformer_heads, dropout) for _ in range(transformer_layers) + ]) + + self.out_proj = nn.Sequential( + nn.LayerNorm(transformer_width), + nn.Linear(transformer_width, visual_dim) + ) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + + def forward(self, text, visual): + B, N, C = visual.shape + visual = self.memory_proj(visual) + x = self.text_proj(text) + + for layer in self.decoder: + x = layer(x, visual) + + return self.out_proj(x) + + +class QuickGELU(nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = nn.LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()), + ('c_proj', nn.Linear(d_model * 4, d_model))])) + self.ln_2 = nn.LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0] + + def forward(self, x: torch.Tensor, key_padding_mask=None): + x = x + self.attention(self.ln_1(x), key_padding_mask=key_padding_mask) + x = x + self.mlp(self.ln_2(x)) + return x + +class Transformer(nn.Module): + + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_checkpoint=False): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5) + attn_std = self.width**-0.5 + fc_std = (2 * self.width)**-0.5 + for block in self.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + self.use_checkpoint = use_checkpoint + + def forward(self, x: torch.Tensor): + for resblock in self.resblocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(resblock, x) + else: + x = resblock(x) + return x + + +class TextTransformer(nn.Module): + + def __init__( + self, + context_length: int, + width: int, + layers: int, + vocab_size, + use_checkpoint=False, + ): + + super().__init__() + heads = width // 64 + self.context_length = context_length + self.width = width + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + attn_mask=self.build_attention_mask(), + use_checkpoint=use_checkpoint) + + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.ln_final = nn.LayerNorm(width) + self.token_embedding = nn.Embedding(vocab_size, width) + nn.init.normal_(self.token_embedding.weight, std=0.02) + + # initialization + nn.init.normal_(self.positional_embedding, std=0.01) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float('-inf')) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text): + x = self.token_embedding(text) + x = x + self.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] + + return x \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/transformer.py b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/transformer.py new file mode 100755 index 00000000..cd075256 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/transformer_decoder/transformer.py @@ -0,0 +1,376 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/transformer.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +""" +Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +import copy +from typing import List, Optional + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + + +class Transformer(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_encoder_layers=6, + num_decoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + return_intermediate_dec=False, + ): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder( + decoder_layer, + num_decoder_layers, + decoder_norm, + return_intermediate=return_intermediate_dec, + ) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, query_embed, pos_embed, task_token=None): + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + if mask is not None: + mask = mask.flatten(1) + + if task_token is None: + tgt = torch.zeros_like(query_embed) + else: + tgt = task_token.repeat(query_embed.shape[0], 1, 1) + + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + hs = self.decoder( + tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed + ) + return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w) + + +class TransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward( + self, + src, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + output = src + + for layer in self.layers: + output = layer( + output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos + ) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class TransformerDecoder(nn.Module): + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + output = tgt + + intermediate = [] + + for layer in self.layers: + output = layer( + output, + memory, + tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, + query_pos=query_pos, + ) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn( + q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask + )[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn( + q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask + )[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn( + query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn( + query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre( + tgt, + memory, + tgt_mask, + memory_mask, + tgt_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + return self.forward_post( + tgt, + memory, + tgt_mask, + memory_mask, + tgt_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") diff --git a/src/perception/camera_segmentation/camera_segmentation/oneformer_model.py b/src/perception/camera_segmentation/camera_segmentation/oneformer_model.py new file mode 100755 index 00000000..1b2f15e5 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/oneformer_model.py @@ -0,0 +1,486 @@ +# ------------------------------------------------------------------------------ +# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/maskformer_model.py +# Modified by Jitesh Jain (https://github.com/praeclarumjj3) +# ------------------------------------------------------------------------------ + +from typing import Tuple + +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.postprocessing import sem_seg_postprocess +from detectron2.structures import Boxes, ImageList, Instances, BitMasks +from detectron2.utils.memory import retry_if_cuda_oom + +from .modeling.criterion import SetCriterion +from .modeling.matcher import HungarianMatcher +from einops import rearrange +from .modeling.transformer_decoder.text_transformer import TextTransformer +from .modeling.transformer_decoder.oneformer_transformer_decoder import MLP +from .data.tokenizer import SimpleTokenizer, Tokenize + +@META_ARCH_REGISTRY.register() +class OneFormer(nn.Module): + """ + Main class for mask classification semantic segmentation architectures. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + task_mlp: nn.Module, + text_encoder: nn.Module, + text_projector: nn.Module, + criterion: nn.Module, + prompt_ctx: nn.Embedding, + num_queries: int, + object_mask_threshold: float, + overlap_threshold: float, + metadata, + size_divisibility: int, + sem_seg_postprocess_before_inference: bool, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + # inference + semantic_on: bool, + panoptic_on: bool, + instance_on: bool, + detection_on: bool, + test_topk_per_image: int, + task_seq_len: int, + max_seq_len: int, + is_demo: bool, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + sem_seg_head: a module that predicts semantic segmentation from backbone features + criterion: a module that defines the loss + num_queries: int, number of queries + object_mask_threshold: float, threshold to filter query based on classification score + for panoptic segmentation inference + overlap_threshold: overlap threshold used in general inference for panoptic segmentation + metadata: dataset meta, get `thing` and `stuff` category names for panoptic + segmentation inference + size_divisibility: Some backbones require the input height and width to be divisible by a + specific integer. We can use this to override such requirement. + sem_seg_postprocess_before_inference: whether to resize the prediction back + to original input size before semantic segmentation inference or after. + For high-resolution dataset like Mapillary, resizing predictions before + inference will cause OOM error. + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + semantic_on: bool, whether to output semantic segmentation prediction + instance_on: bool, whether to output instance segmentation prediction + panoptic_on: bool, whether to output panoptic segmentation prediction + test_topk_per_image: int, instance segmentation parameter, keep topk instances per image + """ + super().__init__() + self.backbone = backbone + self.sem_seg_head = sem_seg_head + self.task_mlp = task_mlp + self.text_encoder = text_encoder + self.text_projector = text_projector + self.prompt_ctx = prompt_ctx + self.criterion = criterion + self.num_queries = num_queries + self.overlap_threshold = overlap_threshold + self.object_mask_threshold = object_mask_threshold + self.metadata = metadata + if size_divisibility < 0: + # use backbone size_divisibility if not set + size_divisibility = self.backbone.size_divisibility + self.size_divisibility = size_divisibility + self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + + # additional args + self.semantic_on = semantic_on + self.instance_on = instance_on + self.panoptic_on = panoptic_on + self.detection_on = detection_on + self.test_topk_per_image = test_topk_per_image + + self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len) + self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len) + self.is_demo = is_demo + + self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()] + + if not self.semantic_on: + assert self.sem_seg_postprocess_before_inference + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + + if cfg.MODEL.IS_TRAIN: + text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH, + width=cfg.MODEL.TEXT_ENCODER.WIDTH, + layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS, + vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE) + text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, + cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS) + if cfg.MODEL.TEXT_ENCODER.N_CTX > 0: + prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH) + else: + prompt_ctx = None + else: + text_encoder = None + text_projector = None + prompt_ctx = None + + task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, + cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2) + + # Loss parameters: + deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION + no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT + + # loss weights + class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT + dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT + mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT + contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT + + # building criterion + matcher = HungarianMatcher( + cost_class=class_weight, + cost_mask=mask_weight, + cost_dice=dice_weight, + num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS, + ) + + weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, + "loss_dice": dice_weight, "loss_contrastive": contrastive_weight} + + + if deep_supervision: + dec_layers = cfg.MODEL.ONE_FORMER.DEC_LAYERS + aux_weight_dict = {} + for i in range(dec_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + + losses = ["labels", "masks", "contrastive"] + + criterion = SetCriterion( + sem_seg_head.num_classes, + matcher=matcher, + weight_dict=weight_dict, + eos_coef=no_object_weight, + contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE, + losses=losses, + num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS, + oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO, + importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO, + ) + + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "task_mlp": task_mlp, + "prompt_ctx": prompt_ctx, + "text_encoder": text_encoder, + "text_projector": text_projector, + "criterion": criterion, + "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES, + "object_mask_threshold": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD, + "overlap_threshold": cfg.MODEL.TEST.OVERLAP_THRESHOLD, + "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), + "size_divisibility": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY, + "sem_seg_postprocess_before_inference": ( + cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE + or cfg.MODEL.TEST.PANOPTIC_ON + or cfg.MODEL.TEST.INSTANCE_ON + ), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + # inference + "semantic_on": cfg.MODEL.TEST.SEMANTIC_ON, + "instance_on": cfg.MODEL.TEST.INSTANCE_ON, + "panoptic_on": cfg.MODEL.TEST.PANOPTIC_ON, + "detection_on": cfg.MODEL.TEST.DETECTION_ON, + "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE, + "task_seq_len": cfg.INPUT.TASK_SEQ_LEN, + "max_seq_len": cfg.INPUT.MAX_SEQ_LEN, + "is_demo": cfg.MODEL.IS_DEMO, + } + + @property + def device(self): + return self.pixel_mean.device + + def encode_text(self, text): + assert text.ndim in [2, 3], text.ndim + b = text.shape[0] + squeeze_dim = False + num_text = 1 + if text.ndim == 3: + num_text = text.shape[1] + text = rearrange(text, 'b n l -> (b n) l', n=num_text) + squeeze_dim = True + + # [B, C] + x = self.text_encoder(text) + + text_x = self.text_projector(x) + + if squeeze_dim: + text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text) + if self.prompt_ctx is not None: + text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1) + text_x = torch.cat([text_x, text_ctx], dim=1) + + return {"texts": text_x} + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + * "image": Tensor, image in (C, H, W) format. + * "instances": per-region ground truth + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model (may be different + from input resolution), used in inference. + Returns: + list[dict]: + each dict has the results for one image. The dict contains the following keys: + * "sem_seg": + A Tensor that represents the + per-pixel segmentation prediced by the head. + The prediction has shape KxHxW that represents the logits of + each class for each pixel. + * "panoptic_seg": + A tuple that represent panoptic output + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.size_divisibility) + + tasks = torch.cat([self.task_tokenizer(x["task"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0) + tasks = self.task_mlp(tasks.float()) + + features = self.backbone(images.tensor) + outputs = self.sem_seg_head(features, tasks) + + if self.training: + texts = torch.cat([self.text_tokenizer(x["text"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0) + texts_x = self.encode_text(texts) + + outputs = {**outputs, **texts_x} + + # mask classification target + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances, images) + else: + targets = None + + # bipartite matching-based loss + losses = self.criterion(outputs, targets) + + for k in list(losses.keys()): + if k in self.criterion.weight_dict: + losses[k] *= self.criterion.weight_dict[k] + else: + # remove this loss if not specified in `weight_dict` + losses.pop(k) + return losses + else: + mask_cls_results = outputs["pred_logits"] + mask_pred_results = outputs["pred_masks"] + # upsample masks + mask_pred_results = F.interpolate( + mask_pred_results, + size=(images.tensor.shape[-2], images.tensor.shape[-1]), + mode="bilinear", + align_corners=False, + ) + + del outputs + + processed_results = [] + for i, data in enumerate(zip( + mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes + )): + mask_cls_result, mask_pred_result, input_per_image, image_size = data + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + processed_results.append({}) + + if self.sem_seg_postprocess_before_inference: + mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)( + mask_pred_result, image_size, height, width + ) + mask_cls_result = mask_cls_result.to(mask_pred_result) + + # semantic segmentation inference + if self.semantic_on: + r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result) + if not self.sem_seg_postprocess_before_inference: + r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width) + processed_results[-1]["sem_seg"] = r + + # panoptic segmentation inference + if self.panoptic_on: + panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["panoptic_seg"] = panoptic_r + + # instance segmentation inference + if self.instance_on: + instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["instances"] = instance_r + + if self.detection_on: + bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["box_instances"] = bbox_r + + return processed_results + + def prepare_targets(self, targets, images): + h_pad, w_pad = images.tensor.shape[-2:] + new_targets = [] + for targets_per_image in targets: + # pad gt + gt_masks = targets_per_image.gt_masks + padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device) + padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks + new_targets.append( + { + "labels": targets_per_image.gt_classes, + "masks": padded_masks, + } + ) + return new_targets + + def semantic_inference(self, mask_cls, mask_pred): + mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1] + mask_pred = mask_pred.sigmoid() + semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) + return semseg + + def panoptic_inference(self, mask_cls, mask_pred): + scores, labels = F.softmax(mask_cls, dim=-1).max(-1) + mask_pred = mask_pred.sigmoid() + + keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold) + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_masks = mask_pred[keep] + cur_mask_cls = mask_cls[keep] + cur_mask_cls = cur_mask_cls[:, :-1] + + cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks + + h, w = cur_masks.shape[-2:] + panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device) + segments_info = [] + + current_segment_id = 0 + + if cur_masks.shape[0] == 0: + # We didn't detect any mask :( + return panoptic_seg, segments_info + else: + # take argmax + cur_mask_ids = cur_prob_masks.argmax(0) + stuff_memory_list = {} + for k in range(cur_classes.shape[0]): + pred_class = cur_classes[k].item() + isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values() + mask_area = (cur_mask_ids == k).sum().item() + original_area = (cur_masks[k] >= 0.5).sum().item() + mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5) + + if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: + if mask_area / original_area < self.overlap_threshold: + continue + + # merge stuff regions + if not isthing: + if int(pred_class) in stuff_memory_list.keys(): + panoptic_seg[mask] = stuff_memory_list[int(pred_class)] + continue + else: + stuff_memory_list[int(pred_class)] = current_segment_id + 1 + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + + segments_info.append( + { + "id": current_segment_id, + "isthing": bool(isthing), + "category_id": int(pred_class), + } + ) + + return panoptic_seg, segments_info + + def instance_inference(self, mask_cls, mask_pred): + # mask_pred is already processed to have the same shape as original input + image_size = mask_pred.shape[-2:] + + # [Q, K] + scores = F.softmax(mask_cls, dim=-1)[:, :-1] + labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1) + + # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False) + scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False) + labels_per_image = labels[topk_indices] + + topk_indices = topk_indices // self.sem_seg_head.num_classes + # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1) + mask_pred = mask_pred[topk_indices] + + # Only consider scores with confidence over [self.object_mask_threshold] for demo + if self.is_demo: + keep = scores_per_image > self.object_mask_threshold + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + # if this is panoptic segmentation, we only keep the "thing" classes + if self.panoptic_on: + keep = torch.zeros_like(scores_per_image).bool() + for i, lab in enumerate(labels_per_image): + keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values() + + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + if 'ade20k' in self.metadata.name: + for i in range(labels_per_image.shape[0]): + labels_per_image[i] = self.thing_indices.index(labels_per_image[i].item()) + + result = Instances(image_size) + # mask (before sigmoid) + result.pred_masks = (mask_pred > 0).float() + if self.detection_on: + # Uncomment the following to get boxes from masks (this is slow) + result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes() + else: + result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4)) + + # calculate average mask prob + mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6) + result.scores = scores_per_image * mask_scores_per_image + result.pred_classes = labels_per_image + return result \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/samples/cityscapes.png b/src/perception/camera_segmentation/camera_segmentation/samples/cityscapes.png new file mode 100644 index 0000000000000000000000000000000000000000..3ca7845181aad889167c4959d89648881a572351 GIT binary patch literal 179215 zcmV)TK(W7xP)7gG)Kn)0JbhiYFB0yHLNS4b=pEs|+5jX6y+buKa9AoHVt{t}(4`I%Uv-fg)nD6_> zH}KK7-Y%mq7e`&AQakak0y`_<{)o2yrTwd%y~t-|K28#mpfue_=LMDyp~ z&&SRBw|@9LAN*$h$*+Gxl8pgW5z#lEUBuwqnZJ3l1xK@4bQFgE1_8eD!|!go{K4P; z%Er=$_K*MMcdeQK`M>|0?Xy<^ii}8LYR=JeHT&B4zP|0U?1y*0{LYVm{Igl>(zxU3 zUOm~QeJZM+@#wT&oW%8JWOUrR{<`b;NsSnWCEfEn+LBSAI!4MJ5 z)JjSm7!kk_%m_gh4Nz2Wu3r4dfBzRRo;@FP{{DA=@B6?117e3r00=+;hG>Rh255kY 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Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, + mode='bilinear', padding_mode='zeros', align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) + return output.transpose(1, 2).contiguous() diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/__init__.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/__init__.py new file mode 100644 index 00000000..6fdbf033 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/__init__.py @@ -0,0 +1,12 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn import MSDeformAttn diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/ms_deform_attn.py b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/ms_deform_attn.py new file mode 100644 index 00000000..55436f35 --- /dev/null +++ b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/ms_deform_attn.py @@ -0,0 +1,129 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +if torch.cuda.is_available(): + from ..functions import MSDeformAttnFunction +else: + MSDeformAttnFunction = None +from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 128 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + # try: + if torch.cuda.is_available(): + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + # except: + else: + # CPU + output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + # # For FLOPs calculation only + # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + output = self.output_proj(output) + return output \ No newline at end of file diff --git a/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.o b/src/perception/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/home/docker/ament_ws/src/camera_segmentation/camera_segmentation/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.o new file mode 100644 index 0000000000000000000000000000000000000000..e9557de93fecadd35ffbbf34283ffddbb9da9f79 GIT binary patch literal 1393992 zcma&P2b|Q@_x?Y37G!6$yDW9-odras2na}1dhbP4P`c8KB8q~ds0c_?u>p#LC@NM^ zumGa)!G?m0qF_M*dqEKk|L2r@b2B^p`+t3zNphd}IrrR>Op=owW_Re`F>XxE{%2yX zW8t1Hjm2(lRm$zO<*l}{%CSnZ--=c`)p_ps>75s5QRz#S@zHr<6WZ>)uobP2(#Pnc zm2aYTKK?9}rQmx`nqKAd36sZsp z;pUl-V?CU93ZGZ+*(c+%9NunOZv(c=^LFcEwCaUr8kU(@;+r9=+O7f$SY3kk-A*kw ztpDT~IcYO&*j2m{izS*ub~V>TwEvfycf;rhEWeTOSCVTcHGrpLSVYZVvxy}(0+Xio ztQU#maW9IVaBkb*3EPqOgPVqBCe`zLaumH#x&+JJPUR@b3Adj%sQr++zs`u8a#WVs zh>}BC(El$dvZQZ{bkFz&MdPTnNcXz5GRIL6|53cWh3&yC_G&_)iA`cA{lW}!kUlJ< zD(b6Jh$__%Pal!dgvzq~vN0K5sEpuPYy^EI8fA5(bw=Z?rkF^TXgfWlS5O-}#aZZ+ zHR>tw&3LCDch~LMzY9xz0u;k?36=+B(&{lt8?77@WgsiBqx64H;qJ~D%%=@<@+Fj` zq)wv>v)SXNMQCOIML}Ac)~M2Ok;9ff` z;Hs};{bm%HSnC%$-ihmMM(z11!=-w~2laJGh+!t?IN`;u3hs%;=KSnwp%M(YzcL92S!f>ExL@ z!%(SC(lboOVepbiT4FdvnO?y~8B4G;j*5e!9JHB_g7^{&GL{&R?a?d_FGnkaW!9&e z3fCZPImWux+X#_on8}425?Rk5+^IfNoQ#NtS;lgXWY!G<>=tVx#LyT?!rtz17R+1pD)Zi z;NnhlQ|C~>3j@k^?(7sYtf*=SmLcH4q^>4a-3%MW@bF}sg{ZHcX<}|8M@~d z6fd=sX-8G@ahx@fb(*D*LEcD6mrxzTYA+P*g7SfLK)gB_>5%Ur|KSj?jOQa26`#Nt)H6ES;wHw0c$MX4&l;Rm{H{+R?b9egCu1GEA3I*%PM9q=DGI$ODxl1jDb9rkTF#C1Zswi z?<)?O5Bbhi$WDhm0eN#OWVb`!gZybK~^H6KeIX;re8q$mQ;>P)IVJ>-K{Jhgvl0$Vi<*z$&Q&q9Vbkwkhu*d%TqBG!n&S-zL^;66XW@*9x{8O zA56uZDGc@43F!27?D>%nK2(tp-2@FtHEL;2RwI*f4z49+xQkL*R&()m9z2{+UT&rl z66rjV2BLVhmAnBLsI@@X0+{EZ+sO&V70bFnbP8e>^hbzULNb5-Wi>I?=#Ub&qp-y6 z!Lm1rJ=at#jv`^Z3VSzP{fWYViRIWu?jtg}D2?l?s#sX|2lc|xMGNR{Eu%GD8oIa; z(nu@&F2;r~u7iH7W4!U46fzG&-;j#ADy(ZK^!JFNUh?O~bs=*M`tPZjn}ner%)&sI z!Q%OmE^jPdE;~(4wFhW464bN&jh>TTKd6iES^mb)Zd#6H!(WxzEsA%bD^XFJl;LXu zQO+wd42qnfv*qrH?sXra`qz*H+A_qb^||7gzt6KT;M*zlD&(EkLas2gJJInKBSZ5! zKE5V-Jq3(#aZS;UWp@5Aq%`;w`Rq{>N5-;yg>nT*)tp>xSO>WQOEiO-ewQuF90x$Y z%kFPNBPiPsG@;sLdu0%V;Lfqj+*RT_+!c4@+bYk8lA3Q0F&TKRQu^3^W zW|k0hFFsZ$u~#?f60$EdONe;`A8$EUT)o9I$94qLcO-K^i2O0jGz6v)#;GV4=?nf6 z5avkeqBiuiEh96j=r5s*PLTRqS&nvb@n6V{gMJk;)ObT%+5JT%AD6gUPbUyA;R{(g$6(gymc*>5s>4~& zzN54L0{{oG#NVb#7Mb-wqWCW>8QOBQo*YGIeR-Ux^02TU^8q>Q1cO*nne_)~OiWt< z-J*~Tyv%y4!iKJH3luWryLWM$S9Vz%OAyJN+b%zBRP zQ%GNs%>5v;%z7FEa|%*nT%}3#^38g7Q3-kt%S2{9yJ!Wevz7DDdS)(zKAsqAyrC^O z>(8MuBD0<=T?S?)Sy0?!5t;Sub~At-1ch!Rvz|SD2HCX{=S5TAJ zcLyDLy&|}T*Y^bDcztiMl-KtKn|XbIu%Fit1m=0NUl~;7_3EH8uOA8q@_J1$gVzrS z%Xqyuc!}5RgG0RD5EOoa?4JzI!Sfh+(*cwMCm12!* z(#kAALHy9GI8Ve96R|y*N{cnFPoRB5S*(*jp)A%V<1duse~87pWgfv+J2vRXU(5%v zA3qLf=5BtbRAnupIPT%HThpy<#~<_vdtz?*Ao>6G2)8)6rdV2HiT_NI@*PuQd4x@B ze=I|sn*R|l9@tXY)hG^Ayqd|p6wx-Nz2bG{>*xVkn?ESNRZfj z)Rb89`U2B;#5-g_tQhnjKRs6boKXHDD&va@b)6?Vo%xF6h&S=qV9p2E>X15BB4s{7F=n@SYN# zdf+taRmhw_yup51ASx+`XRfSkU%e?3S+uc|3^oTXmFyQN570=0Xd_=GpM}tE1wuzc z=t&3hU{i-^Og{)jCA$ea3)SghKVYiTH&uelCAMD#rh-2R_QF0P5)F1~3=aDdXFAw_ zg|_1C->{&awUEK4L5pRUs2?;!FqtJ91nqd;FzChWM!`kAZXD!nBfUvbf!9rg+PpqD zXw2*8!A9m<1Y3E1Ua*VTErWf$ZXMjpT$^Arug?$0bNL0qb-ZpDEaG+hU=^=B25p(^ z6m;ix=U_0ey96V7-7PrGT=(F6UiS!2^15eGi1psVJ-qG{%)+`Fh5%=-YvY;aaBV7+ zd48O)+XvxXWgY=z9><4{#&z+kAJSwdktg#afvLxDB?Dr`fG6|PP+k|6rTgy^ohI{J zq(4pO{NYXJ2L+;%Z!M&A;W3fOBD12k=h?+UOC`?=v%s+OOkPzl2?aL>}xDVMJGw0kLAh zgZ)=1FOSL+-oK*LV82NEyJXHE-e9Mv@$93LeHPNbDkc(H{ADFMb8*m8NjZV`1FD2q z4GeAMtE74ewJQ)hD}=^6hzFZGL}O|!5S7d&=u=dugZ)jsDjj+WDwo)r2}}hW3HHKn zDH09#d)ALQ)4^^V+KRLGVZp!FLe5+?XtB(aO@c2FOlHZZL9HFMJ~z04)}>|#*YkQ# z@GP(A1?7=<%`A0W(1OwifqTohe-x1uy>m|VsUM~+0^ZKsfPhQ^>l-Nmr z?hWem`o5qSuOA2|^Lll#l-Fy5wY+{Lc#hX=gEx7-E;!2TM}zb?$j|zqHm@HK+VFZq zFpSqfmp;mIpD10r6LEi(zJ|+BmcEPEf0h1$*QZK9!Rx7^N~gaGzVz^*2i?k) z9uYjj`=f#yyJ&xOa4xUM1U+~?HW5e zObzPtdV0`}*H;GPd3{x|h}TyKkMMeCu$9->2fKMaH#o@an}T0?eRGh7TzF>bTY@UQ zzBOpZ>)V5&yvF$O`i|fRtofolJ-2gau%@LR>gl=JS=l{s)K{U5r99Wp&5?xv*(h#} zCH_2Y&EvSRk$h=YfU(>XSs7Fmi{Wk(yL~d7Q+CfJ$k`V-GEr7OtI|c-GpF@GF|3?b zp)gA2A#!fDEIyXW)k>5;j3vGW^{Jc}$}dCOMe-XqoEY}-cqMmMX7+tG(1f07RqP@z zJ8lY7)w6l$CsX)k3RR?2rtpln;<3&o<=&M^4=H0G!6iQz&t07f5D%eoe4Jf97Hf