Update to the latest torch

.gitignore

@@ -12,3 +12,7 @@ notebooks/.ipynb_checkpoints/*
 *.zip
 *.pkl
 *.pyc
+
+# Add the images explicitly
+*.jpg
+*.png

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "segmentation"]
+	path = segmentation
+	url = https://github.com/mingyuliutw/semantic-segmentation-pytorch

demo_example3.sh

@@ -1,3 +1,12 @@
+# The model paths are coming from the demo defined here:
+# https://github.com/mingyuliutw/semantic-segmentation-pytorch
+git submodule update --init --recursive
+
+pushd segmentation
+echo -e "\033[33m[WARN]\033[0m Segmentation repo might have dependency on the older version of Pytorch"
+./demo_test.sh
+popd
+
 mkdir images -p && mkdir results -p;
 rm images/content3.png -rf;
 rm images/style3.png -rf;

demo_with_ade20k_ssn.py

@@ -13,7 +13,8 @@
 from segmentation.models import ModelBuilder, SegmentationModule
 from lib.nn import user_scattered_collate, async_copy_to
 from lib.utils import as_numpy, mark_volatile
-from scipy.misc import imread, imresize
+# from scipy.misc import imread, imresize
+from imageio import imread
 import cv2
 from torchvision import transforms
 import numpy as np

photo_smooth.py

@@ -4,7 +4,7 @@
 """
 from __future__ import division
 import torch.nn as nn
-import scipy.misc
+# import scipy.misc
 import numpy as np
 import scipy.sparse
 import scipy.sparse.linalg
@@ -20,13 +20,15 @@ def __init__(self, beta=0.9999):
     def process(self, initImg, contentImg):
 
         if type(contentImg) == str:
-            content = scipy.misc.imread(contentImg, mode='RGB')
+            # content = scipy.misc.imread(contentImg, mode='RGB')
+            content = imageio.imread(contentImg, format='RGB')
         else:
             content = contentImg.copy()
         # content = scipy.misc.imread(contentImg, mode='RGB')
 
         if type(initImg) == str:
-            B = scipy.misc.imread(initImg, mode='RGB').astype(np.float64) / 255
+            # B = scipy.misc.imread(initImg, mode='RGB').astype(np.float64) / 255
+            B = imageio.imread(initImg, mode='RGB').astype(np.float64) / 255
         else:
             B = scipy.asarray(initImg).astype(np.float64) / 255
             # B = self.
@@ -35,7 +37,12 @@ def process(self, initImg, contentImg):
         h = h1 - 4
         w = w1 - 4
         B = B[int((h1-h)/2):int((h1-h)/2+h),int((w1-w)/2):int((w1-w)/2+w),:]
-        content = scipy.misc.imresize(content,(h,w))
+        # content = scipy.misc.imresize(content,(h,w))
+        if isinstance(content, Image.Image):
+            content = content.resize((h, w))
+        else:
+            content = Image.fromarray(content).resize((h, w))
+        content = np.array(content)
         B = self.__replication_padding(B,2)
         content = self.__replication_padding(content,2)
         content = content.astype(np.float64)/255
@@ -57,7 +64,7 @@ def process(self, initImg, contentImg):
         V = V*(1-self.beta)
         V = V.reshape(h1,w1,k)
         V = V[2:2+h,2:2+w,:]
-        
+
         img = Image.fromarray(np.uint8(np.clip(V * 255., 0, 255.)))
         return img
 
@@ -96,4 +103,4 @@ def __replication_padding(self, arr,pad):
     def __rolling_block(self, A, block=(3, 3)):
         shape = (A.shape[0] - block[0] + 1, A.shape[1] - block[1] + 1) + block
         strides = (A.strides[0], A.strides[1]) + A.strides
-        return as_strided(A, shape=shape, strides=strides)
+        return as_strided(A, shape=shape, strides=strides)

requirements.txt

@@ -0,0 +1,7 @@
+torch>=1.5.1
+torchvision>=0.6
+scikit-umfpack
+setuptools
+cupy-cuda101
+pynvrtc
+opencv-contrib-python