yolov5_quant_sample

Introduction

This is 8-bit quantization sample for yolov5. Both PTQ, QAT and partial quantization have been implemented, and present the accuracy results based on yolov5s.

Method	Description
PTQ	TensorRT's native PTQ
QAT	TensorRT's quantization toolkit for PyTorch
Partial Quantization	Leave some quant-sensitive layers in higher precision (fp32/fp16) to improve accuracy

Notes

This repo is based on the release version(v5.0) of yolov5.

Code Structure

Focus on the modifications and additions based on the release version(v5.0) of yolov5.

├───trt                              # Directory for TensorRT's engine build(including PTQ scripts), evaluation and visualization.
│   ├───calibrator.py                # TensorRT native PTQ's calibrator
│   ├───demo.py                      # Run the inference on a picture, and save the visualization result.
│   ├───eval_yolo_trt.py             # Evaluate the tensorRT engine's accuary.
│   ├───onnx_to_trt.py               # Script for building TensorRT's engine.
│   ├───onnx_to_trt_partialquant.py  # Script for building TensorRT's engine with partial quantization.
│   ├───Processor.py                 # Base class for engine runtime.
│   └───Visualizer.py                # Visualize the detection result.   
├───utils_quant                      # Auxiliary scripts for PTQ and QAT.
│   ├───calib_test.sh                # The simple way to search for the best calibration params.
│   ├───check_params.py              # Check the validity of parameters for QAT. 
│   ├───onnxrt_demo.py               # Run the onnx model, generate the results, just for debugging
│   └───print_model_structure.py     # Print the model structure and param, just for debugging               
├───test.py                          # Evaluation            
├───train.py                         # Traning
└───yolo_quant_flow.py               # The main script for QAT expriment.

Results of Quantization

Please refer to yolov5s_Reduced Precision Practice for detail.

Method	Calibrator	mAP^val 0.5:0.95	mAP^val 0.5
PyTorch native fp32		0.367	0.558
TensorRT engine fp32		0.365	0.556
TensorRT's native PTQ	IInt8MinMaxCalibrator	0.350	0.542
TensorRT's native PTQ with Partial Quantization	IInt8MinMaxCalibrator	0.363	0.555

Insert FakeQuant nodes & Disable 4 quant-sensitive layers	IInt8MinMaxCalibrator	0.359	0.551
QAT finetuning 20 epochs & Disable 4 quant-sensitive layers	IInt8MinMaxCalibrator	0.363	0.556

Setup

1. Clone the Sample

git clone https://gitlab-master.nvidia.com/weihuaz/yolov5_quant_sample.git

2. Dataset Preparation

Download the labels and images of coco2017, and unzip to the same level directory as the current project. Please refer to Yolov5 coco2017 Preparation for reference.

.
├── coco                              # Directory for datasets 
│   ├── annotations
│   │   └── instances_val2017.json
│   ├── images
│   │   ├── train2017
│   │   └── val2017
│   ├── labels
│   │   ├── train2017
│   │   └── val2017
│   ├── train2017.txt
│   └── val2017.txt
└── yolov5_quant_sample               # Quantization source code

wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip         # Download the labels needed
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip

3. Docker Build and Launch

It is recommended to use Dockerfile and the scripts under the docker folder(Run these commands under yolov5_quant_sample path).

With the command bash docker/build.sh, you can build the docker.
With the command bash docker/launch.sh, you can launch the docker. By default, docker launches with GPU:0. Change according to your needs.

Tips:

If the format error occurs, such as "$'\r': command not found", please use dos2unix to convert.
If the torch version is not compatible with the cuda driver, try to install another version, for example:

$ pip install --no-cache torch==1.9.1+cu111 torchvision==0.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

4. Download Yolov5s Pretrained Model

$ cd weights
$ wget https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
$ cd ..

Experiments

quant_test.ipynb is a good reference for test.

PTQ

export.py exports a pytorch model to onnx format.

$ python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 --device 0

onnx_to_trt.py aims to build a TensorRT engine from a onnx model file, and save to the weights folder. You can specify to build different precisions(fp32/fp16/int8).
Notes: If you change the setting of the int8 calibrator, please delete the trt\yolov5s_calibration.cache. Otherwise, the change may not take effect. The setting of calibrator the batchsize for calibration, the number of calibration batch, the type of calibrator.
```
$ rm trt/yolov5s_calibration.cache
```
- Example 1: Build a int8 engine using TensorRT's native PTQ.
```
$ python trt/onnx_to_trt.py --model ./weights/yolov5s.onnx --dtype int8 --batch-size 32 --num-calib-batch 16
```
- Example 2: Build a fp32 engine.
```
$ python trt/onnx_to_trt.py --model ./weights/yolov5s.onnx --dtype fp32
```
Evaluate the accurary of TensorRT inference result.
Note: The TensorRT engine name should be modified according to the output of the previous step.
```
$ python trt/eval_yolo_trt.py --model ./weights/yolov5s.trt -l
```

PTQ with Partial Quantization

trt/onnx_to_trt_partialquant.py aims to build a TensorRT engine with partial quantization.

Get the onnx model with export.py.

$ python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 --device 0

Simplify the onnx model and delete useless layers or nodes.

$ python -m onnxsim ./weights/yolov5s.onnx ./weights/yolov5s-simple.onnx

Choose the sensitive layers. Need some manual operation, please refer to the code.
a) Print all the layers ids;
b) Combine the onnx model structure to choose the sensitive layers
```
$ python trt/onnx_to_trt_partialquant.py --model ./weights/yolov5s-simple.onnx --dtype int8 --batch-size 32 --num-calib-batch 16
```
Evaluate the accurary of TensorRT inference result.
Note: The TensorRT engine name should be modified according to the output of the previous step.
```
$ python trt/eval_yolo_trt.py --model ./weights/yolov5s-simple.trt -l
```

Sensitivity Profile

yolo_quant_flow.py is the main script for QAT experiment.
And you can do sensitivity profile by specify the flag --sensitivity, then build_sensitivity_profile() will be called. It takes a long time to complete the entire analysis, please be patient.

```bash
$ python yolo_quant_flow.py --data data/coco.yaml --cfg models/yolov5s.yaml --ckpt-path weights/yolov5s.pt --hyp data/hyp.qat.yaml --sensitivity
```

Skip Sensitive Layers

yolo_quant_flow.py is the main script for QAT experiment.
Add the param --skip-layers, then skip_sensitive_layers() will be called. We will skip 4 quant-sensitive layers based on the sensitivity profile.

QAT Finetuning

yolo_quant_flow.py is the main script for QAT experiment. See the code comments for details.
Run the script as below. The QDQ insert, calibration, QAT-finetuning and evalution will be performed.
QAT-Finetuning takes long time, you can skip this step and download the post-QAT model directly.

    QAT-finetuning
    $ python yolo_quant_flow.py --data data/coco.yaml --cfg models/yolov5s.yaml --ckpt-path weights/yolov5s.pt --hyp data/hyp.qat.yaml --skip-layers
    
    Build TensorRT engine
    $ python trt/onnx_to_trt.py --model ./weights/yolov5s-qat.onnx --dtype int8 --qat
    
    Evaluate the accuray of TensorRT engine
    $ python trt/eval_yolo_trt.py --model ./weights/yolov5s-qat.trt -l

If you have a QAT-finetuning Pytorch checkpoint, you can export to onnx using the command below.

    Export to onnx
    $ python models/export_qat.py --weights ./weights/yolov5s-qat.pt --img 640 --batch 1 --device 0

Dynamic Shape Support

We can export the model with dynamic shape, specify some or all tensor dimensions until runtime. And the inference shape can be adjusted during the runtime.

Export to ONNX with dynamic shape support(with --dynamic)

QAT model 
$ python models/export_qat.py --weights ./weights/yolov5s-qat.pt --img 640 --dynamic --device 0

Common models(take fp16 as a example)
$ python models/export.py --weights ./weights/yolov5s.pt --img 640 --dynamic --device 0

Build the TensorRT engine with dynamic shape support

QAT model
$ python trt/onnx_to_trt.py --model ./weights/yolov5s-qat.onnx --dtype int8 --qat --dynamic-shape

Common models(take fp16 as a example)
$ python trt/onnx_to_trt.py --model ./weights/yolov5s.onnx --dtype fp16 --dynamic-shape

Specify the inference shape and evaluate the engine
Note: The TensorRT engine name should be modified according to the output of the previous step.

QAT model
$ python trt/trt_dynamic/eval_yolo_trt_dynamic.py --model weights/yolov5s-qat.trt -l

Common models(take fp16 as a example)
$ python trt/trt_dynamic/eval_yolo_trt_dynamic.py --model weights/yolov5s.trt -l

Further Optimization (Improve QAT Throughput)

Since the residual block is used in the backbone of yolov5s. TensorRT has extra runtime optimization about the residual add. In order to maximize the throughput of QAT, when inserting QDQ nodes, it's recommended to add extra quantizer to the BasicBlock and Bottleneck.
Take the Bottleneck as a example(models/common.py). We can insert extra quantization/dequantization nodes as below.

class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super(Bottleneck, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2

        # Added by maggie for QDQ debugging in order to improve throughput
        if self.add:
            self.residual_quantizer_1 = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input)
            self.residual_quantizer_2 = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input)

    def forward(self, x):
        #return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

        try:
            if self.add:
                return self.residual_quantizer_1(x) + self.residual_quantizer_2(self.cv2(self.cv1(x)))
            else:
                return self.cv2(self.cv1(x))
        except AttributeError as e:
            # Compatible with PTQ path, handle models without extra residual_quantizer
            # print('\'Bottleneck\' object has no attribute \'residual_quantizer_1\'')
            return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

Please refer to TensorRT OSS/tool/pytorch-quantization/Further optimization for detail. And it is highly recommended to walk through the Q/DQ Layer-Placement Recommendations part of TensorRT Developer Guide before you start.
During the optimization, the following commands are needed. You can compare the results before and after the extra QDQ insertion.

Insert QAT, do calibration and export to onnx file
$ python yolo_quant_flow.py --data data/coco.yaml --cfg models/yolov5s.yaml --ckpt-path weights/yolov5s.pt --hyp data/hyp.qat.yaml --num-finetune-epochs=0 --skip-eval-accuracy

Rename the onnx file
$ mv weights/yolov5s.onnx ./weights/yolov5s_with_residual_quant.onnx

Export to TensorRT engine
$ python trt/onnx_to_trt.py --model ./weights/yolov5s_with_residual_quant.onnx --dtype int8 --qat --verbose

Test the throughput with trtexec
$ trtexec --loadEngine=./weights/yolov5s_with_residual_quant.trt

Check the onnx file with Netron, you will see the extra QDQ nodes as below. Then we can test the throughput with trtexec.

From the engine layer information, we can see some additional Reformat and Scale operations have gone.

Notes

During the practices, there are some common bugs around new features(such as QAT, mixed precision). If you encouter any issues, please leave a message.

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README.md

README.md

yolov5_quant_sample

Introduction

Notes

Code Structure

Results of Quantization

Setup

1. Clone the Sample

2. Dataset Preparation

3. Docker Build and Launch

4. Download Yolov5s Pretrained Model

Experiments

PTQ

PTQ with Partial Quantization

Sensitivity Profile

Skip Sensitive Layers

QAT Finetuning

Dynamic Shape Support

Further Optimization (Improve QAT Throughput)

Notes

Files

README.md

Latest commit

History

README.md

File metadata and controls

yolov5_quant_sample

Introduction

Notes

Code Structure

Results of Quantization

Setup

1. Clone the Sample

2. Dataset Preparation

3. Docker Build and Launch

4. Download Yolov5s Pretrained Model

Experiments

PTQ

PTQ with Partial Quantization

Sensitivity Profile

Skip Sensitive Layers

QAT Finetuning

Dynamic Shape Support

Further Optimization (Improve QAT Throughput)

Notes