pytorch_ Yolox pruning [with code]

Catalog

Environmental Science

Installation package

feature Visualization

Network pruning

Fine tuning training after pruning

Train your own dataset

forecast

Conv And BN The fusion reasoning of layer accelerates

Saving of log files

The weight

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Environmental Science

pytorch 1.7

loguru 0.5.3

NVIDIA 1650 4G

intel i5-9th

torch-pruning 0.2.7

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Installation package

pip install torch_pruning

Note: This project is in b standing up Lord Bubbliiiing Heyuan YOLOX The official code is integrated .

1. Added feature Visualization

2. It can be turned on during training EMA function

3. Network pruning ( Support s,m,l,x)

3.1 Support single convolution pruning

3.2 Support network layer pruning

4. Fine tuning training after pruning

5.Conv And BN The fusion reasoning of layer accelerates

6. preservation log Information

Dataset format ： use voc Dataset format

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feature Visualization

stay tools/Net_Vision.py Implement for visual code . You can import NV function , Realize channel visualization .

eg:

features = [out_features[f] for f in self.in_features]
[x2, x1, x0] = features  # shape is (batch_size,channels,w,h)
NV(x2)

 

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Network pruning

Reference paper ：Pruning Filters for Efficient ConvNets

Import the pruning tool

import torch_pruning as tp

If you need to see yolov4 Of , You can see ：YOLOv4 prune 【 The attached code 】_ Meat loving Peng's blog -CSDN Blog _yolov4 prune

Adopt channel pruning , Instead of weight pruning .

Before pruning, you need to pass tools/prunmodel.py save_whole_model(weights_path, num_classes) Function saves the weight and structure of the model .

weights_path: Weight path

num_classes： Own category quantity

model = YOLOX(num_classes, 's')  #  It needs to be modified according to the number of classes   s finger yolox-s

Support the pruning of a convolution ： call Conv_pruning(whole_model_weights):

pruning_idxs = strategy(v, amount=0.4)  # 0.4 Is the pruning rate   Modify as needed , The larger the number, the more you cut 

For the pruning of a single convolution , Two local values need to be modified , The convolution layer here needs to be obtained by printing the model , Don't guess blindly ：

if k == 'backbone.backbone.dark2.0.conv.weight'

pruning_plan = DG.get_pruning_plan((model.backbone.backbone.dark2)[0].conv, tp.prune_conv, idxs=pruning_idxs)

Support network layer pruning ： call layer_pruning(whole_model_weights):

included_layers = list((model.backbone.backbone.dark2.modules()))  #  Prune for a certain layer 

Note: After successful pruning , It will print the parameter variation of the model ！ If not printed , That means you cut it wrong , Check it out ！

After pruning log The log file will be saved in logs Under the document

Fine tuning training after pruning

take train.py Medium pruned_train Set to True.

False For normal training , And then modify it yourself batch_size.

Pay attention to revision model_path and classes_path, Otherwise, it will report a mistake ！

The size of network input before pruning must be consistent with that during fine tuning training and reasoning ！

Train your own dataset

If you have Bubbliiiing up Code of master , You will soon be able to get started . The dataset uses VOC In the form of

VOCdevkit/
`-- VOC2007
    |-- Annotations  ( Deposit xml Label file )
    |-- ImageSets
    |   `-- Main
    `-- JPEGImages ( Store image )
​

stay model_data Create a new one in new_classes.txt, Write your own class inside . function voc_annotation.py, Will generate... In the current directory 2007_train.txt Document and 2007_val.txt file .( You can check whether it is successfully generated )

stay train.py in , take classes_path It is amended as follows model_data/new_classes.txt【 Wait for the prediction , Also needed in the yolo.py Modify here 】

Then modify other super parameters as needed to train , Training weights will be saved in logs In file ( Save weights by default , Without network structure )

forecast

 

Parameter description ： The inputs of the following terminals are optional

--predict: Prediction model

--pruned: Turn on pruning prediction or training

--image: Image detection

--video: Start video detection

--video_path: Video path

--camid: camera id Default 0

--fps:FPS test

--dir_predict: Predict the image under a folder

--phi: You can choose s,m,l,x etc.

--input_shape: Network input size , Default 640

--confidence: Confidence threshold

--nms_iou:iou threshold

--num_classes: Number of categories , Default 80

--fuse: Whether the convolution layer and BN Layer fusion accelerates , Default False

The input terminal ：

#  Image prediction 
python demo.py --predict --image

#  Video prediction 
python demo.py --predict --video --camdi 0

# fps test 
python demo.py --predict --fps

The default forecasts are yolox_s, If you want to specify another network , Input ：( It is important to note that in yolo.py Modify the weight path , If it is your own data set , It needs to be revised classes_path)

#  Use yolox_l forecast 
python demo.py --predict --image --phi l

Conv And BN The fusion reasoning of layer accelerates

Other commands can be used together , For example, using conv and bn Reasoning in the way of fusion

python demo.py --predict --image --fuse

Found by test FPS Promoted 3 frame /s about ( my GPU yes 1650)

Saving of log files

This project adopts loguru Tools capture logs , Some log records during detection and training will be automatically recorded , Save in logs Under the document , One log The maximum size of the file I set is 1 MB, If beyond this range , Will automatically generate a new .log file , You can modify this value by yourself , Or modify the log save time ( So as not to save too many logs ). If you don't want this feature , You can find the corresponding position and comment it out .

Here is just to help you build wheels , Implement some functions with as simple code as possible , You don't have to look at complex engineering code anymore , The final effect needs to be adjusted patiently , Take your time “ Alchemy ”！

The weight

link ： Baidu SkyDrive Please enter the extraction code https://pan.baidu.com/s/1Jbq8dCv893rZ7RkaANUZgQ Extraction code ：yypn

Code ( If it helps , Excuse me star Chant ~)：

GitHub - YINYIPENG-EN/Pruning_for_YOLOX: Realize to YOLOX Pruning operation , Added convolution and BN Layer fusion reasoning , Add the middle tier visualization function , Prediction and training log saving can be realized Realize to YOLOX Pruning operation , Added convolution and BN Layer fusion reasoning , Add the middle tier visualization function , Prediction and training log saving can be realized - GitHub - YINYIPENG-EN/Pruning_for_YOLOX: Realize to YOLOX Pruning operation , Added convolution and BN Layer fusion reasoning , Add the middle tier visualization function , Prediction and training log saving can be realized https://github.com/YINYIPENG-EN/Pruning_for_YOLOX