├── LICENSE ├── README.md ├── cls_models ├── __init__.py └── model.py ├── cpp_inference ├── .vscode │ └── settings.json ├── CMakeLists.txt ├── classification │ ├── CMakeLists.txt │ ├── include │ │ └── ImgCls.hpp │ ├── src │ │ └── ImgCls.cpp │ └── test_ImgCls.cpp ├── compile.sh └── traced_model │ └── trace_model.py ├── dataset ├── __init__.py ├── cls_dataset.py └── transform.py ├── ensamble ├── kaggle_vote.py └── samples │ ├── method_1.csv │ ├── method_2.csv │ └── method_3.csv ├── run.sh ├── sample_files └── imgs │ ├── cat │ ├── 0.jpg │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ └── 5.jpg │ ├── dog │ ├── 0.jpg │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ ├── 4.jpg │ └── 5.jpg │ └── listfile.txt ├── tools ├── evaluation.py ├── predict.py └── train_val.py ├── trt_inference ├── CMakeLists.txt ├── convert_onnx.py ├── include │ └── TrtCls.hpp ├── labels.txt └── src │ └── TrtCls.cpp ├── utils ├── __init__.py ├── label_smoothing_pytorch.py ├── loss_kd.py ├── lr_scheduler.py ├── opts.py ├── util.py └── warmup_lr.py └── visualization ├── Feature_Visualization.ipynb ├── Feature_Visualization.py ├── f1_conv1.png └── test.png /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 xiangzhe_lu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | ## 简介 3 | 4 | 基于torchision实现的pytorch图像分类功能。 5 | 6 | 7 | ## 近期更新 8 | 9 | * 2022.11.05更新 10 | - 新添加tensorrt c++的推理方案 11 | 12 | * 2022.10.29更新,进行代码重构,基本的功能基本一致。 13 | - 支持pytorch ddp的训练 14 | - 支持c++ libtorch的模型推理 15 | - 支持script脚本一键运行 16 | - 添加日志模块 17 | 18 | 习惯之前版本的请看v1版本的代码:[V1版本](https://github.com/lxztju/pytorch_classification/tree/v1)。 19 | 20 | 21 | 主要功能: 22 | 23 | 利用pytorch实现图像分类,基于torchision可以扩展使用densenet,resnext,mobilenet,efficientnet,swin transformer等图像分类网络 24 | 25 | 如果有用欢迎star 26 | 27 | ## 实现功能 28 | * 基础功能利用pytorch实现图像分类 29 | * 包含带有warmup的cosine学习率调整 30 | * warmup的step学习率优调整 31 | * 多模型融合预测,加权与投票融合 32 | * 利用flask + redis实现模型云端api部署(tag v1) 33 | * c++ libtorch的模型部署 34 | * 使用tta测试时增强进行预测(tag v1) 35 | * 添加label smooth的pytorch实现(标签平滑)(tag v1) 36 | * 添加使用cnn提取特征,并使用SVM,RF,MLP,KNN等分类器进行分类(tag v1)。 37 | * 可视化特征层 38 | 39 | ## 运行环境 40 | * python3.7 41 | * pytorch 1.8.1 42 | * torchvision 0.9.1 43 | * opencv(libtorch cpp推理使用, 版本3.4.6)(可选) 44 | * libtorch cpp推理使用(可选) 45 | 46 | 47 | 48 | ## 快速开始 49 | 50 | ### 数据集形式 51 | 数据集的组织形式,参考[sample_files/imgs/listfile.txt](https://github.com/lxztju/pytorch_classification/blob/master/sample_files/imgs/listfile.txt) 52 | 53 | 54 | ### 训练 测试 55 | 56 | 修改`run.sh`中的参数,直接运行run.sh即可运行 57 | 58 | 59 | 主要修改的参数: 60 | 61 | ``` 62 | OUTPUT_PATH 模型保存和log文件的路径 63 | 64 | TRAIN_LIST 训练数据集的list文件 65 | VAL_LIST 测试集合的list文件 66 | model_name 默认是resnet50 67 | lr 学习率 68 | epochs 训练总的epoch 69 | batch-size batch的大小 70 | j dataloader的num_workers的大小 71 | num_classes 类别数 72 | ``` 73 | 74 | 75 | ### libtorch inference 76 | 77 | 78 | 代码存储在`cpp_inference`文件夹中。 79 | 80 | 1. 利用[cpp_inference/traced_model/trace_model.py](https://github.com/lxztju/pytorch_classification/blob/master/cpp_inference/traced_model/trace_model.py)将训练好的模型导出。 81 | 2. 编译所需的opencv和libtorch代码到`cpp_inference/third_party_library` 82 | 83 | 3. 编译 84 | ``` 85 | sh compile.sh 86 | ``` 87 | 88 | 4. 可执行文件测试 89 | ``` 90 | ./bin/imgCls imgpath 91 | ``` 92 | 93 | 94 | -------------------------------------------------------------------------------- /cls_models/__init__.py: -------------------------------------------------------------------------------- 1 | from .model import ClsModel -------------------------------------------------------------------------------- /cls_models/model.py: -------------------------------------------------------------------------------- 1 | from logging import raiseExceptions 2 | import torch 3 | import torch.nn as nn 4 | import torchvision 5 | 6 | ModelWeights = { 7 | 'mobilenet_v2':'MobileNet_V2_Weights.IMAGENET1K_V1', 8 | 'resnet18':'ResNet18_Weights.IMAGENET1K_V1', 9 | 'resnet50':'ResNet50_Weights.IMAGENET1K_V1', 10 | 'resnet101' : 'ResNet101_Weights.IMAGENET1K_V1', 11 | 'swin_s': 'Swin_S_Weights.IMAGENET1K_V1', 12 | 'swin_b': 'Swin_B_Weights.IMAGENET1K_V1', 13 | 'vit_b_16': 'ViT_B_16_Weights.IMAGENET1K_V1', 14 | 'vit_b_32' : 'ViT_B_32_Weights.IMAGENET1K_V1', 15 | 'vit_l_16': 'ViT_L_16_Weights.IMAGENET1K_V1', 16 | 'vit_l_32': 'ViT_L_32_Weights.IMAGENET1K_V1' 17 | } 18 | 19 | 20 | class ClsModel(nn.Module): 21 | def __init__(self, model_name, num_classes, is_pretrained=False): 22 | super(ClsModel, self).__init__() 23 | self.model_name = model_name 24 | self.num_class = num_classes 25 | self.is_pretrained = is_pretrained 26 | 27 | if self.model_name not in ModelWeights: 28 | raise ValueError('Please confirm the name of model') 29 | 30 | if self.is_pretrained: 31 | self.base_model = getattr(torchvision.models, self.model_name)(weights=ModelWeights[self.model_name]) 32 | else: 33 | self.base_model = getattr(torchvision.models, self.model_name)() 34 | 35 | if hasattr(self.base_model, 'classifier'): 36 | self.base_model.last_layer_name = 'classifier' 37 | feature_dim = self.base_model.classifier[1].in_features 38 | self.base_model.classifier = nn.Linear(feature_dim, self.num_class) 39 | elif hasattr(self.base_model, 'fc'): 40 | self.base_model.last_layer_name = 'fc' 41 | feature_dim = getattr(self.base_model, self.base_model.last_layer_name).in_features 42 | self.base_model.fc = nn.Linear(feature_dim, self.num_class) 43 | elif hasattr(self.base_model, 'head'): 44 | self.base_model.last_layer_name = 'head' 45 | feature_dim = getattr(self.base_model, self.base_model.last_layer_name).in_features 46 | self.base_model.head = nn.Linear(feature_dim, self.num_class) 47 | elif hasattr(self.base_model, 'heads'): 48 | self.base_model.last_layer_name = 'heads' 49 | feature_dim = getattr(self.base_model, self.base_model.last_layer_name).in_features 50 | self.base_model.heads = nn.Linear(feature_dim, self.num_class) 51 | else: 52 | raise ValueError('Please confirm the name of last') 53 | 54 | # self.new_fc = nn.Linear(feature_dim, self.num_class) 55 | 56 | 57 | def forward(self, x): 58 | x = self.base_model(x) 59 | # x = self.new_fc(x) 60 | return x 61 | 62 | 63 | if __name__ == '__main__': 64 | model_name = 'resnet50' 65 | num_classes = 2 66 | is_pretrained = False 67 | 68 | clsmodel = ClsModel(model_name, num_classes, 0, is_pretrained) 69 | print(clsmodel) 70 | -------------------------------------------------------------------------------- /cpp_inference/.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "files.associations": { 3 | "array": "cpp", 4 | "atomic": "cpp", 5 | "bit": "cpp", 6 | "*.tcc": "cpp", 7 | "cctype": "cpp", 8 | "clocale": "cpp", 9 | "cmath": "cpp", 10 | "compare": "cpp", 11 | "concepts": "cpp", 12 | "cstdarg": "cpp", 13 | "cstddef": "cpp", 14 | "cstdint": "cpp", 15 | "cstdio": "cpp", 16 | "cstdlib": "cpp", 17 | "cwchar": "cpp", 18 | "cwctype": "cpp", 19 | "deque": "cpp", 20 | "string": "cpp", 21 | "unordered_map": "cpp", 22 | "vector": "cpp", 23 | "exception": "cpp", 24 | "algorithm": "cpp", 25 | "functional": "cpp", 26 | "iterator": "cpp", 27 | "memory": "cpp", 28 | "memory_resource": "cpp", 29 | "numeric": "cpp", 30 | "random": "cpp", 31 | "string_view": "cpp", 32 | "system_error": "cpp", 33 | "tuple": "cpp", 34 | "type_traits": "cpp", 35 | "utility": "cpp", 36 | "initializer_list": "cpp", 37 | "iosfwd": "cpp", 38 | "limits": "cpp", 39 | "new": "cpp", 40 | "numbers": "cpp", 41 | "ostream": "cpp", 42 | "stdexcept": "cpp", 43 | "streambuf": "cpp", 44 | "typeinfo": "cpp", 45 | "iostream": "cpp", 46 | "istream": "cpp" 47 | } 48 | } -------------------------------------------------------------------------------- /cpp_inference/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.5) 2 | project(cpp_deployment) 3 | 4 | add_definitions(-D _GLIBCXX_USE_CXX11_ABI=1) 5 | 6 | set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/lib) 7 | set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/lib) 8 | set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/bin) 9 | 10 | 11 | set(CMAKE_BUILD_TYPE Release) 12 | 13 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14") 14 | 15 | set(CLASS_3DPARTY ${CMAKE_CURRENT_SOURCE_DIR}/third_party_library) 16 | 17 | 18 | # 两个必要的依赖包 19 | set(OpenCV_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party_library/opencv-3.4.16/share/OpenCV) 20 | set(Torch_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party_library/libtorch/share/cmake/Torch) 21 | find_package(OpenCV REQUIRED) 22 | find_package(Torch REQUIRED) 23 | 24 | message(STATUS "OpenCV_LIBS: ${OpenCV_LIBS}") 25 | message(STATUS "Torch_LIBS: ${TORCH_LIBRARIES}") 26 | 27 | 28 | 29 | ## build 30 | add_subdirectory(classification) -------------------------------------------------------------------------------- /cpp_inference/classification/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.5) 2 | project(classification) 3 | 4 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14") 5 | 6 | 7 | 8 | include_directories( 9 | ${TORCH_INCLUDE_DIRS} 10 | ${OpenCV_INCLUDE_DIRS} 11 | ${CMAKE_CURRENT_SOURCE_DIR}/include 12 | ) 13 | 14 | message(STATUS "Torch_INCLUDE_DIRS: ${TORCH_INCLUDE_DIRS}") 15 | # link_directories( 16 | # ${OpenCV_LIBRARIES_DIRS} 17 | # # ${OpenCV_DIR}/lib 18 | # ${Torch_LIBRARIES_DIRS} 19 | # ) 20 | 21 | 22 | # link_directories( 23 | # ${OpenCV_LIBRARIES_DIRS} 24 | # ) 25 | 26 | # file (GLOB CPP_SRC src/*.cpp) 27 | 28 | add_library(classification SHARED src/ImgCls.cpp) 29 | target_link_libraries( 30 | classification 31 | ${TORCH_LIBRARIES} 32 | ${OpenCV_LIBS} 33 | ) 34 | 35 | 36 | add_executable(imgcls test_ImgCls.cpp) 37 | target_link_libraries( 38 | imgcls 39 | classification 40 | ${TORCH_LIBRARIES} 41 | ${OpenCV_LIBS} 42 | ) -------------------------------------------------------------------------------- /cpp_inference/classification/include/ImgCls.hpp: -------------------------------------------------------------------------------- 1 | #ifndef IMAGE_CLS_H 2 | #define IMAGE_CLS_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | #include 10 | 11 | class ImgCls{ 12 | public: 13 | ImgCls(); 14 | ~ImgCls(); 15 | public: 16 | int Init(std::string model_path, std::string labelmap_path); 17 | int run_img(std::string img_path); 18 | int run(std::vector& input_Mats, std::vector& output_Mats); 19 | 20 | 21 | private: 22 | 23 | torch::jit::script::Module cls_model; 24 | std::vector labels; 25 | std::vector thresh; 26 | bool debug_ = true; 27 | int cls_width = 224; 28 | int cls_height = 224; 29 | cv::Scalar mean = (0.485, 0.456, 0.406); 30 | cv::Scalar std = (0.229, 0.224, 0.225); 31 | }; 32 | #endif -------------------------------------------------------------------------------- /cpp_inference/classification/src/ImgCls.cpp: -------------------------------------------------------------------------------- 1 | #include "ImgCls.hpp" 2 | 3 | #include 4 | #include 5 | #include 6 | #include 7 | 8 | #include 9 | 10 | 11 | // 构造和析构函数的定义 12 | ImgCls::ImgCls(){ 13 | 14 | } 15 | 16 | ImgCls::~ImgCls(){ 17 | 18 | } 19 | 20 | 21 | // 模型初始化的定义 22 | int ImgCls::Init(std::string model_path, std::string labelmap_path){ 23 | 24 | // 模型初始化 25 | cls_model = torch::jit::load(model_path); 26 | if (torch::cuda::is_available()){ 27 | torch::Device device0(torch::kCUDA); 28 | cls_model.to(device0); 29 | } 30 | else{ 31 | torch::Device device0(torch::kCPU); 32 | cls_model.to(device0); 33 | } 34 | cls_model.eval(); 35 | std::cout<<"cls model init done"< inputs; 59 | if(torch::cuda::is_available()){ 60 | torch::Device device0(torch::kCUDA); 61 | inputs.push_back(img_tensor.to(device0)); 62 | } 63 | else{ 64 | torch::Device device0(torch::kCPU); 65 | inputs.push_back(img_tensor.to(device0)); 66 | } 67 | torch::Tensor output = cls_model.forward(inputs).toTensor(); 68 | 69 | auto output_scores = torch::softmax(output, 1); 70 | if (debug_){ 71 | std::cout<<"torch::Tensor output: "< max_result = torch::max(output_scores, 1); 76 | 77 | at::Tensor max_scores = std::get<0>(max_result); 78 | at::Tensor max_indexs = std::get<1>(max_result); 79 | if (debug_){ 80 | std::cout<<"max_score: "<(); 85 | int indexs = max_indexs.item(); 86 | return indexs; 87 | } 88 | 89 | 90 | int ImgCls::run(std::vector& input_Mats, std::vector& output_Mats){ 91 | return 0; 92 | } -------------------------------------------------------------------------------- /cpp_inference/classification/test_ImgCls.cpp: -------------------------------------------------------------------------------- 1 | #include "ImgCls.hpp" 2 | 3 | #include 4 | #include 5 | #include 6 | 7 | #include 8 | 9 | 10 | 11 | 12 | 13 | int main(int argc, const char** argv){ 14 | std::string img_path = argv[1]; 15 | 16 | std::string model_path = "/home/lxz/codes/pytorch_classification/cpp_inference/traced_model/traced_model_res50.pt"; 17 | std::string labelmap_path = ""; 18 | ImgCls imgcls; 19 | imgcls.Init(model_path, labelmap_path); 20 | auto res = imgcls.run_img(img_path); 21 | std::cout<<"res: "< 0: 42 | row = line.strip().split(",") 43 | for l in range(1,weight_list[i]+1): 44 | scores[(e,row[0])].append(row[1]) 45 | for j,k in sorted(scores): 46 | outfile.write("%s,%s\n"%(k,Counter(scores[(j,k)]).most_common(1)[0][0])) 47 | print("wrote to {}".format(loc_outfile)) 48 | 49 | kaggle_bag(glob_files, loc_outfile, weights=weights_strategy) 50 | -------------------------------------------------------------------------------- /ensamble/samples/method_2.csv: -------------------------------------------------------------------------------- 1 | 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AqX4Eb33jG8_05054.jpg,200 3388 | Thpjk69h9P8_00577.jpg,17 3389 | Z68Rulp1fR0_01430.jpg,169 3390 | rugOj1duAeE_02809.jpg,128 3391 | f1SlbCxQjxY_03377.jpg,11 3392 | rugOj1duAeE_00203.jpg,81 3393 | rugOj1duAeE_03021.jpg,81 3394 | rugOj1duAeE_04209.jpg,81 3395 | 9yzxjzPB5F0_01233.jpg,197 3396 | 9yzxjzPB5F0_01469.jpg,197 3397 | DfhwmcVqCP8_00348.jpg,197 3398 | b-MbEx1Iqj8_01052.jpg,111 3399 | r7Gwaoru_gg_01089.jpg,111 3400 | AlvGmMZCUMQ_00499.jpg,180 3401 | AlvGmMZCUMQ_04892.jpg,180 3402 | -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | TIME=$(date "+%Y-%m-%d-%H-%M-%S") 3 | 4 | 5 | OUTPUT_PATH=./outputs 6 | TRAIN_LIST=/home/lxztju/pytorch_classification/sample_files/imgs/listfile.txt 7 | VAL_LIST=/home/lxztju/pytorch_classification/sample_files/imgs/listfile.txt 8 | 9 | #train 10 | # CUDA_VISIBLE_DEVICES=0,1,2,3 \ 11 | # python3 -u -m torch.distributed.launch --nproc_per_node 4 ./tools/train_val.py \ 12 | # --model_name=resnet18 \ 13 | # --lr 0.01 --epochs 70 --batch-size 128 -j 4 \ 14 | # --output=$OUTPUT_PATH/$TIME \ 15 | # --train_list=$TRAIN_LIST \ 16 | # --val_list=$VAL_LIST \ 17 | # --num_classes=2 \ 18 | # --is_pretrained 19 | 20 | 21 | 22 | 23 | # CUDA_VISIBLE_DEVICES=0,1,2,3 \ 24 | # python3 -u -m torch.distributed.launch --nproc_per_node 1 ./tools/evaluation.py \ 25 | # --model_name=resnet18 \ 26 | # --batch-size 64 -j 4 \ 27 | # --output=$OUTPUT_PATH/$TIME \ 28 | # --val_list=$VAL_LIST \ 29 | # --tune_from='/home/lxztju/pytorch_classification/ouputs/xxx/epoch_4.pth' \ 30 | # --num_classes=2 31 | 32 | 33 | # CUDA_VISIBLE_DEVICES=0,1,2,3 \ 34 | # python3 -u -m torch.distributed.launch --nproc_per_node 1 ./tools/predict.py \ 35 | # --model_name=resnet18 \ 36 | # --batch-size 64 -j 4 \ 37 | # --output=$OUTPUT_PATH/$TIME \ 38 | # --val_list=$VAL_LIST \ 39 | # --tune_from='/home/lxztju/pytorch_classification/ouputs/xxx/epoch_4.pth' \ 40 | # --num_classes=2 41 | -------------------------------------------------------------------------------- /sample_files/imgs/cat/0.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lxztju/pytorch_classification/e28cb9f9058abe0b920b85c4740efbb6c135f13f/sample_files/imgs/cat/0.jpg -------------------------------------------------------------------------------- /sample_files/imgs/cat/1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lxztju/pytorch_classification/e28cb9f9058abe0b920b85c4740efbb6c135f13f/sample_files/imgs/cat/1.jpg -------------------------------------------------------------------------------- /sample_files/imgs/cat/2.jpg: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/lxztju/pytorch_classification/e28cb9f9058abe0b920b85c4740efbb6c135f13f/sample_files/imgs/dog/5.jpg -------------------------------------------------------------------------------- /sample_files/imgs/listfile.txt: -------------------------------------------------------------------------------- 1 | /home/lxztju/pytorch_classification/sample_files/imgs/cat/0.jpg 0 2 | /home/lxztju/pytorch_classification/sample_files/imgs/cat/1.jpg 0 3 | /home/lxztju/pytorch_classification/sample_files/imgs/cat/2.jpg 0 4 | /home/lxztju/pytorch_classification/sample_files/imgs/cat/3.jpg 0 5 | /home/lxztju/pytorch_classification/sample_files/imgs/cat/4.jpg 0 6 | /home/lxztju/pytorch_classification/sample_files/imgs/cat/5.jpg 0 7 | /home/lxztju/pytorch_classification/sample_files/imgs/dog/0.jpg 1 8 | /home/lxztju/pytorch_classification/sample_files/imgs/dog/1.jpg 1 9 | /home/lxztju/pytorch_classification/sample_files/imgs/dog/2.jpg 1 10 | /home/lxztju/pytorch_classification/sample_files/imgs/dog/3.jpg 1 11 | /home/lxztju/pytorch_classification/sample_files/imgs/dog/4.jpg 1 12 | /home/lxztju/pytorch_classification/sample_files/imgs/dog/5.jpg 1 13 | -------------------------------------------------------------------------------- /tools/evaluation.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tqdm 3 | import time 4 | import shutil 5 | 6 | 7 | import numpy as np 8 | import torch.nn.parallel 9 | import torch.backends.cudnn as cudnn 10 | import torch.optim 11 | from torch.nn import functional as F 12 | import torch.distributed as dist 13 | 14 | import sys 15 | from pathlib import Path 16 | FILE = Path(__file__).resolve() 17 | 18 | ROOT = FILE.parents[1] 19 | if str(ROOT) not in sys.path: 20 | sys.path.append(str(ROOT)) # add ROOT to PATH 21 | 22 | from utils import init_logger, torch_distributed_zero_first, AverageMeter, distributed_concat 23 | from utils import get_scheduler, parser 24 | 25 | from dataset import ClsDataset, train_transform, val_transform 26 | from cls_models import ClsModel 27 | 28 | def evaluate(rank, local_rank, device, args): 29 | 30 | check_rootfolders() 31 | logger = init_logger(log_file=args.output + f'/log', rank=rank) 32 | 33 | with torch_distributed_zero_first(rank): 34 | val_dataset = ClsDataset( 35 | list_file = args.val_list, 36 | transform = val_transform(size=args.input_size) 37 | ) 38 | 39 | val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, rank=rank,shuffle=False) 40 | 41 | val_loader = torch.utils.data.DataLoader( 42 | val_dataset, 43 | batch_size=args.batch_size, 44 | sampler=val_sampler, 45 | num_workers=args.workers, pin_memory=True) 46 | 47 | print('val_loader is ready!!!') 48 | 49 | 50 | model = ClsModel(args.model_name, args.num_classes, args.is_pretrained) 51 | 52 | 53 | if args.tune_from and os.path.exists(args.tune_from): 54 | print(f'loading model from {args.tune_from}') 55 | sd = torch.load(args.tune_from, map_location='cpu') 56 | model.load_state_dict(sd) 57 | else: 58 | raise ValueError("the path of model weights is not exist!") 59 | 60 | model.to(device) 61 | model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True) 62 | 63 | cudnn.benchmark = True 64 | 65 | 66 | for k, v in sorted(vars(args).items()): 67 | logger.info(f'{k} = {v}') 68 | 69 | model.eval() 70 | with torch.no_grad(): 71 | preds, labels, scores = [], [], [] 72 | eval_pbar = tqdm.tqdm(val_loader, desc=f'evaluating', position=1, disable=False if rank in [-1, 0] else True) 73 | for step, (img, target, _) in enumerate(eval_pbar): 74 | img = img.to(device) 75 | target = target.to(device) 76 | 77 | output = model(img) 78 | 79 | score = torch.softmax(output, dim=1) 80 | predict = torch.max(output, dim=1)[1] 81 | labels.append(target) 82 | scores.append(score) 83 | preds.append(predict) 84 | labels = torch.cat(labels, dim=0) 85 | predicts = torch.cat(preds, dim=0) 86 | scores = torch.cat(scores, dim=0) 87 | if rank != -1: 88 | labels = distributed_concat(labels, len(val_dataset)) 89 | predicts = distributed_concat(predicts, len(val_dataset)) 90 | scores = distributed_concat(scores, len(val_dataset)) 91 | scores = scores.cpu().numpy() 92 | labels = labels.cpu().numpy() 93 | predicts = predicts.cpu().numpy() 94 | if rank == 0: 95 | from sklearn import metrics 96 | report = metrics.classification_report(labels, predicts, target_names=['{}'.format(x) for x in range(args.num_classes)], 97 | digits=4, labels=range(args.num_classes)) 98 | 99 | confusion = metrics.confusion_matrix(labels, predicts) 100 | print(report) 101 | print(confusion) 102 | performance = np.sum(labels==predicts) / len(labels) 103 | print(performance) 104 | np.save(os.path.join(args.output, f"labels"), labels) 105 | np.save(os.path.join(args.output, f"scores"), scores) 106 | np.save(os.path.join(args.output, f"predicts"), predicts) 107 | logger.info(args.output) 108 | 109 | 110 | def check_rootfolders(): 111 | """Create log and model folder""" 112 | folders_util = [args.output] 113 | for folder in folders_util: 114 | os.makedirs(folder, exist_ok=True) 115 | 116 | 117 | def distributed_init(backend="gloo", port=None): 118 | 119 | num_gpus = torch.cuda.device_count() 120 | 121 | rank = int(os.environ["RANK"]) 122 | world_size = int(os.environ["WORLD_SIZE"]) 123 | 124 | torch.cuda.set_device(rank % num_gpus) 125 | 126 | dist.init_process_group( 127 | backend=backend, 128 | world_size=world_size, 129 | rank=rank, 130 | ) 131 | 132 | if __name__ == '__main__': 133 | 134 | args = parser.parse_args() 135 | distributed_init(backend = args.backend) 136 | rank = int(os.environ["RANK"]) 137 | local_rank = int(os.environ["LOCAL_RANK"]) 138 | device = torch.device("cuda", local_rank) 139 | 140 | args.world_size = int(os.environ["WORLD_SIZE"]) 141 | 142 | print(f"[init] == local rank: {local_rank}, global rank: {rank} == devices: {device}") 143 | 144 | evaluate(rank, local_rank, device, args) -------------------------------------------------------------------------------- /tools/predict.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tqdm 3 | import time 4 | import shutil 5 | 6 | 7 | import numpy as np 8 | import torch.nn.parallel 9 | import torch.backends.cudnn as cudnn 10 | import torch.optim 11 | from torch.nn import functional as F 12 | import torch.distributed as dist 13 | from PIL import Image 14 | 15 | import sys 16 | from pathlib import Path 17 | FILE = Path(__file__).resolve() 18 | 19 | ROOT = FILE.parents[1] 20 | if str(ROOT) not in sys.path: 21 | sys.path.append(str(ROOT)) # add ROOT to PATH 22 | 23 | from utils import init_logger, torch_distributed_zero_first, AverageMeter, distributed_concat 24 | from utils import get_scheduler, parser 25 | 26 | from dataset import ClsDataset, train_transform, val_transform 27 | from cls_models import ClsModel 28 | 29 | 30 | def __init_model(args): 31 | logger = init_logger(log_file=args.output + f'/log', rank=-1) 32 | 33 | model = ClsModel(args.model_name, args.num_classes) 34 | if args.tune_from and os.path.exists(args.tune_from): 35 | print(f'loading model from {args.tune_from}') 36 | sd = torch.load(args.tune_from, map_location='cpu') 37 | model.load_state_dict(sd) 38 | device = torch.device("cuda") 39 | model.to(device) 40 | model.eval() 41 | 42 | cudnn.benchmark = True 43 | return logger, model 44 | 45 | 46 | 47 | def predict(img_path): 48 | 49 | img = Image.open(img_path).convert('RGB') 50 | img_tensor = val_transform(size=args.input_size)(img).unsqueeze(0) 51 | 52 | 53 | with torch.no_grad(): 54 | preds, labels, scores = [], [], [] 55 | device = torch.device("cuda") 56 | img_tensor = img_tensor.to(device) 57 | 58 | output = model(img_tensor) 59 | 60 | scores = torch.softmax(output, dim=1) 61 | score = torch.max(scores, dim=1)[0].item() 62 | pred = torch.max(scores, dim=1)[1].item() 63 | 64 | return pred, score 65 | 66 | 67 | def check_rootfolders(): 68 | """Create log and model folder""" 69 | folders_util = [args.output] 70 | for folder in folders_util: 71 | os.makedirs(folder, exist_ok=True) 72 | 73 | 74 | 75 | if __name__ == '__main__': 76 | 77 | args = parser.parse_args() 78 | check_rootfolders() 79 | logger, model = __init_model(args) 80 | 81 | for k, v in sorted(vars(args).items()): 82 | logger.info(f'{k} = {v}') 83 | 84 | pred_file = args.val_list 85 | 86 | datas = open(pred_file, 'r').readlines() 87 | target_res = open(os.path.join(args.output, 'pred_res.txt'), 'w') 88 | for data in tqdm.tqdm(datas): 89 | path, _ = data.strip().split('\t') 90 | pred, score = predict(path) 91 | # print(pred, score) 92 | target_res.write(path + '\t' + str(pred) + '\t' + str(score) +'\n') 93 | target_res.flush() 94 | target_res.close() -------------------------------------------------------------------------------- /tools/train_val.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tqdm 3 | import time 4 | import shutil 5 | 6 | 7 | import numpy as np 8 | import torch.nn.parallel 9 | import torch.backends.cudnn as cudnn 10 | import torch.optim 11 | from torch.nn import functional as F 12 | import torch.distributed as dist 13 | 14 | import sys 15 | from pathlib import Path 16 | FILE = Path(__file__).resolve() 17 | 18 | ROOT = FILE.parents[1] 19 | if str(ROOT) not in sys.path: 20 | sys.path.append(str(ROOT)) # add ROOT to PATH 21 | 22 | from utils import init_logger, torch_distributed_zero_first, AverageMeter, distributed_concat 23 | from utils import get_scheduler, parser 24 | 25 | from dataset import ClsDataset, train_transform, val_transform 26 | from cls_models import ClsModel 27 | 28 | 29 | def train(rank, local_rank, device, args): 30 | check_rootfolders() 31 | logger = init_logger(log_file=args.output + f'/log', rank=rank) 32 | 33 | with torch_distributed_zero_first(rank): 34 | val_dataset = ClsDataset( 35 | list_file = args.val_list, 36 | transform = val_transform(size=args.input_size) 37 | ) 38 | 39 | train_dataset = ClsDataset( 40 | list_file = args.train_list, 41 | transform = train_transform(size=args.input_size) 42 | ) 43 | 44 | logger.info(f"Num train examples = {len(train_dataset)}") 45 | logger.info(f"Num val examples = {len(val_dataset)}") 46 | 47 | 48 | val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, rank=rank,shuffle=False) 49 | 50 | val_loader = torch.utils.data.DataLoader( 51 | val_dataset, 52 | batch_size=args.batch_size, 53 | sampler=val_sampler, 54 | num_workers=args.workers, pin_memory=True) 55 | 56 | 57 | train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, rank=rank,shuffle=True) 58 | train_loader = torch.utils.data.DataLoader(train_dataset, 59 | batch_size=args.batch_size, 60 | sampler=train_sampler, 61 | num_workers=args.workers, pin_memory=True, 62 | drop_last=True) 63 | 64 | 65 | criterion = torch.nn.CrossEntropyLoss().to(device) 66 | 67 | model = ClsModel(args.model_name, args.num_classes, args.is_pretrained) 68 | print(model.base_model) 69 | model.to(device) 70 | model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True) 71 | 72 | optimizer = torch.optim.SGD(model.parameters(),lr=args.lr,momentum=args.momentum,weight_decay=args.weight_decay) 73 | scheduler = get_scheduler(optimizer,len(train_loader), args) 74 | 75 | 76 | cudnn.benchmark = True 77 | 78 | for k, v in sorted(vars(args).items()): 79 | logger.info(f'{k} = {v}') 80 | 81 | epoch = args.start_epoch 82 | model.zero_grad() 83 | eval_results = [] 84 | for epoch in range(args.start_epoch, args.epochs): 85 | losses = AverageMeter() 86 | if local_rank != -1: 87 | train_sampler.set_epoch(epoch) 88 | model.train() 89 | for step, (img, target, _) in enumerate(train_loader): 90 | img = img.to(device) 91 | target = target.to(device) 92 | 93 | output = model(img) 94 | loss = criterion(output, target) 95 | loss.backward() 96 | losses.update(loss.item(), img.size(0)) 97 | if rank == 0: 98 | if step % args.print_freq == 0: 99 | logger.info(f"Epoch: [{epoch}/{args.epochs}][{step}/{len(train_loader)}], lr: {optimizer.param_groups[-1]['lr']:.5f} \t loss = {losses.val:.4f}({losses.avg:.4f})" ) 100 | optimizer.step() 101 | optimizer.zero_grad() 102 | scheduler.step() 103 | 104 | 105 | # evaluate on validation set 106 | if (epoch + 1) % args.eval_freq == 0 or epoch == args.epochs - 1: 107 | model.eval() 108 | with torch.no_grad(): 109 | preds, labels = [], [] 110 | eval_pbar = tqdm.tqdm(val_loader, desc=f'epoch {epoch + 1} / {args.epochs} evaluating', position=1, disable=False if rank in [-1, 0] else True) 111 | for step, (img, target, _) in enumerate(eval_pbar): 112 | img = img.to(device) 113 | target = target.to(device) 114 | 115 | output = model(img) 116 | predict = torch.max(output, dim=1)[1] 117 | 118 | labels.append(target) 119 | preds.append(predict) 120 | 121 | labels = torch.cat(labels, dim=0) 122 | predicts = torch.cat(preds, dim=0) 123 | if rank != -1: 124 | labels = distributed_concat(labels, len(val_dataset)) 125 | predicts = distributed_concat(predicts, len(val_dataset)) 126 | 127 | 128 | labels = labels.cpu().numpy() 129 | predicts = predicts.cpu().numpy() 130 | 131 | if rank == 0: 132 | eval_result = (np.sum(labels == predicts)) / len(labels) 133 | eval_results.append(eval_result) 134 | logger.info(f'precision = {eval_result:.4f}' ) 135 | save_path = os.path.join(args.output, f'precision_{eval_result:.4f}_num_{epoch+1}') 136 | os.makedirs(save_path, exist_ok=True) 137 | model_to_save = (model.module if hasattr(model, "module") else model) 138 | torch.save(model_to_save.state_dict(), os.path.join(save_path, f'epoch_{epoch+1}.pth')) 139 | 140 | 141 | def check_rootfolders(): 142 | """Create log and model folder""" 143 | folders_util = [args.output] 144 | for folder in folders_util: 145 | os.makedirs(folder, exist_ok=True) 146 | 147 | 148 | def distributed_init(backend="gloo", port=None): 149 | 150 | num_gpus = torch.cuda.device_count() 151 | 152 | rank = int(os.environ["RANK"]) 153 | world_size = int(os.environ["WORLD_SIZE"]) 154 | 155 | torch.cuda.set_device(rank % num_gpus) 156 | 157 | dist.init_process_group( 158 | backend=backend, 159 | world_size=world_size, 160 | rank=rank, 161 | ) 162 | 163 | 164 | if __name__ == '__main__': 165 | 166 | args = parser.parse_args() 167 | distributed_init(backend = args.backend) 168 | rank = int(os.environ["RANK"]) 169 | local_rank = int(os.environ["LOCAL_RANK"]) 170 | device = torch.device("cuda", local_rank) 171 | print(args.train_list) 172 | args.world_size = int(os.environ["WORLD_SIZE"]) 173 | 174 | print(f"[init] == local rank: {local_rank}, global rank: {rank} == devices: {device}") 175 | 176 | train(rank, local_rank, device, args) 177 | -------------------------------------------------------------------------------- /trt_inference/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.5) 2 | project(hello_trt) 3 | 4 | set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/lib) 5 | set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/lib) 6 | set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/bin) 7 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14") 8 | SET(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_61,code=sm_61;) 9 | 10 | 11 | message(STATUS "CMAKE_RUNTIME_OUTPUT_DIRECTORY: "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}) 12 | 13 | set(3RDPARTY ${CMAKE_CURRENT_SOURCE_DIR}/3rd) 14 | set(TENSORRT_LIB ${3RDPARTY}/TensorRT-8.0.1.6/targets/x86_64-linux-gnu/lib) 15 | file(GLOB TENSORRT_LIBS "${TENSORRT_LIB}/*.so") 16 | 17 | set(OpenCV_DIR ${3RDPARTY}/opencv-3.4.16/share/OpenCV) 18 | find_package(CUDA REQUIRED) 19 | find_package(OpenCV REQUIRED) 20 | 21 | message(STATUS "CUDA_LIBRARIES: "${CUDA_LIBRARIES}) 22 | message(STATUS "TENSORRT_LIBS: "${TENSORRT_LIBS}) 23 | message(STATUS "Opencv_LIBS: "${OpenCV_LIBS}) 24 | include_directories( 25 | ${CUDA_INCLUDE_DIRS} 26 | ${3RDPARTY}/TensorRT-8.0.1.6/include 27 | ${CMAKE_CURRENT_SOURCE_DIR}/include 28 | ${OpenCV_INCLUDE_DIRS} 29 | ) 30 | 31 | add_executable( 32 | trt_cls 33 | src/TrtCls.cpp 34 | ) 35 | 36 | target_link_libraries( 37 | trt_cls 38 | ${CUDA_LIBRARIES} 39 | ${TENSORRT_LIBS} 40 | ${OpenCV_LIBS} 41 | ) 42 | -------------------------------------------------------------------------------- /trt_inference/convert_onnx.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.onnx 3 | 4 | 5 | def traced_model_convert_onnx(): 6 | model = torch.jit.load('../cpp_inference/traced_model/traced_model_res50.pt') 7 | 8 | dummy_input = torch.randn(1, 3, 224, 224) 9 | 10 | torch.onnx.export( 11 | model, # model 12 | dummy_input, # 模型输入 13 | "./saved_model/traced_res50.onnx", # 转换后模型的存储路径 14 | export_params=True, # 是否将模型参数保存至onnx中 15 | input_names=["input_image"], # 输入节点名称 16 | output_names = ["model_output"], # 输出节点名称 17 | dynamic_axes = { 18 | "input_image":{0: "batch"}, # 可动态shape的维度 19 | "model_output":{0: "batch"} 20 | }, 21 | opset_version=11 # onnx的版本 22 | ) 23 | 24 | 25 | import sys 26 | sys.path.append('/home/netease/codes/pytorch_classification') 27 | print(sys.path) 28 | from cls_models import ClsModel 29 | 30 | def torch_convert_onnx(): 31 | 32 | model = ClsModel('resnet50', num_classes=2, dropout=0, is_pretrained=False) 33 | sd = torch.load('../cpp_inference/traced_model/trained_model.pth', map_location='cpu') 34 | model.load_state_dict(sd) 35 | model.eval() 36 | 37 | dummy_input = torch.randn(1, 3, 224, 224) 38 | torch.onnx.export( 39 | model, 40 | dummy_input, 41 | "./saved_model/torch_res50.onnx", 42 | export_params=True, 43 | input_names=["input_image"], 44 | output_names = ["model_output"], 45 | dynamic_axes = { 46 | "input_image":{0: "batch"}, 47 | "model_output":{0: "batch"} 48 | }, 49 | opset_version=11 50 | ) 51 | 52 | if __name__ == '__main__': 53 | import os 54 | os.makedirs('./saved_model', exist_ok=True) 55 | torch_convert_onnx() 56 | traced_model_convert_onnx() 57 | 58 | 59 | -------------------------------------------------------------------------------- /trt_inference/include/TrtCls.hpp: -------------------------------------------------------------------------------- 1 | #ifndef TRTCLS 2 | #define TRTCLS 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include 10 | #include 11 | #include 12 | #include 13 | 14 | #include 15 | #include 16 | #include 17 | //#include NvInfer.h头文件中包含了这个头文件 18 | 19 | 20 | 21 | inline const char* severity_string(nvinfer1::ILogger::Severity t){ 22 | switch(t){ 23 | case nvinfer1::ILogger::Severity::kINTERNAL_ERROR: return "internal_error"; 24 | case nvinfer1::ILogger::Severity::kERROR: return "error"; 25 | case nvinfer1::ILogger::Severity::kWARNING: return "warning"; 26 | case nvinfer1::ILogger::Severity::kINFO: return "info"; 27 | case nvinfer1::ILogger::Severity::kVERBOSE: return "verbose"; 28 | } 29 | } 30 | 31 | class TrtLogger: public nvinfer1::ILogger{ 32 | public: 33 | virtual void log(Severity severity, nvinfer1::AsciiChar const* msg) noexcept override{ 34 | if (severity <= Severity::kINFO){ 35 | printf("%s : %s \n", severity_string(severity), msg); 36 | } 37 | } 38 | }; 39 | 40 | // enum Severity : int32_t { 41 | // Severity::kINTERNAL_ERROR = 0, 42 | // Severity::kERROR = 1, 43 | // Severity::kWARNING = 2, 44 | // Severity::kINFO = 3, 45 | // Severity::kVERBOSE = 4 46 | // } 47 | 48 | class TrtCls{ 49 | public: 50 | TrtCls(); 51 | ~TrtCls(); 52 | 53 | public: 54 | int build_engine(const std::string onnx_path, const std::string trt_engine_path); 55 | 56 | int init_engine(const std::string onnx_path, const std::string trt_engine_path); 57 | 58 | int run(const std::string img_path); 59 | 60 | private: 61 | 62 | TrtLogger logger; 63 | std::shared_ptr engine; 64 | bool debug_ = true; 65 | 66 | int maxworkspace_bit = 28; // 2的28次方表示的trt workspace空间 67 | 68 | int input_width = 224; 69 | int input_height = 224; 70 | cv::Scalar mean = (0.485, 0.456, 0.406); 71 | cv::Scalar std = (0.229, 0.224, 0.225); 72 | 73 | }; 74 | 75 | 76 | #define checkRuntime(op) _check_cuda_runtime((op), #op, __FILE__, __LINE__) 77 | 78 | bool _check_cuda_runtime(cudaError_t code, const std::string op, const std::string file, int line){ 79 | if (code != cudaSuccess){ 80 | const std::string err_name = cudaGetErrorName(code); 81 | const std::string err_message = cudaGetErrorString(code); 82 | printf("Runtime Error %s: %d %s failed. \n code = %s, message = %s \n", file, line, op, err_name, err_message); 83 | return false; 84 | } 85 | return true; 86 | } 87 | 88 | 89 | 90 | 91 | template 92 | std::shared_ptr<_T> make_nvshared(_T* ptr){ 93 | return std::shared_ptr<_T>(ptr, [](_T* p){p->destroy();}); 94 | } 95 | 96 | bool file_exists(const std::string path){ 97 | std::fstream f(path, std::ios::in); 98 | return f.good(); 99 | } 100 | 101 | 102 | std::vector softmax_cpu(const std::vector& input_data){ 103 | std::vector res(input_data.size(), 0.0); 104 | std::vector exps(input_data.size(), 0.0); 105 | float sums =0; 106 | for(int i = 0; i load_label_file(const std::string &path){ 118 | std::unordered_map index2label; 119 | std::fstream in(path, std::ios::in); 120 | if (!in.is_open()){ 121 | printf("%s open is failed \n", path); 122 | } 123 | std::string line; 124 | while (getline(in, line)){ 125 | std::vector line_item; 126 | std::stringstream ss(line); 127 | std::string tmp; 128 | while (getline(ss, tmp, ' ')){ 129 | line_item.push_back(tmp); 130 | } 131 | index2label.insert({std::stoi(line_item[0]), line_item[1]}); 132 | } 133 | return index2label; 134 | 135 | } 136 | 137 | std::vector load_engine_data(const std::string& path){ 138 | std::ifstream in(path, std::ios::in|std::ios::binary); 139 | if (! in.is_open()){ 140 | printf("%s open failed \n", path); 141 | return {}; 142 | } 143 | in.seekg(0, std::ios::end); // 对输入文件定位,第一个参数是偏移量,第二个是基地址 144 | int length = in.tellg(); // 返回当前定位指针的位置,表示输入流的大小。 145 | std::vector data; 146 | if (length > 0){ 147 | in.seekg(0, std::ios::beg); 148 | data.resize(length); 149 | in.read((char*)&data[0], length); 150 | } 151 | in.close(); 152 | return data; 153 | 154 | } 155 | 156 | #endif 157 | -------------------------------------------------------------------------------- /trt_inference/labels.txt: -------------------------------------------------------------------------------- 1 | 0 cat 2 | 1 dog 3 | -------------------------------------------------------------------------------- /trt_inference/src/TrtCls.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | 7 | #include 8 | #include 9 | 10 | using namespace std; 11 | 12 | 13 | TrtCls::TrtCls(){ 14 | 15 | } 16 | 17 | 18 | TrtCls::~TrtCls(){ 19 | 20 | } 21 | 22 | 23 | int TrtCls::build_engine(const string onnx_path, const string trt_engine_path){ 24 | if (file_exists(trt_engine_path)){ 25 | printf("Engine has existed, there is no need to rebuild \n"); 26 | return 0; 27 | } 28 | 29 | //builder 30 | auto builder = make_nvshared(nvinfer1::createInferBuilder(logger)); 31 | //config 32 | auto config = make_nvshared(builder->createBuilderConfig()); 33 | //network 34 | auto network = make_nvshared(builder->createNetworkV2(1)); // createNetworkV2( 1U << NetworkDefinitionCreationFlag::kEXPLICIT_BATCH) 35 | 36 | /* 37 | enum class NetworkDefinitionCreationFlag : int32_t 38 | { 39 | //! Dynamic shape support requires that the kEXPLICIT_BATCH flag is set. 40 | //! With dynamic shapes, any of the input dimensions can vary at run-time, 41 | //! and there are no implicit dimensions in the network specification. This is specified by using the 42 | //! wildcard dimension value -1. 43 | kEXPLICIT_BATCH = 0, //!< Mark the network to be an explicit batch network 44 | 45 | //! Setting the network to be an explicit precision network has the following implications: 46 | //! 1) Precision of all input tensors to the network have to be specified with ITensor::setType() function 47 | //! 2) Precision of all layer output tensors in the network have to be specified using ILayer::setOutputType() 48 | //! function 49 | //! 3) The builder will not quantize the weights of any layer including those running in lower precision(INT8). It 50 | //! will 51 | //! simply cast the weights into the required precision. 52 | //! 4) Dynamic ranges must not be provided to run the network in int8 mode. Dynamic ranges of each tensor in the 53 | //! explicit 54 | //! precision network is [-127,127]. 55 | //! 5) Quantizing and dequantizing activation values between higher (FP32) and lower (INT8) precision 56 | //! will be performed using explicit Scale layers with input/output precision set appropriately. 57 | kEXPLICIT_PRECISION TRT_DEPRECATED_ENUM = 1, //! <-- Deprecated, used for backward compatibility 58 | }; 59 | */ 60 | 61 | 62 | // onnxparser解析结果,并填充到network中 63 | auto parser = make_nvshared(nvonnxparser::createParser(*network, logger)); 64 | if(! parser->parseFromFile((char*)onnx_path.c_str(), 1)){ 65 | printf("Faile to parse %s \n", onnx_path); 66 | } 67 | //设置workspace的最大内存占用为256MB 68 | config->setMaxWorkspaceSize(1<<28); 69 | 70 | auto profile = builder->createOptimizationProfile(); 71 | auto input_tensor = network->getInput(0); 72 | auto input_dims = input_tensor->getDimensions(); 73 | 74 | input_dims.d[0] = 1; 75 | profile->setDimensions(input_tensor->getName(), nvinfer1::OptProfileSelector::kMIN, input_dims); 76 | profile->setDimensions(input_tensor->getName(), nvinfer1::OptProfileSelector::kOPT, input_dims); 77 | 78 | int max_BatchSize =8; 79 | profile->setDimensions(input_tensor->getName(), nvinfer1::OptProfileSelector::kMAX, input_dims); 80 | config->addOptimizationProfile(profile); 81 | 82 | auto bengine = make_nvshared(builder->buildEngineWithConfig(*network, *config)); 83 | 84 | if(bengine == nullptr){ 85 | printf("build engine failed \n"); 86 | return -1; 87 | } 88 | 89 | auto model_data = make_nvshared(bengine->serialize()); 90 | FILE* f = fopen((char*)trt_engine_path.c_str(), "wb"); 91 | fwrite(model_data->data(), 1, model_data->size(), f); 92 | fclose(f); 93 | 94 | printf("build engine done. \n"); 95 | return 0; 96 | } 97 | 98 | 99 | 100 | 101 | int TrtCls::init_engine(const string onnx_path, const string trt_engine_path){ 102 | if (0 > build_engine(onnx_path, trt_engine_path)){ 103 | printf("build engine failed \n"); 104 | return -1; 105 | } 106 | auto engine_data = load_engine_data(trt_engine_path); 107 | cout<<"engine size: "<deserializeCudaEngine(engine_data.data(), engine_data.size())); 110 | if(engine == nullptr){ 111 | printf("Deserialize cuda engine failed.\n"); 112 | return -1; 113 | } 114 | return 0; 115 | 116 | 117 | } 118 | 119 | 120 | int TrtCls::run(const string img_path ){ 121 | 122 | cudaStream_t stream = nullptr; 123 | checkRuntime(cudaStreamCreate(&stream)); 124 | auto execution_context = make_nvshared(engine->createExecutionContext()); 125 | 126 | int input_batch = 1; 127 | int input_channel = 3; 128 | int input_height = 224; 129 | int input_width = 224; 130 | int input_numel = input_batch * input_channel * input_height * input_width; 131 | float* input_data_host = nullptr; 132 | float* input_data_device = nullptr; 133 | checkRuntime(cudaMallocHost(&input_data_host, input_numel * sizeof(float))); 134 | checkRuntime(cudaMalloc(&input_data_device, input_numel * sizeof(float))); 135 | 136 | /////////////////////////////////////////////////// 137 | // image to float 138 | auto image = cv::imread(img_path); 139 | std::vector mean = {0.406, 0.456, 0.485}; 140 | std::vector _std = {0.225, 0.224, 0.229}; 141 | 142 | // resize 143 | cv::resize(image, image, cv::Size(input_width, input_height)); 144 | int image_area = image.cols * image.rows; 145 | unsigned char* pimage = image.data; 146 | float* phost_b = input_data_host + image_area * 0; 147 | float* phost_g = input_data_host + image_area * 1; 148 | float* phost_r = input_data_host + image_area * 2; 149 | // BGR2RGB 2tensor 150 | for(int i = 0; i < image_area; ++i, pimage += 3){ 151 | *phost_r++ = (pimage[0] / 255.0f - mean[0]) / _std[0]; 152 | *phost_g++ = (pimage[1] / 255.0f - mean[1]) / _std[1]; 153 | *phost_b++ = (pimage[2] / 255.0f - mean[2]) / _std[2]; 154 | } 155 | /////////////////////////////////////////////////// 156 | checkRuntime(cudaMemcpyAsync(input_data_device, input_data_host, input_numel * sizeof(float), cudaMemcpyHostToDevice, stream)); 157 | 158 | // 3x3输入,对应3x3输出 159 | const int num_classes = 2; 160 | float output_data_host[num_classes]; 161 | float* output_data_device = nullptr; 162 | checkRuntime(cudaMalloc(&output_data_device, sizeof(output_data_host))); 163 | 164 | // 明确当前推理时,使用的数据输入大小 165 | auto input_dims = execution_context->getBindingDimensions(0); 166 | input_dims.d[0] = input_batch; 167 | 168 | // 设置当前推理时,input大小 169 | execution_context->setBindingDimensions(0, input_dims); 170 | float* bindings[] = {input_data_device, output_data_device}; 171 | bool success = execution_context->enqueueV2((void**)bindings, stream, nullptr); 172 | checkRuntime(cudaMemcpyAsync(output_data_host, output_data_device, sizeof(output_data_host), cudaMemcpyDeviceToHost, stream)); 173 | checkRuntime(cudaStreamSynchronize(stream)); 174 | vector output_data_host_vector; 175 | for(int i =0; i< num_classes; i++){ 176 | output_data_host_vector.push_back(output_data_host[i]); 177 | } 178 | auto prob = softmax_cpu(output_data_host_vector); 179 | int predict_label = std::max_element(prob.begin(), prob.end())-prob.begin(); // 确定预测类别的下标 180 | auto labels = load_label_file("labels.txt"); 181 | auto predict_name = labels[predict_label]; 182 | float confidence = prob[predict_label]; // 获得预测值的置信度 183 | printf("Predict: %s, confidence = %f, label = %d\n", predict_name.c_str(), confidence, predict_label); 184 | 185 | checkRuntime(cudaStreamDestroy(stream)); 186 | checkRuntime(cudaFreeHost(input_data_host)); 187 | checkRuntime(cudaFree(input_data_device)); 188 | checkRuntime(cudaFree(output_data_device)); 189 | 190 | } 191 | 192 | int main(int argc, const char** argv){ 193 | TrtCls trtclsServer; 194 | const string img_path = argv[1]; 195 | const string onnx_path ="./traced_res50.onnx"; 196 | string trt_engine_path = "./res50_engine.trtmodel"; 197 | if (0 >trtclsServer.init_engine(onnx_path, trt_engine_path)){ 198 | printf("model initialize failed \n"); 199 | return -1; 200 | } 201 | if (0 > trtclsServer.run(img_path)){ 202 | printf("%s inference failed \n", img_path); 203 | return -1; 204 | } 205 | return 0; 206 | } 207 | 208 | 209 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .label_smoothing_pytorch import * 2 | from .warmup_lr import * 3 | from .loss_kd import * 4 | from .util import * 5 | from .lr_scheduler import * 6 | from .opts import * 7 | -------------------------------------------------------------------------------- /utils/label_smoothing_pytorch.py: -------------------------------------------------------------------------------- 1 | 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | class LabelSmoothingCrossEntropy(nn.Module): 6 | def __init__(self, eps=0.1, reduction='mean'): 7 | super(LabelSmoothingCrossEntropy, self).__init__() 8 | self.eps = eps 9 | self.reduction = reduction 10 | 11 | def forward(self, output, target): 12 | c = output.size()[-1] 13 | log_preds = F.log_softmax(output, dim=-1) 14 | if self.reduction=='sum': 15 | loss = -log_preds.sum() 16 | else: 17 | loss = -log_preds.sum(dim=-1) 18 | if self.reduction=='mean': 19 | loss = loss.mean() 20 | return loss*self.eps/c + (1-self.eps) * F.nll_loss(log_preds, target, reduction=self.reduction) 21 | 22 | 23 | if __name__ == '__main__': 24 | torch.manual_seed(15) 25 | criterion = LabelSmoothingCrossEntropy() 26 | out = torch.randn(20, 10) 27 | lbs = torch.randint(10, (20,)) 28 | print('out:', out, out.size()) 29 | print('lbs:', lbs, lbs.size()) 30 | 31 | import torch.nn.functional as F 32 | 33 | loss = criterion(out, lbs) 34 | print('loss:', loss) 35 | -------------------------------------------------------------------------------- /utils/loss_kd.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | def loss_fn_kd(outputs, labels, teacher_outputs, T, alpha): 5 | """ 6 | Compute the knowledge-distillation (KD) loss given outputs, labels. 7 | "Hyperparameters": temperature and alpha 8 | 9 | NOTE: the KL Divergence for PyTorch comparing the softmaxs of teacher 10 | and student expects the input tensor to be log probabilities! 11 | """ 12 | 13 | KD_loss = nn.KLDivLoss()(F.log_softmax(outputs/T, dim=1), 14 | F.softmax(teacher_outputs/T, dim=1)) * (alpha * T * T) + \ 15 | F.cross_entropy(outputs, labels) * (1. - alpha) 16 | 17 | return KD_loss -------------------------------------------------------------------------------- /utils/lr_scheduler.py: -------------------------------------------------------------------------------- 1 | 2 | from torch.optim.lr_scheduler import _LRScheduler, MultiStepLR, CosineAnnealingLR, LambdaLR 3 | 4 | class GradualWarmupScheduler(_LRScheduler): 5 | """ Gradually warm-up(increasing) learning rate in optimizer. 6 | Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'. 7 | Args: 8 | optimizer (Optimizer): Wrapped optimizer. 9 | multiplier: init learning rate = base lr / multiplier 10 | warmup_epoch: target learning rate is reached at warmup_epoch, gradually 11 | after_scheduler: after target_epoch, use this scheduler(eg. ReduceLROnPlateau) 12 | """ 13 | 14 | def __init__(self, optimizer, multiplier, warmup_epoch, after_scheduler, last_epoch=-1): 15 | self.multiplier = multiplier 16 | if self.multiplier <= 1.: 17 | raise ValueError('multiplier should be greater than 1.') 18 | self.warmup_epoch = warmup_epoch 19 | self.after_scheduler = after_scheduler 20 | self.finished = False 21 | super().__init__(optimizer, last_epoch=last_epoch) 22 | 23 | def get_lr(self): 24 | if self.last_epoch > self.warmup_epoch: 25 | return self.after_scheduler.get_lr() 26 | else: 27 | return [base_lr / self.multiplier * ((self.multiplier - 1.) * self.last_epoch / self.warmup_epoch + 1.) 28 | for base_lr in self.base_lrs] 29 | 30 | def step(self, epoch=None): 31 | if epoch is None: 32 | epoch = self.last_epoch + 1 33 | self.last_epoch = epoch 34 | if epoch > self.warmup_epoch: 35 | self.after_scheduler.step(epoch - self.warmup_epoch) 36 | else: 37 | super(GradualWarmupScheduler, self).step(epoch) 38 | 39 | def state_dict(self): 40 | """Returns the state of the scheduler as a :class:`dict`. 41 | 42 | It contains an entry for every variable in self.__dict__ which 43 | is not the optimizer. 44 | """ 45 | 46 | state = {key: value for key, value in self.__dict__.items() if key != 'optimizer' and key != 'after_scheduler'} 47 | state['after_scheduler'] = self.after_scheduler.state_dict() 48 | return state 49 | 50 | def load_state_dict(self, state_dict): 51 | """Loads the schedulers state. 52 | 53 | Arguments: 54 | state_dict (dict): scheduler state. Should be an object returned 55 | from a call to :meth:`state_dict`. 56 | """ 57 | 58 | after_scheduler_state = state_dict.pop('after_scheduler') 59 | self.__dict__.update(state_dict) 60 | self.after_scheduler.load_state_dict(after_scheduler_state) 61 | 62 | 63 | 64 | 65 | def get_scheduler(optimizer, n_iter_per_epoch, args): 66 | if "cosine" in args.lr_type: 67 | scheduler = CosineAnnealingLR( 68 | optimizer=optimizer, 69 | eta_min=0.00001, 70 | T_max=(args.epochs - args.warmup_epoch) * n_iter_per_epoch) 71 | elif "step" in args.lr_type: 72 | scheduler = MultiStepLR( 73 | optimizer=optimizer, 74 | gamma=args.lr_decay_rate, 75 | milestones=[(m - args.warmup_epoch) * n_iter_per_epoch for m in args.lr_steps]) 76 | 77 | else: 78 | raise NotImplementedError("scheduler not supported:" + args.lr_type) 79 | 80 | 81 | if args.warmup_epoch != 0 : 82 | scheduler = GradualWarmupScheduler( 83 | optimizer, 84 | multiplier=args.warmup_multiplier, 85 | after_scheduler=scheduler, 86 | warmup_epoch=args.warmup_epoch * n_iter_per_epoch) 87 | 88 | return scheduler -------------------------------------------------------------------------------- /utils/opts.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | # @time :2022.07.30 3 | # @author :lxztju 4 | # @github : https://github.com/lxztju 5 | 6 | import argparse 7 | parser = argparse.ArgumentParser(description="PyTorch implementation of image classification") 8 | 9 | # ========================= Data Configs ========================== 10 | parser.add_argument('--num_classes', type=int, default=2, help='the numbers of the image classification task') 11 | parser.add_argument('--input_size', default=224, type=int, help="the input feature dimension for the ") 12 | parser.add_argument('--train_list', default='', type=str, help='the path of training samples text file') 13 | parser.add_argument('--val_list', default='', type=str, help='the path of validation samples text file') 14 | 15 | 16 | 17 | # ========================= Model Configs ========================== 18 | parser.add_argument('--model_name', type=str, default="resnet50") 19 | parser.add_argument('--is_pretrained',default=False, action="store_true", help='using imagenet pretrained model') 20 | parser.add_argument('--tune_from', type=str, default='', help='fine-tune from checkpoint') 21 | parser.add_argument('--focal-loss', default=False, action="store_true") 22 | parser.add_argument('--label-smooth', default=False, action="store_true") 23 | parser.add_argument('--resume', type=str, default='', help='resume from checkpoint') 24 | parser.add_argument('--dropout', type=float, default=0.5) 25 | 26 | 27 | # ========================= Learning Configs ========================== 28 | parser.add_argument('--epochs', default=20, type=int, metavar='N', 29 | help='number of total epochs to run') 30 | parser.add_argument('-b', '--batch-size', default=64, type=int, 31 | metavar='N', help='mini-batch size') 32 | parser.add_argument('--warmup_epoch', type=int, default=5) 33 | parser.add_argument('--lr_decay_rate', type=float, default=0.1) 34 | parser.add_argument('--warmup_multiplier', type=int, default=100) 35 | 36 | parser.add_argument('--print-freq', '-p', default=10, type=int, 37 | metavar='N', help='print frequency (default: 10)') 38 | 39 | 40 | parser.add_argument('--lr', '--learning-rate', default=0.01, type=float, 41 | metavar='LR', help='initial learning rate') 42 | parser.add_argument('--lr_type', default='cosine', type=str, 43 | metavar='LRtype', help='learning rate type') 44 | parser.add_argument('--lr_steps', default=[50, 100], type=float, nargs="+", 45 | metavar='LRSteps', help='epochs to decay learning rate by 10') 46 | 47 | parser.add_argument('--momentum', default=0.9, type=float, metavar='M', 48 | help='momentum') 49 | parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float, 50 | metavar='W', help='weight decay (default: 5e-4)') 51 | 52 | 53 | # ========================= Monitor Configs ========================== 54 | parser.add_argument('--eval-freq', '-ef', default=5, type=int, 55 | metavar='N', help='evaluation frequency (default: 5)') 56 | 57 | 58 | # ========================= Runtime Configs ========================== 59 | parser.add_argument('-j', '--workers', default=8, type=int, metavar='N', 60 | help='number of data loading workers (default: 8)') 61 | 62 | 63 | parser.add_argument('--start-epoch', default=0, type=int, metavar='N', 64 | help='manual epoch number (useful on restarts)') 65 | parser.add_argument('--output', type=str, default='./outputs', help='save dir for logs and outputs') 66 | 67 | 68 | parser.add_argument('--backend', default='nccl', type=str, help='Pytorch DDP backend') 69 | parser.add_argument('--local_rank', default=-1, type=int, help='DDP local rank') -------------------------------------------------------------------------------- /utils/util.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import torch 3 | import random 4 | from pathlib import Path 5 | import numpy as np 6 | 7 | 8 | def init_logger(log_file=None, rank=-1): 9 | ''' 10 | Example: 11 | >>> init_logger(log_file) 12 | >>> logger.info("abc'") 13 | ''' 14 | if isinstance(log_file, Path): 15 | log_file = str(log_file) 16 | log_format = logging.Formatter(fmt='%(asctime)s - %(levelname)s - %(name)s - %(message)s', 17 | datefmt='%Y/%m/%d %H:%M:%S') 18 | 19 | logger = logging.getLogger() 20 | # 优先级 logging.basicConfig < handler.setLevel < logger.setLevel 21 | if rank in [-1, 0]: 22 | logger.setLevel(logging.INFO) 23 | else: 24 | logger.setLevel(logging.WARNING) 25 | console_handler = logging.StreamHandler() 26 | console_handler.setFormatter(log_format) 27 | logger.handlers = [console_handler] 28 | if log_file and log_file != '': 29 | file_handler = logging.FileHandler(log_file, encoding='utf8', mode='a') 30 | file_handler.setFormatter(log_format) 31 | logger.addHandler(file_handler) 32 | return logger 33 | 34 | 35 | 36 | def distributed_concat(tensor, num_total_examples): 37 | output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())] 38 | torch.distributed.all_gather(output_tensors, tensor) 39 | concat = torch.cat(output_tensors, dim=0) 40 | # truncate the dummy elements added by SequentialDistributedSampler 41 | return concat[:num_total_examples] 42 | 43 | 44 | from contextlib import contextmanager 45 | # 在某个进程中优先执行A操作,其他进程等待其执行完成后再执行A操作 46 | @contextmanager 47 | def torch_distributed_zero_first(local_rank: int): 48 | """ 49 | Decorator to make all processes in distributed training wait for each local_master to do something. 50 | """ 51 | if local_rank not in [-1, 0]: 52 | torch.distributed.barrier() 53 | yield #中断后执行上下文代码,然后返回到此处继续往下执行 54 | if local_rank == 0: 55 | torch.distributed.barrier() 56 | 57 | 58 | def softmax(scores): 59 | es = np.exp(scores - scores.max(axis=-1)[..., None]) 60 | return es / es.sum(axis=-1)[..., None] 61 | 62 | 63 | class AverageMeter(object): 64 | """Computes and stores the average and current value""" 65 | 66 | def __init__(self): 67 | self.reset() 68 | 69 | def reset(self): 70 | self.val = 0 71 | self.avg = 0 72 | self.sum = 0 73 | self.count = 0 74 | 75 | def update(self, val, n=1): 76 | self.val = val 77 | self.sum += val * n 78 | self.count += n 79 | self.avg = self.sum / self.count -------------------------------------------------------------------------------- /utils/warmup_lr.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | # @time :2020.02.09 3 | # @IDE : pycharm 4 | # @author :lxztju 5 | # @github : https://github.com/lxztju 6 | import numpy as np 7 | 8 | def adjust_learning_rate_step(optimizer, learning_rate_base, gamma, epoch, step_index, iteration, epoch_size): 9 | """Sets the learning rate 10 | # Adapted from PyTorch Imagenet example: 11 | # https://github.com/pytorch/examples/blob/master/imagenet/main.py 12 | """ 13 | if epoch < 4: 14 | lr = 1e-6 + (learning_rate_base-1e-6) * iteration / (epoch_size * 5) 15 | else: 16 | lr = learning_rate_base * (gamma ** (step_index)) 17 | for param_group in optimizer.param_groups: 18 | param_group['lr'] = lr 19 | return lr 20 | 21 | 22 | def adjust_learning_rate_cosine(optimizer, global_step, learning_rate_base, total_steps, warmup_steps): 23 | 24 | lr = cosine_decay_with_warmup(global_step, 25 | learning_rate_base, 26 | total_steps, 27 | warmup_learning_rate=0.0, 28 | warmup_steps=warmup_steps, 29 | hold_base_rate_steps=0) 30 | for param_group in optimizer.param_groups: 31 | param_group['lr'] = lr 32 | 33 | return lr 34 | 35 | 36 | 37 | def cosine_decay_with_warmup(global_step, 38 | learning_rate_base, 39 | total_steps, 40 | warmup_learning_rate=0.0, 41 | warmup_steps=0, 42 | hold_base_rate_steps=0): 43 | """Cosine decay schedule with warm up period. 44 | 45 | Cosine annealing learning rate as described in: 46 | Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts. 47 | ICLR 2017. https://arxiv.org/abs/1608.03983 48 | In this schedule, the learning rate grows linearly from warmup_learning_rate 49 | to learning_rate_base for warmup_steps, then transitions to a cosine decay 50 | schedule. 51 | 52 | Arguments: 53 | global_step {int} -- global step. 54 | learning_rate_base {float} -- base learning rate. 55 | total_steps {int} -- total number of training steps. 56 | 57 | Keyword Arguments: 58 | warmup_learning_rate {float} -- initial learning rate for warm up. (default: {0.0}) 59 | warmup_steps {int} -- number of warmup steps. (default: {0}) 60 | hold_base_rate_steps {int} -- Optional number of steps to hold base learning rate 61 | before decaying. (default: {0}) 62 | Returns: 63 | a float representing learning rate. 64 | 65 | Raises: 66 | ValueError: if warmup_learning_rate is larger than learning_rate_base, 67 | or if warmup_steps is larger than total_steps. 68 | """ 69 | 70 | if total_steps < warmup_steps: 71 | raise ValueError('total_steps must be larger or equal to ' 72 | 'warmup_steps.') 73 | # if global_step % (2*warmup_steps)==0: 74 | # learning_rate = learning_rate_base * 0.1 75 | learning_rate = 0.3 * learning_rate_base * (1 + np.cos( 76 | np.pi * 77 | (global_step - warmup_steps - hold_base_rate_steps 78 | ) / float(total_steps - warmup_steps - hold_base_rate_steps))) 79 | if hold_base_rate_steps > 0: 80 | learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps, 81 | learning_rate, learning_rate_base) 82 | if warmup_steps > 0: 83 | if learning_rate_base < warmup_learning_rate: 84 | raise ValueError('learning_rate_base must be larger or equal to ' 85 | 'warmup_learning_rate.') 86 | slope = (learning_rate_base - warmup_learning_rate) / warmup_steps 87 | warmup_rate = slope * global_step + warmup_learning_rate 88 | learning_rate = np.where(global_step < warmup_steps, warmup_rate, 89 | learning_rate) 90 | return np.where(global_step > total_steps, 0.0, learning_rate) 91 | 92 | 93 | -------------------------------------------------------------------------------- /visualization/Feature_Visualization.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | # @time :2020/8/15 3 | # @IDE : pycharm 4 | # @author :lxztju 5 | # @github : https://github.com/lxztju 6 | 7 | ''' 8 | 以resnet50为例,进行特征可视化 9 | 10 | 模型的定义来自于torchvision中的定义 11 | 12 | 针对特定的模型需要查找模型的定义,针对所需可视化的网络层的输出,然后导出特定的输出featuremap 13 | 14 | ''' 15 | 16 | 17 | import torch 18 | import numpy as np 19 | import matplotlib.pyplot as plt 20 | import torch 21 | import os 22 | from PIL import Image 23 | import numpy as np 24 | 25 | import sys 26 | sys.path.append("..") 27 | import cfg 28 | from data import get_test_transform 29 | 30 | # 对于给定的一个网络层的输出x,x为numpy格式的array,维度为[0, channels, width, height] 31 | # %matplotlib inline 32 | def draw_features(width, height, channels,x,savename): 33 | ''' 34 | x: 输入的array,某一层的网络层输出 35 | savename: 特征可视化的保存路径 36 | width, height: 分别表示可视化子图的个数,二者乘积等于channels 37 | ''' 38 | fig = plt.figure(figsize=(32,32)) 39 | fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05) 40 | for i in range(channels): 41 | plt.subplot(height,width, i + 1) 42 | plt.axis('off') 43 | img = x[0, i, :, :] 44 | pmin = np.min(img) 45 | pmax = np.max(img) 46 | img = (img - pmin) / (pmax - pmin + 0.000001) 47 | plt.imshow(img, cmap='gray') 48 | # print("{}/{}".format(i, channels)) 49 | fig.savefig(savename, dpi=300) 50 | fig.clf() 51 | plt.close() 52 | 53 | 54 | # 读取模型 55 | def load_checkpoint(filepath): 56 | checkpoint = torch.load(filepath) 57 | model = checkpoint['model'] # 提取网络结构 58 | model.load_state_dict(checkpoint['model_state_dict']) # 加载网络权重参数 59 | for parameter in model.parameters(): 60 | parameter.requires_grad = False 61 | model.eval() 62 | # print(model) 63 | # for name in model.state_dict(): 64 | # print(name) 65 | return model 66 | 67 | 68 | savepath = './' 69 | def predict(model): 70 | # 读入模型 71 | model = load_checkpoint(model) 72 | print('..... Finished loading model! ......') 73 | ##将模型放置在gpu上运行 74 | if torch.cuda.is_available(): 75 | model.cuda() 76 | 77 | img = Image.open(img_path).convert('RGB') 78 | 79 | img = get_test_transform(size=cfg.INPUT_SIZE)(img).unsqueeze(0) 80 | 81 | if torch.cuda.is_available(): 82 | img = img.cuda() 83 | with torch.no_grad(): 84 | x = model.conv1(img) 85 | draw_features(8, 8, 64, x.cpu().numpy(), "{}/f1_conv1.png".format(savepath)) 86 | 87 | 88 | 89 | 90 | 91 | if __name__ == "__main__": 92 | 93 | trained_model = '/disk/haihua/weights/resnet50/epoch_39.pth' 94 | img_path = './test.png' 95 | 96 | predict(trained_model) 97 | 98 | 99 | 100 | 101 | -------------------------------------------------------------------------------- /visualization/f1_conv1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lxztju/pytorch_classification/e28cb9f9058abe0b920b85c4740efbb6c135f13f/visualization/f1_conv1.png -------------------------------------------------------------------------------- /visualization/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lxztju/pytorch_classification/e28cb9f9058abe0b920b85c4740efbb6c135f13f/visualization/test.png --------------------------------------------------------------------------------