Skip to content

P
PaddleX

PaddlePaddle / PaddleX

通知 138
Star 4
Fork 0
P
PaddleX
未验证 提交 b7f90835 编写于 作者: S SunAhong1993 提交者： GitHub
浏览文件
操作

Merge pull request #1 from PaddlePaddle/develop 

0
上级 4cc445a4 ae9796c0
展开全部 隐藏空白更改
内联 并排
Showing with 2081 addition and 383 deletion
deploy/README.md
浏览文件 @ b7f90835
# 模型部署
本目录为PaddleX模型部署代码。
本目录为PaddleX模型部署代码, 编译和使用的教程参考：
- [C++部署文档](../docs/deploy/deploy.md#C部署)
deploy/cpp/CMakeLists.txt
浏览文件 @ b7f90835
......@@ -3,9 +3,10 @@ project(PaddleX CXX C)
option(WITH_MKL "Compile demo with MKL/OpenBlas support,defaultuseMKL." ON)
option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." ON)
option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." ON)
option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." OFF)
option(WITH_TENSORRT "Compile demo with TensorRT." OFF)
SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
SET(PADDLE_DIR "" CACHE PATH "Location of libraries")
SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
SET(CUDA_LIB "" CACHE PATH "Location of libraries")
......@@ -111,8 +112,8 @@ endif()
if (NOT WIN32)
if (WITH_TENSORRT AND WITH_GPU)
include_directories("${PADDLE_DIR}/third_party/install/tensorrt/include")
link_directories("${PADDLE_DIR}/third_party/install/tensorrt/lib")
include_directories("${TENSORRT_DIR}/include")
link_directories("${TENSORRT_DIR}/lib")
endif()
endif(NOT WIN32)
......@@ -169,7 +170,7 @@ endif()
if (NOT WIN32)
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf z xxhash yaml-cpp
)
if(EXISTS "${PADDLE_DIR}/third_party/install/snappystream/lib")
......@@ -194,8 +195,8 @@ endif(NOT WIN32)
if(WITH_GPU)
if(NOT WIN32)
if (WITH_TENSORRT)
set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer${CMAKE_STATIC_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${PADDLE_DIR}/third_party/install/tensorrt/lib/libnvinfer_plugin${CMAKE_STATIC_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
......@@ -211,7 +212,7 @@ if (NOT WIN32)
set(DEPS ${DEPS} ${EXTERNAL_LIB})
endif()
set(DEPS ${DEPS} ${OpenCV_LIBS})
set(DEPS ${DEPS} ${OpenCV_LIBS})
add_executable(classifier src/classifier.cpp src/transforms.cpp src/paddlex.cpp)
ADD_DEPENDENCIES(classifier ext-yaml-cpp)
target_link_libraries(classifier ${DEPS})
......@@ -251,4 +252,3 @@ if (WIN32 AND WITH_MKL)
)
endif()
deploy/cpp/include/paddlex/paddlex.h
浏览文件 @ b7f90835
......@@ -38,12 +38,14 @@ class Model {
public:
void Init(const std::string& model_dir,
bool use_gpu = false,
bool use_trt = false,
int gpu_id = 0) {
create_predictor(model_dir, use_gpu, gpu_id);
create_predictor(model_dir, use_gpu, use_trt, gpu_id);
}
void create_predictor(const std::string& model_dir,
bool use_gpu = false,
bool use_trt = false,
int gpu_id = 0);
bool load_config(const std::string& model_dir);
......
deploy/cpp/include/paddlex/transforms.h
浏览文件 @ b7f90835
......@@ -35,10 +35,8 @@ class ImageBlob {
std::vector<int> ori_im_size_ = std::vector<int>(2);
// Newest image height and width after process
std::vector<int> new_im_size_ = std::vector<int>(2);
// Image height and width before padding
std::vector<int> im_size_before_padding_ = std::vector<int>(2);
// Image height and width before resize
std::vector<int> im_size_before_resize_ = std::vector<int>(2);
std::vector<std::vector<int>> im_size_before_resize_;
// Reshape order
std::vector<std::string> reshape_order_;
// Resize scale
......@@ -49,7 +47,6 @@ class ImageBlob {
void clear() {
ori_im_size_.clear();
new_im_size_.clear();
im_size_before_padding_.clear();
im_size_before_resize_.clear();
reshape_order_.clear();
im_data_.clear();
......@@ -155,12 +152,13 @@ class Padding : public Transform {
virtual void Init(const YAML::Node& item) {
if (item["coarsest_stride"].IsDefined()) {
coarsest_stride_ = item["coarsest_stride"].as<int>();
if (coarsest_stride_ <= 1) {
if (coarsest_stride_ < 1) {
std::cerr << "[Padding] coarest_stride should greater than 0"
<< std::endl;
exit(-1);
}
} else {
}
if (item["target_size"].IsDefined()) {
if (item["target_size"].IsScalar()) {
width_ = item["target_size"].as<int>();
height_ = item["target_size"].as<int>();
......
deploy/cpp/scripts/build.sh
浏览文件 @ b7f90835
# 是否使用GPU(即是否使用 CUDA)
WITH_GPU=ON
WITH_GPU=OFF
# 使用MKL or openblas
WITH_MKL=ON
# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
WITH_TENSORRT=OFF
# TensorRT 的lib路径
TENSORRT_DIR=/path/to/TensorRT/
# Paddle 预测库路径
PADDLE_DIR=/path/to/fluid_inference/
# Paddle 的预测库是否使用静态库来编译
# 使用TensorRT时，Paddle的预测库通常为动态库
WITH_STATIC_LIB=OFF
# CUDA 的 lib 路径
CUDA_LIB=/path/to/cuda/lib/
# CUDNN 的 lib 路径
......@@ -19,8 +26,11 @@ mkdir -p build
cd build
cmake .. \
-DWITH_GPU=${WITH_GPU} \
-DWITH_MKL=${WITH_MKL} \
-DWITH_TENSORRT=${WITH_TENSORRT} \
-DTENSORRT_DIR=${TENSORRT_DIR} \
-DPADDLE_DIR=${PADDLE_DIR} \
-DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
-DCUDA_LIB=${CUDA_LIB} \
-DCUDNN_LIB=${CUDNN_LIB} \
-DOPENCV_DIR=${OPENCV_DIR}
......
deploy/cpp/src/classifier.cpp
浏览文件 @ b7f90835
......@@ -23,6 +23,7 @@
DEFINE_string(model_dir, "", "Path of inference model");
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
DEFINE_bool(use_trt, false, "Infering with TensorRT");
DEFINE_int32(gpu_id, 0, "GPU card id");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
......@@ -42,7 +43,7 @@ int main(int argc, char** argv) {
// 加载模型
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_gpu_id);
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_trt, FLAGS_gpu_id);
// 进行预测
if (FLAGS_image_list != "") {
......
deploy/cpp/src/detector.cpp
浏览文件 @ b7f90835
......@@ -24,6 +24,7 @@
DEFINE_string(model_dir, "", "Path of inference model");
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
DEFINE_bool(use_trt, false, "Infering with TensorRT");
DEFINE_int32(gpu_id, 0, "GPU card id");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
......@@ -44,7 +45,7 @@ int main(int argc, char** argv) {
// 加载模型
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_gpu_id);
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_trt, FLAGS_gpu_id);
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
std::string save_dir = "output";
......@@ -68,7 +69,7 @@ int main(int argc, char** argv) {
<< result.boxes[i].coordinate[0] << ", "
<< result.boxes[i].coordinate[1] << ", "
<< result.boxes[i].coordinate[2] << ", "
<< result.boxes[i].coordinate[3] << std::endl;
<< result.boxes[i].coordinate[3] << ")" << std::endl;
}
// 可视化
......@@ -91,7 +92,7 @@ int main(int argc, char** argv) {
<< result.boxes[i].coordinate[0] << ", "
<< result.boxes[i].coordinate[1] << ", "
<< result.boxes[i].coordinate[2] << ", "
<< result.boxes[i].coordinate[3] << std::endl;
<< result.boxes[i].coordinate[3] << ")" << std::endl;
}
// 可视化
......
deploy/cpp/src/paddlex.cpp
浏览文件 @ b7f90835
......@@ -18,6 +18,7 @@ namespace PaddleX {
void Model::create_predictor(const std::string& model_dir,
bool use_gpu,
bool use_trt,
int gpu_id) {
// 读取配置文件
if (!load_config(model_dir)) {
......@@ -37,6 +38,15 @@ void Model::create_predictor(const std::string& model_dir,
config.SwitchSpecifyInputNames(true);
// 开启内存优化
config.EnableMemoryOptim();
if (use_trt) {
config.EnableTensorRtEngine(
1 << 20 /* workspace_size*/,
32 /* max_batch_size*/,
20 /* min_subgraph_size*/,
paddle::AnalysisConfig::Precision::kFloat32 /* precision*/,
true /* use_static*/,
false /* use_calib_mode*/);
}
predictor_ = std::move(CreatePaddlePredictor(config));
}
......@@ -246,7 +256,6 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
auto im_tensor = predictor_->GetInputTensor("image");
im_tensor->Reshape({1, 3, h, w});
im_tensor->copy_from_cpu(inputs_.im_data_.data());
std::cout << "input image: " << h << " " << w << std::endl;
// 使用加载的模型进行预测
predictor_->ZeroCopyRun();
......@@ -286,19 +295,24 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
result->score_map.shape[3],
CV_32FC1,
result->score_map.data.data());
int idx = 1;
int len_postprocess = inputs_.im_size_before_resize_.size();
for (std::vector<std::string>::reverse_iterator iter =
inputs_.reshape_order_.rbegin();
iter != inputs_.reshape_order_.rend();
++iter) {
if (*iter == "padding") {
auto padding_w = inputs_.im_size_before_padding_[0];
auto padding_h = inputs_.im_size_before_padding_[1];
auto before_shape = inputs_.im_size_before_resize_[len_postprocess - idx];
inputs_.im_size_before_resize_.pop_back();
auto padding_w = before_shape[0];
auto padding_h = before_shape[1];
mask_label = mask_label(cv::Rect(0, 0, padding_w, padding_h));
mask_score = mask_score(cv::Rect(0, 0, padding_w, padding_h));
} else if (*iter == "resize") {
auto resize_w = inputs_.im_size_before_resize_[0];
auto resize_h = inputs_.im_size_before_resize_[1];
auto before_shape = inputs_.im_size_before_resize_[len_postprocess - idx];
inputs_.im_size_before_resize_.pop_back();
auto resize_w = before_shape[0];
auto resize_h = before_shape[1];
cv::resize(mask_label,
mask_label,
cv::Size(resize_h, resize_w),
......@@ -312,6 +326,7 @@ bool Model::predict(const cv::Mat& im, SegResult* result) {
0,
cv::INTER_NEAREST);
}
++idx;
}
result->label_map.data.assign(mask_label.begin<uint8_t>(),
mask_label.end<uint8_t>());
......
deploy/cpp/src/segmenter.cpp
浏览文件 @ b7f90835
......@@ -24,6 +24,7 @@
DEFINE_string(model_dir, "", "Path of inference model");
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU");
DEFINE_bool(use_trt, false, "Infering with TensorRT");
DEFINE_int32(gpu_id, 0, "GPU card id");
DEFINE_string(image, "", "Path of test image file");
DEFINE_string(image_list, "", "Path of test image list file");
......@@ -44,7 +45,8 @@ int main(int argc, char** argv) {
// 加载模型
PaddleX::Model model;
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_gpu_id);
model.Init(FLAGS_model_dir, FLAGS_use_gpu, FLAGS_use_trt, FLAGS_gpu_id);
auto colormap = PaddleX::GenerateColorMap(model.labels.size());
// 进行预测
if (FLAGS_image_list != "") {
......
deploy/cpp/src/transforms.cpp
浏览文件 @ b7f90835
......@@ -56,8 +56,7 @@ float ResizeByShort::GenerateScale(const cv::Mat& im) {
}
bool ResizeByShort::Run(cv::Mat* im, ImageBlob* data) {
data->im_size_before_resize_[0] = im->rows;
data->im_size_before_resize_[1] = im->cols;
data->im_size_before_resize_.push_back({im->rows, im->cols});
data->reshape_order_.push_back("resize");
float scale = GenerateScale(*im);
......@@ -88,21 +87,21 @@ bool CenterCrop::Run(cv::Mat* im, ImageBlob* data) {
}
bool Padding::Run(cv::Mat* im, ImageBlob* data) {
data->im_size_before_padding_[0] = im->rows;
data->im_size_before_padding_[1] = im->cols;
data->im_size_before_resize_.push_back({im->rows, im->cols});
data->reshape_order_.push_back("padding");
int padding_w = 0;
int padding_h = 0;
if (width_ > 0 & height_ > 0) {
if (width_ > 1 & height_ > 1) {
padding_w = width_ - im->cols;
padding_h = height_ - im->rows;
} else if (coarsest_stride_ > 0) {
} else if (coarsest_stride_ > 1) {
padding_h =
ceil(im->rows * 1.0 / coarsest_stride_) * coarsest_stride_ - im->rows;
padding_w =
ceil(im->cols * 1.0 / coarsest_stride_) * coarsest_stride_ - im->cols;
}
if (padding_h < 0 || padding_w < 0) {
std::cerr << "[Padding] Computed padding_h=" << padding_h
<< ", padding_w=" << padding_w
......@@ -122,8 +121,7 @@ bool ResizeByLong::Run(cv::Mat* im, ImageBlob* data) {
<< std::endl;
return false;
}
data->im_size_before_resize_[0] = im->rows;
data->im_size_before_resize_[1] = im->cols;
data->im_size_before_resize_.push_back({im->rows, im->cols});
data->reshape_order_.push_back("resize");
int origin_w = im->cols;
int origin_h = im->rows;
......@@ -149,8 +147,7 @@ bool Resize::Run(cv::Mat* im, ImageBlob* data) {
<< std::endl;
return false;
}
data->im_size_before_resize_[0] = im->rows;
data->im_size_before_resize_[1] = im->cols;
data->im_size_before_resize_.push_back({im->rows, im->cols});
data->reshape_order_.push_back("resize");
cv::resize(
......
docs/apis/load_model.md
浏览文件 @ b7f90835
......@@ -34,7 +34,7 @@ pred_result = model.predict('./xiaoduxiong_ins_det/JPEGImages/WechatIMG114.jpeg'
# 在验证集上进行评估
eval_reader = pdx.cv.datasets.CocoDetection(data_dir=data_dir,
ann_file=ann_file
ann_file=ann_file,
transforms=model.eval_transforms)
eval_result = model.evaluate(eval_reader, batch_size=1)
```
docs/apis/models.md
浏览文件 @ b7f90835
......@@ -17,7 +17,7 @@ paddlex.cls.ResNet50(num_classes=1000)
#### 分类器训练函数接口
> ```python
> train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.025, lr_decay_epochs=[30, 60, 90], lr_decay_gamma=0.1, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5)
> train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.025, lr_decay_epochs=[30, 60, 90], lr_decay_gamma=0.1, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None)
> ```
>
> **参数：**
......@@ -39,6 +39,7 @@ paddlex.cls.ResNet50(num_classes=1000)
> > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。
> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。
> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。
> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
#### 分类器评估函数接口
......@@ -89,7 +90,7 @@ paddlex.cls.ResNet50(num_classes=1000)
paddlex.det.YOLOv3(num_classes=80, backbone='MobileNetV1', anchors=None, anchor_masks=None, ignore_threshold=0.7, nms_score_threshold=0.01, nms_topk=1000, nms_keep_topk=100, nms_iou_threshold=0.45, label_smooth=False, train_random_shapes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608])
```
构建YOLOv3检测器，并实现其训练、评估和预测。
构建YOLOv3检测器，并实现其训练、评估和预测。 **注意在YOLOv3，num_classes不需要包含背景类，如目标包括human、dog两种，则num_classes设为2即可，这里与FasterRCNN/MaskRCNN有差别**
**参数:**
......@@ -111,7 +112,7 @@ paddlex.det.YOLOv3(num_classes=80, backbone='MobileNetV1', anchors=None, anchor_
#### YOLOv3训练函数接口
> ```python
> train(self, num_epochs, train_dataset, train_batch_size=8, eval_dataset=None, save_interval_epochs=20, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=1.0/8000, warmup_steps=1000, warmup_start_lr=0.0, lr_decay_epochs=[213, 240], lr_decay_gamma=0.1, metric=None, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5)
> train(self, num_epochs, train_dataset, train_batch_size=8, eval_dataset=None, save_interval_epochs=20, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=1.0/8000, warmup_steps=1000, warmup_start_lr=0.0, lr_decay_epochs=[213, 240], lr_decay_gamma=0.1, metric=None, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None)
> ```
>
> **参数：**
......@@ -136,6 +137,7 @@ paddlex.det.YOLOv3(num_classes=80, backbone='MobileNetV1', anchors=None, anchor_
> > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。
> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。
> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。
> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
#### YOLOv3评估函数接口
......@@ -177,12 +179,12 @@ paddlex.det.FasterRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspec
```
构建FasterRCNN检测器，并实现其训练、评估和预测。
构建FasterRCNN检测器，并实现其训练、评估和预测。 **注意在FasterRCNN中，num_classes需要设置为类别数+背景类，如目标包括human、dog两种，则num_classes需设为3，多的一种为背景background类别**
**参数:**
> - **num_classes** (int): 包含了背景类的类别数。默认为81。
> - **backbone** (str): FasterRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50vd', 'ResNet101', 'ResNet101vd']。默认为'ResNet50'。
> - **backbone** (str): FasterRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd']。默认为'ResNet50'。
> - **with_fpn** (bool): 是否使用FPN结构。默认为True。
> - **aspect_ratios** (list): 生成anchor高宽比的可选值。默认为[0.5, 1.0, 2.0]。
> - **anchor_sizes** (list): 生成anchor大小的可选值。默认为[32, 64, 128, 256, 512]。
......@@ -190,7 +192,7 @@ paddlex.det.FasterRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspec
#### FasterRCNN训练函数接口
> ```python
> train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2,save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.0025, warmup_steps=500, warmup_start_lr=1.0/1200, lr_decay_epochs=[8, 11], lr_decay_gamma=0.1, metric=None, use_vdl=False, early_stop=False, early_stop_patience=5)
> train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2,save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.0025, warmup_steps=500, warmup_start_lr=1.0/1200, lr_decay_epochs=[8, 11], lr_decay_gamma=0.1, metric=None, use_vdl=False, early_stop=False, early_stop_patience=5, resume_checkpoint=None)
>
> ```
>
......@@ -214,6 +216,7 @@ paddlex.det.FasterRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspec
> > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认值为False。
> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。
> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。
> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
#### FasterRCNN评估函数接口
......@@ -257,12 +260,12 @@ paddlex.det.MaskRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspect_
```
构建MaskRCNN检测器，并实现其训练、评估和预测。
构建MaskRCNN检测器，并实现其训练、评估和预测。**注意在MaskRCNN中，num_classes需要设置为类别数+背景类，如目标包括human、dog两种，则num_classes需设为3，多的一种为背景background类别**
**参数:**
> - **num_classes** (int): 包含了背景类的类别数。默认为81。
> - **backbone** (str): MaskRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50vd', 'ResNet101', 'ResNet101vd']。默认为'ResNet50'。
> - **backbone** (str): MaskRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd']。默认为'ResNet50'。
> - **with_fpn** (bool): 是否使用FPN结构。默认为True。
> - **aspect_ratios** (list): 生成anchor高宽比的可选值。默认为[0.5, 1.0, 2.0]。
> - **anchor_sizes** (list): 生成anchor大小的可选值。默认为[32, 64, 128, 256, 512]。
......@@ -270,7 +273,7 @@ paddlex.det.MaskRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspect_
#### MaskRCNN训练函数接口
> ```python
> train(self, num_epochs, train_dataset, train_batch_size=1, eval_dataset=None, save_interval_epochs=1, log_interval_steps=20, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=1.0/800, warmup_steps=500, warmup_start_lr=1.0 / 2400, lr_decay_epochs=[8, 11], lr_decay_gamma=0.1, metric=None, use_vdl=False, early_stop=False, early_stop_patience=5)
> train(self, num_epochs, train_dataset, train_batch_size=1, eval_dataset=None, save_interval_epochs=1, log_interval_steps=20, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=1.0/800, warmup_steps=500, warmup_start_lr=1.0 / 2400, lr_decay_epochs=[8, 11], lr_decay_gamma=0.1, metric=None, use_vdl=False, early_stop=False, early_stop_patience=5, resume_checkpoint=None)
>
> ```
>
......@@ -294,6 +297,7 @@ paddlex.det.MaskRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspect_
> > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认值为False。
> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。
> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。
> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
#### MaskRCNN评估函数接口
......@@ -358,7 +362,7 @@ paddlex.seg.DeepLabv3p(num_classes=2, backbone='MobileNetV2_x1.0', output_stride
#### DeepLabv3训练函数接口
> ```python
> train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, eval_batch_size=1, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.01, lr_decay_power=0.9, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5):
> train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, eval_batch_size=1, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.01, lr_decay_power=0.9, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None):
>
> ```
>
......@@ -380,6 +384,7 @@ paddlex.seg.DeepLabv3p(num_classes=2, backbone='MobileNetV2_x1.0', output_stride
> > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。
> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。
> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。
> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
#### DeepLabv3评估函数接口
......@@ -437,7 +442,7 @@ paddlex.seg.UNet(num_classes=2, upsample_mode='bilinear', use_bce_loss=False, us
#### Unet训练函数接口
> ```python
> train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, eval_batch_size=1, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='COCO', optimizer=None, learning_rate=0.01, lr_decay_power=0.9, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5):
> train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, eval_batch_size=1, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='COCO', optimizer=None, learning_rate=0.01, lr_decay_power=0.9, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None):
> ```
>
> **参数：**
......@@ -458,6 +463,7 @@ paddlex.seg.UNet(num_classes=2, upsample_mode='bilinear', use_bce_loss=False, us
> > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。
> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。
> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。
> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
#### Unet评估函数接口
......
docs/apis/transforms/cls_transforms.md
浏览文件 @ b7f90835
......@@ -109,7 +109,9 @@ paddlex.cls.transforms.RandomDistort(brightness_range=0.9, brightness_prob=0.5,
以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作。
1. 对变换的操作顺序进行随机化操作。
2. 按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
2. 按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
【注意】该数据增强必须在数据增强Normalize之前使用。
### 参数
* **brightness_range** (float): 明亮度因子的范围。默认为0.9。
......
docs/apis/transforms/det_transforms.md
浏览文件 @ b7f90835
......@@ -85,7 +85,9 @@ paddlex.det.transforms.RandomDistort(brightness_range=0.5, brightness_prob=0.5,
以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作。
1. 对变换的操作顺序进行随机化操作。
2. 按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
2. 按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
【注意】该数据增强必须在数据增强Normalize之前使用。
### 参数
* **brightness_range** (float): 明亮度因子的范围。默认为0.5。
......@@ -135,7 +137,9 @@ paddlex.det.transforms.RandomExpand(ratio=4., prob=0.5, fill_value=[123.675, 116
### 参数
* **ratio** (float): 图像扩张的最大比例。默认为4.0。
* **prob** (float): 随机扩张的概率。默认为0.5。
* **fill_value** (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
* **fill_value** (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
【注意】该数据增强必须在数据增强Resize、ResizeByShort之前使用。
## RandomCrop类
```python
......@@ -152,7 +156,9 @@ paddlex.det.transforms.RandomCrop(aspect_ratio=[.5, 2.], thresholds=[.0, .1, .3,
(4) 如果cover_all_box为True且存在真实标注框的IoU小于thresh，则继续第3步。
(5) 筛选出位于候选裁剪区域内的真实标注框，若有效框的个数为0，则继续第3步，否则进行第4步。
4. 换算有效真值标注框相对候选裁剪区域的位置坐标。
5. 换算有效分割区域相对候选裁剪区域的位置坐标。
5. 换算有效分割区域相对候选裁剪区域的位置坐标。
【注意】该数据增强必须在数据增强Resize、ResizeByShort之前使用。
### 参数
* **aspect_ratio** (list): 裁剪后短边缩放比例的取值范围，以[min, max]形式表示。默认值为[.5, 2.]。
......
docs/apis/transforms/seg_transforms.md
浏览文件 @ b7f90835
......@@ -153,7 +153,9 @@ paddlex.seg.transforms.RandomDistort(brightness_range=0.5, brightness_prob=0.5,
以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作。
1.对变换的操作顺序进行随机化操作。
2.按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
2.按照1中的顺序以一定的概率对图像在范围[-range, range]内进行随机像素内容变换。
【注意】该数据增强必须在数据增强Normalize之前使用。
### 参数
* **brightness_range** (float): 明亮度因子的范围。默认为0.5。
......
docs/deploy/deploy.md
浏览文件 @ b7f90835
......@@ -7,20 +7,29 @@
### 导出inference模型
在服务端部署的模型需要首先将模型导出为inference格式模型，导出的模型将包括`__model__`、`__params__`和`model.yml`三个文名，分别为模型的网络结构，模型权重和模型的配置文件（包括数据预处理参数等等）。在安装完PaddleX后，在命令行终端使用如下命令导出模型到当前目录`inferece_model`下。
> 可直接下载小度熊分拣模型测试本文档的流程[xiaoduxiong_epoch_12.tar.gz](https://bj.bcebos.com/paddlex/models/xiaoduxiong_epoch_12.tar.gz)
> 可直接下载垃圾检测模型测试本文档的流程[garbage_epoch_12.tar.gz](https://bj.bcebos.com/paddlex/models/garbage_epoch_12.tar.gz)
```
paddlex --export_inference --model_dir=./xiaoduxiong_epoch_12 --save_dir=./inference_model
```
使用TensorRT预测时，需指定模型的图像输入shape:[w,h]。
**注**：
- 分类模型请保持于训练时输入的shape一致。
- 指定[w,h]时，w和h中间逗号隔开，不允许存在空格等其他字符
```
paddlex --export_inference --model_dir=./garbage_epoch_12 --save_dir=./inference_model
paddlex --export_inference --model_dir=./xiaoduxiong_epoch_12 --save_dir=./inference_model --fixed_input_shape=[640,960]
```
### Python部署
PaddleX已经集成了基于Python的高性能预测接口，在安装PaddleX后，可参照如下代码示例，进行预测。相关的接口文档可参考[paddlex.deploy](apis/deploy.md)
> 点击下载测试图片 [garbage.bmp](https://bj.bcebos.com/paddlex/datasets/garbage.bmp)
> 点击下载测试图片 [xiaoduxiong_test_image.tar.gz](https://bj.bcebos.com/paddlex/datasets/xiaoduxiong_test_image.tar.gz)
```
import paddlex as pdx
predictorpdx.deploy.create_predictor('./inference_model')
result = predictor.predict(image='garbage.bmp')
predictor = pdx.deploy.create_predictor('./inference_model')
result = predictor.predict(image='xiaoduxiong_test_image/JPEGImages/WeChatIMG110.jpeg')
```
### C++部署
......
docs/deploy/deploy_cpp_linux.md
浏览文件 @ b7f90835
......@@ -19,8 +19,18 @@
### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference
PaddlePaddle C++ 预测库针对不同的`CPU`，`CUDA`，以及是否支持TensorRT，提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html#id1)
PaddlePaddle C++ 预测库针对不同的`CPU`，`CUDA`，以及是否支持TensorRT，提供了不同的预编译版本，目前PaddleX依赖于Paddle1.7版本，以下提供了多个不同版本的Paddle预测库:
| 版本说明 | 预测库(1.7.2版本) |
| ---- | ---- |
| ubuntu14.04_cpu_avx_mkl | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-cpu-avx-mkl/fluid_inference.tgz) |
| ubuntu14.04_cpu_avx_openblas | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-cpu-avx-openblas/fluid_inference.tgz) |
| ubuntu14.04_cpu_noavx_openblas | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-cpu-noavx-openblas/fluid_inference.tgz) |
| ubuntu14.04_cuda9.0_cudnn7_avx_mkl | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-gpu-cuda9-cudnn7-avx-mkl/fluid_inference.tgz) |
| ubuntu14.04_cuda10.0_cudnn7_avx_mkl | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-gpu-cuda10-cudnn7-avx-mkl/fluid_inference.tgz ) |
| ubuntu14.04_cuda10.1_cudnn7.6_avx_mkl_trt6 | [fluid_inference.tgz](https://paddle-inference-lib.bj.bcebos.com/1.7.2-gpu-cuda10.1-cudnn7.6-avx-mkl-trt6%2Ffluid_inference.tgz) |
更多和更新的版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/windows_cpp_inference.html#id1)
下载并解压后`/root/projects/fluid_inference`目录包含内容为：
```
......@@ -40,17 +50,24 @@ fluid_inference
编译`cmake`的命令在`scripts/build.sh`中，请根据实际情况修改主要参数，其主要内容说明如下：
```
# 是否使用GPU(即是否使用 CUDA)
WITH_GPU=ON
WITH_GPU=OFF
# 使用MKL or openblas
WITH_MKL=ON
# 是否集成 TensorRT(仅WITH_GPU=ON 有效)
WITH_TENSORRT=OFF
# 上一步下载的 Paddle 预测库路径
PADDLE_DIR=/root/projects/deps/fluid_inference/
# TensorRT 的lib路径
TENSORRT_DIR=/path/to/TensorRT/
# Paddle 预测库路径
PADDLE_DIR=/path/to/fluid_inference/
# Paddle 的预测库是否使用静态库来编译
# 使用TensorRT时，Paddle的预测库通常为动态库
WITH_STATIC_LIB=ON
# CUDA 的 lib 路径
CUDA_LIB=/usr/local/cuda/lib64/
CUDA_LIB=/path/to/cuda/lib/
# CUDNN 的 lib 路径
CUDNN_LIB=/usr/local/cudnn/lib64/
CUDNN_LIB=/path/to/cudnn/lib/
# OPENCV 路径, 如果使用自带预编译版本可不设置
# OPENCV 路径, 如果使用自带预编译版本可不修改
OPENCV_DIR=$(pwd)/deps/opencv3gcc4.8/
sh $(pwd)/scripts/bootstrap.sh
......@@ -60,8 +77,11 @@ mkdir -p build
cd build
cmake .. \
-DWITH_GPU=${WITH_GPU} \
-DWITH_MKL=${WITH_MKL} \
-DWITH_TENSORRT=${WITH_TENSORRT} \
-DTENSORRT_DIR=${TENSORRT_DIR} \
-DPADDLE_DIR=${PADDLE_DIR} \
-DWITH_STATIC_LIB=${WITH_STATIC_LIB} \
-DCUDA_LIB=${CUDA_LIB} \
-DCUDNN_LIB=${CUDNN_LIB} \
-DOPENCV_DIR=${OPENCV_DIR}
......@@ -83,19 +103,20 @@ make
| image | 要预测的图片文件路径 |
| image_list | 按行存储图片路径的.txt文件 |
| use_gpu | 是否使用 GPU 预测, 支持值为0或1(默认值为0) |
| use_trt | 是否使用 TensorTr 预测, 支持值为0或1(默认值为0) |
| gpu_id | GPU 设备ID, 默认值为0 |
| save_dir | 保存可视化结果的路径, 默认值为"output"，classfier无该参数 |
## 样例
可使用[垃圾检测模型](deploy.md#导出inference模型)中生成的`inference_model`模型和测试图片进行预测。
可使用[小度熊识别模型](deploy.md#导出inference模型)中导出的`inference_model`和测试图片进行预测。
`样例一`：
不使用`GPU`测试图片 `/path/to/garbage.bmp`
不使用`GPU`测试图片 `/path/to/xiaoduxiong.jpeg`
```shell
./build/detector --model_dir=/path/to/inference_model --image=/path/to/garbage.bmp --save_dir=output
./build/detector --model_dir=/path/to/inference_model --image=/path/to/xiaoduxiong.jpeg --save_dir=output
```
图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。
......@@ -104,13 +125,12 @@ make
使用`GPU`预测多个图片`/path/to/image_list.txt`，image_list.txt内容的格式如下：
```
/path/to/images/garbage1.jpeg
/path/to/images/garbage2.jpeg
/path/to/images/xiaoduxiong1.jpeg
/path/to/images/xiaoduxiong2.jpeg
...
/path/to/images/garbagen.jpeg
/path/to/images/xiaoduxiongn.jpeg
```
```shell
./build/detector --model_dir=/path/to/models/inference_model --image_list=/root/projects/images_list.txt --use_gpu=1 --save_dir=output
```
图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。
docs/deploy/deploy_cpp_win_vs2019.md
浏览文件 @ b7f90835
......@@ -27,7 +27,18 @@ git clone https://github.com/PaddlePaddle/PaddleX.git
### Step2: 下载PaddlePaddle C++ 预测库 fluid_inference
PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/windows_cpp_inference.html)
PaddlePaddle C++ 预测库针对不同的`CPU`，`CUDA`，以及是否支持TensorRT，提供了不同的预编译版本，目前PaddleX依赖于Paddle1.7版本，以下提供了多个不同版本的Paddle预测库:
| 版本说明 | 预测库(1.7.2版本) | 编译器 | 构建工具| cuDNN | CUDA
| ---- | ---- | ---- | ---- | ---- | ---- |
| cpu_avx_mkl | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/mkl/cpu/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 |
| cpu_avx_openblas | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/open/cpu/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 |
| cuda9.0_cudnn7_avx_mkl | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/mkl/post97/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | 7.4.1 | 9.0 |
| cuda9.0_cudnn7_avx_openblas | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/open/post97/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | 7.4.1 | 9.0 |
| cuda10.0_cudnn7_avx_mkl | [fluid_inference.zip](https://paddle-wheel.bj.bcebos.com/1.7.2/win-infer/mkl/post107/fluid_inference_install_dir.zip) | MSVC 2015 update 3 | CMake v3.16.0 | 7.5.0 | 9.0 |
更多和更新的版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/build_and_install_lib_cn.html#id1)
解压后`D:\projects\fluid_inference*\`目录下主要包含的内容为：
```
......@@ -109,14 +120,14 @@ cd D:\projects\PaddleX\deploy\cpp\out\build\x64-Release
## 样例
可使用[垃圾检测模型](deploy.md#导出inference模型)中生成的`inference_model`模型和测试图片进行预测。
可使用[小度熊识别模型](deploy.md#导出inference模型)中导出的`inference_model`和测试图片进行预测。
`样例一`：
不使用`GPU`测试图片 `\\path\\to\\garbage.bmp`
不使用`GPU`测试图片 `\\path\\to\\xiaoduxiong.jpeg`
```shell
.\detector --model_dir=\\path\\to\\inference_model --image=D:\\images\\garbage.bmp --save_dir=output
.\detector --model_dir=\\path\\to\\inference_model --image=D:\\images\\xiaoduxiong.jpeg --save_dir=output
```
图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。
......@@ -126,13 +137,12 @@ cd D:\projects\PaddleX\deploy\cpp\out\build\x64-Release
使用`GPU`预测多个图片`\\path\\to\\image_list.txt`，image_list.txt内容的格式如下：
```
\\path\\to\\images\\garbage1.jpeg
\\path\\to\\images\\garbage2.jpeg
\\path\\to\\images\\xiaoduxiong1.jpeg
\\path\\to\\images\\xiaoduxiong2.jpeg
...
\\path\\to\\images\\garbagen.jpeg
\\path\\to\\images\\xiaoduxiongn.jpeg
```
```shell
.\detector --model_dir=\\path\\to\\inference_model --image_list=\\path\\to\\images_list.txt --use_gpu=1 --save_dir=output
```
图片文件`可视化预测结果`会保存在`save_dir`参数设置的目录下。
docs/index.rst
浏览文件 @ b7f90835
......@@ -22,7 +22,7 @@ PaddleX是基于飞桨技术生态的深度学习全流程开发工具。具备
client_use.md
FAQ.md
* PaddleX版本: v0.1.6
* PaddleX版本: v0.1.7
* 项目官网: http://www.paddlepaddle.org.cn/paddle/paddlex
* 项目GitHub: https://github.com/PaddlePaddle/PaddleX/tree/develop
* 官方QQ用户群: 1045148026
......
paddlex/__init__.py
浏览文件 @ b7f90835
......@@ -13,27 +13,39 @@
# limitations under the License.
from __future__ import absolute_import
import os
if 'FLAGS_eager_delete_tensor_gb' not in os.environ:
os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
if 'FLAGS_allocator_strategy' not in os.environ:
os.environ['FLAGS_allocator_strategy'] = 'auto_growth'
if "CUDA_VISIBLE_DEVICES" in os.environ:
if os.environ["CUDA_VISIBLE_DEVICES"].count("-1") > 0:
os.environ["CUDA_VISIBLE_DEVICES"] = ""
from .utils.utils import get_environ_info
from . import cv
from . import det
from . import seg
from . import cls
from . import slim
from . import tools
try:
import pycocotools
except:
print("[WARNING] pycocotools is not installed, detection model is not available now.")
print("[WARNING] pycocotools install: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/install.md")
import paddlehub as hub
if hub.version.hub_version < '1.6.2':
raise Exception("[ERROR] paddlehub >= 1.6.2 is required")
print(
"[WARNING] pycocotools is not installed, detection model is not available now."
)
print(
"[WARNING] pycocotools install: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/install.md"
)
#import paddlehub as hub
#if hub.version.hub_version < '1.6.2':
# raise Exception("[ERROR] paddlehub >= 1.6.2 is required")
env_info = get_environ_info()
load_model = cv.models.load_model
datasets = cv.datasets
log_level = 2
__version__ = '0.1.6.github'
__version__ = '0.1.7.github'
paddlex/command.py
浏览文件 @ b7f90835
......@@ -29,7 +29,11 @@ def arg_parser():
action="store_true",
default=False,
help="export inference model for C++/Python deployment")
parser.add_argument(
"--fixed_input_shape",
"-fs",
default=None,
help="export inference model with fixed input shape:[w,h]")
return parser
......@@ -53,9 +57,23 @@ def main():
if args.export_inference:
assert args.model_dir is not None, "--model_dir should be defined while exporting inference model"
assert args.save_dir is not None, "--save_dir should be defined to save inference model"
model = pdx.load_model(args.model_dir)
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape) == 2, "len of fixed input shape must == 2"
model = pdx.load_model(args.model_dir, fixed_input_shape)
model.export_inference_model(args.save_dir)
if args.export_onnx:
assert args.model_dir is not None, "--model_dir should be defined while exporting onnx model"
assert args.save_dir is not None, "--save_dir should be defined to save onnx model"
fixed_input_shape = eval(args.fixed_input_shape)
assert len(
fixed_input_shape) == 2, "len of fixed input shape must == 2"
model = pdx.load_model(args.model_dir, fixed_input_shape)
model.export_onnx_model(args.save_dir)
if __name__ == "__main__":
main()
paddlex/cv/datasets/coco.py
浏览文件 @ b7f90835
......@@ -100,7 +100,7 @@ class CocoDetection(VOCDetection):
gt_score = np.ones((num_bbox, 1), dtype=np.float32)
is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
gt_poly = [None] * num_bbox
gt_poly = None
for i, box in enumerate(bboxes):
catid = box['category_id']
......@@ -108,20 +108,24 @@ class CocoDetection(VOCDetection):
gt_bbox[i, :] = box['clean_bbox']
is_crowd[i][0] = box['iscrowd']
if 'segmentation' in box:
if gt_poly is None:
gt_poly = [None] * num_bbox
gt_poly[i] = box['segmentation']
im_info = {
'im_id': np.array([img_id]).astype('int32'),
'origin_shape': np.array([im_h, im_w]).astype('int32'),
'image_shape': np.array([im_h, im_w]).astype('int32'),
}
label_info = {
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'gt_poly': gt_poly,
'difficult': difficult
}
if gt_poly is not None:
label_info['gt_poly'] = gt_poly
coco_rec = (im_info, label_info)
self.file_list.append([im_fname, coco_rec])
......
paddlex/cv/datasets/dataset.py
浏览文件 @ b7f90835
......@@ -254,3 +254,11 @@ class Dataset:
buffer_size=self.buffer_size,
batch_size=batch_size,
drop_last=drop_last)
def set_num_samples(self, num_samples):
if num_samples > len(self.file_list):
logging.warning(
"You want set num_samples to {}, but your dataset only has {} samples, so we will keep your dataset num_samples as {}"
.format(num_samples, len(self.file_list), len(self.file_list)))
num_samples = len(self.file_list)
self.num_samples = num_samples
paddlex/cv/datasets/easydata_det.py
浏览文件 @ b7f90835
......@@ -143,7 +143,7 @@ class EasyDataDet(VOCDetection):
ann_ct += 1
im_info = {
'im_id': im_id,
'origin_shape': np.array([im_h, im_w]).astype('int32'),
'image_shape': np.array([im_h, im_w]).astype('int32'),
}
label_info = {
'is_crowd': is_crowd,
......
paddlex/cv/datasets/voc.py
浏览文件 @ b7f90835
......@@ -146,14 +146,13 @@ class VOCDetection(Dataset):
im_info = {
'im_id': im_id,
'origin_shape': np.array([im_h, im_w]).astype('int32'),
'image_shape': np.array([im_h, im_w]).astype('int32'),
}
label_info = {
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'gt_poly': [],
'difficult': difficult
}
voc_rec = (im_info, label_info)
......
paddlex/cv/models/base.py
浏览文件 @ b7f90835
......@@ -70,6 +70,8 @@ class BaseAPI:
self.sync_bn = False
# 当前模型状态
self.status = 'Normal'
# 已完成迭代轮数，为恢复训练时的起始轮数
self.completed_epochs = 0
def _get_single_card_bs(self, batch_size):
if batch_size % len(self.places) == 0:
......@@ -182,35 +184,62 @@ class BaseAPI:
fuse_bn=False,
save_dir='.',
sensitivities_file=None,
eval_metric_loss=0.05):
pretrain_dir = osp.join(save_dir, 'pretrain')
if not os.path.isdir(pretrain_dir):
if os.path.exists(pretrain_dir):
os.remove(pretrain_dir)
os.makedirs(pretrain_dir)
if hasattr(self, 'backbone'):
backbone = self.backbone
else:
backbone = self.__class__.__name__
pretrain_weights = get_pretrain_weights(
pretrain_weights, self.model_type, backbone, pretrain_dir)
eval_metric_loss=0.05,
resume_checkpoint=None):
if not resume_checkpoint:
pretrain_dir = osp.join(save_dir, 'pretrain')
if not os.path.isdir(pretrain_dir):
if os.path.exists(pretrain_dir):
os.remove(pretrain_dir)
os.makedirs(pretrain_dir)
if hasattr(self, 'backbone'):
backbone = self.backbone
else:
backbone = self.__class__.__name__
pretrain_weights = get_pretrain_weights(
pretrain_weights, self.model_type, backbone, pretrain_dir)
if startup_prog is None:
startup_prog = fluid.default_startup_program()
self.exe.run(startup_prog)
if pretrain_weights is not None:
if resume_checkpoint:
logging.info(
"Load pretrain weights from {}.".format(pretrain_weights))
"Resume checkpoint from {}.".format(resume_checkpoint),
use_color=True)
paddlex.utils.utils.load_pretrain_weights(
self.exe, self.train_prog, resume_checkpoint, resume=True)
if not osp.exists(osp.join(resume_checkpoint, "model.yml")):
raise Exception(
"There's not model.yml in {}".format(resume_checkpoint))
with open(osp.join(resume_checkpoint, "model.yml")) as f:
info = yaml.load(f.read(), Loader=yaml.Loader)
self.completed_epochs = info['completed_epochs']
elif pretrain_weights is not None:
logging.info(
"Load pretrain weights from {}.".format(pretrain_weights),
use_color=True)
paddlex.utils.utils.load_pretrain_weights(
self.exe, self.train_prog, pretrain_weights, fuse_bn)
# 进行裁剪
if sensitivities_file is not None:
import paddleslim
from .slim.prune_config import get_sensitivities
sensitivities_file = get_sensitivities(sensitivities_file, self,
save_dir)
from .slim.prune import get_params_ratios, prune_program
logging.info(
"Start to prune program with eval_metric_loss = {}".format(
eval_metric_loss),
use_color=True)
origin_flops = paddleslim.analysis.flops(self.test_prog)
prune_params_ratios = get_params_ratios(
sensitivities_file, eval_metric_loss=eval_metric_loss)
prune_program(self, prune_params_ratios)
current_flops = paddleslim.analysis.flops(self.test_prog)
remaining_ratio = current_flops / origin_flops
logging.info(
"Finish prune program, before FLOPs:{}, after prune FLOPs:{}, remaining ratio:{}"
.format(origin_flops, current_flops, remaining_ratio),
use_color=True)
self.status = 'Prune'
def get_model_info(self):
......@@ -248,6 +277,7 @@ class BaseAPI:
name = op.__class__.__name__
attr = op.__dict__
info['Transforms'].append({name: attr})
info['completed_epochs'] = self.completed_epochs
return info
def save_model(self, save_dir):
......@@ -255,7 +285,10 @@ class BaseAPI:
if osp.exists(save_dir):
os.remove(save_dir)
os.makedirs(save_dir)
fluid.save(self.train_prog, osp.join(save_dir, 'model'))
if self.train_prog is not None:
fluid.save(self.train_prog, osp.join(save_dir, 'model'))
else:
fluid.save(self.test_prog, osp.join(save_dir, 'model'))
model_info = self.get_model_info()
model_info['status'] = self.status
with open(
......@@ -317,11 +350,11 @@ class BaseAPI:
model_info['_ModelInputsOutputs']['test_outputs'] = [
[k, v.name] for k, v in self.test_outputs.items()
]
with open(
osp.join(save_dir, 'model.yml'), encoding='utf-8',
mode='w') as f:
yaml.dump(model_info, f)
# 模型保存成功的标志
open(osp.join(save_dir, '.success'), 'w').close()
logging.info(
......@@ -404,8 +437,9 @@ class BaseAPI:
earlystop = EarlyStop(early_stop_patience, thresh)
best_accuracy_key = ""
best_accuracy = -1.0
best_model_epoch = 1
for i in range(num_epochs):
best_model_epoch = -1
start_epoch = self.completed_epochs
for i in range(start_epoch, num_epochs):
records = list()
step_start_time = time.time()
epoch_start_time = time.time()
......@@ -477,7 +511,7 @@ class BaseAPI:
current_save_dir = osp.join(save_dir, "epoch_{}".format(i + 1))
if not osp.isdir(current_save_dir):
os.makedirs(current_save_dir)
if eval_dataset is not None:
if eval_dataset is not None and eval_dataset.num_samples > 0:
self.eval_metrics, self.eval_details = self.evaluate(
eval_dataset=eval_dataset,
batch_size=eval_batch_size,
......@@ -485,6 +519,7 @@ class BaseAPI:
return_details=True)
logging.info('[EVAL] Finished, Epoch={}, {} .'.format(
i + 1, dict2str(self.eval_metrics)))
self.completed_epochs += 1
# 保存最优模型
best_accuracy_key = list(self.eval_metrics.keys())[0]
current_accuracy = self.eval_metrics[best_accuracy_key]
......@@ -509,10 +544,11 @@ class BaseAPI:
self.save_model(save_dir=current_save_dir)
time_eval_one_epoch = time.time() - eval_epoch_start_time
eval_epoch_start_time = time.time()
logging.info(
'Current evaluated best model in eval_dataset is epoch_{}, {}={}'
.format(best_model_epoch, best_accuracy_key,
best_accuracy))
if best_model_epoch > 0:
logging.info(
'Current evaluated best model in eval_dataset is epoch_{}, {}={}'
.format(best_model_epoch, best_accuracy_key,
best_accuracy))
if eval_dataset is not None and early_stop:
if earlystop(current_accuracy):
break
paddlex/cv/models/classifier.py
浏览文件 @ b7f90835
......@@ -46,10 +46,18 @@ class BaseClassifier(BaseAPI):
self.model_name = model_name
self.labels = None
self.num_classes = num_classes
self.fixed_input_shape = None
def build_net(self, mode='train'):
image = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
image = fluid.data(
dtype='float32', shape=input_shape, name='image')
else:
image = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if mode != 'test':
label = fluid.data(dtype='int64', shape=[None, 1], name='label')
model = getattr(paddlex.cv.nets, str.lower(self.model_name))
......@@ -104,7 +112,8 @@ class BaseClassifier(BaseAPI):
sensitivities_file=None,
eval_metric_loss=0.05,
early_stop=False,
early_stop_patience=5):
early_stop_patience=5,
resume_checkpoint=None):
"""训练。
Args:
......@@ -129,6 +138,7 @@ class BaseClassifier(BaseAPI):
early_stop (bool): 是否使用提前终止训练策略。默认值为False。
early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
连续下降或持平，则终止训练。默认值为5。
resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
Raises:
ValueError: 模型从inference model进行加载。
......@@ -152,8 +162,8 @@ class BaseClassifier(BaseAPI):
pretrain_weights=pretrain_weights,
save_dir=save_dir,
sensitivities_file=sensitivities_file,
eval_metric_loss=eval_metric_loss)
eval_metric_loss=eval_metric_loss,
resume_checkpoint=resume_checkpoint)
# 训练
self.train_loop(
num_epochs=num_epochs,
......
paddlex/cv/models/deeplabv3p.py
浏览文件 @ b7f90835
......@@ -48,7 +48,6 @@ class DeepLabv3p(BaseAPI):
自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None时，各类的权重1，
即平时使用的交叉熵损失函数。
ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。默认255。
Raises:
ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
ValueError: backbone取值不在['Xception65', 'Xception41', 'MobileNetV2_x0.25',
......@@ -118,6 +117,7 @@ class DeepLabv3p(BaseAPI):
self.enable_decoder = enable_decoder
self.labels = None
self.sync_bn = True
self.fixed_input_shape = None
def _get_backbone(self, backbone):
def mobilenetv2(backbone):
......@@ -182,7 +182,8 @@ class DeepLabv3p(BaseAPI):
use_bce_loss=self.use_bce_loss,
use_dice_loss=self.use_dice_loss,
class_weight=self.class_weight,
ignore_index=self.ignore_index)
ignore_index=self.ignore_index,
fixed_input_shape=self.fixed_input_shape)
inputs = model.generate_inputs()
model_out = model.build_net(inputs)
outputs = OrderedDict()
......@@ -233,7 +234,8 @@ class DeepLabv3p(BaseAPI):
sensitivities_file=None,
eval_metric_loss=0.05,
early_stop=False,
early_stop_patience=5):
early_stop_patience=5,
resume_checkpoint=None):
"""训练。
Args:
......@@ -257,6 +259,7 @@ class DeepLabv3p(BaseAPI):
early_stop (bool): 是否使用提前终止训练策略。默认值为False。
early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
连续下降或持平，则终止训练。默认值为5。
resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
Raises:
ValueError: 模型从inference model进行加载。
......@@ -283,7 +286,8 @@ class DeepLabv3p(BaseAPI):
pretrain_weights=pretrain_weights,
save_dir=save_dir,
sensitivities_file=sensitivities_file,
eval_metric_loss=eval_metric_loss)
eval_metric_loss=eval_metric_loss,
resume_checkpoint=resume_checkpoint)
# 训练
self.train_loop(
num_epochs=num_epochs,
......@@ -396,13 +400,14 @@ class DeepLabv3p(BaseAPI):
fetch_list=list(self.test_outputs.values()))
pred = result[0]
pred = np.squeeze(pred).astype('uint8')
keys = list(im_info.keys())
for k in keys[::-1]:
if k == 'shape_before_resize':
h, w = im_info[k][0], im_info[k][1]
for info in im_info[::-1]:
if info[0] == 'resize':
w, h = info[1][1], info[1][0]
pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
elif k == 'shape_before_padding':
h, w = im_info[k][0], im_info[k][1]
elif info[0] == 'padding':
w, h = info[1][1], info[1][0]
pred = pred[0:h, 0:w]
else:
raise Exception("Unexpected info '{}' in im_info".format(
info[0]))
return {'label_map': pred, 'score_map': result[1]}
paddlex/cv/models/faster_rcnn.py
浏览文件 @ b7f90835
......@@ -32,7 +32,7 @@ class FasterRCNN(BaseAPI):
Args:
num_classes (int): 包含了背景类的类别数。默认为81。
backbone (str): FasterRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50',
'ResNet50vd', 'ResNet101', 'ResNet101vd']。默认为'ResNet50'。
'ResNet50_vd', 'ResNet101', 'ResNet101_vd']。默认为'ResNet50'。
with_fpn (bool): 是否使用FPN结构。默认为True。
aspect_ratios (list): 生成anchor高宽比的可选值。默认为[0.5, 1.0, 2.0]。
anchor_sizes (list): 生成anchor大小的可选值。默认为[32, 64, 128, 256, 512]。
......@@ -47,7 +47,7 @@ class FasterRCNN(BaseAPI):
self.init_params = locals()
super(FasterRCNN, self).__init__('detector')
backbones = [
'ResNet18', 'ResNet50', 'ResNet50vd', 'ResNet101', 'ResNet101vd'
'ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd'
]
assert backbone in backbones, "backbone should be one of {}".format(
backbones)
......@@ -57,6 +57,7 @@ class FasterRCNN(BaseAPI):
self.aspect_ratios = aspect_ratios
self.anchor_sizes = anchor_sizes
self.labels = None
self.fixed_input_shape = None
def _get_backbone(self, backbone_name):
norm_type = None
......@@ -66,7 +67,7 @@ class FasterRCNN(BaseAPI):
elif backbone_name == 'ResNet50':
layers = 50
variant = 'b'
elif backbone_name == 'ResNet50vd':
elif backbone_name == 'ResNet50_vd':
layers = 50
variant = 'd'
norm_type = 'affine_channel'
......@@ -74,7 +75,7 @@ class FasterRCNN(BaseAPI):
layers = 101
variant = 'b'
norm_type = 'affine_channel'
elif backbone_name == 'ResNet101vd':
elif backbone_name == 'ResNet101_vd':
layers = 101
variant = 'd'
norm_type = 'affine_channel'
......@@ -109,7 +110,8 @@ class FasterRCNN(BaseAPI):
aspect_ratios=self.aspect_ratios,
anchor_sizes=self.anchor_sizes,
train_pre_nms_top_n=train_pre_nms_top_n,
test_pre_nms_top_n=test_pre_nms_top_n)
test_pre_nms_top_n=test_pre_nms_top_n,
fixed_input_shape=self.fixed_input_shape)
inputs = model.generate_inputs()
if mode == 'train':
model_out = model.build_net(inputs)
......@@ -165,7 +167,8 @@ class FasterRCNN(BaseAPI):
metric=None,
use_vdl=False,
early_stop=False,
early_stop_patience=5):
early_stop_patience=5,
resume_checkpoint=None):
"""训练。
Args:
......@@ -191,6 +194,7 @@ class FasterRCNN(BaseAPI):
early_stop (bool): 是否使用提前终止训练策略。默认值为False。
early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
连续下降或持平，则终止训练。默认值为5。
resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
Raises:
ValueError: 评估类型不在指定列表中。
......@@ -229,7 +233,9 @@ class FasterRCNN(BaseAPI):
startup_prog=fluid.default_startup_program(),
pretrain_weights=pretrain_weights,
fuse_bn=fuse_bn,
save_dir=save_dir)
save_dir=save_dir,
resume_checkpoint=resume_checkpoint)
# 训练
self.train_loop(
num_epochs=num_epochs,
......
paddlex/cv/models/load_model.py
浏览文件 @ b7f90835
......@@ -23,7 +23,7 @@ import paddlex
import paddlex.utils.logging as logging
def load_model(model_dir):
def load_model(model_dir, fixed_input_shape=None):
if not osp.exists(osp.join(model_dir, "model.yml")):
raise Exception("There's not model.yml in {}".format(model_dir))
with open(osp.join(model_dir, "model.yml")) as f:
......@@ -44,6 +44,7 @@ def load_model(model_dir):
else:
model = getattr(paddlex.cv.models,
info['Model'])(**info['_init_params'])
model.fixed_input_shape = fixed_input_shape
if status == "Normal" or \
status == "Prune" or status == "fluid.save":
startup_prog = fluid.Program()
......@@ -78,6 +79,8 @@ def load_model(model_dir):
model.test_outputs[var_desc[0]] = out
if 'Transforms' in info:
transforms_mode = info.get('TransformsMode', 'RGB')
# 固定模型的输入shape
fix_input_shape(info, fixed_input_shape=fixed_input_shape)
if transforms_mode == 'RGB':
to_rgb = True
else:
......@@ -102,6 +105,33 @@ def load_model(model_dir):
return model
def fix_input_shape(info, fixed_input_shape=None):
if fixed_input_shape is not None:
resize = {'ResizeByShort': {}}
padding = {'Padding': {}}
if info['_Attributes']['model_type'] == 'classifier':
crop_size = 0
for transform in info['Transforms']:
if 'CenterCrop' in transform:
crop_size = transform['CenterCrop']['crop_size']
break
assert crop_size == fixed_input_shape[
0], "fixed_input_shape must == CenterCrop:crop_size:{}".format(
crop_size)
assert crop_size == fixed_input_shape[
1], "fixed_input_shape must == CenterCrop:crop_size:{}".format(
crop_size)
if crop_size == 0:
logging.warning(
"fixed_input_shape must == input shape when trainning")
else:
resize['ResizeByShort']['short_size'] = min(fixed_input_shape)
resize['ResizeByShort']['max_size'] = max(fixed_input_shape)
padding['Padding']['target_size'] = list(fixed_input_shape)
info['Transforms'].append(resize)
info['Transforms'].append(padding)
def build_transforms(model_type, transforms_info, to_rgb=True):
if model_type == "classifier":
import paddlex.cv.transforms.cls_transforms as T
......
paddlex/cv/models/mask_rcnn.py
浏览文件 @ b7f90835
......@@ -32,7 +32,7 @@ class MaskRCNN(FasterRCNN):
Args:
num_classes (int): 包含了背景类的类别数。默认为81。
backbone (str): MaskRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50',
'ResNet50vd', 'ResNet101', 'ResNet101vd']。默认为'ResNet50'。
'ResNet50_vd', 'ResNet101', 'ResNet101_vd']。默认为'ResNet50'。
with_fpn (bool): 是否使用FPN结构。默认为True。
aspect_ratios (list): 生成anchor高宽比的可选值。默认为[0.5, 1.0, 2.0]。
anchor_sizes (list): 生成anchor大小的可选值。默认为[32, 64, 128, 256, 512]。
......@@ -46,7 +46,7 @@ class MaskRCNN(FasterRCNN):
anchor_sizes=[32, 64, 128, 256, 512]):
self.init_params = locals()
backbones = [
'ResNet18', 'ResNet50', 'ResNet50vd', 'ResNet101', 'ResNet101vd'
'ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd'
]
assert backbone in backbones, "backbone should be one of {}".format(
backbones)
......@@ -60,6 +60,7 @@ class MaskRCNN(FasterRCNN):
self.mask_head_resolution = 28
else:
self.mask_head_resolution = 14
self.fixed_input_shape = None
def build_net(self, mode='train'):
train_pre_nms_top_n = 2000 if self.with_fpn else 12000
......@@ -73,7 +74,8 @@ class MaskRCNN(FasterRCNN):
train_pre_nms_top_n=train_pre_nms_top_n,
test_pre_nms_top_n=test_pre_nms_top_n,
num_convs=num_convs,
mask_head_resolution=self.mask_head_resolution)
mask_head_resolution=self.mask_head_resolution,
fixed_input_shape=self.fixed_input_shape)
inputs = model.generate_inputs()
if mode == 'train':
model_out = model.build_net(inputs)
......@@ -130,7 +132,8 @@ class MaskRCNN(FasterRCNN):
metric=None,
use_vdl=False,
early_stop=False,
early_stop_patience=5):
early_stop_patience=5,
resume_checkpoint=None):
"""训练。
Args:
......@@ -156,6 +159,7 @@ class MaskRCNN(FasterRCNN):
early_stop (bool): 是否使用提前终止训练策略。默认值为False。
early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
连续下降或持平，则终止训练。默认值为5。
resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
Raises:
ValueError: 评估类型不在指定列表中。
......@@ -167,7 +171,8 @@ class MaskRCNN(FasterRCNN):
metric = 'COCO'
else:
raise Exception(
"train_dataset should be datasets.COCODetection or datasets.EasyDataDet.")
"train_dataset should be datasets.COCODetection or datasets.EasyDataDet."
)
assert metric in ['COCO', 'VOC'], "Metric only support 'VOC' or 'COCO'"
self.metric = metric
if not self.trainable:
......@@ -195,7 +200,8 @@ class MaskRCNN(FasterRCNN):
startup_prog=fluid.default_startup_program(),
pretrain_weights=pretrain_weights,
fuse_bn=fuse_bn,
save_dir=save_dir)
save_dir=save_dir,
resume_checkpoint=resume_checkpoint)
# 训练
self.train_loop(
num_epochs=num_epochs,
......
paddlex/cv/models/slim/prune_config.py
浏览文件 @ b7f90835
......@@ -15,7 +15,7 @@
import numpy as np
import os.path as osp
import paddle.fluid as fluid
import paddlehub as hub
#import paddlehub as hub
import paddlex
sensitivities_data = {
......@@ -105,22 +105,26 @@ def get_sensitivities(flag, model, save_dir):
model_type)
url = sensitivities_data[model_type]
fname = osp.split(url)[-1]
try:
hub.download(fname, save_path=save_dir)
except Exception as e:
if isinstance(e, hub.ResourceNotFoundError):
raise Exception(
"Resource for model {}(key='{}') not found".format(
model_type, fname))
elif isinstance(e, hub.ServerConnectionError):
raise Exception(
"Cannot get reource for model {}(key='{}'), please check your internet connecgtion"
.format(model_type, fname))
else:
raise Exception(
"Unexpected error, please make sure paddlehub >= 1.6.2 {}".
format(str(e)))
paddlex.utils.download(url, path=save_dir)
return osp.join(save_dir, fname)
# try:
# hub.download(fname, save_path=save_dir)
# except Exception as e:
# if isinstance(e, hub.ResourceNotFoundError):
# raise Exception(
# "Resource for model {}(key='{}') not found".format(
# model_type, fname))
# elif isinstance(e, hub.ServerConnectionError):
# raise Exception(
# "Cannot get reource for model {}(key='{}'), please check your internet connecgtion"
# .format(model_type, fname))
# else:
# raise Exception(
# "Unexpected error, please make sure paddlehub >= 1.6.2 {}".
# format(str(e)))
# return osp.join(save_dir, fname)
else:
raise Exception(
"sensitivities need to be defined as directory path or `DEFAULT`(download sensitivities automatically)."
......
paddlex/cv/models/unet.py
浏览文件 @ b7f90835
......@@ -77,6 +77,7 @@ class UNet(DeepLabv3p):
self.class_weight = class_weight
self.ignore_index = ignore_index
self.labels = None
self.fixed_input_shape = None
def build_net(self, mode='train'):
model = paddlex.cv.nets.segmentation.UNet(
......@@ -86,7 +87,8 @@ class UNet(DeepLabv3p):
use_bce_loss=self.use_bce_loss,
use_dice_loss=self.use_dice_loss,
class_weight=self.class_weight,
ignore_index=self.ignore_index)
ignore_index=self.ignore_index,
fixed_input_shape=self.fixed_input_shape)
inputs = model.generate_inputs()
model_out = model.build_net(inputs)
outputs = OrderedDict()
......@@ -119,7 +121,8 @@ class UNet(DeepLabv3p):
sensitivities_file=None,
eval_metric_loss=0.05,
early_stop=False,
early_stop_patience=5):
early_stop_patience=5,
resume_checkpoint=None):
"""训练。
Args:
......@@ -143,14 +146,14 @@ class UNet(DeepLabv3p):
early_stop (bool): 是否使用提前终止训练策略。默认值为False。
early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
连续下降或持平，则终止训练。默认值为5。
resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
Raises:
ValueError: 模型从inference model进行加载。
"""
return super(
UNet,
self).train(num_epochs, train_dataset, train_batch_size,
eval_dataset, save_interval_epochs, log_interval_steps,
save_dir, pretrain_weights, optimizer, learning_rate,
lr_decay_power, use_vdl, sensitivities_file,
eval_metric_loss, early_stop, early_stop_patience)
return super(UNet, self).train(
num_epochs, train_dataset, train_batch_size, eval_dataset,
save_interval_epochs, log_interval_steps, save_dir,
pretrain_weights, optimizer, learning_rate, lr_decay_power,
use_vdl, sensitivities_file, eval_metric_loss, early_stop,
early_stop_patience, resume_checkpoint)
paddlex/cv/models/utils/pretrain_weights.py
浏览文件 @ b7f90835
import paddlex
import paddlehub as hub
#import paddlehub as hub
import os
import os.path as osp
......@@ -85,40 +85,53 @@ def get_pretrain_weights(flag, model_type, backbone, save_dir):
backbone = 'DetResNet50'
assert backbone in image_pretrain, "There is not ImageNet pretrain weights for {}, you may try COCO.".format(
backbone)
try:
hub.download(backbone, save_path=new_save_dir)
except Exception as e:
if isinstance(e, hub.ResourceNotFoundError):
raise Exception(
"Resource for backbone {} not found".format(backbone))
elif isinstance(e, hub.ServerConnectionError):
raise Exception(
"Cannot get reource for backbone {}, please check your internet connecgtion"
.format(backbone))
else:
raise Exception(
"Unexpected error, please make sure paddlehub >= 1.6.2")
return osp.join(new_save_dir, backbone)
url = image_pretrain[backbone]
fname = osp.split(url)[-1].split('.')[0]
paddlex.utils.download_and_decompress(url, path=new_save_dir)
return osp.join(new_save_dir, fname)
# try:
# hub.download(backbone, save_path=new_save_dir)
# except Exception as e:
# if isinstance(e, hub.ResourceNotFoundError):
# raise Exception(
# "Resource for backbone {} not found".format(backbone))
# elif isinstance(e, hub.ServerConnectionError):
# raise Exception(
# "Cannot get reource for backbone {}, please check your internet connecgtion"
# .format(backbone))
# else:
# raise Exception(
# "Unexpected error, please make sure paddlehub >= 1.6.2")
# return osp.join(new_save_dir, backbone)
elif flag == 'COCO':
new_save_dir = save_dir
if hasattr(paddlex, 'pretrain_dir'):
new_save_dir = paddlex.pretrain_dir
assert backbone in coco_pretrain, "There is not COCO pretrain weights for {}, you may try ImageNet.".format(
backbone)
try:
hub.download(backbone, save_path=new_save_dir)
except Exception as e:
if isinstance(hub.ResourceNotFoundError):
raise Exception(
"Resource for backbone {} not found".format(backbone))
elif isinstance(hub.ServerConnectionError):
raise Exception(
"Cannot get reource for backbone {}, please check your internet connecgtion"
.format(backbone))
else:
raise Exception(
"Unexpected error, please make sure paddlehub >= 1.6.2")
return osp.join(new_save_dir, backbone)
url = coco_pretrain[backbone]
fname = osp.split(url)[-1].split('.')[0]
paddlex.utils.download_and_decompress(url, path=new_save_dir)
return osp.join(new_save_dir, fname)
# new_save_dir = save_dir
# if hasattr(paddlex, 'pretrain_dir'):
# new_save_dir = paddlex.pretrain_dir
# assert backbone in coco_pretrain, "There is not COCO pretrain weights for {}, you may try ImageNet.".format(
# backbone)
# try:
# hub.download(backbone, save_path=new_save_dir)
# except Exception as e:
# if isinstance(hub.ResourceNotFoundError):
# raise Exception(
# "Resource for backbone {} not found".format(backbone))
# elif isinstance(hub.ServerConnectionError):
# raise Exception(
# "Cannot get reource for backbone {}, please check your internet connecgtion"
# .format(backbone))
# else:
# raise Exception(
# "Unexpected error, please make sure paddlehub >= 1.6.2")
# return osp.join(new_save_dir, backbone)
else:
raise Exception(
"pretrain_weights need to be defined as directory path or `IMAGENET` or 'COCO' (download pretrain weights automatically)."
......
paddlex/cv/models/utils/visualize.py
浏览文件 @ b7f90835
......@@ -16,6 +16,7 @@ import os
import cv2
import colorsys
import numpy as np
import time
import paddlex.utils.logging as logging
from .detection_eval import fixed_linspace, backup_linspace, loadRes
......@@ -25,8 +26,12 @@ def visualize_detection(image, result, threshold=0.5, save_dir='./'):
Visualize bbox and mask results
"""
image_name = os.path.split(image)[-1]
image = cv2.imread(image)
if isinstance(image, np.ndarray):
image_name = str(int(time.time())) + '.jpg'
else:
image_name = os.path.split(image)[-1]
image = cv2.imread(image)
image = draw_bbox_mask(image, result, threshold=threshold)
if save_dir is not None:
if not os.path.exists(save_dir):
......@@ -56,13 +61,18 @@ def visualize_segmentation(image, result, weight=0.6, save_dir='./'):
c3 = cv2.LUT(label_map, color_map[:, 2])
pseudo_img = np.dstack((c1, c2, c3))
im = cv2.imread(image)
if isinstance(image, np.ndarray):
im = image
image_name = str(int(time.time())) + '.jpg'
else:
image_name = os.path.split(image)[-1]
im = cv2.imread(image)
vis_result = cv2.addWeighted(im, weight, pseudo_img, 1 - weight, 0)
if save_dir is not None:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image_name = os.path.split(image)[-1]
out_path = os.path.join(save_dir, 'visualize_{}'.format(image_name))
cv2.imwrite(out_path, vis_result)
logging.info('The visualized result is saved as {}'.format(out_path))
......
paddlex/cv/models/yolo_v3.py
浏览文件 @ b7f90835
......@@ -80,6 +80,7 @@ class YOLOv3(BaseAPI):
self.label_smooth = label_smooth
self.sync_bn = True
self.train_random_shapes = train_random_shapes
self.fixed_input_shape = None
def _get_backbone(self, backbone_name):
if backbone_name == 'DarkNet53':
......@@ -113,7 +114,8 @@ class YOLOv3(BaseAPI):
nms_topk=self.nms_topk,
nms_keep_topk=self.nms_keep_topk,
nms_iou_threshold=self.nms_iou_threshold,
train_random_shapes=self.train_random_shapes)
train_random_shapes=self.train_random_shapes,
fixed_input_shape=self.fixed_input_shape)
inputs = model.generate_inputs()
model_out = model.build_net(inputs)
outputs = OrderedDict([('bbox', model_out)])
......@@ -164,7 +166,8 @@ class YOLOv3(BaseAPI):
sensitivities_file=None,
eval_metric_loss=0.05,
early_stop=False,
early_stop_patience=5):
early_stop_patience=5,
resume_checkpoint=None):
"""训练。
Args:
......@@ -193,6 +196,7 @@ class YOLOv3(BaseAPI):
early_stop (bool): 是否使用提前终止训练策略。默认值为False。
early_stop_patience (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内
连续下降或持平，则终止训练。默认值为5。
resume_checkpoint (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。
Raises:
ValueError: 评估类型不在指定列表中。
......@@ -234,7 +238,8 @@ class YOLOv3(BaseAPI):
pretrain_weights=pretrain_weights,
save_dir=save_dir,
sensitivities_file=sensitivities_file,
eval_metric_loss=eval_metric_loss)
eval_metric_loss=eval_metric_loss,
resume_checkpoint=resume_checkpoint)
# 训练
self.train_loop(
num_epochs=num_epochs,
......
paddlex/cv/nets/detection/faster_rcnn.py
浏览文件 @ b7f90835
......@@ -76,7 +76,8 @@ class FasterRCNN(object):
fg_thresh=.5,
bg_thresh_hi=.5,
bg_thresh_lo=0.,
bbox_reg_weights=[0.1, 0.1, 0.2, 0.2]):
bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
fixed_input_shape=None):
super(FasterRCNN, self).__init__()
self.backbone = backbone
self.mode = mode
......@@ -148,6 +149,7 @@ class FasterRCNN(object):
self.bg_thresh_lo = bg_thresh_lo
self.bbox_reg_weights = bbox_reg_weights
self.rpn_only = rpn_only
self.fixed_input_shape = fixed_input_shape
def build_net(self, inputs):
im = inputs['image']
......@@ -219,8 +221,16 @@ class FasterRCNN(object):
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
inputs['image'] = fluid.data(
dtype='float32', shape=input_shape, name='image')
else:
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.mode == 'train':
inputs['im_info'] = fluid.data(
dtype='float32', shape=[None, 3], name='im_info')
......
paddlex/cv/nets/detection/mask_rcnn.py
浏览文件 @ b7f90835
......@@ -86,7 +86,8 @@ class MaskRCNN(object):
fg_thresh=.5,
bg_thresh_hi=.5,
bg_thresh_lo=0.,
bbox_reg_weights=[0.1, 0.1, 0.2, 0.2]):
bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
fixed_input_shape=None):
super(MaskRCNN, self).__init__()
self.backbone = backbone
self.mode = mode
......@@ -167,6 +168,7 @@ class MaskRCNN(object):
self.bg_thresh_lo = bg_thresh_lo
self.bbox_reg_weights = bbox_reg_weights
self.rpn_only = rpn_only
self.fixed_input_shape = fixed_input_shape
def build_net(self, inputs):
im = inputs['image']
......@@ -306,8 +308,16 @@ class MaskRCNN(object):
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
inputs['image'] = fluid.data(
dtype='float32', shape=input_shape, name='image')
else:
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.mode == 'train':
inputs['im_info'] = fluid.data(
dtype='float32', shape=[None, 3], name='im_info')
......
paddlex/cv/nets/detection/yolo_v3.py
浏览文件 @ b7f90835
......@@ -33,7 +33,8 @@ class YOLOv3:
nms_iou_threshold=0.45,
train_random_shapes=[
320, 352, 384, 416, 448, 480, 512, 544, 576, 608
]):
],
fixed_input_shape=None):
if anchors is None:
anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]]
......@@ -54,6 +55,7 @@ class YOLOv3:
self.norm_decay = 0.0
self.prefix_name = ''
self.train_random_shapes = train_random_shapes
self.fixed_input_shape = fixed_input_shape
def _head(self, feats):
outputs = []
......@@ -247,8 +249,15 @@ class YOLOv3:
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
inputs['image'] = fluid.data(
dtype='float32', shape=input_shape, name='image')
else:
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.mode == 'train':
inputs['gt_box'] = fluid.data(
dtype='float32', shape=[None, None, 4], name='gt_box')
......
paddlex/cv/nets/segmentation/deeplabv3p.py
浏览文件 @ b7f90835
......@@ -61,6 +61,7 @@ class DeepLabv3p(object):
自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1，
即平时使用的交叉熵损失函数。
ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。
fixed_input_shape (list): 长度为2，维度为1的list，如:[640,720]，用来固定模型输入:'image'的shape，默认为None。
Raises:
ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
......@@ -81,7 +82,8 @@ class DeepLabv3p(object):
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255):
ignore_index=255,
fixed_input_shape=None):
# dice_loss或bce_loss只适用两类分割中
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise ValueError(
......@@ -115,6 +117,7 @@ class DeepLabv3p(object):
self.decoder_use_sep_conv = decoder_use_sep_conv
self.encoder_with_aspp = encoder_with_aspp
self.enable_decoder = enable_decoder
self.fixed_input_shape = fixed_input_shape
def _encoder(self, input):
# 编码器配置，采用ASPP架构，pooling + 1x1_conv + 三个不同尺度的空洞卷积并行, concat后1x1conv
......@@ -310,8 +313,16 @@ class DeepLabv3p(object):
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
inputs['image'] = fluid.data(
dtype='float32', shape=input_shape, name='image')
else:
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.mode == 'train':
inputs['label'] = fluid.data(
dtype='int32', shape=[None, 1, None, None], name='label')
......
paddlex/cv/nets/segmentation/unet.py
浏览文件 @ b7f90835
......@@ -54,6 +54,7 @@ class UNet(object):
自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1，
即平时使用的交叉熵损失函数。
ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。
fixed_input_shape (list): 长度为2，维度为1的list，如:[640,720]，用来固定模型输入:'image'的shape，默认为None。
Raises:
ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
......@@ -69,7 +70,8 @@ class UNet(object):
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255):
ignore_index=255,
fixed_input_shape=None):
# dice_loss或bce_loss只适用两类分割中
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise Exception(
......@@ -97,6 +99,7 @@ class UNet(object):
self.use_dice_loss = use_dice_loss
self.class_weight = class_weight
self.ignore_index = ignore_index
self.fixed_input_shape = fixed_input_shape
def _double_conv(self, data, out_ch):
param_attr = fluid.ParamAttr(
......@@ -226,8 +229,16 @@ class UNet(object):
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
inputs['image'] = fluid.data(
dtype='float32', shape=input_shape, name='image')
else:
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.mode == 'train':
inputs['label'] = fluid.data(
dtype='int32', shape=[None, 1, None, None], name='label')
......
paddlex/cv/transforms/cls_transforms.py
浏览文件 @ b7f90835
......@@ -13,13 +13,22 @@
# limitations under the License.
from .ops import *
from .imgaug_support import execute_imgaug
import random
import os.path as osp
import numpy as np
from PIL import Image, ImageEnhance
class Compose:
class ClsTransform:
"""分类Transform的基类
"""
def __init__(self):
pass
class Compose(ClsTransform):
"""根据数据预处理/增强算子对输入数据进行操作。
所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
......@@ -39,6 +48,15 @@ class Compose:
'must be equal or larger than 1!')
self.transforms = transforms
# 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
for op in self.transforms:
if not isinstance(op, ClsTransform):
import imgaug.augmenters as iaa
if not isinstance(op, iaa.Augmenter):
raise Exception(
"Elements in transforms should be defined in 'paddlex.cls.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
)
def __call__(self, im, label=None):
"""
Args:
......@@ -48,20 +66,34 @@ class Compose:
tuple: 根据网络所需字段所组成的tuple；
字段由transforms中的最后一个数据预处理操作决定。
"""
try:
im = cv2.imread(im).astype('float32')
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
if isinstance(im, np.ndarray):
if len(im.shape) != 3:
raise Exception(
"im should be 3-dimension, but now is {}-dimensions".
format(len(im.shape)))
else:
try:
im = cv2.imread(im).astype('float32')
except:
raise TypeError('Can\'t read The image file {}!'.format(im))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
for op in self.transforms:
outputs = op(im, label)
im = outputs[0]
if len(outputs) == 2:
label = outputs[1]
if isinstance(op, ClsTransform):
outputs = op(im, label)
im = outputs[0]
if len(outputs) == 2:
label = outputs[1]
else:
import imgaug.augmenters as iaa
if isinstance(op, iaa.Augmenter):
im, = execute_imgaug(op, im)
output = (im, )
if label is not None:
output = (im, label)
return outputs
class RandomCrop:
class RandomCrop(ClsTransform):
"""对图像进行随机剪裁，模型训练时的数据增强操作。
1. 根据lower_scale、lower_ratio、upper_ratio计算随机剪裁的高、宽。
......@@ -104,7 +136,7 @@ class RandomCrop:
return (im, label)
class RandomHorizontalFlip:
class RandomHorizontalFlip(ClsTransform):
"""以一定的概率对图像进行随机水平翻转，模型训练时的数据增强操作。
Args:
......@@ -132,7 +164,7 @@ class RandomHorizontalFlip:
return (im, label)
class RandomVerticalFlip:
class RandomVerticalFlip(ClsTransform):
"""以一定的概率对图像进行随机垂直翻转，模型训练时的数据增强操作。
Args:
......@@ -160,7 +192,7 @@ class RandomVerticalFlip:
return (im, label)
class Normalize:
class Normalize(ClsTransform):
"""对图像进行标准化。
1. 对图像进行归一化到区间[0.0, 1.0]。
......@@ -195,7 +227,7 @@ class Normalize:
return (im, label)
class ResizeByShort:
class ResizeByShort(ClsTransform):
"""根据图像短边对图像重新调整大小（resize）。
1. 获取图像的长边和短边长度。
......@@ -242,7 +274,7 @@ class ResizeByShort:
return (im, label)
class CenterCrop:
class CenterCrop(ClsTransform):
"""以图像中心点扩散裁剪长宽为`crop_size`的正方形
1. 计算剪裁的起始点。
......@@ -272,7 +304,7 @@ class CenterCrop:
return (im, label)
class RandomRotate:
class RandomRotate(ClsTransform):
def __init__(self, rotate_range=30, prob=0.5):
"""以一定的概率对图像在[-rotate_range, rotaterange]角度范围内进行旋转，模型训练时的数据增强操作。
......@@ -306,7 +338,7 @@ class RandomRotate:
return (im, label)
class RandomDistort:
class RandomDistort(ClsTransform):
"""以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作。
1. 对变换的操作顺序进行随机化操作。
......@@ -397,7 +429,7 @@ class RandomDistort:
return (im, label)
class ArrangeClassifier:
class ArrangeClassifier(ClsTransform):
"""获取训练/验证/预测所需信息。注意：此操作不需用户自己显示调用
Args:
......
paddlex/cv/transforms/imgaug_support.py 0 → 100644
浏览文件 @ b7f90835
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
segment_map=None):
# 预处理，将bboxes, polygons转换成imgaug格式
import imgaug.augmentables.polys as polys
import imgaug.augmentables.bbs as bbs
aug_im = im.astype('uint8')
aug_bboxes = None
if bboxes is not None:
aug_bboxes = list()
for i in range(len(bboxes)):
x1 = bboxes[i, 0] - 1
y1 = bboxes[i, 1]
x2 = bboxes[i, 2]
y2 = bboxes[i, 3]
aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
aug_polygons = None
lod_info = list()
if polygons is not None:
aug_polygons = list()
for i in range(len(polygons)):
num = len(polygons[i])
lod_info.append(num)
for j in range(num):
points = np.reshape(polygons[i][j], (-1, 2))
aug_polygons.append(polys.Polygon(points))
aug_segment_map = None
if segment_map is not None:
if len(segment_map.shape) == 2:
h, w = segment_map.shape
aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
elif len(segment_map.shape) == 3:
h, w, c = segment_map.shape
aug_segment_map = np.reshape(segment_map, (1, h, w, c))
else:
raise Exception(
"Only support 2-dimensions for 3-dimensions for segment_map")
aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(
image=aug_im,
bounding_boxes=aug_bboxes,
polygons=aug_polygons,
segmentation_maps=aug_segment_map)
aug_im = aug_im.astype('float32')
if aug_polygons is not None:
assert len(aug_bboxes) == len(
lod_info
), "Number of aug_bboxes should be equal to number of aug_polygons"
if aug_bboxes is not None:
# 裁剪掉在图像之外的bbox和polygon
for i in range(len(aug_bboxes)):
aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
if aug_polygons is not None:
for i in range(len(aug_polygons)):
aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)
# 过滤掉无效的bbox和polygon，并转换为训练数据格式
converted_bboxes = list()
converted_polygons = list()
poly_index = 0
for i in range(len(aug_bboxes)):
# 过滤width或height不足1像素的框
if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
continue
if aug_polygons is None:
converted_bboxes.append([
aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
aug_bboxes[i].y2
])
continue
# 如若有polygons，将会继续执行下面代码
polygons_this_box = list()
for ps in aug_polygons[poly_index:poly_index + lod_info[i]]:
if len(ps) == 0:
continue
for p in ps:
# 没有3个point的polygon被过滤
if len(p.exterior) < 3:
continue
polygons_this_box.append(p.exterior.flatten().tolist())
poly_index += lod_info[i]
if len(polygons_this_box) == 0:
continue
converted_bboxes.append([
aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
aug_bboxes[i].y2
])
converted_polygons.append(polygons_this_box)
if len(converted_bboxes) == 0:
aug_im = im
converted_bboxes = bboxes
converted_polygons = polygons
result = [aug_im]
if bboxes is not None:
result.append(np.array(converted_bboxes))
if polygons is not None:
result.append(converted_polygons)
if segment_map is not None:
n, h, w, c = aug_seg_map.shape
if len(segment_map.shape) == 2:
aug_seg_map = np.reshape(aug_seg_map, (h, w))
elif len(segment_map.shape) == 3:
aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
result.append(aug_seg_map)
return result
paddlex/tools/__init__.py 0 → 100644
浏览文件 @ b7f90835
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .x2imagenet import EasyData2ImageNet
from .x2coco import LabelMe2COCO
from .x2coco import EasyData2COCO
from .x2voc import LabelMe2VOC
from .x2voc import EasyData2VOC
from .x2seg import JingLing2Seg
from .x2seg import LabelMe2Seg
from .x2seg import EasyData2Seg
paddlex/tools/base.py 0 → 100644
浏览文件 @ b7f90835
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import chardet
import numpy as np
class MyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(MyEncoder, self).default(obj)
def is_pic(img_name):
valid_suffix = ["JPEG", "jpeg", "JPG", "jpg", "BMP", "bmp", "PNG", "png"]
suffix = img_name.split(".")[-1]
if suffix not in valid_suffix:
return False
return True
def get_encoding(path):
f = open(path, 'rb')
data = f.read()
file_encoding = chardet.detect(data).get('encoding')
return file_encoding
\ No newline at end of file
paddlex/tools/x2coco.py 0 → 100644
浏览文件 @ b7f90835
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import json
import os
import os.path as osp
import shutil
import numpy as np
import PIL.ImageDraw
from .base import MyEncoder, is_pic, get_encoding
class X2COCO(object):
def __init__(self):
self.images_list = []
self.categories_list = []
self.annotations_list = []
def generate_categories_field(self, label, labels_list):
category = {}
category["supercategory"] = "component"
category["id"] = len(labels_list) + 1
category["name"] = label
return category
def generate_rectangle_anns_field(self, points, label, image_id, object_id, label_to_num):
annotation = {}
seg_points = np.asarray(points).copy()
seg_points[1, :] = np.asarray(points)[2, :]
seg_points[2, :] = np.asarray(points)[1, :]
annotation["segmentation"] = [list(seg_points.flatten())]
annotation["iscrowd"] = 0
annotation["image_id"] = image_id + 1
annotation["bbox"] = list(
map(float, [
points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
1] - points[0][1]
]))
annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
annotation["category_id"] = label_to_num[label]
annotation["id"] = object_id + 1
return annotation
def convert(self, image_dir, json_dir, dataset_save_dir):
"""转换。
Args:
image_dir (str): 图像文件存放的路径。
json_dir (str): 与每张图像对应的json文件的存放路径。
dataset_save_dir (str): 转换后数据集存放路径。
"""
assert osp.exists(image_dir), "he image folder does not exist!"
assert osp.exists(json_dir), "The json folder does not exist!"
assert osp.exists(dataset_save_dir), "The save folder does not exist!"
# Convert the image files.
new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
if osp.exists(new_image_dir):
shutil.rmtree(new_image_dir)
os.makedirs(new_image_dir)
for img_name in os.listdir(image_dir):
if is_pic(img_name):
shutil.copyfile(
osp.join(image_dir, img_name),
osp.join(new_image_dir, img_name))
# Convert the json files.
self.parse_json(new_image_dir, json_dir)
coco_data = {}
coco_data["images"] = self.images_list
coco_data["categories"] = self.categories_list
coco_data["annotations"] = self.annotations_list
json_path = osp.join(dataset_save_dir, "annotations.json")
json.dump(
coco_data,
open(json_path, "w"),
indent=4,
cls=MyEncoder)
class LabelMe2COCO(X2COCO):
"""将使用LabelMe标注的数据集转换为COCO数据集。
"""
def __init__(self):
super(LabelMe2COCO, self).__init__()
def generate_images_field(self, json_info, image_id):
image = {}
image["height"] = json_info["imageHeight"]
image["width"] = json_info["imageWidth"]
image["id"] = image_id + 1
image["file_name"] = json_info["imagePath"].split("/")[-1]
return image
def generate_polygon_anns_field(self, height, width,
points, label, image_id,
object_id, label_to_num):
annotation = {}
annotation["segmentation"] = [list(np.asarray(points).flatten())]
annotation["iscrowd"] = 0
annotation["image_id"] = image_id + 1
annotation["bbox"] = list(map(float, get_bbox(height, width, points)))
annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
annotation["category_id"] = label_to_num[label]
annotation["id"] = object_id + 1
return annotation
def get_bbox(self, height, width, points):
polygons = points
mask = np.zeros([height, width], dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
xy = list(map(tuple, polygons))
PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
left_top_r = np.min(rows)
left_top_c = np.min(clos)
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
return [
left_top_c, left_top_r, right_bottom_c - left_top_c,
right_bottom_r - left_top_r
]
def parse_json(self, img_dir, json_dir):
image_id = -1
object_id = -1
labels_list = []
label_to_num = {}
for img_file in os.listdir(img_dir):
img_name_part = osp.splitext(img_file)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_file)))
continue
image_id = image_id + 1
with open(json_file, mode='r', \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
img_info = self.generate_images_field(json_info, image_id)
self.images_list.append(img_info)
for shapes in json_info["shapes"]:
object_id = object_id + 1
label = shapes["label"]
if label not in labels_list:
self.categories_list.append(\
self.generate_categories_field(label, labels_list))
labels_list.append(label)
label_to_num[label] = len(labels_list)
points = shapes["points"]
p_type = shapes["shape_type"]
if p_type == "polygon":
self.annotations_list.append(
self.generate_polygon_anns_field(json_info["imageHeight"], json_info[
"imageWidth"], points, label, image_id,
object_id, label_to_num))
if p_type == "rectangle":
points.append([points[0][0], points[1][1]])
points.append([points[1][0], points[0][1]])
self.annotations_list.append(
self.generate_rectangle_anns_field(points, label, image_id,
object_id, label_to_num))
class EasyData2COCO(X2COCO):
"""将使用EasyData标注的检测或分割数据集转换为COCO数据集。
"""
def __init__(self):
super(EasyData2COCO, self).__init__()
def generate_images_field(self, img_path, image_id):
image = {}
img = cv2.imread(img_path)
image["height"] = img.shape[0]
image["width"] = img.shape[1]
image["id"] = image_id + 1
image["file_name"] = osp.split(img_path)[-1]
return image
def generate_polygon_anns_field(self, points, segmentation,
label, image_id, object_id,
label_to_num):
annotation = {}
annotation["segmentation"] = segmentation
annotation["iscrowd"] = 1 if len(segmentation) > 1 else 0
annotation["image_id"] = image_id + 1
annotation["bbox"] = list(map(float, [
points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
1] - points[0][1]
]))
annotation["area"] = annotation["bbox"][2] * annotation["bbox"][3]
annotation["category_id"] = label_to_num[label]
annotation["id"] = object_id + 1
return annotation
def parse_json(self, img_dir, json_dir):
from pycocotools.mask import decode
image_id = -1
object_id = -1
labels_list = []
label_to_num = {}
for img_file in os.listdir(img_dir):
img_name_part = osp.splitext(img_file)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_file)))
continue
image_id = image_id + 1
with open(json_file, mode='r', \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
img_info = self.generate_images_field(osp.join(img_dir, img_file), image_id)
self.images_list.append(img_info)
for shapes in json_info["labels"]:
object_id = object_id + 1
label = shapes["name"]
if label not in labels_list:
self.categories_list.append(\
self.generate_categories_field(label, labels_list))
labels_list.append(label)
label_to_num[label] = len(labels_list)
points = [[shapes["x1"], shapes["y1"]],
[shapes["x2"], shapes["y2"]]]
if "mask" not in shapes:
points.append([points[0][0], points[1][1]])
points.append([points[1][0], points[0][1]])
self.annotations_list.append(
self.generate_rectangle_anns_field(points, label, image_id,
object_id, label_to_num))
else:
mask_dict = {}
mask_dict['size'] = [img_info["height"], img_info["width"]]
mask_dict['counts'] = shapes['mask'].encode()
mask = decode(mask_dict)
contours, hierarchy = cv2.findContours(
(mask).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
segmentation = []
for contour in contours:
contour_list = contour.flatten().tolist()
if len(contour_list) > 4:
segmentation.append(contour_list)
self.annotations_list.append(
self.generate_polygon_anns_field(points, segmentation, label, image_id, object_id,
label_to_num))
\ No newline at end of file
paddlex/tools/x2imagenet.py 0 → 100644
浏览文件 @ b7f90835
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import json
import os
import os.path as osp
import shutil
import numpy as np
from .base import MyEncoder, is_pic, get_encoding
class EasyData2ImageNet(object):
"""将使用EasyData标注的分类数据集转换为COCO数据集。
"""
def __init__(self):
pass
def convert(self, image_dir, json_dir, dataset_save_dir):
"""转换。
Args:
image_dir (str): 图像文件存放的路径。
json_dir (str): 与每张图像对应的json文件的存放路径。
dataset_save_dir (str): 转换后数据集存放路径。
"""
assert osp.exists(image_dir), "The image folder does not exist!"
assert osp.exists(json_dir), "The json folder does not exist!"
assert osp.exists(dataset_save_dir), "The save folder does not exist!"
assert len(os.listdir(dataset_save_dir)) == 0, "The save folder must be empty!"
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for output in json_info['labels']:
cls_name = output['name']
new_image_dir = osp.join(dataset_save_dir, cls_name)
if not osp.exists(new_image_dir):
os.makedirs(new_image_dir)
if is_pic(img_name):
shutil.copyfile(
osp.join(image_dir, img_name),
osp.join(new_image_dir, img_name))
\ No newline at end of file
paddlex/tools/x2seg.py 0 → 100644
浏览文件 @ b7f90835
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import uuid
import json
import os
import os.path as osp
import shutil
import numpy as np
import PIL.Image
from .base import MyEncoder, is_pic, get_encoding
class X2Seg(object):
def __init__(self):
self.labels2ids = {'_background_': 0}
def shapes_to_label(self, img_shape, shapes, label_name_to_value):
# 该函数基于https://github.com/wkentaro/labelme/blob/master/labelme/utils/shape.py实现。
def shape_to_mask(img_shape, points, shape_type=None,
line_width=10, point_size=5):
mask = np.zeros(img_shape[:2], dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
draw = PIL.ImageDraw.Draw(mask)
xy = [tuple(point) for point in points]
if shape_type == 'circle':
assert len(xy) == 2, 'Shape of shape_type=circle must have 2 points'
(cx, cy), (px, py) = xy
d = math.sqrt((cx - px) ** 2 + (cy - py) ** 2)
draw.ellipse([cx - d, cy - d, cx + d, cy + d], outline=1, fill=1)
elif shape_type == 'rectangle':
assert len(xy) == 2, 'Shape of shape_type=rectangle must have 2 points'
draw.rectangle(xy, outline=1, fill=1)
elif shape_type == 'line':
assert len(xy) == 2, 'Shape of shape_type=line must have 2 points'
draw.line(xy=xy, fill=1, width=line_width)
elif shape_type == 'linestrip':
draw.line(xy=xy, fill=1, width=line_width)
elif shape_type == 'point':
assert len(xy) == 1, 'Shape of shape_type=point must have 1 points'
cx, cy = xy[0]
r = point_size
draw.ellipse([cx - r, cy - r, cx + r, cy + r], outline=1, fill=1)
else:
assert len(xy) > 2, 'Polygon must have points more than 2'
draw.polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
cls = np.zeros(img_shape[:2], dtype=np.int32)
ins = np.zeros_like(cls)
instances = []
for shape in shapes:
points = shape['points']
label = shape['label']
group_id = shape.get('group_id')
if group_id is None:
group_id = uuid.uuid1()
shape_type = shape.get('shape_type', None)
cls_name = label
instance = (cls_name, group_id)
if instance not in instances:
instances.append(instance)
ins_id = instances.index(instance) + 1
cls_id = label_name_to_value[cls_name]
mask = shape_to_mask(img_shape[:2], points, shape_type)
cls[mask] = cls_id
ins[mask] = ins_id
return cls, ins
def get_color_map_list(self, num_classes):
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
return color_map
def convert(self, image_dir, json_dir, dataset_save_dir):
"""转换。
Args:
image_dir (str): 图像文件存放的路径。
json_dir (str): 与每张图像对应的json文件的存放路径。
dataset_save_dir (str): 转换后数据集存放路径。
"""
assert osp.exists(image_dir), "The image folder does not exist!"
assert osp.exists(json_dir), "The json folder does not exist!"
assert osp.exists(dataset_save_dir), "The save folder does not exist!"
# Convert the image files.
new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
if osp.exists(new_image_dir):
shutil.rmtree(new_image_dir)
os.makedirs(new_image_dir)
for img_name in os.listdir(image_dir):
if is_pic(img_name):
shutil.copyfile(
osp.join(image_dir, img_name),
osp.join(new_image_dir, img_name))
# Convert the json files.
png_dir = osp.join(dataset_save_dir, "Annotations")
if osp.exists(png_dir):
shutil.rmtree(png_dir)
os.makedirs(png_dir)
self.get_labels2ids(new_image_dir, json_dir)
self.json2png(new_image_dir, json_dir, png_dir)
# Generate the labels.txt
ids2labels = {v : k for k, v in self.labels2ids.items()}
with open(osp.join(dataset_save_dir, 'labels.txt'), 'w') as fw:
for i in range(len(ids2labels)):
fw.write(ids2labels[i] + '\n')
class JingLing2Seg(X2Seg):
"""将使用标注精灵标注的数据集转换为Seg数据集。
"""
def __init__(self):
super(JingLing2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
if 'outputs' in json_info:
for output in json_info['outputs']['object']:
cls_name = output['name']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def json2png(self, image_dir, json_dir, png_dir):
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
data_shapes = []
if 'outputs' in json_info:
for output in json_info['outputs']['object']:
if 'polygon' in output.keys():
polygon = output['polygon']
name = output['name']
points = []
for i in range(1, int(len(polygon) / 2) + 1):
points.append(
[polygon['x' + str(i)], polygon['y' + str(i)]])
shape = {
'label': name,
'points': points,
'shape_type': 'polygon'
}
data_shapes.append(shape)
if 'size' not in json_info:
continue
img_shape = (json_info['size']['height'],
json_info['size']['width'],
json_info['size']['depth'])
lbl, _ = self.shapes_to_label(
img_shape=img_shape,
shapes=data_shapes,
label_name_to_value=self.labels2ids,
)
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
class LabelMe2Seg(X2Seg):
"""将使用LabelMe标注的数据集转换为Seg数据集。
"""
def __init__(self):
super(LabelMe2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info['shapes']:
cls_name = shape['label']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def json2png(self, image_dir, json_dir, png_dir):
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
img_file = osp.join(image_dir, img_name)
img = np.asarray(PIL.Image.open(img_file))
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
lbl, _ = self.shapes_to_label(
img_shape=img.shape,
shapes=json_info['shapes'],
label_name_to_value=self.labels2ids,
)
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
class EasyData2Seg(X2Seg):
"""将使用EasyData标注的分割数据集转换为Seg数据集。
"""
def __init__(self):
super(EasyData2Seg, self).__init__()
def get_labels2ids(self, image_dir, json_dir):
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info["labels"]:
cls_name = shape['name']
if cls_name not in self.labels2ids:
self.labels2ids[cls_name] = len(self.labels2ids)
def mask2polygon(self, mask, label):
contours, hierarchy = cv2.findContours(
(mask).astype(np.uint8), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
segmentation = []
for contour in contours:
contour_list = contour.flatten().tolist()
if len(contour_list) > 4:
points = []
for i in range(0, len(contour_list), 2):
points.append(
[contour_list[i], contour_list[i + 1]])
shape = {
'label': label,
'points': points,
'shape_type': 'polygon'
}
segmentation.append(shape)
return segmentation
def json2png(self, image_dir, json_dir, png_dir):
from pycocotools.mask import decode
color_map = self.get_color_map_list(256)
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
img_file = osp.join(image_dir, img_name)
img = np.asarray(PIL.Image.open(img_file))
img_h = img.shape[0]
img_w = img.shape[1]
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
data_shapes = []
for shape in json_info['labels']:
mask_dict = {}
mask_dict['size'] = [img_h, img_w]
mask_dict['counts'] = shape['mask'].encode()
mask = decode(mask_dict)
polygon = self.mask2polygon(mask, shape["name"])
data_shapes.extend(polygon)
lbl, _ = self.shapes_to_label(
img_shape=img.shape,
shapes=data_shapes,
label_name_to_value=self.labels2ids,
)
out_png_file = osp.join(png_dir, img_name_part + '.png')
if lbl.min() >= 0 and lbl.max() <= 255:
lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
lbl_pil.save(out_png_file)
else:
raise ValueError(
'[%s] Cannot save the pixel-wise class label as PNG. '
'Please consider using the .npy format.' % out_png_file)
paddlex/tools/x2voc.py 0 → 100644
浏览文件 @ b7f90835
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import json
import os
import os.path as osp
import shutil
import numpy as np
from .base import MyEncoder, is_pic, get_encoding
class X2VOC(object):
def __init__(self):
pass
def convert(self, image_dir, json_dir, dataset_save_dir):
"""转换。
Args:
image_dir (str): 图像文件存放的路径。
json_dir (str): 与每张图像对应的json文件的存放路径。
dataset_save_dir (str): 转换后数据集存放路径。
"""
assert osp.exists(image_dir), "The image folder does not exist!"
assert osp.exists(json_dir), "The json folder does not exist!"
assert osp.exists(dataset_save_dir), "The save folder does not exist!"
# Convert the image files.
new_image_dir = osp.join(dataset_save_dir, "JPEGImages")
if osp.exists(new_image_dir):
shutil.rmtree(new_image_dir)
os.makedirs(new_image_dir)
for img_name in os.listdir(image_dir):
if is_pic(img_name):
shutil.copyfile(
osp.join(image_dir, img_name),
osp.join(new_image_dir, img_name))
# Convert the json files.
xml_dir = osp.join(dataset_save_dir, "Annotations")
if osp.exists(xml_dir):
shutil.rmtree(xml_dir)
os.makedirs(xml_dir)
self.json2xml(new_image_dir, json_dir, xml_dir)
class LabelMe2VOC(X2VOC):
"""将使用LabelMe标注的数据集转换为VOC数据集。
"""
def __init__(self):
pass
def json2xml(self, image_dir, json_dir, xml_dir):
import xml.dom.minidom as minidom
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
xml_doc = minidom.Document()
root = xml_doc.createElement("annotation")
xml_doc.appendChild(root)
node_folder = xml_doc.createElement("folder")
node_folder.appendChild(xml_doc.createTextNode("JPEGImages"))
root.appendChild(node_folder)
node_filename = xml_doc.createElement("filename")
node_filename.appendChild(xml_doc.createTextNode(img_name))
root.appendChild(node_filename)
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
h = json_info["imageHeight"]
w = json_info["imageWidth"]
node_size = xml_doc.createElement("size")
node_width = xml_doc.createElement("width")
node_width.appendChild(xml_doc.createTextNode(str(w)))
node_size.appendChild(node_width)
node_height = xml_doc.createElement("height")
node_height.appendChild(xml_doc.createTextNode(str(h)))
node_size.appendChild(node_height)
node_depth = xml_doc.createElement("depth")
node_depth.appendChild(xml_doc.createTextNode(str(3)))
node_size.appendChild(node_depth)
root.appendChild(node_size)
for shape in json_info["shapes"]:
if shape["shape_type"] != "rectangle":
continue
label = shape["label"]
(xmin, ymin), (xmax, ymax) = shape["points"]
xmin, xmax = sorted([xmin, xmax])
ymin, ymax = sorted([ymin, ymax])
node_obj = xml_doc.createElement("object")
node_name = xml_doc.createElement("name")
node_name.appendChild(xml_doc.createTextNode(label))
node_obj.appendChild(node_name)
node_diff = xml_doc.createElement("difficult")
node_diff.appendChild(xml_doc.createTextNode(str(0)))
node_obj.appendChild(node_diff)
node_box = xml_doc.createElement("bndbox")
node_xmin = xml_doc.createElement("xmin")
node_xmin.appendChild(xml_doc.createTextNode(str(xmin)))
node_box.appendChild(node_xmin)
node_ymin = xml_doc.createElement("ymin")
node_ymin.appendChild(xml_doc.createTextNode(str(ymin)))
node_box.appendChild(node_ymin)
node_xmax = xml_doc.createElement("xmax")
node_xmax.appendChild(xml_doc.createTextNode(str(xmax)))
node_box.appendChild(node_xmax)
node_ymax = xml_doc.createElement("ymax")
node_ymax.appendChild(xml_doc.createTextNode(str(ymax)))
node_box.appendChild(node_ymax)
node_obj.appendChild(node_box)
root.appendChild(node_obj)
with open(osp.join(xml_dir, img_name_part + ".xml"), 'w') as fxml:
xml_doc.writexml(fxml, indent='\t', addindent='\t', newl='\n', encoding="utf-8")
class EasyData2VOC(X2VOC):
"""将使用EasyData标注的分割数据集转换为VOC数据集。
"""
def __init__(self):
pass
def json2xml(self, image_dir, json_dir, xml_dir):
import xml.dom.minidom as minidom
for img_name in os.listdir(image_dir):
img_name_part = osp.splitext(img_name)[0]
json_file = osp.join(json_dir, img_name_part + ".json")
if not osp.exists(json_file):
os.remove(os.remove(osp.join(image_dir, img_name)))
continue
xml_doc = minidom.Document()
root = xml_doc.createElement("annotation")
xml_doc.appendChild(root)
node_folder = xml_doc.createElement("folder")
node_folder.appendChild(xml_doc.createTextNode("JPEGImages"))
root.appendChild(node_folder)
node_filename = xml_doc.createElement("filename")
node_filename.appendChild(xml_doc.createTextNode(img_name))
root.appendChild(node_filename)
img = cv2.imread(osp.join(image_dir, img_name))
h = img.shape[0]
w = img.shape[1]
node_size = xml_doc.createElement("size")
node_width = xml_doc.createElement("width")
node_width.appendChild(xml_doc.createTextNode(str(w)))
node_size.appendChild(node_width)
node_height = xml_doc.createElement("height")
node_height.appendChild(xml_doc.createTextNode(str(h)))
node_size.appendChild(node_height)
node_depth = xml_doc.createElement("depth")
node_depth.appendChild(xml_doc.createTextNode(str(3)))
node_size.appendChild(node_depth)
root.appendChild(node_size)
with open(json_file, mode="r", \
encoding=get_encoding(json_file)) as j:
json_info = json.load(j)
for shape in json_info["labels"]:
label = shape["name"]
xmin = shape["x1"]
ymin = shape["y1"]
xmax = shape["x2"]
ymax = shape["y2"]
node_obj = xml_doc.createElement("object")
node_name = xml_doc.createElement("name")
node_name.appendChild(xml_doc.createTextNode(label))
node_obj.appendChild(node_name)
node_diff = xml_doc.createElement("difficult")
node_diff.appendChild(xml_doc.createTextNode(str(0)))
node_obj.appendChild(node_diff)
node_box = xml_doc.createElement("bndbox")
node_xmin = xml_doc.createElement("xmin")
node_xmin.appendChild(xml_doc.createTextNode(str(xmin)))
node_box.appendChild(node_xmin)
node_ymin = xml_doc.createElement("ymin")
node_ymin.appendChild(xml_doc.createTextNode(str(ymin)))
node_box.appendChild(node_ymin)
node_xmax = xml_doc.createElement("xmax")
node_xmax.appendChild(xml_doc.createTextNode(str(xmax)))
node_box.appendChild(node_xmax)
node_ymax = xml_doc.createElement("ymax")
node_ymax.appendChild(xml_doc.createTextNode(str(ymax)))
node_box.appendChild(node_ymax)
node_obj.appendChild(node_box)
root.appendChild(node_obj)
with open(osp.join(xml_dir, img_name_part + ".xml"), 'w') as fxml:
xml_doc.writexml(fxml, indent='\t', addindent='\t', newl='\n', encoding="utf-8")
paddlex/utils/utils.py
浏览文件 @ b7f90835
......@@ -31,18 +31,7 @@ def seconds_to_hms(seconds):
return hms_str
def setting_environ_flags():
if 'FLAGS_eager_delete_tensor_gb' not in os.environ:
os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
if 'FLAGS_allocator_strategy' not in os.environ:
os.environ['FLAGS_allocator_strategy'] = 'auto_growth'
if "CUDA_VISIBLE_DEVICES" in os.environ:
if os.environ["CUDA_VISIBLE_DEVICES"].count("-1") > 0:
os.environ["CUDA_VISIBLE_DEVICES"] = ""
def get_environ_info():
setting_environ_flags()
import paddle.fluid as fluid
info = dict()
info['place'] = 'cpu'
......@@ -181,11 +170,85 @@ def load_pdparams(exe, main_prog, model_dir):
len(vars_to_load), model_dir))
def load_pretrain_weights(exe, main_prog, weights_dir, fuse_bn=False):
def is_persistable(var):
import paddle.fluid as fluid
from paddle.fluid.proto.framework_pb2 import VarType
if var.desc.type() == fluid.core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == fluid.core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == fluid.core.VarDesc.VarType.READER:
return False
return var.persistable
def is_belong_to_optimizer(var):
import paddle.fluid as fluid
from paddle.fluid.proto.framework_pb2 import VarType
if not (isinstance(var, fluid.framework.Parameter)
or var.desc.need_check_feed()):
return is_persistable(var)
return False
def load_pdopt(exe, main_prog, model_dir):
import paddle.fluid as fluid
optimizer_var_list = list()
vars_to_load = list()
import pickle
with open(osp.join(model_dir, 'model.pdopt'), 'rb') as f:
opt_dict = pickle.load(f) if six.PY2 else pickle.load(
f, encoding='latin1')
optimizer_var_list = list(
filter(is_belong_to_optimizer, main_prog.list_vars()))
exception_message = "the training process can not be resumed due to optimizer set now and last time is different. Recommend to use `pretrain_weights` instead of `resume_checkpoint`"
if len(optimizer_var_list) > 0:
for var in optimizer_var_list:
if var.name not in opt_dict:
raise Exception(
"{} is not in saved paddlex optimizer, {}".format(
var.name, exception_message))
if var.shape != opt_dict[var.name].shape:
raise Exception(
"Shape of optimizer variable {} doesn't match.(Last: {}, Now: {}), {}"
.format(var.name, opt_dict[var.name].shape,
var.shape), exception_message)
optimizer_varname_list = [var.name for var in optimizer_var_list]
for k, v in opt_dict.items():
if k not in optimizer_varname_list:
raise Exception(
"{} in saved paddlex optimizer is not in the model, {}".
format(k, exception_message))
fluid.io.set_program_state(main_prog, opt_dict)
if len(optimizer_var_list) == 0:
raise Exception(
"There is no optimizer parameters in the model, please set the optimizer!"
)
else:
logging.info(
"There are {} optimizer parameters in {} are loaded.".format(
len(optimizer_var_list), model_dir))
def load_pretrain_weights(exe,
main_prog,
weights_dir,
fuse_bn=False,
resume=False):
if not osp.exists(weights_dir):
raise Exception("Path {} not exists.".format(weights_dir))
if osp.exists(osp.join(weights_dir, "model.pdparams")):
return load_pdparams(exe, main_prog, weights_dir)
load_pdparams(exe, main_prog, weights_dir)
if resume:
if osp.exists(osp.join(weights_dir, "model.pdopt")):
load_pdopt(exe, main_prog, weights_dir)
else:
raise Exception(
"Optimizer file {} does not exist. Stop resumming training. Recommend to use `pretrain_weights` instead of `resume_checkpoint`"
.format(osp.join(weights_dir, "model.pdopt")))
return
import paddle.fluid as fluid
vars_to_load = list()
for var in main_prog.list_vars():
......@@ -220,6 +283,45 @@ def load_pretrain_weights(exe, main_prog, weights_dir, fuse_bn=False):
len(vars_to_load), weights_dir))
if fuse_bn:
fuse_bn_weights(exe, main_prog, weights_dir)
if resume:
exception_message = "the training process can not be resumed due to optimizer set now and last time is different. Recommend to use `pretrain_weights` instead of `resume_checkpoint`"
optimizer_var_list = list(
filter(is_belong_to_optimizer, main_prog.list_vars()))
if len(optimizer_var_list) > 0:
for var in optimizer_var_list:
if not osp.exists(osp.join(weights_dir, var.name)):
raise Exception(
"Optimizer parameter {} doesn't exist, {}".format(
osp.join(weights_dir, var.name),
exception_message))
pretrained_shape = parse_param_file(
osp.join(weights_dir, var.name))
actual_shape = tuple(var.shape)
if pretrained_shape != actual_shape:
raise Exception(
"Shape of optimizer variable {} doesn't match.(Last: {}, Now: {}), {}"
.format(var.name, pretrained_shape,
actual_shape), exception_message)
optimizer_varname_list = [var.name for var in optimizer_var_list]
if os.exists(osp.join(weights_dir, 'learning_rate')
) and 'learning_rate' not in optimizer_varname_list:
raise Exception(
"Optimizer parameter {}/learning_rate is not in the model, {}"
.format(weights_dir, exception_message))
fluid.io.load_vars(
executor=exe,
dirname=weights_dir,
main_program=main_prog,
vars=optimizer_var_list)
if len(optimizer_var_list) == 0:
raise Exception(
"There is no optimizer parameters in the model, please set the optimizer!"
)
else:
logging.info(
"There are {} optimizer parameters in {} are loaded.".format(
len(optimizer_var_list), weights_dir))
class EarlyStop:
......
setup.py
浏览文件 @ b7f90835
......@@ -19,7 +19,7 @@ long_description = "PaddleX. A end-to-end deeplearning model development toolkit
setuptools.setup(
name="paddlex",
version='0.1.6',
version='0.1.7',
author="paddlex",
author_email="paddlex@baidu.com",
description=long_description,
......@@ -29,9 +29,8 @@ setuptools.setup(
packages=setuptools.find_packages(),
setup_requires=['cython', 'numpy', 'sklearn'],
install_requires=[
"pycocotools;platform_system!='Windows'",
'pyyaml', 'colorama', 'tqdm', 'visualdl==1.3.0',
'paddleslim==1.0.1', 'paddlehub>=1.6.2'
"pycocotools;platform_system!='Windows'", 'pyyaml', 'colorama', 'tqdm',
'visualdl==1.3.0', 'paddleslim==1.0.1'
],
classifiers=[
"Programming Language :: Python :: 3",
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或