optimize code structure for TensorRT(deploy)

deploy/cpp/CMakeLists.txt

@@ -114,8 +114,6 @@ if (NOT WIN32)
   if (WITH_TENSORRT AND WITH_GPU)
       include_directories("${TENSORRT_DIR}/include")
       link_directories("${TENSORRT_DIR}/lib")
-      #include_directories("${PADDLE_DIR}/third_party/install/tensorrt/include")
-      #link_directories("${PADDLE_DIR}/third_party/install/tensorrt/lib")
   endif()
 endif(NOT WIN32)
 
@@ -172,7 +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")
@@ -199,8 +197,6 @@ if(WITH_GPU)
     if (WITH_TENSORRT)
       set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
       set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
-      #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})
     endif()
     set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
     set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
@@ -216,7 +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})
@@ -256,4 +252,3 @@ if (WIN32 AND WITH_MKL)
     )
 
 endif()
-

deploy/cpp/include/paddlex/transforms.h

@@ -152,18 +152,19 @@ 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>();
       } else if (item["target_size"].IsSequence()) {
-        width_ = item["target_size"].as<std::vector<int>>()[1];
-        height_ = item["target_size"].as<std::vector<int>>()[0];
+        width_ = item["target_size"].as<std::vector<int>>()[0];
+        height_ = item["target_size"].as<std::vector<int>>()[1];
       }
     }
     if (item["im_padding_value"].IsDefined()) {

deploy/cpp/scripts/build.sh

@@ -6,6 +6,9 @@ WITH_TENSORRT=OFF
 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=/path/to/cuda/lib/
 # CUDNN 的 lib 路径
@@ -24,6 +27,7 @@ cmake .. \
     -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/detector.cpp

@@ -69,7 +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
@@ -92,7 +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

@@ -39,11 +39,11 @@ void Model::create_predictor(const std::string& model_dir,
   // 开启内存优化
   config.EnableMemoryOptim();
   if (use_trt){
-    config.EnableTensorRtEngine(1 << 20      /* workspace_size*/,  
-                        32        /* max_batch_size*/,  
+    config.EnableTensorRtEngine(1 << 20      /* workspace_size*/,
+                        32        /* max_batch_size*/,
                         20                 /* min_subgraph_size*/,
                         paddle::AnalysisConfig::Precision::kFloat32 /* precision*/,
-                        false             /* use_static*/,
+                        true             /* use_static*/,
                         false             /* use_calib_mode*/);
   }
   predictor_ = std::move(CreatePaddlePredictor(config));

deploy/cpp/src/transforms.cpp

@@ -92,15 +92,16 @@ bool Padding::Run(cv::Mat* im, ImageBlob* data) {
 
   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

docs/deploy/deploy.md

@@ -14,6 +14,12 @@
 paddlex --export_inference --model_dir=./garbage_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]，需要注意的是分类模型请保持于训练时输入的shape一致。
+
+```
+paddlex --export_inference --model_dir=./garbage_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)

docs/deploy/deploy_cpp_linux.md

@@ -39,18 +39,24 @@ 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
 # 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
 
@@ -61,7 +67,9 @@ cd build
 cmake .. \
     -DWITH_GPU=${WITH_GPU} \
     -DWITH_TENSORRT=${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}
@@ -83,6 +91,7 @@ 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 |
 
@@ -113,4 +122,3 @@ make
 ./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。
-

paddlex/command.py

@@ -33,7 +33,7 @@ def arg_parser():
         "--fixed_input_shape",
         "-fs",
         default=None,
-        help="export inference model with fixed input shape(TensorRT need)")
+        help="export inference model with fixed input shape:[w,h]")
     return parser
 
 
@@ -58,10 +58,11 @@ def main():
         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"
         fixed_input_shape = eval(args.fixed_input_shape)
-        assert len(fixed_input_shape) == 2, "len of fixed input shape must == 2"
+        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, fixed_input_shape)
+        model.export_inference_model(args.save_dir)
 
 
 if __name__ == "__main__":

paddlex/cv/models/base.py

@@ -316,25 +316,6 @@ class BaseAPI:
         model_info['_ModelInputsOutputs']['test_outputs'] = [
             [k, v.name] for k, v in self.test_outputs.items()
         ]
-        resize = {'ResizeByShort': {}}
-        padding = {'Padding':{}}
-
-        if model_info['_Attributes']['model_type'] == 'classifier':
-            crop_size = 0
-            for transform in model_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)
-            model_info['Transforms'].append(resize)
-            model_info['Transforms'].append(padding)
         with open(
                 osp.join(save_dir, 'model.yml'), encoding='utf-8',
                 mode='w') as f:

paddlex/cv/models/classifier.py

@@ -35,10 +35,9 @@ class BaseClassifier(BaseAPI):
                           'MobileNetV1', 'MobileNetV2', 'Xception41',
                           'Xception65', 'Xception71']。默认为'ResNet50'。
         num_classes (int): 类别数。默认为1000。
-        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。
     """
 
-    def __init__(self, model_name='ResNet50', num_classes=1000, fixed_input_shape=None):
+    def __init__(self, model_name='ResNet50', num_classes=1000):
         self.init_params = locals()
         super(BaseClassifier, self).__init__('classifier')
         if not hasattr(paddlex.cv.nets, str.lower(model_name)):
@@ -47,11 +46,13 @@ class BaseClassifier(BaseAPI):
         self.model_name = model_name
         self.labels = None
         self.num_classes = num_classes
-        self.fixed_input_shape = fixed_input_shape
+        self.fixed_input_shape = None
 
     def build_net(self, mode='train'):
         if self.fixed_input_shape is not None:
-            input_shape =[None, 3, self.fixed_input_shape[0], self.fixed_input_shape[1]]
+            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:

paddlex/cv/models/deeplabv3p.py

@@ -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。
-        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。
         ValueError: backbone取值不在['Xception65', 'Xception41', 'MobileNetV2_x0.25',
@@ -69,8 +68,7 @@ class DeepLabv3p(BaseAPI):
                  use_bce_loss=False,
                  use_dice_loss=False,
                  class_weight=None,
-                 ignore_index=255,
-                 fixed_input_shape=None):
+                 ignore_index=255):
         self.init_params = locals()
         super(DeepLabv3p, self).__init__('segmenter')
         # dice_loss或bce_loss只适用两类分割中
@@ -119,7 +117,7 @@ class DeepLabv3p(BaseAPI):
         self.enable_decoder = enable_decoder
         self.labels = None
         self.sync_bn = True
-        self.fixed_input_shape = fixed_input_shape
+        self.fixed_input_shape = None
 
     def _get_backbone(self, backbone):
         def mobilenetv2(backbone):
@@ -185,7 +183,7 @@ class DeepLabv3p(BaseAPI):
             use_dice_loss=self.use_dice_loss,
             class_weight=self.class_weight,
             ignore_index=self.ignore_index,
-            fixed_input_shape = self.fixed_input_shape)
+            fixed_input_shape=self.fixed_input_shape)
         inputs = model.generate_inputs()
         model_out = model.build_net(inputs)
         outputs = OrderedDict()

paddlex/cv/models/faster_rcnn.py

@@ -44,8 +44,7 @@ class FasterRCNN(BaseAPI):
                  backbone='ResNet50',
                  with_fpn=True,
                  aspect_ratios=[0.5, 1.0, 2.0],
-                 anchor_sizes=[32, 64, 128, 256, 512],
-                fixed_input_shape=None):
+                 anchor_sizes=[32, 64, 128, 256, 512]):
         self.init_params = locals()
         super(FasterRCNN, self).__init__('detector')
         backbones = [
@@ -59,7 +58,7 @@ class FasterRCNN(BaseAPI):
         self.aspect_ratios = aspect_ratios
         self.anchor_sizes = anchor_sizes
         self.labels = None
-        self.fixed_input_shape = fixed_input_shape
+        self.fixed_input_shape = None
 
     def _get_backbone(self, backbone_name):
         norm_type = None
@@ -113,7 +112,7 @@ class FasterRCNN(BaseAPI):
             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,
-            fixed_input_shape = self.fixed_input_shape)
+            fixed_input_shape=self.fixed_input_shape)
         inputs = model.generate_inputs()
         if mode == 'train':
             model_out = model.build_net(inputs)

paddlex/cv/models/load_model.py

@@ -39,13 +39,12 @@ def load_model(model_dir, fixed_input_shape=None):
         raise Exception("There's no attribute {} in paddlex.cv.models".format(
             info['Model']))
 
-    info['_init_params']['fixed_input_shape'] = fixed_input_shape
-
     if info['_Attributes']['model_type'] == 'classifier':
         model = paddlex.cv.models.BaseClassifier(**info['_init_params'])
     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()
@@ -80,6 +79,8 @@ def load_model(model_dir, fixed_input_shape=None):
             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:
@@ -104,6 +105,34 @@ def load_model(model_dir, fixed_input_shape=None):
     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:
+            print("*" * 10)
+            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

@@ -36,7 +36,6 @@ class MaskRCNN(FasterRCNN):
         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]。
-        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。
     """
 
     def __init__(self,
@@ -44,8 +43,7 @@ class MaskRCNN(FasterRCNN):
                  backbone='ResNet50',
                  with_fpn=True,
                  aspect_ratios=[0.5, 1.0, 2.0],
-                 anchor_sizes=[32, 64, 128, 256, 512],
-                 fixed_input_shape=None):
+                 anchor_sizes=[32, 64, 128, 256, 512]):
         self.init_params = locals()
         backbones = [
             'ResNet18', 'ResNet50', 'ResNet50vd', 'ResNet101', 'ResNet101vd'
@@ -62,7 +60,7 @@ class MaskRCNN(FasterRCNN):
             self.mask_head_resolution = 28
         else:
             self.mask_head_resolution = 14
-        self.fixed_input_shape = fixed_input_shape
+        self.fixed_input_shape = None
 
     def build_net(self, mode='train'):
         train_pre_nms_top_n = 2000 if self.with_fpn else 12000
@@ -77,7 +75,7 @@ class MaskRCNN(FasterRCNN):
             test_pre_nms_top_n=test_pre_nms_top_n,
             num_convs=num_convs,
             mask_head_resolution=self.mask_head_resolution,
-            fixed_input_shape = self.fixed_input_shape)
+            fixed_input_shape=self.fixed_input_shape)
         inputs = model.generate_inputs()
         if mode == 'train':
             model_out = model.build_net(inputs)

paddlex/cv/models/yolo_v3.py

@@ -60,8 +60,7 @@ class YOLOv3(BaseAPI):
                  label_smooth=False,
                  train_random_shapes=[
                      320, 352, 384, 416, 448, 480, 512, 544, 576, 608
-                 ],
-                 fixed_input_shape=None):
+                 ]):
         self.init_params = locals()
         super(YOLOv3, self).__init__('detector')
         backbones = [
@@ -81,7 +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 = fixed_input_shape
+        self.fixed_input_shape = None
 
     def _get_backbone(self, backbone_name):
         if backbone_name == 'DarkNet53':
@@ -116,7 +115,7 @@ class YOLOv3(BaseAPI):
             nms_keep_topk=self.nms_keep_topk,
             nms_iou_threshold=self.nms_iou_threshold,
             train_random_shapes=self.train_random_shapes,
-            fixed_input_shape = self.fixed_input_shape)
+            fixed_input_shape=self.fixed_input_shape)
         inputs = model.generate_inputs()
         model_out = model.build_net(inputs)
         outputs = OrderedDict([('bbox', model_out)])

paddlex/cv/nets/detection/faster_rcnn.py

@@ -223,7 +223,9 @@ class FasterRCNN(object):
         inputs = OrderedDict()
 
         if self.fixed_input_shape is not None:
-            input_shape =[None, 3, self.fixed_input_shape[0], self.fixed_input_shape[1]]
+            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:

paddlex/cv/nets/detection/mask_rcnn.py

@@ -310,7 +310,9 @@ class MaskRCNN(object):
         inputs = OrderedDict()
 
         if self.fixed_input_shape is not None:
-            input_shape =[None, 3, self.fixed_input_shape[0], self.fixed_input_shape[1]]
+            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:

paddlex/cv/nets/detection/yolo_v3.py

@@ -250,7 +250,9 @@ class YOLOv3:
     def generate_inputs(self):
         inputs = OrderedDict()
         if self.fixed_input_shape is not None:
-            input_shape =[None, 3, self.fixed_input_shape[0], self.fixed_input_shape[1]]
+            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:

paddlex/cv/nets/segmentation/deeplabv3p.py

@@ -315,7 +315,9 @@ class DeepLabv3p(object):
         inputs = OrderedDict()
 
         if self.fixed_input_shape is not None:
-            input_shape =[None, 3, self.fixed_input_shape[0], self.fixed_input_shape[1]]
+            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:

paddlex/cv/nets/segmentation/unet.py

@@ -231,7 +231,9 @@ class UNet(object):
         inputs = OrderedDict()
 
         if self.fixed_input_shape is not None:
-            input_shape =[None, 3, self.fixed_input_shape[0], self.fixed_input_shape[1]]
+            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:

paddlex/cv/transforms/det_transforms.py

@@ -211,7 +211,7 @@ class Padding:
         target_size (int|list): 填充后的图像长、宽，默认为1。
     """
 
-    def __init__(self, coarsest_stride=1, target_size=None):
+    def __init__(self, coarsest_stride=1, target_size=1):
         self.coarsest_stride = coarsest_stride
         self.target_size = target_size
 
@@ -233,11 +233,12 @@ class Padding:
             ValueError: target_size小于原图的大小。
         """
 
-        if self.coarsest_stride == 1 and self.target_size is None:
-            if label_info is None:
-                return (im, im_info)
-            else:
-                return (im, im_info, label_info)
+        if self.coarsest_stride == 1:
+            if isinstance(self.target_size, int) and self.target_size == 1:
+                if label_info is None:
+                    return (im, im_info)
+                else:
+                    return (im, im_info, label_info)
         if im_info is None:
             im_info = dict()
         if not isinstance(im, np.ndarray):
@@ -250,18 +251,17 @@ class Padding:
                 np.ceil(im_h / self.coarsest_stride) * self.coarsest_stride)
             padding_im_w = int(
                 np.ceil(im_w / self.coarsest_stride) * self.coarsest_stride)
-        if self.target_size is not None:
-            if isinstance(self.target_size, int):
-                padding_im_h = self.target_size
-                padding_im_w = self.target_size
-            else:
-                padding_im_h = self.target_size[0]
-                padding_im_w = self.target_size[1]
-            pad_height = padding_im_h - im_h
-            pad_width = padding_im_w - im_w
 
-            if pad_height < 0 or pad_width < 0:
-                raise ValueError(
+        if isinstance(self.target_size, int) and self.target_size != 1:
+            padding_im_h = self.target_size
+            padding_im_w = self.target_size
+        elif isinstance(self.target_size, list):
+            padding_im_w = self.target_size[0]
+            padding_im_h = self.target_size[1]
+        pad_height = padding_im_h - im_h
+        pad_width = padding_im_w - im_w
+        if pad_height < 0 or pad_width < 0:
+            raise ValueError(
                 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
                 .format(im_w, im_h, padding_im_w, padding_im_h))
         padding_im = np.zeros((padding_im_h, padding_im_w, im_c),
@@ -562,7 +562,7 @@ class RandomDistort:
             params = params_dict[ops[id].__name__]
             prob = prob_dict[ops[id].__name__]
             params['im'] = im
-            
+
             if np.random.uniform(0, 1) < prob:
                 im = ops[id](**params)
         if label_info is None: