some details modify

docs/apis/slim.md

@@ -9,7 +9,7 @@ paddlex.slim.cal_params_sensitivities(model, save_file, eval_dataset, batch_size
 1. 获取模型中可裁剪卷积Kernel的名称。
 2. 计算每个可裁剪卷积Kernel不同裁剪率下的敏感度。
 【注意】卷积的敏感度是指在不同裁剪率下评估数据集预测精度的损失，通过得到的敏感度，可以决定最终模型需要裁剪的参数列表和各裁剪参数对应的裁剪率。  
-[查看使用示例](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/compress/classification/cal_sensitivities_file.py#L33) [查看裁剪教程](../tutorials/compress/classification.md)
+[查看使用示例](https://github.com/PaddlePaddle/PaddleX/blob/develop/tutorials/compress/classification/cal_sensitivities_file.py#L33)
 
 ### 参数
 

paddlex/cv/models/base.py

@@ -192,22 +192,31 @@ class BaseAPI:
                 if self.model_type == 'classifier':
                     if pretrain_weights not in ['IMAGENET']:
                         logging.warning(
-                            "Pretrain_weights for classifier should be defined as directory path or parameter file or 'IMAGENET' or None, but it is {}, so we force to set it as 'IMAGENET'".
+                            "Path of pretrain_weights('{}') is not exists!".
                             format(pretrain_weights))
+                        logging.warning(
+                            "Pretrain_weights will be forced to set as 'IMAGENET', if you don't want to use pretrain weights, set pretrain_weights=None."
+                        )
                         pretrain_weights = 'IMAGENET'
                 elif self.model_type == 'detector':
                     if pretrain_weights not in ['IMAGENET', 'COCO']:
                         logging.warning(
-                            "Pretrain_weights for detector should be defined as directory path or parameter file or 'IMAGENET' or 'COCO' or None, but it is {}, so we force to set it as 'IMAGENET'".
+                            "Path of pretrain_weights('{}') is not exists!".
                             format(pretrain_weights))
+                        logging.warning(
+                            "Pretrain_weights will be forced to set as 'IMAGENET', if you don't want to use pretrain weights, set pretrain_weights=None."
+                        )
                         pretrain_weights = 'IMAGENET'
                 elif self.model_type == 'segmenter':
                     if pretrain_weights not in [
                             'IMAGENET', 'COCO', 'CITYSCAPES'
                     ]:
                         logging.warning(
-                            "Pretrain_weights for segmenter should be defined as directory path or parameter file or 'IMAGENET' or 'COCO' or 'CITYSCAPES', but it is {}, so we force to set it as 'IMAGENET'".
+                            "Path of pretrain_weights('{}') is not exists!".
                             format(pretrain_weights))
+                        logging.warning(
+                            "Pretrain_weights will be forced to set as 'IMAGENET', if you don't want to use pretrain weights, set pretrain_weights=None."
+                        )
                         pretrain_weights = 'IMAGENET'
             if hasattr(self, 'backbone'):
                 backbone = self.backbone
@@ -237,8 +246,8 @@ class BaseAPI:
             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)
+            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
@@ -342,9 +351,7 @@ class BaseAPI:
         logging.info("Model saved in {}.".format(save_dir))
 
     def export_inference_model(self, save_dir):
-        test_input_names = [
-            var.name for var in list(self.test_inputs.values())
-        ]
+        test_input_names = [var.name for var in list(self.test_inputs.values())]
         test_outputs = list(self.test_outputs.values())
         with fluid.scope_guard(self.scope):
             if self.__class__.__name__ == 'MaskRCNN':
@@ -382,8 +389,7 @@ class BaseAPI:
 
         # 模型保存成功的标志
         open(osp.join(save_dir, '.success'), 'w').close()
-        logging.info("Model for inference deploy saved in {}.".format(
-            save_dir))
+        logging.info("Model for inference deploy saved in {}.".format(save_dir))
 
     def train_loop(self,
                    num_epochs,
@@ -510,13 +516,11 @@ class BaseAPI:
                         eta = ((num_epochs - i) * total_num_steps - step - 1
                                ) * avg_step_time
                     if time_eval_one_epoch is not None:
-                        eval_eta = (
-                            total_eval_times - i // save_interval_epochs
-                        ) * time_eval_one_epoch
+                        eval_eta = (total_eval_times - i // save_interval_epochs
+                                    ) * time_eval_one_epoch
                     else:
-                        eval_eta = (
-                            total_eval_times - i // save_interval_epochs
-                        ) * total_num_steps_eval * avg_step_time
+                        eval_eta = (total_eval_times - i // save_interval_epochs
+                                    ) * total_num_steps_eval * avg_step_time
                     eta_str = seconds_to_hms(eta + eval_eta)
 
                     logging.info(

paddlex/cv/models/load_model.py

@@ -26,6 +26,8 @@ from paddlex.cv.transforms import build_transforms, build_transforms_v1
 
 def load_model(model_dir, fixed_input_shape=None):
     model_scope = fluid.Scope()
+    if not osp.exists(model_dir):
+        logging.error("model_dir '{}' is not exists!".format(model_dir))
     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:
@@ -101,8 +103,8 @@ def load_model(model_dir, fixed_input_shape=None):
                 model.model_type, info['Transforms'], info['BatchTransforms'])
             model.eval_transforms = copy.deepcopy(model.test_transforms)
         else:
-            model.test_transforms = build_transforms(
-                model.model_type, info['Transforms'], to_rgb)
+            model.test_transforms = build_transforms(model.model_type,
+                                                     info['Transforms'], to_rgb)
             model.eval_transforms = copy.deepcopy(model.test_transforms)
 
     if '_Attributes' in info:

paddlex/cv/models/utils/pretrain_weights.py

@@ -144,8 +144,7 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir):
             logging.warning(warning_info.format(class_name, flag, 'IMAGENET'))
             flag = 'IMAGENET'
         elif class_name == 'FastSCNN':
-            logging.warning(
-                warning_info.format(class_name, flag, 'CITYSCAPES'))
+            logging.warning(warning_info.format(class_name, flag, 'CITYSCAPES'))
             flag = 'CITYSCAPES'
     elif flag == 'CITYSCAPES':
         model_name = '{}_{}'.format(class_name, backbone)
@@ -168,8 +167,7 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir):
             logging.warning(warning_info.format(class_name, flag, 'COCO'))
             flag = 'COCO'
         elif class_name == 'FastSCNN':
-            logging.warning(
-                warning_info.format(class_name, flag, 'CITYSCAPES'))
+            logging.warning(warning_info.format(class_name, flag, 'CITYSCAPES'))
             flag = 'CITYSCAPES'
 
     if flag == 'IMAGENET':
@@ -196,8 +194,14 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir):
         #            paddlex.utils.download_and_decompress(url, path=new_save_dir)
         #            return osp.join(new_save_dir, fname)
         try:
+            logging.info(
+                "Connecting PaddleHub server to get pretrain weights...")
             hub.download(backbone, save_path=new_save_dir)
         except Exception as e:
+            logging.error(
+                "Couldn't download pretrain weight, you can download it manualy from {} (decompress the file if it is a compressed file), and set pretrain weights by your self".
+                format(image_pretrain[backbone]),
+                exit=False)
             if isinstance(e, hub.ResourceNotFoundError):
                 raise Exception("Resource for backbone {} not found".format(
                     backbone))
@@ -224,8 +228,14 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir):
         #        paddlex.utils.download_and_decompress(url, path=new_save_dir)
         #        return osp.join(new_save_dir, fname)
         try:
+            logging.info(
+                "Connecting PaddleHub server to get pretrain weights...")
             hub.download(backbone, save_path=new_save_dir)
         except Exception as e:
+            logging.error(
+                "Couldn't download pretrain weight, you can download it manualy from {} (decompress the file if it is a compressed file), and set pretrain weights by your self".
+                format(url),
+                exit=False)
             if isinstance(hub.ResourceNotFoundError):
                 raise Exception("Resource for backbone {} not found".format(
                     backbone))
@@ -238,6 +248,5 @@ def get_pretrain_weights(flag, class_name, backbone, save_dir):
                     "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' or 'Cityscapes' (download pretrain weights automatically)."
-        )
+        logging.error("Path of retrain weights '{}' is not exists!".format(
+            flag))

tutorials/interpret/lime.py

@@ -1,8 +1,3 @@
-import os
-# 选择使用0号卡
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-import os.path as osp
 import paddlex as pdx
 
 # 下载和解压Imagenet果蔬分类数据集
@@ -18,6 +13,4 @@ model = pdx.load_model('mini_imagenet_veg_mobilenetv2')
 
 # 可解释性可视化
 pdx.interpret.lime(
-         'mini_imagenet_veg/mushroom/n07734744_1106.JPEG', 
-          model,
-          save_dir='./')
+    'mini_imagenet_veg/mushroom/n07734744_1106.JPEG', model, save_dir='./')