modify warmup error info

docs/appendix/parameters.md

@@ -38,7 +38,7 @@ Batch Size指模型在训练过程中，一次性处理的样本数量, 如若
 
 `lr_decay_epochs`用于让学习率在模型训练后期逐步衰减，它一般是一个list，如[6, 8, 10]，表示学习率在第6个epoch时衰减一次，第8个epoch时再衰减一次，第10个epoch时再衰减一次。每次学习率衰减为之前的学习率*lr_decay_gamma
 
-### PaddleX中的warmup_steps和lr_decay_epochs
+### Notice
 
 在PaddleX中，限制warmup需要在第一个学习率decay衰减前结束，因此要满足下面的公式
 ```

paddlex/cv/models/classifier.py

@@ -96,10 +96,12 @@ class BaseClassifier(BaseAPI):
                     "In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
                     exit=False)
                 logging.error(
-                    "See this doc for more information: xxxx", exit=False)
+                    "See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
+                    exit=False)
                 logging.error(
-                    "warmup_steps should less than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
-                    format(lr_decay_epochs[0] * num_steps_each_epoch))
+                    "warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
+                    format(lr_decay_epochs[0] * num_steps_each_epoch,
+                           warmup_steps // num_steps_each_epoch))
 
             lr_decay = fluid.layers.linear_lr_warmup(
                 learning_rate=lr_decay,

paddlex/cv/models/faster_rcnn.py

@@ -138,8 +138,16 @@ class FasterRCNN(BaseAPI):
                           lr_decay_epochs, lr_decay_gamma,
                           num_steps_each_epoch):
         if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
-            raise Exception("warmup_steps should less than {}".format(
-                lr_decay_epochs[0] * num_steps_each_epoch))
+            logging.error(
+                "In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
+                exit=False)
+            logging.error(
+                "See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
+                exit=False)
+            logging.error(
+                "warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
+                format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
+                       // num_steps_each_epoch))
         boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
         values = [(lr_decay_gamma**i) * learning_rate
                   for i in range(len(lr_decay_epochs) + 1)]
@@ -282,8 +290,7 @@ class FasterRCNN(BaseAPI):
                 eval_details为dict，包含关键字：'bbox'，对应元素预测结果列表，每个预测结果由图像id、
                 预测框类别id、预测框坐标、预测框得分；’gt‘：真实标注框相关信息。
         """
-        self.arrange_transforms(
-            transforms=eval_dataset.transforms, mode='eval')
+        self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
         if metric is None:
             if hasattr(self, 'metric') and self.metric is not None:
                 metric = self.metric
@@ -302,14 +309,12 @@ class FasterRCNN(BaseAPI):
             logging.warning(
                 "Faster RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1."
             )
-        dataset = eval_dataset.generator(
-            batch_size=batch_size, drop_last=False)
+        dataset = eval_dataset.generator(batch_size=batch_size, drop_last=False)
 
         total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size)
         results = list()
-        logging.info(
-            "Start to evaluating(total_samples={}, total_steps={})...".format(
-                eval_dataset.num_samples, total_steps))
+        logging.info("Start to evaluating(total_samples={}, total_steps={})...".
+                     format(eval_dataset.num_samples, total_steps))
         for step, data in tqdm.tqdm(enumerate(dataset()), total=total_steps):
             images = np.array([d[0] for d in data]).astype('float32')
             im_infos = np.array([d[1] for d in data]).astype('float32')

paddlex/cv/models/mask_rcnn.py

@@ -97,8 +97,16 @@ class MaskRCNN(FasterRCNN):
                           lr_decay_epochs, lr_decay_gamma,
                           num_steps_each_epoch):
         if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
-            raise Exception("warmup_step should less than {}".format(
-                lr_decay_epochs[0] * num_steps_each_epoch))
+            logging.error(
+                "In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
+                exit=False)
+            logging.error(
+                "See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
+                exit=False)
+            logging.error(
+                "warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
+                format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
+                       // num_steps_each_epoch))
         boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
         values = [(lr_decay_gamma**i) * learning_rate
                   for i in range(len(lr_decay_epochs) + 1)]
@@ -244,8 +252,7 @@ class MaskRCNN(FasterRCNN):
                 预测框坐标、预测框得分；'mask'，对应元素预测区域结果列表，每个预测结果由图像id、
                 预测区域类别id、预测区域坐标、预测区域得分；’gt‘：真实标注框和标注区域相关信息。
         """
-        self.arrange_transforms(
-            transforms=eval_dataset.transforms, mode='eval')
+        self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
         if metric is None:
             if hasattr(self, 'metric') and self.metric is not None:
                 metric = self.metric
@@ -266,9 +273,8 @@ class MaskRCNN(FasterRCNN):
 
         total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size)
         results = list()
-        logging.info(
-            "Start to evaluating(total_samples={}, total_steps={})...".format(
-                eval_dataset.num_samples, total_steps))
+        logging.info("Start to evaluating(total_samples={}, total_steps={})...".
+                     format(eval_dataset.num_samples, total_steps))
         for step, data in tqdm.tqdm(
                 enumerate(data_generator()), total=total_steps):
             images = np.array([d[0] for d in data]).astype('float32')
@@ -310,8 +316,7 @@ class MaskRCNN(FasterRCNN):
                     zip(['bbox_map', 'segm_map'],
                         [ap_stats[0][1], ap_stats[1][1]]))
             else:
-                metrics = OrderedDict(
-                    zip(['bbox_map', 'segm_map'], [0.0, 0.0]))
+                metrics = OrderedDict(zip(['bbox_map', 'segm_map'], [0.0, 0.0]))
         elif metric == 'COCO':
             if isinstance(ap_stats[0], np.ndarray) and isinstance(ap_stats[1],
                                                                   np.ndarray):

paddlex/cv/models/yolo_v3.py

@@ -128,8 +128,16 @@ class YOLOv3(BaseAPI):
                           lr_decay_epochs, lr_decay_gamma,
                           num_steps_each_epoch):
         if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
-            raise Exception("warmup_steps should less than {}".format(
-                lr_decay_epochs[0] * num_steps_each_epoch))
+            logging.error(
+                "In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
+                exit=False)
+            logging.error(
+                "See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
+                exit=False)
+            logging.error(
+                "warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
+                format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
+                       // num_steps_each_epoch))
         boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
         values = [(lr_decay_gamma**i) * learning_rate
                   for i in range(len(lr_decay_epochs) + 1)]
@@ -277,8 +285,7 @@ class YOLOv3(BaseAPI):
                 eval_details为dict，包含关键字：'bbox'，对应元素预测结果列表，每个预测结果由图像id、
                 预测框类别id、预测框坐标、预测框得分；’gt‘：真实标注框相关信息。
         """
-        self.arrange_transforms(
-            transforms=eval_dataset.transforms, mode='eval')
+        self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
         if metric is None:
             if hasattr(self, 'metric') and self.metric is not None:
                 metric = self.metric
@@ -298,9 +305,8 @@ class YOLOv3(BaseAPI):
 
         data_generator = eval_dataset.generator(
             batch_size=batch_size, drop_last=False)
-        logging.info(
-            "Start to evaluating(total_samples={}, total_steps={})...".format(
-                eval_dataset.num_samples, total_steps))
+        logging.info("Start to evaluating(total_samples={}, total_steps={})...".
+                     format(eval_dataset.num_samples, total_steps))
         for step, data in tqdm.tqdm(
                 enumerate(data_generator()), total=total_steps):
             images = np.array([d[0] for d in data])