!3949 support pretrain for maskrcnn

Merge pull request !3949 from meixiaowei/master

model_zoo/official/cv/maskrcnn/README.md

@@ -35,7 +35,7 @@ MaskRcnn is a two-stage target detection network,This network uses a region prop
           └─train2017
     
         ```
-
+        Notice that the coco2017 dataset will be converted to MindRecord which is a data format in MindSpore. The dataset conversion may take about 4 hours.
     2. If your own dataset is used. **Select dataset to other when run script.**
         Organize the dataset infomation into a TXT file, each row in the file is as follows:
 

model_zoo/official/cv/maskrcnn/src/config.py

@@ -134,6 +134,7 @@ config = ed({
     "loss_scale": 1,
     "momentum": 0.91,
     "weight_decay": 1e-4,
+    "pretrain_epoch_size": 0,
     "epoch_size": 12,
     "save_checkpoint": True,
     "save_checkpoint_epochs": 1,

model_zoo/official/cv/maskrcnn/src/lr_schedule.py

@@ -25,7 +25,7 @@ def a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
     learning_rate = (1 + math.cos(base * math.pi)) / 2 * base_lr
     return learning_rate
 
-def dynamic_lr(config, rank_size=1):
+def dynamic_lr(config, rank_size=1, start_steps=0):
     """dynamic learning rate generator"""
     base_lr = config.base_lr
 
@@ -38,5 +38,5 @@ def dynamic_lr(config, rank_size=1):
             lr.append(linear_warmup_learning_rate(i, warmup_steps, base_lr, base_lr * config.warmup_ratio))
         else:
             lr.append(a_cosine_learning_rate(i, base_lr, warmup_steps, total_steps))
-
-    return lr
+    learning_rate = lr[start_steps:]
+    return learning_rate

model_zoo/official/cv/maskrcnn/train.py

@@ -108,13 +108,15 @@ if __name__ == '__main__':
         load_path = args_opt.pre_trained
         if load_path != "":
             param_dict = load_checkpoint(load_path)
+            if config.pretrain_epoch_size == 0:
                 for item in list(param_dict.keys()):
                     if not (item.startswith('backbone') or item.startswith('rcnn_mask')):
                         param_dict.pop(item)
             load_param_into_net(net, param_dict)
 
         loss = LossNet()
-        lr = Tensor(dynamic_lr(config, rank_size=device_num), mstype.float32)
+        lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),
+                    mstype.float32)
         opt = SGD(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
                   weight_decay=config.weight_decay, loss_scale=config.loss_scale)