!9163 Remove network useless parameters .

From: @linqingke
Reviewed-by: @c_34,@oacjiewen
Signed-off-by: @c_34

model_zoo/official/cv/faster_rcnn/src/FasterRcnn/faster_rcnn_r50.py

@@ -111,7 +111,7 @@ class Faster_Rcnn_Resnet50(nn.Cell):
         # Assign and sampler stage two
         self.bbox_assigner_sampler_for_rcnn = BboxAssignSampleForRcnn(config, self.train_batch_size,
                                                                       config.num_bboxes_stage2, True)
-        self.decode = P.BoundingBoxDecode(max_shape=(768, 1280), means=self.target_means, \
+        self.decode = P.BoundingBoxDecode(max_shape=(config.img_height, config.img_width), means=self.target_means, \
                                           stds=self.target_stds)
 
         # Roi

model_zoo/official/cv/faster_rcnn/src/FasterRcnn/roi_align.py

@@ -172,7 +172,8 @@ class SingleRoIExtractor(nn.Cell):
             mask = self.equal(target_lvls, P.ScalarToArray()(i))
             mask = P.Reshape()(mask, (-1, 1, 1, 1))
             roi_feats_t = self.roi_layers[i](feats[i], rois)
-            mask = self.cast(P.Tile()(self.cast(mask, mstype.int32), (1, 256, 7, 7)), mstype.bool_)
+            mask = self.cast(P.Tile()(self.cast(mask, mstype.int32),\
+                                      (1, 256, self.out_size, self.out_size)), mstype.bool_)
             res = self.select(mask, roi_feats_t, res)
 
         return res

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

@@ -17,17 +17,17 @@
 from easydict import EasyDict as ed
 
 config = ed({
-    'INFER_LONG_SIZE': 1920,
-    'KERNEL_NUM': 7,
-    'INFERENCE': True,  # INFER MODE\TRAIN MODE
+    "INFER_LONG_SIZE": 1920,
+    "KERNEL_NUM": 7,
+    "INFERENCE": True,  # INFER MODE\TRAIN MODE
 
     # backbone
-    'BACKBONE_LAYER_NUMS': [3, 4, 6, 3],
-    'BACKBONE_IN_CHANNELS': [64, 256, 512, 1024],
-    'BACKBONE_OUT_CHANNELS': [256, 512, 1024, 2048],
+    "BACKBONE_LAYER_NUMS": [3, 4, 6, 3],
+    "BACKBONE_IN_CHANNELS": [64, 256, 512, 1024],
+    "BACKBONE_OUT_CHANNELS": [256, 512, 1024, 2048],
 
     # neck
-    'NECK_OUT_CHANNEL': 256,
+    "NECK_OUT_CHANNEL": 256,
 
     # lr
     "BASE_LR": 2e-3,
@@ -36,20 +36,20 @@ config = ed({
     "WARMUP_RATIO": 1/3,
 
     # dataset for train
-    "TRAIN_ROOT_DIR": 'psenet/ic15/',
+    "TRAIN_ROOT_DIR": "psenet/ic15/",
     "TRAIN_IS_TRANSFORM": True,
     "TRAIN_LONG_SIZE": 640,
     "TRAIN_MIN_SCALE": 0.4,
     "TRAIN_BATCH_SIZE": 4,
     "TRAIN_REPEAT_NUM": 1800,
     "TRAIN_DROP_REMAINDER": True,
-    "TRAIN_MODEL_SAVE_PATH": './checkpoints/',
+    "TRAIN_MODEL_SAVE_PATH": "./checkpoints/",
 
     # dataset for test
-    "TEST_ROOT_DIR": 'psenet/ic15/',
+    "TEST_ROOT_DIR": "psenet/ic15/",
     "TEST_BUFFER_SIZE": 4,
     "TEST_DROP_REMAINDER": False,
 
     # air config
-    'air_filename': 'psenet_bs_1.air',
+    "air_filename": "psenet_bs_1.air",
 })

model_zoo/official/nlp/mass/config/config.json

@@ -26,8 +26,7 @@
     "label_smoothing": 0.1,
     "beam_width": 4,
     "length_penalty_weight": 1.0,
-    "max_decode_length": 64,
-    "input_mask_from_dataset": true
+    "max_decode_length": 64
   },
   "loss_scale_config": {
     "loss_scale_mode": "dynamic",

model_zoo/official/nlp/mass/config/config.py

@@ -100,8 +100,6 @@ class TransformerConfig:
         beam_width (int): Beam width for beam search in inferring. Default: 4.
         length_penalty_weight (float): Penalty for sentence length. Default: 1.0.
         label_smoothing (float): Label smoothing setting. Default: 0.1.
-        input_mask_from_dataset (bool): Specifies whether to use the input mask that loaded from
-            dataset. Default: True.
         save_graphs (bool): Whether to save graphs, please set to True if mindinsight
             is wanted.
         dtype (mstype): Data type of the input. Default: mstype.float32.
@@ -148,7 +146,6 @@ class TransformerConfig:
                  beam_width=5,
                  length_penalty_weight=1.0,
                  label_smoothing=0.1,
-                 input_mask_from_dataset=True,
                  save_graphs=False,
                  dtype=mstype.float32,
                  max_decode_length=64):
@@ -190,7 +187,6 @@ class TransformerConfig:
         self.beam_width = beam_width
         self.length_penalty_weight = length_penalty_weight
         self.max_decode_length = max_decode_length
-        self.input_mask_from_dataset = input_mask_from_dataset
         self.compute_type = mstype.float16
         self.dtype = dtype
 

model_zoo/official/nlp/mass/src/transformer/create_attn_mask.py

@@ -33,11 +33,8 @@ class CreateAttentionMaskFromInputMask(nn.Cell):
 
     def __init__(self, config):
         super(CreateAttentionMaskFromInputMask, self).__init__()
-        self.input_mask_from_dataset = config.input_mask_from_dataset
         self.input_mask = None
 
-        assert self.input_mask_from_dataset
-
         self.cast = P.Cast()
         self.shape = P.Shape()
         self.reshape = P.Reshape()

model_zoo/official/nlp/mass/src/transformer/transformer.py

@@ -59,7 +59,6 @@ class Transformer(nn.Cell):
             config.hidden_dropout_prob = 0.0
             config.attention_dropout_prob = 0.0
 
-        self.input_mask_from_dataset = config.input_mask_from_dataset
         self.batch_size = config.batch_size
         self.max_positions = config.seq_length
         self.attn_embed_dim = config.hidden_size

model_zoo/official/nlp/mass/src/transformer/transformer_for_infer.py

@@ -212,7 +212,6 @@ class TransformerInferModel(nn.Cell):
         config.hidden_dropout_prob = 0.0
         config.attention_dropout_prob = 0.0
 
-        self.input_mask_from_dataset = config.input_mask_from_dataset
         self.batch_size = config.batch_size
         self.seq_length = config.seq_length
         self.hidden_size = config.hidden_size