modify networks

research/cv/SE-Net/export.py

@@ -28,7 +28,7 @@ if config.device_target == "Ascend":
 def run_export():
     """run export."""
     if config.network_dataset == 'se-resnet50_imagenet2012':
-        from src.resnet import resnet50 as resnet
+        from src.resnet import se_resnet50 as resnet
     elif config.network_dataset == 'se-resnet101_imagenet2012':
         from src.resnet import resnet101 as resnet
     else:

research/cv/u2net/export.py

@@ -31,7 +31,6 @@ args = parser.parse_args()
 context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
 if __name__ == '__main__':
-    context.set_context(device_id="Ascend")
     net = U2NET()
     param_dict = load_checkpoint(args.ckpt_file)
     load_param_into_net(net, param_dict)

research/cv/vit_base/src/modeling_ms.py

@@ -27,9 +27,6 @@ def swish(x):
     return x * P.Sigmoid()(x)
 
 
-ACT2FN = {"gelu": nn.GELU(), "relu": P.ReLU(), "swish": swish}
-
-
 class Attention(nn.Cell):
     """Attention"""
     def __init__(self, config):
@@ -87,7 +84,7 @@ class Mlp(nn.Cell):
                             weight_init='XavierUniform', bias_init='Normal')
         self.fc2 = nn.Dense(config.transformer_mlp_dim, config.hidden_size,
                             weight_init='XavierUniform', bias_init='Normal')
-        self.act_fn = ACT2FN["gelu"]
+        self.act_fn = nn.GELU()
         self.dropout = nn.Dropout(config.transformer_dropout_rate)
 
     def construct(self, x):

research/cv/yolov3_tiny/scripts/run_standalone_train.sh

@@ -50,7 +50,9 @@ then
 fi
 mkdir ./train
 cp ../*.py ./train
+cp ../*.yaml ./train
 cp -r ../src ./train
+cp -r ../model_utils ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log