timemonitor

mindspore/nn/layer/basic.py

@@ -709,7 +709,7 @@ class ResizeBilinear(Cell):
         ValueError: If `size` is a list or tuple whose length is not equal to 2.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> tensor = Tensor([[[[1, 2, 3, 4], [5, 6, 7, 8]]]], mindspore.float32)

mindspore/ops/operations/array_ops.py

@@ -3297,7 +3297,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer):
         ValueError: If length of `size` is not equal to 2.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_tensor = Tensor(np.array([[[[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]]]), mindspore.float32)

mindspore/ops/operations/nn_ops.py

@@ -3148,7 +3148,7 @@ class ResizeBilinear(PrimitiveWithInfer):
         ValueError: If length of shape of `input` is not equal to 4.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> tensor = Tensor([[[[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]]]], mindspore.float32)

model_zoo/research/audio/deepspeech2/train.py

@@ -21,7 +21,7 @@ import argparse
 from mindspore import context, Tensor, ParameterTuple
 from mindspore.context import ParallelMode
 from mindspore.communication.management import init, get_rank, get_group_size
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.nn.optim import Adam
 from mindspore.nn import TrainOneStepCell
@@ -89,7 +89,7 @@ if __name__ == '__main__':
         print('Successfully loading the pre-trained model')
 
     model = Model(train_net)
-    callback_list = [LossMonitor()]
+    callback_list = [TimeMonitor(steps_size), LossMonitor()]
 
     if args.is_distributed:
         config.CheckpointConfig.ckpt_file_name_prefix = config.CheckpointConfig.ckpt_file_name_prefix + str(get_rank())