forked from mindspore-Ecosystem/mindspore
update the documentation of MaxPoolWithArgmax, BroadcastTo, GlobalBatchNorm and DistributedGradReducer operators.
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wangshuide2020
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882a582eb4
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0f75370377
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@ -575,7 +575,7 @@ class GlobalBatchNorm(_BatchNorm):
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>>>
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>>> device_id = int(os.environ["DEVICE_ID"])
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>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
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>>> device_id=int(device_id))
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... device_id=int(device_id))
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>>> init()
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>>> context.reset_auto_parallel_context()
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>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
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@ -268,47 +268,45 @@ class DistributedGradReducer(Cell):
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>>> context.reset_auto_parallel_context()
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>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
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>>>
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>>>
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>>> class TrainingWrapper(nn.Cell):
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>>> def __init__(self, network, optimizer, sens=1.0):
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>>> super(TrainingWrapper, self).__init__(auto_prefix=False)
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>>> self.network = network
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>>> self.network.add_flags(defer_inline=True)
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>>> self.weights = optimizer.parameters
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>>> self.optimizer = optimizer
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>>> self.grad = C.GradOperation(get_by_list=True, sens_param=True)
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>>> self.sens = sens
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>>> self.reducer_flag = False
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>>> self.grad_reducer = None
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>>> self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
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>>> if self.parallel_mode in [ParallelMode.DATA_PARALLEL,
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>>> ParallelMode.HYBRID_PARALLEL]:
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>>> self.reducer_flag = True
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>>> if self.reducer_flag:
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>>> mean = _get_gradients_mean()
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>>> degree = _get_device_num()
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>>> self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
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>>>
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>>> def construct(self, *args):
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>>> weights = self.weights
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>>> loss = self.network(*args)
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>>> sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
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>>> grads = self.grad(self.network, weights)(*args, sens)
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>>> if self.reducer_flag:
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>>> # apply grad reducer on grads
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>>> grads = self.grad_reducer(grads)
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>>> return F.depend(loss, self.optimizer(grads))
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... def __init__(self, network, optimizer, sens=1.0):
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... super(TrainingWrapper, self).__init__(auto_prefix=False)
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... self.network = network
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... self.network.add_flags(defer_inline=True)
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... self.weights = optimizer.parameters
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... self.optimizer = optimizer
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... self.grad = C.GradOperation(get_by_list=True, sens_param=True)
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... self.sens = sens
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... self.reducer_flag = False
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... self.grad_reducer = None
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... self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
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... if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
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... self.reducer_flag = True
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... if self.reducer_flag:
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... mean = _get_gradients_mean()
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... degree = _get_device_num()
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... self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
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...
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... def construct(self, *args):
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... weights = self.weights
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... loss = self.network(*args)
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... sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
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... grads = self.grad(self.network, weights)(*args, sens)
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... if self.reducer_flag:
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... # apply grad reducer on grads
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... grads = self.grad_reducer(grads)
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... return F.depend(loss, self.optimizer(grads))
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>>>
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>>> class Net(nn.Cell):
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>>> def __init__(self, in_features, out_features):
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>>> super(Net, self).__init__()
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>>> self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
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>>> name='weight')
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>>> self.matmul = P.MatMul()
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>>>
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>>> def construct(self, x):
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>>> output = self.matmul(x, self.weight)
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>>> return output
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... def __init__(self, in_features, out_features):
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... super(Net, self).__init__()
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... self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
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... name='weight')
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... self.matmul = P.MatMul()
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...
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... def construct(self, x):
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... output = self.matmul(x, self.weight)
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... return output
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>>>
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>>> size, in_features, out_features = 16, 16, 10
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>>> network = Net(in_features, out_features)
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@ -4105,12 +4105,12 @@ class BroadcastTo(PrimitiveWithInfer):
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When input shape is broadcast to target shape, it starts with the trailing dimensions.
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Raises:
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ValueError: Given a shape tuple, if it has several -1s; or if the -1 is in an invalid position
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such as one that does not have a opposing dimension in an input tensor; of if the target and
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ValueError: Given a shape tuple, if it has several -1; or if the -1 is in an invalid position
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such as one that does not have a opposing dimension in an input tensor; or if the target and
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input shapes are incompatiable.
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Args:
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shape (tuple): The target shape to broadcast. Can be fully specified, or have '-1's in one position
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shape (tuple): The target shape to broadcast. Can be fully specified, or have -1 in one position
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where it will be substituted by the input tensor's shape in that position, see example.
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Inputs:
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@ -1566,7 +1566,8 @@ class MaxPoolWithArgmax(_Pool):
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Tuple of 2 Tensors, representing the maxpool result and where the max values are generated.
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- **output** (Tensor) - Maxpooling result, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
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- **mask** (Tensor) - Max values' index represented by the mask.
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It has the same data type as `input`.
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- **mask** (Tensor) - Max values' index represented by the mask. Data type is int32.
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Supported Platforms:
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``Ascend``
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