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@ -13,36 +13,52 @@
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# limitations under the License.
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# limitations under the License.
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# ============================================================================
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# ============================================================================
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"""pooling"""
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"""pooling"""
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from mindspore.ops import operations as P
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from mindspore.ops import operations as P
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from mindspore._checkparam import ParamValidator as validator
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from mindspore._checkparam import ParamValidator as validator
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from mindspore._checkparam import Rel
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from mindspore._checkparam import Rel
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from ... import context
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from ..cell import Cell
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from ..cell import Cell
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class _PoolNd(Cell):
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class _PoolNd(Cell):
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"""N-D AvgPool"""
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"""N-D AvgPool"""
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def __init__(self,
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def __init__(self, kernel_size, stride, pad_mode):
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kernel_size,
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name = self.__class__.__name__
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stride,
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pad_mode,
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padding=0,
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pool=None):
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super(_PoolNd, self).__init__()
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super(_PoolNd, self).__init__()
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validator.check_type('kernel_size', kernel_size, [int, tuple])
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validator.check_type('stride', stride, [int, tuple])
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self.pad_mode = validator.check_string('pad_mode', pad_mode.upper(), ['VALID', 'SAME'])
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if isinstance(kernel_size, int):
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validator.check_integer("kernel_size", kernel_size, 1, Rel.GE)
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else:
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if (len(kernel_size) != 2 or
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(not isinstance(kernel_size[0], int)) or
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(not isinstance(kernel_size[1], int)) or
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kernel_size[0] <= 0 or
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kernel_size[1] <= 0):
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raise ValueError(f'The kernel_size passed to cell {name} should be an positive int number or'
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f'a tuple of two positive int numbers, but got {kernel_size}')
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self.kernel_size = kernel_size
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self.kernel_size = kernel_size
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if isinstance(stride, int):
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validator.check_integer("stride", stride, 1, Rel.GE)
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else:
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if (len(stride) != 2 or
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(not isinstance(stride[0], int)) or
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(not isinstance(stride[1], int)) or
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stride[0] <= 0 or
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stride[1] <= 0):
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raise ValueError(f'The stride passed to cell {name} should be an positive int number or'
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f'a tuple of two positive int numbers, but got {stride}')
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self.stride = stride
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self.stride = stride
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self.pad_mode = pad_mode
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self.padding = validator.check_integer('padding', padding, 0, Rel.GE)
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self.pool = pool
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if self.pool is None:
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raise NotImplementedError
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def construct(self, x):
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def construct(self, *inputs):
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return self.pool(x)
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pass
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def extend_repr(self):
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def extend_repr(self):
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return 'kernel_size={kernel_size}, stride={stride}, pad_mode={pad_mode}'.format(**self.__dict__)
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return 'kernel_size={kernel_size}, strides={strides}, pad_mode={pad_mode}'.format(**self.__dict__)
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class MaxPool2d(_PoolNd):
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class MaxPool2d(_PoolNd):
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@ -63,19 +79,23 @@ class MaxPool2d(_PoolNd):
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pad_mode for training only supports "same" and "valid".
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pad_mode for training only supports "same" and "valid".
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Args:
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Args:
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kernel_size (int): Size of the window to take a max over. Default 1.
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kernel_size (Union[int, tuple[int]]): The size of kernel used to take the max value,
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stride (int): Stride size of the window. Default: 1.
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is an int number that represents height and width are both kernel_size,
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pad_mode (str): Select the mode of the pad. The optional values are
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or a tuple of two int numbers that represent height and width respectively.
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"same" and "valid". Default: "valid".
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Default: 1.
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stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
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the height and width of movement are both strides, or a tuple of two int numbers that
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represent height and width of movement respectively. Default: 1.
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pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
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Default: "valid".
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- same: Adopts the way of completion. Output height and width will be the same as
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- same: Adopts the way of completion. Output height and width will be the same as
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the input. Total number of padding will be calculated for horizontal and vertical
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the input. Total number of padding will be calculated for horizontal and vertical
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direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the
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direction and evenly distributed to top and bottom, left and right if possible.
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last extra padding will be done from the bottom and the right side.
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Otherwise, the last extra padding will be done from the bottom and the right side.
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- valid: Adopts the way of discarding. The possibly largest height and width of output will be return
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- valid: Adopts the way of discarding. The possibly largest height and width of output
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without padding. Extra pixels will be discarded.
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will be return without padding. Extra pixels will be discarded.
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padding (int): Implicit zero padding to be added on both sides. Default: 0.
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Inputs:
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Inputs:
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
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@ -103,31 +123,22 @@ class MaxPool2d(_PoolNd):
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[[7. 8.]
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[[7. 8.]
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[8. 8.]]]]
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[8. 8.]]]]
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"""
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"""
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def __init__(self,
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kernel_size=1,
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def __init__(self, kernel_size=1, stride=1, pad_mode="valid"):
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stride=1,
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super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode)
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pad_mode="VALID",
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self.max_pool = P.MaxPool(ksize=self.kernel_size,
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padding=0):
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strides=self.stride,
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max_pool = P.MaxPool(ksize=kernel_size,
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padding=self.pad_mode)
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strides=stride,
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self.max_pool_with_arg_max = P.MaxPoolWithArgmax(ksize=self.kernel_size,
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padding=pad_mode)
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strides=self.stride,
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self.is_autodiff_backend = False
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padding=self.pad_mode)
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if self.is_autodiff_backend:
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self.is_tbe = context.get_context("device_target") == "Ascend"
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# At present, pad mode of max pool is not unified, so it is a temporarily avoided
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pad_mode = validator.check_string('pad_mode', pad_mode.lower(), ['valid', 'same'])
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max_pool = P.MaxPoolWithArgmax(window=kernel_size,
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stride=stride,
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pad_mode=pad_mode,
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pad=padding)
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super(MaxPool2d, self).__init__(kernel_size, stride, pad_mode, padding, max_pool)
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def construct(self, x):
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def construct(self, x):
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if self.is_autodiff_backend:
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if self.is_tbe and self.training:
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out = self.pool(x)[0]
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out = self.max_pool_with_arg_max(x)[0]
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else:
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else:
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out = self.pool(x)
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out = self.max_pool(x)
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return out
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return out
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@ -149,19 +160,24 @@ class AvgPool2d(_PoolNd):
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pad_mode for training only supports "same" and "valid".
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pad_mode for training only supports "same" and "valid".
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Args:
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Args:
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kernel_size (int): Size of the window to take a max over. Default: 1.
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kernel_size (Union[int, tuple[int]]): The size of kernel used to take the average value,
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stride (int): Stride size of the window. Default: 1.
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is an int number that represents height and width are both kernel_size,
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pad_mode (str): Select the mode of the pad. The optional values are
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or a tuple of two int numbers that represent height and width respectively.
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"same", "valid". Default: "valid".
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Default: 1.
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stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
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the height and width of movement are both strides, or a tuple of two int numbers that
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represent height and width of movement respectively. Default: 1.
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pad_mode (str): The optional values for pad mode, is "same" or "valid", not case sensitive.
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Default: "valid".
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- same: Adopts the way of completion. Output height and width will be the same as
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- same: Adopts the way of completion. Output height and width will be the same as
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the input. Total number of padding will be calculated for horizontal and vertical
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the input. Total number of padding will be calculated for horizontal and vertical
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direction and evenly distributed to top and bottom, left and right if possible. Otherwise, the
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direction and evenly distributed to top and bottom, left and right if possible.
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last extra padding will be done from the bottom and the right side.
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Otherwise, the last extra padding will be done from the bottom and the right side.
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- valid: Adopts the way of discarding. The possibly largest height and width of output
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will be return without padding. Extra pixels will be discarded.
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- valid: Adopts the way of discarding. The possibly largest height and width of output will be return
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without padding. Extra pixels will be discarded.
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padding (int): Implicit zero padding to be added on both sides. Default: 0.
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Inputs:
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Inputs:
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
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- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
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@ -170,7 +186,7 @@ class AvgPool2d(_PoolNd):
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Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
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Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
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Examples:
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Examples:
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>>> pool = AvgPool2d(kernel_size=3, stride=1)
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>>> pool = AvgPool2d(kernel_size=3, strides=1)
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>>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
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>>> x = Tensor(np.random.randint(0, 10, [1, 2, 4, 4]), mindspore.float32)
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[[[[5. 5. 9. 9.]
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[[[[5. 5. 9. 9.]
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[8. 4. 3. 0.]
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[8. 4. 3. 0.]
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@ -189,12 +205,15 @@ class AvgPool2d(_PoolNd):
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[[4.2222223 4.5555553]
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[[4.2222223 4.5555553]
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[3.2222223 4.5555553]]]]
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[3.2222223 4.5555553]]]]
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"""
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"""
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def __init__(self,
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def __init__(self,
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kernel_size=1,
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kernel_size=1,
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stride=1,
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stride=1,
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pad_mode="VALID",
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pad_mode="valid"):
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padding=0):
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super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode)
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avg_pool = P.AvgPool(ksize=kernel_size,
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self.avg_pool = P.AvgPool(ksize=self.kernel_size,
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strides=stride,
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strides=self.stride,
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padding=pad_mode)
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padding=self.pad_mode)
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super(AvgPool2d, self).__init__(kernel_size, stride, pad_mode, padding, avg_pool)
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def construct(self, x):
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return self.avg_pool(x)
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buxue
5 years ago