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@ -810,13 +810,13 @@ class _PoolGrad(PrimitiveWithInfer):
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"""Gradients of the max/avg pool operation."""
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@prim_attr_register
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def __init__(self, ksize, strides, padding="VALID", data_format="NCHW"):
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def __init__(self, kernel_size, strides, pad_mode="VALID", data_format="NCHW"):
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self.init_prim_io_names(inputs=['x_origin', 'out_origin', 'grad'], outputs=['output'])
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validator.check_value_type('ksize', ksize, [int, tuple], self.name)
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validator.check_value_type('kernel_size', kernel_size, [int, tuple], self.name)
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validator.check_value_type('strides', strides, [int, tuple], self.name)
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self.padding = validator.check_string(padding.upper(), ['VALID', 'SAME'], 'padding', self.name)
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self.add_prim_attr("padding", self.padding)
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self.pad_mode = validator.check_string(pad_mode.upper(), ['VALID', 'SAME'], 'pad_mode', self.name)
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self.add_prim_attr("pad_mode", self.pad_mode)
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self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.name)
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if context.get_context("device_target") != "GPU" and self.format == "NHWC":
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raise ValueError("NHWC format only support in GPU target.")
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@ -842,9 +842,10 @@ class _PoolGrad(PrimitiveWithInfer):
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raise error_msg
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return ret
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ksize = _grad_check_int_or_tuple("ksize", ksize, self.is_maxpoolgradwithargmax)
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self.ksize = ksize if self.format == "NCHW" else [ksize[0], ksize[2], ksize[3], ksize[1]]
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self.add_prim_attr("ksize", self.ksize)
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kernel_size = _grad_check_int_or_tuple("kernel_size", kernel_size, self.is_maxpoolgradwithargmax)
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self.kernel_size = kernel_size if self.format == "NCHW" else [kernel_size[0], kernel_size[2],
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kernel_size[3], kernel_size[1]]
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self.add_prim_attr("kernel_size", self.kernel_size)
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strides = _grad_check_int_or_tuple("strides", strides, self.is_maxpoolgradwithargmax)
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self.strides = strides if self.format == "NCHW" else [strides[0], strides[2], strides[3], strides[1]]
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@ -855,8 +856,8 @@ class AvgPoolGrad(_PoolGrad):
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"""Gradients of the avg pool operation for ge."""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID"):
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super(AvgPoolGrad, self).__init__(ksize, strides, padding)
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID"):
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super(AvgPoolGrad, self).__init__(kernel_size, strides, pad_mode)
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def __infer__(self, origin_input, dout):
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out = {
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@ -872,8 +873,8 @@ class AvgPoolGradVm(_PoolGrad):
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"""Gradients of the avg pool operation for vm."""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID"):
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super(AvgPoolGradVm, self).__init__(ksize, strides, padding)
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID"):
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super(AvgPoolGradVm, self).__init__(kernel_size, strides, pad_mode)
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self.init_prim_io_names(inputs=['x_origin', 'grad', 'mean_matrix', 'kernel_matrix'], outputs=['output'])
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def __infer__(self, origin_input, dout, mean_matrix, kernel_matrix):
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@ -890,8 +891,8 @@ class AvgPoolGradGpu(_PoolGrad):
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"""Gradients of the avg pool operation for gpu."""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID", data_format="NCHW"):
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super(AvgPoolGradGpu, self).__init__(ksize, strides, padding, data_format)
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID", data_format="NCHW"):
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super(AvgPoolGradGpu, self).__init__(kernel_size, strides, pad_mode, data_format)
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def infer_shape(self, x1_shape, x2_shape, grad_shape):
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return x1_shape
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@ -904,8 +905,8 @@ class AvgPoolGradCpu(_PoolGrad):
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"""Gradients of the avg pool operation for cpu."""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID", data_format="NCHW"):
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super(AvgPoolGradCpu, self).__init__(ksize, strides, padding, data_format)
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID", data_format="NCHW"):
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super(AvgPoolGradCpu, self).__init__(kernel_size, strides, pad_mode, data_format)
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def infer_shape(self, x1_shape, x2_shape, grad_shape):
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return x1_shape
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@ -918,8 +919,8 @@ class MaxPoolGrad(_PoolGrad):
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"""Performs gradients of the max pool operation."""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID", data_format="NCHW"):
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super(MaxPoolGrad, self).__init__(ksize, strides, padding, data_format)
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID", data_format="NCHW"):
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super(MaxPoolGrad, self).__init__(kernel_size, strides, pad_mode, data_format)
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def infer_shape(self, x1_shape, x2_shape, grad_shape):
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return x1_shape
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@ -933,13 +934,13 @@ class MaxPoolGradGrad(_PoolGrad):
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Performs gradients of the MaxPoolGrad operation.
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Args:
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ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value,
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is an int number that represents height and width are both ksize, or a tuple
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kernel_size (Union[int, tuple[int]]): The size of kernel used to take the maximum value,
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is an int number that represents height and width are both kernel_size, or a tuple
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of two int numbers that represent height and width respectively. Default: 1.
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strides (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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padding (str): The optional value for pad mode, is "same" or "valid", not case sensitive.
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pad_mode (str): The optional value 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. The height and width of the output will be the same as
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@ -961,8 +962,8 @@ class MaxPoolGradGrad(_PoolGrad):
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"""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID"):
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super(MaxPoolGradGrad, self).__init__(ksize, strides, padding)
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID"):
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super(MaxPoolGradGrad, self).__init__(kernel_size, strides, pad_mode)
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def infer_shape(self, x1_shape, x2_shape, grad_shape):
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return x1_shape
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@ -985,9 +986,9 @@ class MaxPoolGradWithArgmax(_PoolGrad):
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"""Computes the gradients of MaxPoolWithArgmax."""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID"):
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID"):
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self.init_prim_io_names(inputs=['x', 'grad', 'argmax'], outputs=['output'])
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super(MaxPoolGradWithArgmax, self).__init__(ksize, strides, padding)
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super(MaxPoolGradWithArgmax, self).__init__(kernel_size, strides, pad_mode)
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def infer_shape(self, x_shape, grad_shape, argmax_shape):
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if not grad_shape:
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@ -1003,13 +1004,13 @@ class MaxPoolGradGradWithArgmax(_PoolGrad):
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Computes the gradients of MaxPoolGradWithArgmax.
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Args:
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ksize (Union[int, tuple[int]]): The size of kernel used to take the maximum value,
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is an int number that represents height and width are both ksize, or a tuple
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kernel_size (Union[int, tuple[int]]): The size of kernel used to take the maximum value,
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is an int number that represents height and width are both kernel_size, or a tuple
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of two int numbers that represent height and width respectively. Default: 1.
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strides (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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padding (str): The optional value for pad mode, is "same" or "valid", not case sensitive.
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pad_mode (str): The optional value 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. The height and width of the output will be the same as
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@ -1031,9 +1032,9 @@ class MaxPoolGradGradWithArgmax(_PoolGrad):
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"""
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@prim_attr_register
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def __init__(self, ksize=1, strides=1, padding="VALID"):
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def __init__(self, kernel_size=1, strides=1, pad_mode="VALID"):
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self.init_prim_io_names(inputs=['x', 'grad', 'argmax'], outputs=['output'])
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super(MaxPoolGradGradWithArgmax, self).__init__(ksize, strides, padding)
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super(MaxPoolGradGradWithArgmax, self).__init__(kernel_size, strides, pad_mode)
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def infer_shape(self, x_shape, grad_shape, argmax_shape):
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if not grad_shape:
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yuchaojie
5 years ago