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@ -17,7 +17,7 @@ from mindspore import log as logger
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from mindspore.ops import operations as P
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative, check_int
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from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative
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from mindspore._extends import cell_attr_register
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from ..cell import Cell
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@ -42,17 +42,23 @@ class _Conv(Cell):
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self.in_channels = check_int_positive(in_channels)
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self.out_channels = check_int_positive(out_channels)
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self.kernel_size = kernel_size
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self.stride = check_int_positive(stride)
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self.stride = stride
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self.pad_mode = pad_mode
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self.padding = check_int_non_negative(padding)
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self.dilation = check_int(dilation)
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self.dilation = dilation
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self.group = check_int_positive(group)
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self.has_bias = has_bias
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if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \
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(not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
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kernel_size[0] < 1 or kernel_size[1] < 1:
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if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
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kernel_size[0] < 1 or kernel_size[1] < 1:
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raise ValueError("Attr 'kernel_size' of 'Conv2D' Op passed "
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+ str(self.kernel_size) + ", should be a int or tuple and equal to or greater than 1.")
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if (not isinstance(stride[0], int)) or (not isinstance(stride[1], int)) or stride[0] < 1 or stride[1] < 1:
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raise ValueError("Attr 'stride' of 'Conv2D' Op passed "
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+ str(self.stride) + ", should be a int or tuple and equal to or greater than 1.")
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if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \
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dilation[0] < 1 or dilation[1] < 1:
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raise ValueError("Attr 'dilation' of 'Conv2D' Op passed "
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+ str(self.dilation) + ", should equal to or greater than 1.")
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if in_channels % group != 0:
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raise ValueError("Attr 'in_channels' of 'Conv2D' Op must be divisible by "
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"attr 'group' of 'Conv2D' Op.")
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@ -107,12 +113,13 @@ class Conv2d(_Conv):
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Args:
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in_channels (int): The number of input channel :math:`C_{in}`.
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out_channels (int): The number of output channel :math:`C_{out}`.
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kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
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kernel_size (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the height
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and width of the 2D convolution window. Single int means the value if for both height and width of
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the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
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width of the kernel.
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stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
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greater or equal to 1 but bounded by the height and width of the input. 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): Specifies padding mode. The optional values are
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"same", "valid", "pad". Default: "same".
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@ -130,9 +137,11 @@ class Conv2d(_Conv):
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Tensor borders. `padding` should be greater than or equal to 0.
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padding (int): Implicit paddings on both sides of the input. Default: 0.
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dilation (int): Specifying the dilation rate to use for dilated convolution. If set to be :math:`k > 1`,
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there will be :math:`k - 1` pixels skipped for each sampling location. Its value should be greater
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or equal to 1 and bounded by the height and width of the input. Default: 1.
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dilation (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the dilation rate
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to use for dilated convolution. If set to be :math:`k > 1`, there will
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be :math:`k - 1` pixels skipped for each sampling location. Its value should
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be greater or equal to 1 and bounded by the height and width of the
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input. Default: 1.
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group (int): Split filter into groups, `in_ channels` and `out_channels` should be
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divisible by the number of groups. Default: 1.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
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@ -172,6 +181,8 @@ class Conv2d(_Conv):
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weight_init='normal',
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bias_init='zeros'):
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kernel_size = twice(kernel_size)
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stride = twice(stride)
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dilation = twice(dilation)
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super(Conv2d, self).__init__(
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in_channels,
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out_channels,
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@ -241,7 +252,9 @@ class Conv2dTranspose(_Conv):
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and width of the 2D convolution window. Single int means the value is for both height and width of
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the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
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width of the kernel.
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stride (int): Specifies the same value for all spatial dimensions. 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): Select the mode of the pad. The optional values are
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"pad", "same", "valid". Default: "same".
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@ -251,8 +264,11 @@ class Conv2dTranspose(_Conv):
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- valid: Adopted the way of discarding.
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padding (int): Implicit paddings on both sides of the input. Default: 0.
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dilation (int): Specifies the dilation rate to use for dilated
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convolution. Default: 1.
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dilation (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the dilation rate
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to use for dilated convolution. If set to be :math:`k > 1`, there will
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be :math:`k - 1` pixels skipped for each sampling location. Its value should
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be greater or equal to 1 and bounded by the height and width of the
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input. Default: 1.
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group (int): Split filter into groups, `in_channels` and `out_channels` should be
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divisible by the number of groups. Default: 1.
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has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
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@ -290,6 +306,8 @@ class Conv2dTranspose(_Conv):
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weight_init='normal',
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bias_init='zeros'):
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kernel_size = twice(kernel_size)
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stride = twice(stride)
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dilation = twice(dilation)
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# out_channels and in_channels swap.
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# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
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# then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
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@ -333,26 +351,26 @@ class Conv2dTranspose(_Conv):
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self.conv2d_transpose.set_strategy(strategy)
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return self
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def _deconv_output_length(self, input_length, filter_size):
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def _deconv_output_length(self, input_length, filter_size, stride_size, dilation_size):
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"""Calculate the width and height of output."""
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length = 0
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if self.is_valid:
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if filter_size - self.stride > 0:
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length = input_length * self.stride + filter_size - self.stride
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if filter_size - stride_size > 0:
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length = input_length * stride_size + filter_size - stride_size
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else:
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length = input_length * self.stride
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length = input_length * stride_size
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elif self.is_same:
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length = input_length * self.stride
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length = input_length * stride_size
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elif self.is_pad:
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length = input_length * self.stride - 2 * self.padding + filter_size + \
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(filter_size - 1) * (self.dilation - 1) - self.stride
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length = input_length * stride_size - 2 * self.padding + filter_size + \
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(filter_size - 1) * (dilation_size - 1) - stride_size
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return length
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def construct(self, x):
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n, _, h, w = self.shape(x)
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h_out = self._deconv_output_length(h, self.kernel_size[0])
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w_out = self._deconv_output_length(w, self.kernel_size[1])
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h_out = self._deconv_output_length(h, self.kernel_size[0], self.stride[0], self.dilation[0])
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w_out = self._deconv_output_length(w, self.kernel_size[1], self.stride[1], self.dilation[1])
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if self.has_bias:
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return self.bias_add(self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out)),
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self.bias)
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wangnan39@huawei.com
5 years ago