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63 lines
2.0 KiB
63 lines
2.0 KiB
2 years ago
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from typing import Optional
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from typing import Union
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import paddle
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import paddle.nn.functional as F
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from paddle.nn.layer.conv import _ConvNd
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__all__ = ['Conv2DValid']
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class Conv2DValid(_ConvNd):
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"""
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Conv2d operator for VALID mode padding.
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"""
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def __init__(self,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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stride: int=1,
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padding: Union[str, int]=0,
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dilation: int=1,
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groups: int=1,
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padding_mode: str='zeros',
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weight_attr=None,
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bias_attr=None,
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data_format="NCHW",
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valid_trigx: bool=False,
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valid_trigy: bool=False) -> None:
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super(Conv2DValid, self).__init__(
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in_channels,
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out_channels,
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kernel_size,
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False,
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2,
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stride=stride,
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padding=padding,
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padding_mode=padding_mode,
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dilation=dilation,
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groups=groups,
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weight_attr=weight_attr,
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bias_attr=bias_attr,
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data_format=data_format)
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self.valid_trigx = valid_trigx
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self.valid_trigy = valid_trigy
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def _conv_forward(self,
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input: paddle.Tensor,
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weight: paddle.Tensor,
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bias: Optional[paddle.Tensor]):
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validx, validy = 0, 0
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if self.valid_trigx:
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validx = (input.shape[-2] *
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(self._stride[-2] - 1) - 1 + self._kernel_size[-2]) // 2
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if self.valid_trigy:
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validy = (input.shape[-1] *
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(self._stride[-1] - 1) - 1 + self._kernel_size[-1]) // 2
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return F.conv2d(input, weight, bias, self._stride, (validx, validy),
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self._dilation, self._groups)
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def forward(self, input: paddle.Tensor) -> paddle.Tensor:
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return self._conv_forward(input, self.weight, self.bias)
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