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Running
on
Zero
File size: 2,638 Bytes
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# Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Conv2d Module with Valid Padding"""
import torch.nn.functional as F
from torch.nn.modules.conv import _ConvNd, _size_2_t, Union, _pair, Tensor, Optional
class Conv2dValid(_ConvNd):
"""
Conv2d operator for VALID mode padding.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_2_t,
stride: _size_2_t = 1,
padding: Union[str, _size_2_t] = 0,
dilation: _size_2_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
device=None,
dtype=None,
valid_trigx: bool = False,
valid_trigy: bool = False) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
kernel_size_ = _pair(kernel_size)
stride_ = _pair(stride)
padding_ = padding if isinstance(padding, str) else _pair(padding)
dilation_ = _pair(dilation)
super(Conv2dValid,
self).__init__(in_channels, out_channels,
kernel_size_, stride_, padding_, dilation_, False,
_pair(0), groups, bias, padding_mode,
**factory_kwargs)
self.valid_trigx = valid_trigx
self.valid_trigy = valid_trigy
def _conv_forward(self, input: Tensor, weight: Tensor,
bias: Optional[Tensor]):
validx, validy = 0, 0
if self.valid_trigx:
validx = (input.size(-2) *
(self.stride[-2] - 1) - 1 + self.kernel_size[-2]) // 2
if self.valid_trigy:
validy = (input.size(-1) *
(self.stride[-1] - 1) - 1 + self.kernel_size[-1]) // 2
return F.conv2d(input, weight, bias, self.stride, (validx, validy),
self.dilation, self.groups)
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias)
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