219 lines
7.4 KiB
Python
219 lines
7.4 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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"""NAS ops (adopted from DARTS)."""
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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OPS = {
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'none': lambda C, stride, affine:
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Zero(stride),
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'noise': lambda C, stride, affine: NoiseOp(stride, 0., 1.),
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'avg_pool_2x2': lambda C, stride, affine:
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nn.AvgPool2d(2, stride=stride, padding=0, count_include_pad=False),
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'avg_pool_3x3': lambda C, stride, affine:
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nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
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'avg_pool_5x5': lambda C, stride, affine:
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nn.AvgPool2d(5, stride=stride, padding=2, count_include_pad=False),
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'max_pool_2x2': lambda C, stride, affine:
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nn.MaxPool2d(2, stride=stride, padding=0),
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'max_pool_3x3': lambda C, stride, affine:
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nn.MaxPool2d(3, stride=stride, padding=1),
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'max_pool_5x5': lambda C, stride, affine:
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nn.MaxPool2d(5, stride=stride, padding=2),
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'max_pool_7x7': lambda C, stride, affine:
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nn.MaxPool2d(7, stride=stride, padding=3),
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'skip_connect': lambda C, stride, affine:
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Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
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'conv_1x1': lambda C, stride, affine:
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=stride, padding=0, bias=False),
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nn.BatchNorm2d(C, affine=affine)
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),
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'conv_3x3': lambda C, stride, affine:
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(C, affine=affine)
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),
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'sep_conv_3x3': lambda C, stride, affine:
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SepConv(C, C, 3, stride, 1, affine=affine),
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'sep_conv_5x5': lambda C, stride, affine:
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SepConv(C, C, 5, stride, 2, affine=affine),
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'sep_conv_7x7': lambda C, stride, affine:
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SepConv(C, C, 7, stride, 3, affine=affine),
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'dil_conv_3x3': lambda C, stride, affine:
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DilConv(C, C, 3, stride, 2, 2, affine=affine),
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'dil_conv_5x5': lambda C, stride, affine:
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DilConv(C, C, 5, stride, 4, 2, affine=affine),
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'dil_sep_conv_3x3': lambda C, stride, affine:
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DilSepConv(C, C, 3, stride, 2, 2, affine=affine),
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'conv_3x1_1x3': lambda C, stride, affine:
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, (1,3), stride=(1, stride), padding=(0, 1), bias=False),
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nn.Conv2d(C, C, (3,1), stride=(stride, 1), padding=(1, 0), bias=False),
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nn.BatchNorm2d(C, affine=affine)
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),
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'conv_7x1_1x7': lambda C, stride, affine:
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
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nn.Conv2d(C, C, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
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nn.BatchNorm2d(C, affine=affine)
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),
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}
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class NoiseOp(nn.Module):
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def __init__(self, stride, mean, std):
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super(NoiseOp, self).__init__()
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self.stride = stride
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self.mean = mean
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self.std = std
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def forward(self, x, block_input=False):
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if block_input:
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x = x*0
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if self.stride != 1:
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x_new = x[:,:,::self.stride,::self.stride]
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else:
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x_new = x
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noise = Variable(x_new.data.new(x_new.size()).normal_(self.mean, self.std))
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return noise
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class ReLUConvBN(nn.Module):
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def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
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super(ReLUConvBN, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in, C_out, kernel_size, stride=stride,
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padding=padding, bias=False
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),
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nn.BatchNorm2d(C_out, affine=affine)
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)
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def forward(self, x):
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return self.op(x)
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class DilConv(nn.Module):
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def __init__(
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self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True
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):
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super(DilConv, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in, C_in, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=C_in, bias=False
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),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_out, affine=affine),
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)
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def forward(self, x):
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return self.op(x)
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class SepConv(nn.Module):
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def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
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super(SepConv, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in, C_in, kernel_size=kernel_size, stride=stride,
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padding=padding, groups=C_in, bias=False
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),
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nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_in, affine=affine),
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in, C_in, kernel_size=kernel_size, stride=1,
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padding=padding, groups=C_in, bias=False
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),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_out, affine=affine),
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)
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def forward(self, x):
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return self.op(x)
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class DilSepConv(nn.Module):
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def __init__(
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self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True
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):
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super(DilSepConv, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in, C_in, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, groups=C_in, bias=False
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),
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nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_in, affine=affine),
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in, C_in, kernel_size=kernel_size, stride=1,
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padding=padding, dilation=dilation, groups=C_in, bias=False
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),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_out, affine=affine),
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)
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def forward(self, x):
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return self.op(x)
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class Identity(nn.Module):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, x):
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return x
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class Zero(nn.Module):
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def __init__(self, stride):
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super(Zero, self).__init__()
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self.stride = stride
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def forward(self, x):
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if self.stride == 1:
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return x.mul(0.)
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return x[:,:,::self.stride,::self.stride].mul(0.)
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class FactorizedReduce(nn.Module):
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def __init__(self, C_in, C_out, affine=True):
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super(FactorizedReduce, self).__init__()
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assert C_out % 2 == 0
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self.relu = nn.ReLU(inplace=False)
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self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
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self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
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self.bn = nn.BatchNorm2d(C_out, affine=affine)
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self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
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def forward(self, x):
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x = self.relu(x)
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y = self.pad(x)
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out = torch.cat([self.conv_1(x), self.conv_2(y[:,:,1:,1:])], dim=1)
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out = self.bn(out)
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return out |