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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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from torch import nn
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def get_nonlinear(config_str, channels):
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nonlinear = nn.Sequential()
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for name in config_str.split('-'):
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if name == 'relu':
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nonlinear.add_module('relu', nn.ReLU(inplace=True))
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elif name == 'prelu':
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nonlinear.add_module('prelu', nn.PReLU(channels))
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elif name == 'batchnorm':
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nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels))
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elif name == 'batchnorm_':
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nonlinear.add_module('batchnorm',
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nn.BatchNorm1d(channels, affine=False))
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else:
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raise ValueError('Unexpected module ({}).'.format(name))
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return nonlinear
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def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
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mean = x.mean(dim=dim)
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std = x.std(dim=dim, unbiased=unbiased)
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stats = torch.cat([mean, std], dim=-1)
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if keepdim:
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stats = stats.unsqueeze(dim=dim)
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return stats
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class StatsPool(nn.Module):
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def forward(self, x):
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return statistics_pooling(x)
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class TDNNLayer(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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bias=False,
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config_str='batchnorm-relu'):
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super(TDNNLayer, self).__init__()
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if padding < 0:
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assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
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kernel_size)
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padding = (kernel_size - 1) // 2 * dilation
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self.linear = nn.Conv1d(in_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=bias)
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self.nonlinear = get_nonlinear(config_str, out_channels)
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def forward(self, x):
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x = self.linear(x)
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x = self.nonlinear(x)
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return x
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class CAMLayer(nn.Module):
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def __init__(self,
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bn_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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bias,
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reduction=2):
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super(CAMLayer, self).__init__()
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self.linear_local = nn.Conv1d(bn_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=bias)
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self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1)
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self.relu = nn.ReLU(inplace=True)
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self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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y = self.linear_local(x)
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context = x.mean(-1, keepdim=True)+self.seg_pooling(x)
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context = self.relu(self.linear1(context))
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m = self.sigmoid(self.linear2(context))
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return y*m
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def seg_pooling(self, x, seg_len=100, stype='avg'):
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if stype == 'avg':
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seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
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elif stype == 'max':
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seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
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else:
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raise ValueError('Wrong segment pooling type.')
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shape = seg.shape
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seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
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seg = seg[..., :x.shape[-1]]
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return seg
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class CAMDenseTDNNLayer(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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bn_channels,
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kernel_size,
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stride=1,
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dilation=1,
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bias=False,
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config_str='batchnorm-relu',
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memory_efficient=False):
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super(CAMDenseTDNNLayer, self).__init__()
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assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
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kernel_size)
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padding = (kernel_size - 1) // 2 * dilation
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self.memory_efficient = memory_efficient
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self.nonlinear1 = get_nonlinear(config_str, in_channels)
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self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
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self.nonlinear2 = get_nonlinear(config_str, bn_channels)
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self.cam_layer = CAMLayer(bn_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=bias)
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def bn_function(self, x):
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return self.linear1(self.nonlinear1(x))
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def forward(self, x):
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if self.training and self.memory_efficient:
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x = cp.checkpoint(self.bn_function, x)
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else:
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x = self.bn_function(x)
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x = self.cam_layer(self.nonlinear2(x))
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return x
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class CAMDenseTDNNBlock(nn.ModuleList):
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def __init__(self,
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num_layers,
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in_channels,
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out_channels,
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bn_channels,
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kernel_size,
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stride=1,
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dilation=1,
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bias=False,
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config_str='batchnorm-relu',
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memory_efficient=False):
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super(CAMDenseTDNNBlock, self).__init__()
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for i in range(num_layers):
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layer = CAMDenseTDNNLayer(in_channels=in_channels + i * out_channels,
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out_channels=out_channels,
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bn_channels=bn_channels,
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kernel_size=kernel_size,
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stride=stride,
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dilation=dilation,
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bias=bias,
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config_str=config_str,
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memory_efficient=memory_efficient)
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self.add_module('tdnnd%d' % (i + 1), layer)
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def forward(self, x):
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for layer in self:
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x = torch.cat([x, layer(x)], dim=1)
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return x
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class TransitLayer(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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bias=True,
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config_str='batchnorm-relu'):
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super(TransitLayer, self).__init__()
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self.nonlinear = get_nonlinear(config_str, in_channels)
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self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
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def forward(self, x):
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x = self.nonlinear(x)
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x = self.linear(x)
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return x
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class DenseLayer(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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bias=False,
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config_str='batchnorm-relu'):
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super(DenseLayer, self).__init__()
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self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
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self.nonlinear = get_nonlinear(config_str, out_channels)
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def forward(self, x):
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if len(x.shape) == 2:
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x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
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else:
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x = self.linear(x)
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x = self.nonlinear(x)
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return x
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class BasicResBlock(nn.Module):
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(BasicResBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_planes,
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planes,
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kernel_size=3,
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stride=(stride, 1),
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padding=1,
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bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes,
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planes,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes,
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self.expansion * planes,
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kernel_size=1,
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stride=(stride, 1),
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bias=False),
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nn.BatchNorm2d(self.expansion * planes))
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x)
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out = F.relu(out)
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return out |
