116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .initialization import initialize_resnet
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class WideBasicblock(nn.Module):
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def __init__(self, inplanes, planes, stride, dropout=False):
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super(WideBasicblock, self).__init__()
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self.bn_a = nn.BatchNorm2d(inplanes)
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self.conv_a = nn.Conv2d(
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inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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self.bn_b = nn.BatchNorm2d(planes)
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if dropout:
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self.dropout = nn.Dropout2d(p=0.5, inplace=True)
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else:
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self.dropout = None
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self.conv_b = nn.Conv2d(
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planes, planes, kernel_size=3, stride=1, padding=1, bias=False
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)
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if inplanes != planes:
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self.downsample = nn.Conv2d(
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inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False
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)
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else:
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self.downsample = None
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def forward(self, x):
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basicblock = self.bn_a(x)
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basicblock = F.relu(basicblock)
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basicblock = self.conv_a(basicblock)
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basicblock = self.bn_b(basicblock)
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basicblock = F.relu(basicblock)
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if self.dropout is not None:
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basicblock = self.dropout(basicblock)
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basicblock = self.conv_b(basicblock)
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if self.downsample is not None:
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x = self.downsample(x)
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return x + basicblock
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class CifarWideResNet(nn.Module):
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"""
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ResNet optimized for the Cifar dataset, as specified in
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https://arxiv.org/abs/1512.03385.pdf
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"""
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def __init__(self, depth, widen_factor, num_classes, dropout):
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super(CifarWideResNet, self).__init__()
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# Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
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assert (depth - 4) % 6 == 0, "depth should be one of 20, 32, 44, 56, 110"
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layer_blocks = (depth - 4) // 6
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print(
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"CifarPreResNet : Depth : {} , Layers for each block : {}".format(
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depth, layer_blocks
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)
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)
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self.num_classes = num_classes
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self.dropout = dropout
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self.conv_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
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self.message = "Wide ResNet : depth={:}, widen_factor={:}, class={:}".format(
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depth, widen_factor, num_classes
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)
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self.inplanes = 16
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self.stage_1 = self._make_layer(
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WideBasicblock, 16 * widen_factor, layer_blocks, 1
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)
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self.stage_2 = self._make_layer(
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WideBasicblock, 32 * widen_factor, layer_blocks, 2
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)
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self.stage_3 = self._make_layer(
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WideBasicblock, 64 * widen_factor, layer_blocks, 2
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)
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self.lastact = nn.Sequential(
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nn.BatchNorm2d(64 * widen_factor), nn.ReLU(inplace=True)
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)
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self.avgpool = nn.AvgPool2d(8)
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self.classifier = nn.Linear(64 * widen_factor, num_classes)
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self.apply(initialize_resnet)
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def get_message(self):
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return self.message
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def _make_layer(self, block, planes, blocks, stride):
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layers = []
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layers.append(block(self.inplanes, planes, stride, self.dropout))
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self.inplanes = planes
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, 1, self.dropout))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv_3x3(x)
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x = self.stage_1(x)
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x = self.stage_2(x)
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x = self.stage_3(x)
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x = self.lastact(x)
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x = self.avgpool(x)
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features = x.view(x.size(0), -1)
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outs = self.classifier(features)
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return features, outs
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