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195 lines
6.8 KiB
195 lines
6.8 KiB
import torch.nn as nn
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import torch.nn.functional as F
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import math
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import torch.utils.model_zoo as model_zoo
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from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d
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__all__ = ['ResNeXt', 'resnext50', 'resnext101', 'resnext152']
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def conv3x3(in_planes, out_planes, stride=1):
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"3x3 convolution with padding"
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return nn.Conv2d(
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in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
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class ResNeXtBottleneckC(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=32, base_width=4):
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super(ResNeXtBottleneckC, self).__init__()
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width = math.floor(planes / 64 * cardinality * base_width)
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self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(width)
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self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
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padding=1, bias=False, groups=cardinality)
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self.bn2 = nn.BatchNorm2d(width)
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self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNeXt(nn.Module):
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def __init__(self, block, layers, num_classes=1000, cardinality=32, base_width=4, shortcut='C',
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drop_rate=0., global_pool='avg'):
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self.num_classes = num_classes
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self.inplanes = 64
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self.cardinality = cardinality
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self.base_width = base_width
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self.shortcut = shortcut
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self.drop_rate = drop_rate
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super(ResNeXt, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
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self.num_features = 512 * block.expansion
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self.fc = nn.Linear(self.num_features, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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reshape = stride != 1 or self.inplanes != planes * block.expansion
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use_conv = (self.shortcut == 'C') or (self.shortcut == 'B' and reshape)
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if use_conv:
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downsample = nn.Sequential(
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nn.Conv2d(
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self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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elif reshape:
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downsample = nn.AvgPool2d(3, stride=stride)
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layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width)]
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self.inplanes = planes * block.expansion
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if self.shortcut == 'C':
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shortcut = nn.Sequential(
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nn.Conv2d(
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self.inplanes, planes * block.expansion, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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else:
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shortcut = None
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, 1, shortcut, self.cardinality, self.base_width))
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return nn.Sequential(*layers)
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def get_classifier(self):
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return self.fc
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
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self.num_classes = num_classes
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del self.fc
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if num_classes:
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self.fc = nn.Linear(self.num_features, num_classes)
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else:
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self.fc = None
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def forward_features(self, x, pool=True):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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if pool:
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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return x
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def forward(self, x):
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x = self.forward_features(x)
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if self.drop_rate > 0.:
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x = F.dropout(x, p=self.drop_rate, training=self.training)
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x = self.fc(x)
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return x
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def resnext50(cardinality=32, base_width=4, shortcut='C', pretrained=False, **kwargs):
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"""Constructs a ResNeXt-50 model.
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Args:
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cardinality (int): Cardinality of the aggregated transform
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base_width (int): Base width of the grouped convolution
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shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
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"""
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model = ResNeXt(
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ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality,
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base_width=base_width, shortcut=shortcut, **kwargs)
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return model
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def resnext101(cardinality=32, base_width=4, shortcut='C', pretrained=False, **kwargs):
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"""Constructs a ResNeXt-101 model.
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Args:
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cardinality (int): Cardinality of the aggregated transform
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base_width (int): Base width of the grouped convolution
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shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
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"""
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model = ResNeXt(
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ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality,
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base_width=base_width, shortcut=shortcut, **kwargs)
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return model
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def resnext152(cardinality=32, base_width=4, shortcut='C', pretrained=False, **kwargs):
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"""Constructs a ResNeXt-152 model.
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Args:
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cardinality (int): Cardinality of the aggregated transform
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base_width (int): Base width of the grouped convolution
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shortcut ('A'|'B'|'C'): 'B' use 1x1 conv to downsample, 'C' use 1x1 conv on every residual connection
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"""
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model = ResNeXt(
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ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality,
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base_width=base_width, shortcut=shortcut, **kwargs)
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return model
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