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