Source code for super_gradients.training.models.resnext

"""ResNeXt in PyTorch.

See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.

Code adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.nn.functional as F
from super_gradients.training.models.sg_module import SgModule


[docs]def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation)
[docs]def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
[docs]class GroupedConvBlock(nn.Module): """Grouped convolution block.""" expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(GroupedConvBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self.norm_layer = norm_layer width = int(planes * (base_width / 64.)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride
[docs] def forward(self, x): identity = 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: identity = self.downsample(x) out += identity out = self.relu(out) return out
[docs]class ResNeXt(SgModule): def __init__(self, layers, cardinality, bottleneck_width, num_classes=10, replace_stride_with_dilation=None): super(ResNeXt, self).__init__() if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.cardinality = cardinality self.dilation = 1 self.inplanes = 64 self.base_width = bottleneck_width self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.inplanes) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(GroupedConvBlock, 64, layers[0]) self.layer2 = self._make_layer(GroupedConvBlock, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(GroupedConvBlock, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) if len(layers) == 4: self.layer4 = self._make_layer(GroupedConvBlock, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) end_width = 512 if len(layers) == 4 else 256 self.fc = nn.Linear(end_width * GroupedConvBlock.expansion, num_classes) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = nn.BatchNorm2d downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [block(self.inplanes, planes, stride, downsample, self.cardinality, self.base_width, previous_dilation, norm_layer)] self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.cardinality, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer)) return nn.Sequential(*layers)
[docs] def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) if self.layer4 is not None: out = self.layer4(out) out = self.avgpool(out) out = out.view(out.size(0), -1) out = self.fc(out) return out
[docs]def CustomizedResNeXt(arch_params): return ResNeXt(layers=arch_params.structure if hasattr(arch_params, "structure") else [3, 3, 3], bottleneck_width=arch_params.num_init_features if hasattr(arch_params, "bottleneck_width") else 64, cardinality=arch_params.bn_size if hasattr(arch_params, "cardinality") else 32, num_classes=arch_params.num_classes, replace_stride_with_dilation=arch_params.replace_stride_with_dilation if hasattr(arch_params, "replace_stride_with_dilation") else None)
[docs]def ResNeXt50(arch_params): return ResNeXt(layers=[3, 4, 6, 3], cardinality=32, bottleneck_width=4, num_classes=arch_params.num_classes)
[docs]def ResNeXt101(arch_params): return ResNeXt(layers=[3, 4, 23, 3], cardinality=32, bottleneck_width=8, num_classes=arch_params.num_classes)