From e65d9ef0a05ad9a2c2aa4db9e85a920576b81195 Mon Sep 17 00:00:00 2001 From: Jaeyong Kang Date: Fri, 3 Nov 2023 09:14:53 +0800 Subject: [PATCH] Delete utilities/resnext.py --- utilities/resnext.py | 175 ------------------------------------------- 1 file changed, 175 deletions(-) delete mode 100755 utilities/resnext.py diff --git a/utilities/resnext.py b/utilities/resnext.py deleted file mode 100755 index 47328772..00000000 --- a/utilities/resnext.py +++ /dev/null @@ -1,175 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Variable -import math -from functools import partial - -__all__ = ['ResNeXt', 'resnet50', 'resnet101'] - -def conv3x3x3(in_planes, out_planes, stride=1): - # 3x3x3 convolution with padding - return nn.Conv3d(in_planes, out_planes, kernel_size=3, - stride=stride, padding=1, bias=False) - - -def downsample_basic_block(x, planes, stride): - out = F.avg_pool3d(x, kernel_size=1, stride=stride) - zero_pads = torch.Tensor(out.size(0), planes - out.size(1), - out.size(2), out.size(3), - out.size(4)).zero_() - if isinstance(out.data, torch.cuda.FloatTensor): - zero_pads = zero_pads.cuda() - - out = Variable(torch.cat([out.data, zero_pads], dim=1)) - - return out - - -class ResNeXtBottleneck(nn.Module): - expansion = 2 - - def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None): - super(ResNeXtBottleneck, self).__init__() - mid_planes = cardinality * int(planes / 32) - self.conv1 = nn.Conv3d(inplanes, mid_planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm3d(mid_planes) - self.conv2 = nn.Conv3d(mid_planes, mid_planes, kernel_size=3, stride=stride, - padding=1, groups=cardinality, bias=False) - self.bn2 = nn.BatchNorm3d(mid_planes) - self.conv3 = nn.Conv3d(mid_planes, planes * self.expansion, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm3d(planes * self.expansion) - 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, sample_size, sample_duration, shortcut_type='B', cardinality=32, num_classes=400, last_fc=True): - self.last_fc = last_fc - - self.inplanes = 64 - super(ResNeXt, self).__init__() - self.conv1 = nn.Conv3d(3, 64, kernel_size=7, stride=(1, 2, 2), - padding=(3, 3, 3), bias=False) - self.bn1 = nn.BatchNorm3d(64) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1) - self.layer1 = self._make_layer(block, 128, layers[0], shortcut_type, cardinality) - self.layer2 = self._make_layer(block, 256, layers[1], shortcut_type, cardinality, stride=2) - self.layer3 = self._make_layer(block, 512, layers[2], shortcut_type, cardinality, stride=2) - self.layer4 = self._make_layer(block, 1024, layers[3], shortcut_type, cardinality, stride=2) - last_duration = math.ceil(sample_duration / 16) - last_size = math.ceil(sample_size / 32) - self.avgpool = nn.AvgPool3d((last_duration, last_size, last_size), stride=1) - self.fc = nn.Linear(cardinality * 32 * block.expansion, num_classes) - - for m in self.modules(): - if isinstance(m, nn.Conv3d): - 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.BatchNorm3d): - m.weight.data.fill_(1) - m.bias.data.zero_() - - def _make_layer(self, block, planes, blocks, shortcut_type, cardinality, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - if shortcut_type == 'A': - downsample = partial(downsample_basic_block, - planes=planes * block.expansion, - stride=stride) - else: - downsample = nn.Sequential( - nn.Conv3d(self.inplanes, planes * block.expansion, - kernel_size=1, stride=stride, bias=False), - nn.BatchNorm3d(planes * block.expansion) - ) - - layers = [] - layers.append(block(self.inplanes, planes, cardinality, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes, cardinality)) - - return nn.Sequential(*layers) - - def forward(self, x): - 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) - - x = self.avgpool(x) - - x = x.view(x.size(0), -1) - if self.last_fc: - x = self.fc(x) - - return x - -def get_fine_tuning_parameters(model, ft_begin_index): - if ft_begin_index == 0: - return model.parameters() - - ft_module_names = [] - for i in range(ft_begin_index, 5): - ft_module_names.append('layer{}'.format(ft_begin_index)) - ft_module_names.append('fc') - - parameters = [] - for k, v in model.named_parameters(): - for ft_module in ft_module_names: - if ft_module in k: - parameters.append({'params': v}) - break - else: - parameters.append({'params': v, 'lr': 0.0}) - - return parameters - -def resnet50(**kwargs): - """Constructs a ResNet-50 model. - """ - model = ResNeXt(ResNeXtBottleneck, [3, 4, 6, 3], **kwargs) - return model - -def resnet101(**kwargs): - """Constructs a ResNet-101 model. - """ - model = ResNeXt(ResNeXtBottleneck, [3, 4, 23, 3], **kwargs) - return model - -def resnet152(**kwargs): - """Constructs a ResNet-101 model. - """ - model = ResNeXt(ResNeXtBottleneck, [3, 8, 36, 3], **kwargs) - return model