From e504fb0f82a3be50b06710c91ecce07358d4e625 Mon Sep 17 00:00:00 2001 From: wangwl Date: Mon, 22 Dec 2025 01:46:45 +0000 Subject: [PATCH 1/3] add SEResNet --- .../SEResNet/SEResNet/README.md | 5 + .../SEResNet/SEResNet/Se-ResNet.png | Bin 0 -> 109948 bytes .../SEResNet/SEResNet/model/__init__.py | 2 + .../SEResNet/SEResNet/model/resnet.py | 379 ++++++++++ .../SEResNet/SEResNet/model/se.py | 19 + .../SEResNet/SEResNet/model/seresnet.py | 186 +++++ .../Classification/SEResNet/coverage.txt | 3 + .../Classification/SEResNet/model/__init__.py | 2 + .../Classification/SEResNet/model/resnet.py | 379 ++++++++++ .../Classification/SEResNet/model/se.py | 19 + .../Classification/SEResNet/model/seresnet.py | 187 +++++ .../Classification/SEResNet/seresnet.py | 28 + .../Classification/SEResNet/seresnet_loss.jpg | Bin 0 -> 35249 bytes .../Classification/SEResNet/seresnet_loss.txt | 29 + .../Classification/SEResNet/weloTrainStep.py | 692 ++++++++++++++++++ 15 files changed, 1930 insertions(+) create mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/README.md create mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/Se-ResNet.png create mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/__init__.py create mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py create mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py create mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py create mode 100644 PyTorch/build-in/Classification/SEResNet/coverage.txt create mode 100644 PyTorch/build-in/Classification/SEResNet/model/__init__.py create mode 100644 PyTorch/build-in/Classification/SEResNet/model/resnet.py create mode 100644 PyTorch/build-in/Classification/SEResNet/model/se.py create mode 100644 PyTorch/build-in/Classification/SEResNet/model/seresnet.py create mode 100644 PyTorch/build-in/Classification/SEResNet/seresnet.py create mode 100644 PyTorch/build-in/Classification/SEResNet/seresnet_loss.jpg create mode 100644 PyTorch/build-in/Classification/SEResNet/seresnet_loss.txt create mode 100644 PyTorch/build-in/Classification/SEResNet/weloTrainStep.py diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/README.md b/PyTorch/build-in/Classification/SEResNet/SEResNet/README.md new file mode 100644 index 000000000..ab4a62afd --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/SEResNet/README.md @@ -0,0 +1,5 @@ +# SEResNet +SEResNet Code Snippet +
+ +
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znQ{{)&ypcP!reMFYB>|p0~z=&JWe19gxD{{$k2sz_x!yjTn*uP82Wkmr#{pDsX~uP z@a1}RR3PqFy6Z>9!Ec#-4cm;WD z%4{x2Hor?Ag*g{A_3Ll7=`7C+`GzUC8JE-6`47XbWB^NDioZ&*uSpSxyS?+KPkAk) z-?3YaERbBn+V8#ZCfK~cWggFI+X4`YggaL4N5*eo3j4*Uv$G7YOoxB7tsL-mJooa# z9|yVZhahL!Eo_jqhkn1gsV<$4L{Hc0ealvSGt>9j_k;-_!l*Bs>AU1d6(Us3?D=a|Huo82iwnprV7KHf|7A zM_qt|UIo35=ist{tc}=K%-3FD-pxr#p^zYVOtAN4%^U)|`g{Btt+ z^R^N2%yKOXwS@RDEB{+q59q*qvh|i}{O3*I-|OlRCxsaMNwI^VYOnP_N%J40?-J48 z5y4EtglBmF=feLv(u4X*@kRinH2%K~U9FV-Lw1ort8V}Im-Rh=QcQ-e$)5IaL;YVN aKBC61;`=3MT{}K|dgP>(B`d_=1^x%iMU{{M literal 0 HcmV?d00001 diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/__init__.py b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/__init__.py new file mode 100644 index 000000000..16ed6037d --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/__init__.py @@ -0,0 +1,2 @@ +from .resnet import * +from .seresnet import * diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py new file mode 100644 index 000000000..b4bb1c2ab --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py @@ -0,0 +1,379 @@ +import torch +import torch.nn as nn +from torchvision.models.utils import load_state_dict_from_url + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', + 'wide_resnet50_2', 'wide_resnet101_2', 'model_urls'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +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) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + 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) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + 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 + + 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 + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + 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.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + 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, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + 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 = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + 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.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + 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 = torch.flatten(x, 1) + x = self.fc(x) + + return x + + def forward(self, x): + return self._forward_impl(x) + + def load_from_resnet(self, state_dict): + loaded_param_names = [] + for module_name, module in self.named_modules(): + if isinstance(module, nn.Conv2d) or isinstance(module, nn.BatchNorm2d): + if module_name + '.weight' not in state_dict: + print(f'Module not exist in the state_dict: {module_name}') + elif isinstance(module, nn.Conv2d): + weight_name = module_name + '.weight' + module.weight.data.copy_(state_dict[weight_name]) + loaded_param_names.append(weight_name) + elif isinstance(module, nn.BatchNorm2d): + for param_name, param in module.named_parameters(): + name = f'{module_name}.{param_name}' + param.data.copy_(state_dict[name]) + loaded_param_names.append(name) + for param_name, param in module.named_buffers(): + name = f'{module_name}.{param_name}' + # some buffers like num_batches_tracked may not exist in old + # checkpoints + if name in state_dict: + param.data.copy_(state_dict[name]) + loaded_param_names.append(name) + # check if any parameters in the 2d checkpoint are not loaded + remaining_names = set(state_dict.keys()) - set(loaded_param_names) + if remaining_names: + print(f'These parameters in the 2d checkpoint are not loaded: {remaining_names}') + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_state_dict(state_dict) + model.fc = nn.Linear(512 * block.expansion, 2) + return model + + +def resnet18(pretrained=False, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet101(pretrained=False, progress=True, **kwargs): + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def resnet152(pretrained=False, progress=True, **kwargs): + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def resnext50_32x4d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def resnext101_32x8d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) + + +def wide_resnet50_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def wide_resnet101_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py new file mode 100644 index 000000000..53b4bbef0 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py @@ -0,0 +1,19 @@ +import torch.nn as nn + + +class SE(nn.Module): + def __init__(self, channels, reduction=16): + super(SE, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + out = self.avg_pool(x).view(b, c) + out = self.fc(out).view(b, c, 1, 1) + return x * out.expand_as(x) diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py new file mode 100644 index 000000000..ba649e822 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py @@ -0,0 +1,186 @@ +import torch.nn as nn +from torchvision.models.utils import load_state_dict_from_url +from .se import SE +from .resnet import ResNet +from .resnet import model_urls + + +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) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class SEBasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, reduction=16): + super(SEBasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.se = SE(planes, reduction) + self.downsample = downsample + self.stride = stride + + 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.se(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class SEBottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, reduction=16): + super(SEBottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + 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.se = SE(planes * self.expansion, reduction) + self.downsample = downsample + self.stride = stride + + 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) + out = self.se(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +def _seresnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + model.load_from_resnet(state_dict) + return model + + +def seresnet18(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet18', SEBasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def seresnet34(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet34', SEBasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def seresnet50(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet50', SEBottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def seresnet101(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet101', SEBottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def seresnet152(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet152', SEBottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def seresnext50(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _seresnet('resnext50_32x4d', SEBottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def seresnext101(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _seresnet('resnext101_32x8d', SEBottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SEResNet/coverage.txt b/PyTorch/build-in/Classification/SEResNet/coverage.txt new file mode 100644 index 000000000..eb3ebe943 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/coverage.txt @@ -0,0 +1,3 @@ +all api: ['_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale_', '_copy_from', '_has_compatible_shallow_copy_type', '_local_scalar_dense', '_log_softmax', '_log_softmax_backward_data', '_pin_memory', '_reshape_alias', 'add_', 'addmm', 'as_strided', 'as_strided_', 'convolution', 'convolution_backward', 'copy_stride', 'div', 'eq', 'fill_', 'fused_sgd', 'is_pinned', 'linear', 'matmul', 'max_pool2d', 'maxpool2d_backward', 'maxpool2d_forward', 'mean', 'mm', 'mul', 'mul_', 'native_batch_norm', 'native_batch_norm_backward', 'nll_loss_backward', 'nll_loss_forward', 'reciprocal', 'relu_', 'set_', 'sigmoid', 'sigmoid_backward', 'sum', 'threshold_backward', 'topk_out', 'view', 'zero_'], total: 44 +fallback op: [], total: 0 +coverage rate: 100.00% diff --git a/PyTorch/build-in/Classification/SEResNet/model/__init__.py b/PyTorch/build-in/Classification/SEResNet/model/__init__.py new file mode 100644 index 000000000..16ed6037d --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/model/__init__.py @@ -0,0 +1,2 @@ +from .resnet import * +from .seresnet import * diff --git a/PyTorch/build-in/Classification/SEResNet/model/resnet.py b/PyTorch/build-in/Classification/SEResNet/model/resnet.py new file mode 100644 index 000000000..d69df5d88 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/model/resnet.py @@ -0,0 +1,379 @@ +import torch +import torch.nn as nn +# from torchvision.models.utils import load_state_dict_from_url + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', + 'wide_resnet50_2', 'wide_resnet101_2', 'model_urls'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +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) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + 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) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + 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 + + 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 + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + 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.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + 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, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + 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 = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + 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.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + 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 = torch.flatten(x, 1) + x = self.fc(x) + + return x + + def forward(self, x): + return self._forward_impl(x) + + def load_from_resnet(self, state_dict): + loaded_param_names = [] + for module_name, module in self.named_modules(): + if isinstance(module, nn.Conv2d) or isinstance(module, nn.BatchNorm2d): + if module_name + '.weight' not in state_dict: + print(f'Module not exist in the state_dict: {module_name}') + elif isinstance(module, nn.Conv2d): + weight_name = module_name + '.weight' + module.weight.data.copy_(state_dict[weight_name]) + loaded_param_names.append(weight_name) + elif isinstance(module, nn.BatchNorm2d): + for param_name, param in module.named_parameters(): + name = f'{module_name}.{param_name}' + param.data.copy_(state_dict[name]) + loaded_param_names.append(name) + for param_name, param in module.named_buffers(): + name = f'{module_name}.{param_name}' + # some buffers like num_batches_tracked may not exist in old + # checkpoints + if name in state_dict: + param.data.copy_(state_dict[name]) + loaded_param_names.append(name) + # check if any parameters in the 2d checkpoint are not loaded + remaining_names = set(state_dict.keys()) - set(loaded_param_names) + if remaining_names: + print(f'These parameters in the 2d checkpoint are not loaded: {remaining_names}') + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + # if pretrained: + # state_dict = load_state_dict_from_url(model_urls[arch], + # progress=progress) + # model.load_state_dict(state_dict) + model.fc = nn.Linear(512 * block.expansion, 2) + return model + + +def resnet18(pretrained=False, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet101(pretrained=False, progress=True, **kwargs): + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def resnet152(pretrained=False, progress=True, **kwargs): + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def resnext50_32x4d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def resnext101_32x8d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) + + +def wide_resnet50_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def wide_resnet101_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SEResNet/model/se.py b/PyTorch/build-in/Classification/SEResNet/model/se.py new file mode 100644 index 000000000..53b4bbef0 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/model/se.py @@ -0,0 +1,19 @@ +import torch.nn as nn + + +class SE(nn.Module): + def __init__(self, channels, reduction=16): + super(SE, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channels, channels // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channels // reduction, channels, bias=False), + nn.Sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + out = self.avg_pool(x).view(b, c) + out = self.fc(out).view(b, c, 1, 1) + return x * out.expand_as(x) diff --git a/PyTorch/build-in/Classification/SEResNet/model/seresnet.py b/PyTorch/build-in/Classification/SEResNet/model/seresnet.py new file mode 100644 index 000000000..4a576468b --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/model/seresnet.py @@ -0,0 +1,187 @@ +import torch.nn as nn +# from torchvision.models.utils import load_state_dict_from_url +# from torch.hub import load_state_dict_from_url +from .se import SE +from .resnet import ResNet +from .resnet import model_urls + + +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) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class SEBasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, reduction=16): + super(SEBasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.se = SE(planes, reduction) + self.downsample = downsample + self.stride = stride + + 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.se(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class SEBottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, reduction=16): + super(SEBottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + 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.se = SE(planes * self.expansion, reduction) + self.downsample = downsample + self.stride = stride + + 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) + out = self.se(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +def _seresnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + # if pretrained: + # state_dict = load_state_dict_from_url(model_urls[arch], + # progress=progress) + # model.load_from_resnet(state_dict) + return model + + +def seresnet18(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet18', SEBasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def seresnet34(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet34', SEBasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def seresnet50(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet50', SEBottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def seresnet101(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet101', SEBottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def seresnet152(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _seresnet('resnet152', SEBottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def seresnext50(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _seresnet('resnext50_32x4d', SEBottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def seresnext101(pretrained=False, progress=True, **kwargs): + r""" + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _seresnet('resnext101_32x8d', SEBottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SEResNet/seresnet.py b/PyTorch/build-in/Classification/SEResNet/seresnet.py new file mode 100644 index 000000000..a91a463b7 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/seresnet.py @@ -0,0 +1,28 @@ +import model.seresnet as seresnet + +def Model(num_classes=1000, variant='18', pretrained=False): + """ + Simple factory returning a SE-ResNet / SE-ResNeXt variant. + variant: '18', '34', '50', '101', '152', 'next50', 'next101' + """ + if variant == '18': + return seresnet.seresnet18(pretrained=pretrained, num_classes=num_classes) + elif variant == '34': + return seresnet.seresnet34(pretrained=pretrained, num_classes=num_classes) + elif variant == '50': + return seresnet.seresnet50(pretrained=pretrained, num_classes=num_classes) + elif variant == '101': + return seresnet.seresnet101(pretrained=pretrained, num_classes=num_classes) + elif variant == '152': + return seresnet.seresnet152(pretrained=pretrained, num_classes=num_classes) + elif variant.lower() == 'next50': + return seresnet.seresnext50(pretrained=pretrained, num_classes=num_classes) + elif variant.lower() == 'next101': + return seresnet.seresnext101(pretrained=pretrained, num_classes=num_classes) + else: + raise ValueError(f"Unknown variant: {variant}") + +# 测试 +if __name__ == "__main__": + m = Model(num_classes=100, variant='50', pretrained=False) + print(m) \ No newline at end of file diff --git a/PyTorch/build-in/Classification/SEResNet/seresnet_loss.jpg b/PyTorch/build-in/Classification/SEResNet/seresnet_loss.jpg new file mode 100644 index 0000000000000000000000000000000000000000..45419a78f1ed1f8ff87b8cf435c9c00a9ca033ed GIT binary patch literal 35249 zcmeEu1zcQNmVV(HNP>HU1cHSi!Kn~}2M=xu8a%iKia;Q^1_p@nOLyIbMz?obr} 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+5.243700 4.860000 4.901700 4.972000 5.217600 5.257200 5.172400 4.954800 4.766100 4.733300 +4.817900 4.636500 4.600600 4.679000 4.723000 4.554800 4.635600 4.738800 4.598000 4.688700 +4.503400 4.537000 4.530600 4.524300 4.748800 4.348600 4.677400 4.480400 4.628000 4.406400 +4.279100 4.536600 4.501700 4.544200 4.625900 4.618700 4.645000 4.531900 4.632300 4.532500 +4.444700 4.552500 4.445000 4.595700 4.423200 4.563300 4.466200 4.277400 4.460700 4.536500 +4.452100 4.384400 4.355400 4.371800 4.386600 4.504900 4.364500 4.567600 4.362700 4.366000 +4.266800 4.490200 4.527800 4.241300 4.365700 4.196000 4.123600 4.373100 4.352100 4.619600 +4.412000 4.571500 4.294300 4.252300 4.580000 4.244400 4.426400 4.422000 4.171400 4.378200 + +=== SDAA === +4.602800 4.731700 4.830000 4.766100 4.891000 5.397000 5.432600 5.317800 5.863900 5.501600 +5.457100 5.346400 6.533400 5.124500 5.276400 5.267400 5.038100 5.121800 5.358700 5.454500 +4.813100 5.272200 5.198100 5.094100 5.284500 5.408300 5.481300 4.846000 4.872300 4.808600 +4.660000 4.657000 4.653100 4.599700 4.572500 4.637400 4.947900 4.489300 4.650700 4.717900 +4.861600 4.580800 4.686800 4.619700 4.634500 4.542100 4.379000 4.431700 4.826700 4.346300 +4.705800 4.580100 4.716400 4.448000 4.369200 4.503800 4.443400 4.491000 4.508300 4.872600 +4.615700 4.301000 4.593000 4.433000 4.428700 4.405500 4.361100 4.385100 4.493400 4.370100 +4.424200 4.407500 4.355100 4.535700 4.399500 4.194800 4.240400 4.206800 4.275900 4.276500 +4.489900 4.542500 4.198100 4.294400 4.433000 4.338900 4.693600 4.418000 4.445000 4.181000 +4.365200 4.454500 4.264700 4.420800 4.562200 4.226300 4.210100 4.245600 4.312500 4.325800 + +=== RESULT === +MeanRelativeError: 0.0037770064676498137 +MeanAbsoluteError: 0.01585200000000009 +Rule,mean_relative_error 0.0037770064676498137 +pass mean_relative_error=0.0037770064676498137 <= 0.05 or mean_absolute_error=0.01585200000000009 <= 0.0002 diff --git a/PyTorch/build-in/Classification/SEResNet/weloTrainStep.py b/PyTorch/build-in/Classification/SEResNet/weloTrainStep.py new file mode 100644 index 000000000..13297c11b --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/weloTrainStep.py @@ -0,0 +1,692 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import os +import random +import sys +import time +import json +import argparse +from collections import OrderedDict +from pathlib import Path +import numpy as np +import pandas as pd +from tqdm import tqdm +import importlib + +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" # 强烈推荐在 shell/最顶端设置 +os.environ["PYTHONHASHSEED"] = "12345" +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +def ensure_cublas_workspace(config=":4096:8"): + """ + 尝试为 cuBLAS 设置可复现 workspace。强烈建议在主脚本入口处（import torch 之前） + 通过 export 设置该 env。此函数会在运行时设置，但如果 torch 已经被 import， + 则可能为时已晚——函数会打印提醒。 + """ + already = os.environ.get("CUBLAS_WORKSPACE_CONFIG") + if already: + print(f"[seed_utils] CUBLAS_WORKSPACE_CONFIG 已存在：{already}") + else: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = config + print(f"[seed_utils] 已设置 CUBLAS_WORKSPACE_CONFIG={config} （注意：请在 import torch 前设置以保证生效）") + +def set_global_seed(seed: int = 42, set_threads: bool = True): + """ + 统一随机性设置。注意：若希望完全发挥效果，请在主脚本入口（import torch 之前） + 先调用 ensure_cublas_workspace(...) 或在 shell 中 export CUBLAS_WORKSPACE_CONFIG。 + """ + ensure_cublas_workspace() # 会设置 env 并提醒 + os.environ["PYTHONHASHSEED"] = str(seed) + + if set_threads: + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["MKL_NUM_THREADS"] = "1" + + random.seed(seed) + np.random.seed(seed) + + # 现在导入 torch（晚导入以便前面 env 生效） + import torch + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # 强制确定性（如果存在不确定性算子，PyTorch 会报错并提示） + try: + torch.use_deterministic_algorithms(True) + except Exception as e: + print("[seed_utils] 设置 deterministic 模式时出错：", e) + print("[seed_utils] 请确认 CUBLAS_WORKSPACE_CONFIG 已在 import torch 之前设置。") + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + if set_threads: + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + + print(f"[seed_utils] 全局 seed 已设置为 {seed}") + +set_global_seed(2025) + +""" +通用训练模版（优先从本地导入 Model -> 支持 DDP / 单卡，AMP，resume，日志，checkpoint） +保存为 train_template_localmodel.py +""" +import torch +import torch.nn as nn +import torch.optim as optim +import torch.backends.cudnn as cudnn +import torchvision.transforms as transforms +import torchvision.datasets as datasets +import torchvision.models as tv_models + +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from torch.sdaa import amp +# from torch.cuda import amp + + +# ---------------------------- +# Helper utilities (self-contained) +# ---------------------------- +class AverageMeter(object): + def __init__(self, name='Meter', fmt=':.4f'): + self.name = name + self.fmt = fmt + self.reset() + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / max(1, self.count) + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} (avg {avg' + self.fmt + '})' + return fmtstr.format(name=self.name, val=self.val, avg=self.avg) + +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k + 返回一个 list，每个元素是 tensor（百分比形式） + """ + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + # output: (N, C) -> pred: (maxk, N) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() # (maxk, N) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (maxk, N) bool + + res = [] + for k in topk: + # 把前 k 行展平后求和（返回 0-dim tensor），随后换算为百分比 + correct_k = correct[:k].reshape(-1).float().sum() # 注意：不传 keepdim + # 乘以 100.0 / batch_size，保持返回 tensor（和之前代码兼容） + res.append(correct_k.mul_(100.0 / batch_size)) + return res + +def save_checkpoint(state, is_best, save_dir, filename='checkpoint.pth'): + save_path = os.path.join(save_dir, filename) + torch.save(state, save_path) + if is_best: + best_path = os.path.join(save_dir, 'model_best.pth') + torch.save(state, best_path) + +def set_seed(seed, deterministic=False): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if deterministic: + cudnn.deterministic = True + cudnn.benchmark = False + else: + cudnn.deterministic = False + cudnn.benchmark = True + +# ---------------------------- +# Argument parser +# ---------------------------- +def parse_args(): + parser = argparse.ArgumentParser(description='Generic PyTorch training template (DDP/AMP) with LocalModel priority') + parser.add_argument('--name', default='run', type=str, help='experiment name (log/checkpoints dir)') + parser.add_argument('--seed', default=42, type=int, help='random seed') + parser.add_argument('--arch', default='None', type=str, help='model name') + parser.add_argument('--deterministic', action='store_true', help='set cudnn deterministic (may be slower)') + parser.add_argument('--dataset', default='cifar10', choices=['cifar10','cifar100','imagenet','custom'], help='which dataset') + parser.add_argument('--datapath', default='./data', type=str, help='dataset root / imagenet root / custom root') + parser.add_argument('--imagenet_dir', default='./imagenet', type=str, help='if dataset=imagenet, path to imagenet root') + parser.add_argument('--custom_eval_dir', default=None, help='if dataset=custom, provide val dir') + parser.add_argument('--num_workers', default=4, type=int, help='dataloader workers per process') + parser.add_argument('--epochs', default=200, type=int) + parser.add_argument('--steps', default=0, type=int, help='max steps to run (if >0, training will stop when global_step reaches this).') + parser.add_argument('--batch_size', default=128, type=int) + parser.add_argument('--model_name', default='resnet18', help='torchvision model name or python path e.g. mypkg.mymodule.Model (used if no local Model)') + parser.add_argument('--num_classes', default=None, type=int, help='override num classes (auto-detect for common sets)') + parser.add_argument('--pretrained', action='store_true', help='use torchvision pretrained weights when available') + parser.add_argument('--optimizer', default='sgd', choices=['sgd','adam','adamw'], help='optimizer') + parser.add_argument('--lr', '--learning_rate', default=0.1, type=float) + parser.add_argument('--momentum', default=0.9, type=float) + parser.add_argument('--weight_decay', default=5e-4, type=float) + parser.add_argument('--nesterov', action='store_true') + parser.add_argument('--scheduler', default='multistep', choices=['multistep','step','cosine','none'], help='lr scheduler') + parser.add_argument('--milestones', default='100,150', type=str, help='milestones for multistep (comma sep)') + parser.add_argument('--step_size', default=30, type=int, help='step size for StepLR or cosine max epochs') + parser.add_argument('--gamma', default=0.1, type=float) + parser.add_argument('--scheduler_step_per_batch', action='store_true', help='call scheduler.step() per batch (for some schedulers)') + parser.add_argument('--resume', default='', type=str, help='path to checkpoint to resume from') + parser.add_argument('--start_epoch', default=0, type=int) + parser.add_argument('--print_freq', default=100, type=int) + parser.add_argument('--save_freq', default=10, type=int, help='save checkpoint every N epochs (rank0 only)') + parser.add_argument('--amp', action='store_true', default = True,help='use automatic mixed precision (AMP)') + parser.add_argument('--grad_accum_steps', default=1, type=int, help='gradient accumulation steps') + parser.add_argument('--local_rank', default=None, type=int, help='local rank passed by torchrun (if any). Use -1 or None for non-distributed') + parser.add_argument('--cutmix_prob', default=0.0, type=float) + parser.add_argument('--beta', default=1.0, type=float) + parser.add_argument('--seed_sampler', default=False, action='store_true', help='set sampler epoch seeds to make deterministic distributed shuffling') + args = parser.parse_args() + args.milestones = [int(x) for x in args.milestones.split(',')] if args.milestones else [] + return args + +# ---------------------------- +# build model (优先 LocalModel) +# ---------------------------- +def build_model_with_local_priority(args, device=None): + """ + 用参数 args.arch 作为模块名导入 Model() + 如果模块不存在或没有 Model 类，则报错停止。 + """ + try: + # 动态导入模块，比如 args.arch = "rexnet" + mod = importlib.import_module(args.arch) + Model = getattr(mod, "Model") # 从模块中获取 Model 类 + except Exception as e: + raise RuntimeError( + f"无法导入模型模块 '{args.arch}' 或未找到类 Model。" + f"\n错误信息：{e}" + ) + + # 解析数据集类别数 + if args.dataset == 'cifar10': + num_classes = 10 + elif args.dataset == 'cifar100': + num_classes = 100 + else: + print(f"[ERROR] 不支持的数据集类型：{args.dataset}，无法确定类别数。程序终止。") + sys.exit(1) + + + # 实例化 + try: + model = Model(num_classes) + except Exception as e: + raise RuntimeError( + f"Model() 实例化失败，请检查模型构造函数。\n错误信息：{e}" + ) + + return model + +# ---------------------------- +# Data loader factory +# ---------------------------- +def build_dataloaders(args, rank, world_size): + if args.dataset == 'cifar10' or args.dataset == 'cifar100': + mean = (0.4914, 0.4822, 0.4465) + std = (0.2470, 0.2435, 0.2616) if args.dataset == 'cifar10' else (0.2023, 0.1994, 0.2010) + # train_transform = transforms.Compose([ + # transforms.RandomCrop(32, padding=4), + # transforms.RandomHorizontalFlip(), + # transforms.ToTensor(), + # transforms.Normalize(mean, std), + # ]) + # test_transform = transforms.Compose([ + # transforms.ToTensor(), + # transforms.Normalize(mean, std), + # ]) + + train_transform = transforms.Compose([ # 2025/12/3 从visformer模型开始 + transforms.Resize(256), # 先放大到 256 + transforms.RandomCrop(224), # 再随机裁剪为 224（更符合 ImageNet 风格增强） + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + test_transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + root = args.datapath + if args.dataset == 'cifar10': + train_set = datasets.CIFAR10(root=root, train=True, download=False, transform=train_transform) + val_set = datasets.CIFAR10(root=root, train=False, download=False, transform=test_transform) + num_classes = 10 + else: + train_set = datasets.CIFAR100(root=root, train=True, download=False, transform=train_transform) + val_set = datasets.CIFAR100(root=root, train=False, download=False, transform=test_transform) + num_classes = 100 + + elif args.dataset == 'imagenet': + train_dir = os.path.join(args.imagenet_dir, 'train') + val_dir = os.path.join(args.imagenet_dir, 'val') + train_transform = transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225)), + ]) + test_transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225)), + ]) + train_set = datasets.ImageFolder(train_dir, train_transform) + val_set = datasets.ImageFolder(val_dir, test_transform) + num_classes = args.num_classes or 1000 + + elif args.dataset == 'custom': + train_dir = os.path.join(args.datapath, 'train') + val_dir = args.custom_eval_dir or os.path.join(args.datapath, 'val') + train_transform = transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ]) + test_transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + ]) + train_set = datasets.ImageFolder(train_dir, train_transform) + val_set = datasets.ImageFolder(val_dir, test_transform) + num_classes = len(train_set.classes) + else: + raise ValueError("Unknown dataset") + + if dist.is_initialized() and world_size > 1: + train_sampler = DistributedSampler(train_set, num_replicas=world_size, rank=rank, shuffle=True) + else: + train_sampler = None + + train_loader = DataLoader(train_set, + batch_size=args.batch_size, + shuffle=(train_sampler is None), + num_workers=args.num_workers, + pin_memory=True, + sampler=train_sampler, + drop_last=False) + val_loader = DataLoader(val_set, + batch_size=args.batch_size, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True) + + return train_loader, val_loader, num_classes, train_sampler + +# ---------------------------- +# Train & validate +# ---------------------------- +def train_one_epoch(args, epoch, model, criterion, optimizer, train_loader, device, scaler, scheduler=None, train_sampler=None, global_step_start=0, max_global_steps=None): + """ + 现在支持：若 max_global_steps 非 None，则当 global_step 达到该值时提前退出 + 返回: epoch_summary_dict, step_logs_list, global_step_end + step_logs_list: list of dicts with per-step info (for logging to CSV if需要) + """ + batch_time = AverageMeter('Time') + data_time = AverageMeter('Data') + losses = AverageMeter('Loss') + top1 = AverageMeter('Acc@1') + top5 = AverageMeter('Acc@5') + + model.train() + end = time.time() + optimizer.zero_grad() + + iters = len(train_loader) + step_logs = [] + global_step = global_step_start + + for i, (images, targets) in enumerate(train_loader): + # check global steps limit + if (max_global_steps is not None) and (global_step >= max_global_steps): + break + + data_time.update(time.time() - end) + images = images.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + + if args.amp: + with amp.autocast(): + outputs = model(images) + loss = criterion(outputs, targets) / args.grad_accum_steps + else: + outputs = model(images) + loss = criterion(outputs, targets) / args.grad_accum_steps + + if args.amp: + scaler.scale(loss).backward() + else: + loss.backward() + + # 每当累积步满足 grad_accum_steps 就 step + if (i + 1) % args.grad_accum_steps == 0: + if args.amp: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + optimizer.zero_grad() + if scheduler is not None and args.scheduler_step_per_batch: + scheduler.step() + + with torch.no_grad(): + acc1, acc5 = accuracy(outputs, targets, topk=(1,5)) + losses.update(loss.item() * args.grad_accum_steps, images.size(0)) + top1.update(acc1.item(), images.size(0)) + top5.update(acc5.item(), images.size(0)) + + batch_time.update(time.time() - end) + end = time.time() + + # increment global step AFTER processing this batch + global_step += 1 + + # per-step print (controlled by print_freq) + if ((global_step % args.print_freq == 0) or (i == iters - 1)) and ((dist.get_rank() if dist.is_initialized() else 0) == 0): + lr = optimizer.param_groups[0]['lr'] + print(f"Epoch[{epoch}]:step[{i+1}/{iters}] step_train_loss {losses.val:.4f} acc1 {top1.val:.2f} acc5 {top5.val:.2f}") + + # collect per-step log + step_logs.append({ + 'epoch': epoch, + 'batch_idx': i, + 'global_step': global_step, + 'lr': optimizer.param_groups[0]['lr'], + 'loss': losses.val, + 'loss_avg': losses.avg, + 'acc1': top1.val, + 'acc1_avg': top1.avg, + 'acc5': top5.val, + 'acc5_avg': top5.avg, + 'time': batch_time.val + }) + + # if reached max_global_steps inside epoch, break (handled at loop start next iter) + if (max_global_steps is not None) and (global_step >= max_global_steps): + if (dist.get_rank() if dist.is_initialized() else 0) == 0: + print(f"[Info] 达到 max_global_steps={max_global_steps}，将在 epoch 内提前停止。") + break + + # --- flush remaining grads if needed (handle gradient accumulation leftovers) --- + processed_batches = global_step - global_step_start # 实际处理的 batch 数 + if args.grad_accum_steps > 1 and (processed_batches % args.grad_accum_steps) != 0: + # only step if there are gradients + grads_present = any((p.grad is not None and p.requires_grad) for p in model.parameters()) + if grads_present: + if args.amp: + try: + scaler.step(optimizer) + scaler.update() + except Exception as e: + # 防御性：若 scaler.step 因某些原因失败，尝试普通 step（只在极端情况下） + print("[Warning] scaler.step 失败，尝试普通 optimizer.step():", e) + optimizer.step() + else: + optimizer.step() + optimizer.zero_grad() + if scheduler is not None and args.scheduler_step_per_batch: + scheduler.step() + if (dist.get_rank() if dist.is_initialized() else 0) == 0: + print(f"[Info] flushed remaining gradients after early stop (processed_batches={processed_batches}, grad_accum={args.grad_accum_steps}).") + + if scheduler is not None and not args.scheduler_step_per_batch: + scheduler.step() + + return OrderedDict([('loss', losses.avg), ('acc1', top1.avg), ('acc5', top5.avg)]), step_logs, global_step + +def validate(args, model, val_loader, criterion, device, max_batches=None): + """ + Validate on the val_loader. + If max_batches is not None, only process up to that many batches (useful for quick checks). + Returns an OrderedDict with loss/acc1/acc5 (averaged over processed samples). + """ + losses = AverageMeter('Loss') + top1 = AverageMeter('Acc@1') + top5 = AverageMeter('Acc@5') + + model.eval() + processed_batches = 0 + processed_samples = 0 + with torch.no_grad(): + for i, (images, targets) in enumerate(tqdm(val_loader)): + images = images.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + outputs = model(images) + loss = criterion(outputs, targets) + acc1, acc5 = accuracy(outputs, targets, topk=(1,5)) + batch_n = images.size(0) + losses.update(loss.item(), batch_n) + top1.update(acc1.item(), batch_n) + top5.update(acc5.item(), batch_n) + + processed_batches += 1 + processed_samples += batch_n + + if (max_batches is not None) and (processed_batches >= max_batches): + break + + # 如果没处理任何样本，避免除0（不太可能，但防御性） + if processed_samples == 0: + return OrderedDict([('loss', 0.0), ('acc1', 0.0), ('acc5', 0.0)]) + return OrderedDict([('loss', losses.avg), ('acc1', top1.avg), ('acc5', top5.avg)]) + +# ---------------------------- +# Main +# ---------------------------- +def main(): + args = parse_args() + + # handle local_rank from env if not provided + local_rank_env = os.environ.get('LOCAL_RANK', None) + if args.local_rank is None and local_rank_env is not None: + args.local_rank = int(local_rank_env) + + distributed = (args.local_rank is not None and args.local_rank != -1) + if distributed: + dist.init_process_group(backend='nccl', init_method='env://') + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + + if distributed: + torch.cuda.set_device(args.local_rank) + device = torch.device('cuda', args.local_rank) + else: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + set_seed(args.seed + (rank if distributed else 0), deterministic=args.deterministic) + + save_dir = os.path.join('models', args.name) + if rank == 0: + os.makedirs(save_dir, exist_ok=True) + with open(os.path.join(save_dir, 'args.json'), 'w') as f: + json.dump(vars(args), f, indent=2) + if distributed: + dist.barrier() + + train_loader, val_loader, auto_num_classes, train_sampler = build_dataloaders(args, rank, world_size) + if args.num_classes is None: + args.num_classes = auto_num_classes + + # 使用本地 Model 优先（LocalModel 已在文件顶部尝试导入） + model = build_model_with_local_priority(args, device) + model.to(device) + + if distributed: + model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) + + criterion = nn.CrossEntropyLoss().to(device) + params = [p for p in model.parameters() if p.requires_grad] + if args.optimizer == 'sgd': + optimizer = optim.SGD(params, lr=args.lr, momentum=args.momentum, + weight_decay=args.weight_decay, nesterov=args.nesterov) + elif args.optimizer == 'adam': + optimizer = optim.Adam(params, lr=args.lr, weight_decay=args.weight_decay) + elif args.optimizer == 'adamw': + optimizer = optim.AdamW(params, lr=args.lr, weight_decay=args.weight_decay) + else: + raise ValueError('Unknown optimizer') + + scheduler = None + if args.scheduler == 'multistep': + scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=args.gamma) + elif args.scheduler == 'step': + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=args.gamma) + elif args.scheduler == 'cosine': + scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs) + elif args.scheduler == 'none': + scheduler = None + + scaler = amp.GradScaler() if args.amp else None + + start_epoch = args.start_epoch + best_acc = 0.0 + if args.resume: + if os.path.isfile(args.resume): + ckpt = torch.load(args.resume, map_location='cpu') + model_state = ckpt.get('state_dict', ckpt) + if isinstance(model, DDP): + model.module.load_state_dict(model_state) + else: + model.load_state_dict(model_state) + if 'optimizer' in ckpt: + optimizer.load_state_dict(ckpt['optimizer']) + start_epoch = ckpt.get('epoch', start_epoch) + best_acc = ckpt.get('best_acc', best_acc) + print(f"=> resumed from {args.resume}, start_epoch={start_epoch}") + else: + print(f"=> resume path {args.resume} not found") + + log_columns = ['epoch', 'lr', 'loss', 'acc1', 'acc5', 'val_loss', 'val_acc1', 'val_acc5'] + log_df = pd.DataFrame(columns=log_columns) + # step-level log + step_log_columns = ['epoch', 'batch_idx', 'global_step', 'lr', 'loss', 'loss_avg', 'acc1', 'acc1_avg', 'acc5', 'acc5_avg', 'time'] + step_log_df = pd.DataFrame(columns=step_log_columns) + + total_epochs = args.epochs + # global_step计数器（训练过程中跨epoch持续） + global_step = 0 + + epoch = start_epoch + # loop until either epoch criteria or step criteria met + while True: + if train_sampler is not None: + if args.seed_sampler: + train_sampler.set_epoch(epoch + args.seed) + else: + train_sampler.set_epoch(epoch) + + if rank == 0: + print(f"==== Epoch {epoch}/{total_epochs - 1} ====") + + # 如果传入了 args.steps (>0)，则把剩余允许的 step 数传给 train_one_epoch， + # 否则 max_global_steps=None（按整 epoch 执行完） + if args.steps and args.steps > 0: + max_global_steps = args.steps + else: + max_global_steps = None + + train_log, step_logs, global_step = train_one_epoch( + args, epoch, model, criterion, optimizer, train_loader, device, scaler, + scheduler, train_sampler, global_step_start=global_step, max_global_steps=max_global_steps + ) + + # 如果启用了按 steps 的模式且已经达到上限，标记需要在做一次验证后退出 + if max_global_steps is not None and global_step >= max_global_steps: + if rank == 0: + print(f"[Main] 达到 max_global_steps={max_global_steps}（global_step={global_step}），将在完成验证后退出训练。") + # 我们不 return 立刻退出；后面的 validate / 保存逻辑会执行一次，然后 main 返回/结束 + end_due_to_steps = True + else: + end_due_to_steps = False + + # 验证并记录 epoch 级别日志（如果在 step 模式下很可能在中间某个 epoch 提前结束，但我们仍做一次 validate） + val_log = validate(args, model, val_loader, criterion, device, args.batch_size) + current_lr = optimizer.param_groups[0]['lr'] + + if rank == 0: + # epoch summary print, 格式与示例对齐 + print(f"Epoch[{epoch}]: epoch_train_loss {train_log['loss']:.4f} acc1 {train_log['acc1']:.2f} acc5 {train_log['acc5']:.2f} | " + f"val_loss {val_log['loss']:.4f} acc1 {val_log['acc1']:.2f} acc5 {val_log['acc5']:.2f} lr {current_lr:.6f}") + row = { + 'epoch': epoch, + 'lr': current_lr, + 'loss': train_log['loss'], + 'acc1': train_log['acc1'], + 'acc5': train_log['acc5'], + 'val_loss': val_log['loss'], + 'val_acc1': val_log['acc1'], + 'val_acc5': val_log['acc5'], + } + new_row_df = pd.DataFrame([row]) + log_df = pd.concat([log_df, new_row_df], ignore_index=True) + log_df.to_csv(os.path.join(save_dir, 'log.csv'), index=False) + + is_best = val_log['acc1'] > best_acc + if is_best: + best_acc = val_log['acc1'] + if (epoch % args.save_freq == 0) or is_best or ( (max_global_steps is None) and (epoch == total_epochs - 1) ) : + state = { + 'epoch': epoch, + 'state_dict': model.module.state_dict() if isinstance(model, DDP) else model.state_dict(), + 'best_acc': best_acc, + 'optimizer': optimizer.state_dict(), + 'args': vars(args) + } + save_checkpoint(state, is_best, save_dir, filename=f'checkpoint_epoch_{epoch}.pth') + + # 如果是因为 steps 模式达到上限，则在完成 validation / 保存后退出训练 + if end_due_to_steps: + if rank == 0: + print(f"[Main] 已在 steps 模式下完成最后一次验证并保存，训练结束（global_step={global_step}）。") + break + + # increment epoch + epoch += 1 + + # stopping conditions: + # 1) if steps mode enabled and reached steps -> stop + if args.steps and args.steps > 0: + if global_step >= args.steps: + if rank == 0: + print(f"[Main] 已达到指定 steps={args.steps}（global_step={global_step}），训练结束。") + break + + # 2) if steps not used, stop when epoch >= epochs + else: + if epoch >= total_epochs: + if rank == 0: + print(f"[Main] 已达到指定 epochs={total_epochs}（epoch={epoch}），训练结束。") + break + + if dist.is_initialized(): + dist.barrier() + if rank == 0: + print("Training finished. Best val acc1: {:.2f}".format(best_acc)) + +if __name__ == '__main__': + main() \ No newline at end of file From cd7c91fba01bcdc973e5bc69b54effc0ea49eddb Mon Sep 17 00:00:00 2001 From: wangwl Date: Wed, 7 Jan 2026 06:42:50 +0000 Subject: [PATCH 2/3] fix: cleanup code and update --- .../SEResNet/SEResNet/README.md | 5 - .../SEResNet/SEResNet/Se-ResNet.png | Bin 109948 -> 0 bytes .../SEResNet/SEResNet/model/__init__.py | 2 - .../SEResNet/SEResNet/model/resnet.py | 379 ------------------ .../SEResNet/SEResNet/model/se.py | 19 - .../SEResNet/SEResNet/model/seresnet.py | 186 --------- .../Classification/SEResNet/coverage.txt | 3 - .../build-in/Classification/SEResNet/readme | 65 +++ .../SEResNet/requirements_exact.txt | 89 ++++ .../Classification/SEResNet/seresnet_loss.jpg | Bin 35249 -> 0 bytes .../Classification/SEResNet/seresnet_loss.txt | 29 -- 11 files changed, 154 insertions(+), 623 deletions(-) delete mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/README.md delete mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/Se-ResNet.png delete mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/__init__.py delete mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py delete mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py delete mode 100644 PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py delete mode 100644 PyTorch/build-in/Classification/SEResNet/coverage.txt create mode 100644 PyTorch/build-in/Classification/SEResNet/readme create mode 100644 PyTorch/build-in/Classification/SEResNet/requirements_exact.txt delete mode 100644 PyTorch/build-in/Classification/SEResNet/seresnet_loss.jpg delete mode 100644 PyTorch/build-in/Classification/SEResNet/seresnet_loss.txt diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/README.md b/PyTorch/build-in/Classification/SEResNet/SEResNet/README.md deleted file mode 100644 index ab4a62afd..000000000 --- a/PyTorch/build-in/Classification/SEResNet/SEResNet/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# SEResNet -SEResNet Code Snippet -
- -
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b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py deleted file mode 100644 index b4bb1c2ab..000000000 --- a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/resnet.py +++ /dev/null @@ -1,379 +0,0 @@ -import torch -import torch.nn as nn -from torchvision.models.utils import load_state_dict_from_url - -__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', - 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', - 'wide_resnet50_2', 'wide_resnet101_2', 'model_urls'] - -model_urls = { - 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', - 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', - 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', - 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', - 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', - 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', - 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', - 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', - 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', -} - - -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) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None): - super(BasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - if groups != 1 or base_width != 64: - raise ValueError('BasicBlock only supports groups=1 and base_width=64') - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = norm_layer(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = norm_layer(planes) - self.downsample = downsample - self.stride = stride - - 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) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) - # while original implementation places the stride at the first 1x1 convolution(self.conv1) - # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. - # This variant is also known as ResNet V1.5 and improves accuracy according to - # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. - - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None): - super(Bottleneck, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - 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 - - 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 - - -class ResNet(nn.Module): - - def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, - groups=1, width_per_group=64, replace_stride_with_dilation=None, - norm_layer=None): - super(ResNet, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - self._norm_layer = norm_layer - - self.inplanes = 64 - self.dilation = 1 - 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.groups = groups - self.base_width = width_per_group - self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, - bias=False) - self.bn1 = norm_layer(self.inplanes) - 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, - dilate=replace_stride_with_dilation[0]) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2, - dilate=replace_stride_with_dilation[1]) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2, - dilate=replace_stride_with_dilation[2]) - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - self.fc = nn.Linear(512 * block.expansion, num_classes) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - # Zero-initialize the last BN in each residual branch, - # so that the residual branch starts with zeros, and each residual block behaves like an identity. - # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 - if zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - nn.init.constant_(m.bn3.weight, 0) - elif isinstance(m, BasicBlock): - nn.init.constant_(m.bn2.weight, 0) - - def _make_layer(self, block, planes, blocks, stride=1, dilate=False): - norm_layer = self._norm_layer - 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 = [] - layers.append(block(self.inplanes, planes, stride, downsample, self.groups, - 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.groups, - base_width=self.base_width, dilation=self.dilation, - norm_layer=norm_layer)) - - return nn.Sequential(*layers) - - def _forward_impl(self, x): - # See note [TorchScript super()] - 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 = torch.flatten(x, 1) - x = self.fc(x) - - return x - - def forward(self, x): - return self._forward_impl(x) - - def load_from_resnet(self, state_dict): - loaded_param_names = [] - for module_name, module in self.named_modules(): - if isinstance(module, nn.Conv2d) or isinstance(module, nn.BatchNorm2d): - if module_name + '.weight' not in state_dict: - print(f'Module not exist in the state_dict: {module_name}') - elif isinstance(module, nn.Conv2d): - weight_name = module_name + '.weight' - module.weight.data.copy_(state_dict[weight_name]) - loaded_param_names.append(weight_name) - elif isinstance(module, nn.BatchNorm2d): - for param_name, param in module.named_parameters(): - name = f'{module_name}.{param_name}' - param.data.copy_(state_dict[name]) - loaded_param_names.append(name) - for param_name, param in module.named_buffers(): - name = f'{module_name}.{param_name}' - # some buffers like num_batches_tracked may not exist in old - # checkpoints - if name in state_dict: - param.data.copy_(state_dict[name]) - loaded_param_names.append(name) - # check if any parameters in the 2d checkpoint are not loaded - remaining_names = set(state_dict.keys()) - set(loaded_param_names) - if remaining_names: - print(f'These parameters in the 2d checkpoint are not loaded: {remaining_names}') - - -def _resnet(arch, block, layers, pretrained, progress, **kwargs): - model = ResNet(block, layers, **kwargs) - if pretrained: - state_dict = load_state_dict_from_url(model_urls[arch], - progress=progress) - model.load_state_dict(state_dict) - model.fc = nn.Linear(512 * block.expansion, 2) - return model - - -def resnet18(pretrained=False, progress=True, **kwargs): - r"""ResNet-18 model from - `"Deep Residual Learning for Image Recognition" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, - **kwargs) - - -def resnet34(pretrained=False, progress=True, **kwargs): - r"""ResNet-34 model from - `"Deep Residual Learning for Image Recognition" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, - **kwargs) - - -def resnet50(pretrained=False, progress=True, **kwargs): - r"""ResNet-50 model from - `"Deep Residual Learning for Image Recognition" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, - **kwargs) - - -def resnet101(pretrained=False, progress=True, **kwargs): - r"""ResNet-101 model from - `"Deep Residual Learning for Image Recognition" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, - **kwargs) - - -def resnet152(pretrained=False, progress=True, **kwargs): - r"""ResNet-152 model from - `"Deep Residual Learning for Image Recognition" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, - **kwargs) - - -def resnext50_32x4d(pretrained=False, progress=True, **kwargs): - r"""ResNeXt-50 32x4d model from - `"Aggregated Residual Transformation for Deep Neural Networks" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 4 - return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], - pretrained, progress, **kwargs) - - -def resnext101_32x8d(pretrained=False, progress=True, **kwargs): - r"""ResNeXt-101 32x8d model from - `"Aggregated Residual Transformation for Deep Neural Networks" `_ - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 8 - return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], - pretrained, progress, **kwargs) - - -def wide_resnet50_2(pretrained=False, progress=True, **kwargs): - r"""Wide ResNet-50-2 model from - `"Wide Residual Networks" `_ - - The model is the same as ResNet except for the bottleneck number of channels - which is twice larger in every block. The number of channels in outer 1x1 - convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 - channels, and in Wide ResNet-50-2 has 2048-1024-2048. - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - kwargs['width_per_group'] = 64 * 2 - return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], - pretrained, progress, **kwargs) - - -def wide_resnet101_2(pretrained=False, progress=True, **kwargs): - r"""Wide ResNet-101-2 model from - `"Wide Residual Networks" `_ - - The model is the same as ResNet except for the bottleneck number of channels - which is twice larger in every block. The number of channels in outer 1x1 - convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 - channels, and in Wide ResNet-50-2 has 2048-1024-2048. - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - kwargs['width_per_group'] = 64 * 2 - return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], - pretrained, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py deleted file mode 100644 index 53b4bbef0..000000000 --- a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/se.py +++ /dev/null @@ -1,19 +0,0 @@ -import torch.nn as nn - - -class SE(nn.Module): - def __init__(self, channels, reduction=16): - super(SE, self).__init__() - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.fc = nn.Sequential( - nn.Linear(channels, channels // reduction, bias=False), - nn.ReLU(inplace=True), - nn.Linear(channels // reduction, channels, bias=False), - nn.Sigmoid() - ) - - def forward(self, x): - b, c, _, _ = x.size() - out = self.avg_pool(x).view(b, c) - out = self.fc(out).view(b, c, 1, 1) - return x * out.expand_as(x) diff --git a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py b/PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py deleted file mode 100644 index ba649e822..000000000 --- a/PyTorch/build-in/Classification/SEResNet/SEResNet/model/seresnet.py +++ /dev/null @@ -1,186 +0,0 @@ -import torch.nn as nn -from torchvision.models.utils import load_state_dict_from_url -from .se import SE -from .resnet import ResNet -from .resnet import model_urls - - -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) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -class SEBasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None, reduction=16): - super(SEBasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - if groups != 1 or base_width != 64: - raise ValueError('BasicBlock only supports groups=1 and base_width=64') - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = norm_layer(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = norm_layer(planes) - self.se = SE(planes, reduction) - self.downsample = downsample - self.stride = stride - - 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.se(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - - return out - - -class SEBottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, - base_width=64, dilation=1, norm_layer=None, reduction=16): - super(SEBottleneck, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - 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.se = SE(planes * self.expansion, reduction) - self.downsample = downsample - self.stride = stride - - 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) - out = self.se(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - - return out - - -def _seresnet(arch, block, layers, pretrained, progress, **kwargs): - model = ResNet(block, layers, **kwargs) - if pretrained: - state_dict = load_state_dict_from_url(model_urls[arch], - progress=progress) - model.load_from_resnet(state_dict) - return model - - -def seresnet18(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _seresnet('resnet18', SEBasicBlock, [2, 2, 2, 2], pretrained, progress, - **kwargs) - - -def seresnet34(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _seresnet('resnet34', SEBasicBlock, [3, 4, 6, 3], pretrained, progress, - **kwargs) - - -def seresnet50(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _seresnet('resnet50', SEBottleneck, [3, 4, 6, 3], pretrained, progress, - **kwargs) - - -def seresnet101(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _seresnet('resnet101', SEBottleneck, [3, 4, 23, 3], pretrained, progress, - **kwargs) - - -def seresnet152(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _seresnet('resnet152', SEBottleneck, [3, 8, 36, 3], pretrained, progress, - **kwargs) - - -def seresnext50(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 4 - return _seresnet('resnext50_32x4d', SEBottleneck, [3, 4, 6, 3], - pretrained, progress, **kwargs) - - -def seresnext101(pretrained=False, progress=True, **kwargs): - r""" - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 8 - return _seresnet('resnext101_32x8d', SEBottleneck, [3, 4, 23, 3], - pretrained, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SEResNet/coverage.txt b/PyTorch/build-in/Classification/SEResNet/coverage.txt deleted file mode 100644 index eb3ebe943..000000000 --- a/PyTorch/build-in/Classification/SEResNet/coverage.txt +++ /dev/null @@ -1,3 +0,0 @@ -all api: ['_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale_', '_copy_from', '_has_compatible_shallow_copy_type', '_local_scalar_dense', '_log_softmax', '_log_softmax_backward_data', '_pin_memory', '_reshape_alias', 'add_', 'addmm', 'as_strided', 'as_strided_', 'convolution', 'convolution_backward', 'copy_stride', 'div', 'eq', 'fill_', 'fused_sgd', 'is_pinned', 'linear', 'matmul', 'max_pool2d', 'maxpool2d_backward', 'maxpool2d_forward', 'mean', 'mm', 'mul', 'mul_', 'native_batch_norm', 'native_batch_norm_backward', 'nll_loss_backward', 'nll_loss_forward', 'reciprocal', 'relu_', 'set_', 'sigmoid', 'sigmoid_backward', 'sum', 'threshold_backward', 'topk_out', 'view', 'zero_'], total: 44 -fallback op: [], total: 0 -coverage rate: 100.00% diff --git a/PyTorch/build-in/Classification/SEResNet/readme b/PyTorch/build-in/Classification/SEResNet/readme new file mode 100644 index 000000000..f56c6462b --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/readme @@ -0,0 +1,65 @@ +```markdown +## 1. 模型链接 +- 原始仓库链接： +https://github.com/huggingface/pytorch-image-models?tab=readme-ov-file#models + +## 2. 快速开始 + +使用本模型执行训练的主要流程如下： + +1. **基础环境安装**：介绍训练前需要完成的基础环境检查和安装。 +2. **获取数据集**：介绍如何获取训练所需的数据集。 +3. **构建环境**：介绍如何构建模型运行所需要的环境。 +4. **启动训练**：介绍如何运行训练。 + +### 2.1 基础环境安装 + +请参考主仓库的基础环境安装章节，完成训练前的基础环境检查和安装（如驱动、固件等）。 + +### 2.2 准备数据集 + +#### 2.2.1 获取数据集 + +训练使用 **CIFAR-100** 数据集。该数据集为开源数据集，包含 100 个类别的 60000 张彩色图像。 + +#### 2.2.2 处理数据集 + +请确保数据集已下载并解压。根据训练脚本的默认配置，建议将数据集存放在模型目录的上级 `data` 目录中（即 `../data`），或者根据实际路径修改训练命令中的 `--datapath` 参数。 + +### 2.3 构建环境 + +所使用的环境下需包含 PyTorch 框架虚拟环境。 + +1. 执行以下命令，启动虚拟环境（根据实际环境名称修改）： + + ```bash + conda activate torch_env_py310 + +``` + +2. 安装 Python 依赖。确保已安装项目所需的依赖包： +```bash +pip install -r requirements_exact.txt + +``` + + + +### 2.4 启动训练 + +1. 在构建好的环境中，进入模型训练脚本所在目录。 + +2. 运行训练。该模型支持单机单卡训练。 +执行以下命令启动训练（使用 CIFAR-100 数据集，Batch Size 为 128）： +```bash +python weloTrainStep.py \ + --name train \ + --arch seresnet \ + --print_freq 1 \ + --steps 100 \ + --dataset cifar100 \ + --datapath ../data \ + --batch_size 32 \ + --epochs 100 + +``` diff --git a/PyTorch/build-in/Classification/SEResNet/requirements_exact.txt b/PyTorch/build-in/Classification/SEResNet/requirements_exact.txt new file mode 100644 index 000000000..7394b3319 --- /dev/null +++ b/PyTorch/build-in/Classification/SEResNet/requirements_exact.txt @@ -0,0 +1,89 @@ +addict==2.4.0 +aliyun-python-sdk-core==2.16.0 +aliyun-python-sdk-kms==2.16.5 +anyio==4.11.0 +astunparse==1.6.3 +certifi==2024.12.14 +cffi==2.0.0 +charset-normalizer==3.4.1 +click==8.3.1 +colorama==0.4.6 +contourpy==1.3.2 +crcmod==1.7 +cryptography==46.0.3 +cycler==0.12.1 +einops==0.8.1 +exceptiongroup==1.3.1 +filelock==3.14.0 +fonttools==4.60.1 +fsspec==2024.12.0 +future @ file:///croot/future_1730902796226/work +git-filter-repo==2.47.0 +h11==0.16.0 +hf-xet==1.2.0 +httpcore==1.0.9 +httpx==0.28.1 +huggingface_hub==1.1.5 +idna==3.10 +inplace-abn @ git+https://github.com/mapillary/inplace_abn.git@b50bfe9c7cd7116a3ab091a352b48d6ba5ee701c +Jinja2==3.1.5 +jmespath==0.10.0 +joblib==1.5.2 +kiwisolver==1.4.9 +Markdown==3.10 +markdown-it-py==4.0.0 +MarkupSafe==3.0.2 +matplotlib==3.10.7 +mdurl==0.1.2 +mmdet==3.3.0 +mmengine==0.10.7 +model-index==0.1.11 +mpmath==1.3.0 +networkx==3.4.2 +numpy==1.23.5 +opencv-python==4.12.0.88 +opendatalab==0.0.10 +openmim==0.3.9 +openxlab==0.1.3 +ordered-set==4.1.0 +oss2==2.17.0 +packaging @ file:///croot/packaging_1734472117206/work +pandas==2.3.3 +pillow==11.1.0 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5.123900 5.286200 4.771400 -5.243700 4.860000 4.901700 4.972000 5.217600 5.257200 5.172400 4.954800 4.766100 4.733300 -4.817900 4.636500 4.600600 4.679000 4.723000 4.554800 4.635600 4.738800 4.598000 4.688700 -4.503400 4.537000 4.530600 4.524300 4.748800 4.348600 4.677400 4.480400 4.628000 4.406400 -4.279100 4.536600 4.501700 4.544200 4.625900 4.618700 4.645000 4.531900 4.632300 4.532500 -4.444700 4.552500 4.445000 4.595700 4.423200 4.563300 4.466200 4.277400 4.460700 4.536500 -4.452100 4.384400 4.355400 4.371800 4.386600 4.504900 4.364500 4.567600 4.362700 4.366000 -4.266800 4.490200 4.527800 4.241300 4.365700 4.196000 4.123600 4.373100 4.352100 4.619600 -4.412000 4.571500 4.294300 4.252300 4.580000 4.244400 4.426400 4.422000 4.171400 4.378200 - -=== SDAA === -4.602800 4.731700 4.830000 4.766100 4.891000 5.397000 5.432600 5.317800 5.863900 5.501600 -5.457100 5.346400 6.533400 5.124500 5.276400 5.267400 5.038100 5.121800 5.358700 5.454500 -4.813100 5.272200 5.198100 5.094100 5.284500 5.408300 5.481300 4.846000 4.872300 4.808600 -4.660000 4.657000 4.653100 4.599700 4.572500 4.637400 4.947900 4.489300 4.650700 4.717900 -4.861600 4.580800 4.686800 4.619700 4.634500 4.542100 4.379000 4.431700 4.826700 4.346300 -4.705800 4.580100 4.716400 4.448000 4.369200 4.503800 4.443400 4.491000 4.508300 4.872600 -4.615700 4.301000 4.593000 4.433000 4.428700 4.405500 4.361100 4.385100 4.493400 4.370100 -4.424200 4.407500 4.355100 4.535700 4.399500 4.194800 4.240400 4.206800 4.275900 4.276500 -4.489900 4.542500 4.198100 4.294400 4.433000 4.338900 4.693600 4.418000 4.445000 4.181000 -4.365200 4.454500 4.264700 4.420800 4.562200 4.226300 4.210100 4.245600 4.312500 4.325800 - -=== RESULT === -MeanRelativeError: 0.0037770064676498137 -MeanAbsoluteError: 0.01585200000000009 -Rule,mean_relative_error 0.0037770064676498137 -pass mean_relative_error=0.0037770064676498137 <= 0.05 or mean_absolute_error=0.01585200000000009 <= 0.0002 From 21b3103f69a386e499677a76cc3d85a56df01a5b Mon Sep 17 00:00:00 2001 From: root Date: Thu, 8 Jan 2026 10:28:20 +0000 Subject: [PATCH 3/3] fix: rename files and update code --- PyTorch/build-in/Classification/SEResNet/{readme => readme.md} | 0 .../SEResNet/{requirements_exact.txt => requirements.txt} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename PyTorch/build-in/Classification/SEResNet/{readme => readme.md} (100%) rename PyTorch/build-in/Classification/SEResNet/{requirements_exact.txt => requirements.txt} (100%) diff --git a/PyTorch/build-in/Classification/SEResNet/readme b/PyTorch/build-in/Classification/SEResNet/readme.md similarity index 100% rename from PyTorch/build-in/Classification/SEResNet/readme rename to PyTorch/build-in/Classification/SEResNet/readme.md diff --git a/PyTorch/build-in/Classification/SEResNet/requirements_exact.txt b/PyTorch/build-in/Classification/SEResNet/requirements.txt similarity index 100% rename from PyTorch/build-in/Classification/SEResNet/requirements_exact.txt rename to PyTorch/build-in/Classification/SEResNet/requirements.txt