(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False) Model.fc = torch.nn.Linear(in_features=512, out_features=fc_out_features, bias=True)īut the above looks wrong, there are so many attributes…and the old ones seem to be there still too!!! # module = BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
# if type(module) = torch.nn.BatchNorm2d: This deosn’t seem to work either as now there are a bunch of extra fields that probably shouldn’t be there… for name, module in epcopy(model).named_modules(): How is this supposed to be done properly? RuntimeError: OrderedDict mutated during iterationīut I am ok with mutating it…I am doing this on purpose to build off the resnet models given… ack_running_stats = False Ok pytorch doesn’t like what I’m trying to do: for name, module in self._ems(): Torch.Size([1, could you please help me out here #self.avg_pool = nn.AdaptiveAvgPool2d((7,7)) Self.classifier = nn.Linear(512*512, num_classes)
Self.features = nn.Sequential(*list(originalModel.features)) I want to add the above code snippet to the transfer learning tutorial available on the pytorch website. # outer production of features on each position over height*width average pooling Self.classifier = nn.Linear(512 * 512, args.numClasses) Self.features = nn.Sequential(*list(original_vgg16.features)) # feature extraction from Conv5_3 with relu Super(VggBasedNet_bilinear, self)._init_() I have designed the code snipper that I want to attach after the final layers of VGG-Net but I don’t know-how. I am trying to modify the pretrained VGG-Net Classifier and modify the final layers for fine-grained classification. I am working on Bilinear CNN for Image Classification.
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