![]() ![]() ![]() It consist of 10 layers? it will run these 10, it’s a single one? no problem. They return a nn.Sequential because they can run it without the functions knows “what’s inside” So in the code you are pointing to, they build different ResNet architectures with the same function. It makes the forward to be readable and compact. So nn.Sequential is a construction which is used when you want to run certain layers sequentially. # our inputīut using a nn.Sequential the same code can be called in a single line So this two blocks of code are equivalent: x =. Secondly, nn.Sequential runs the three layers at once, this is, it takes the input, run layer1, take output1 and feed layer2 with it, take output2 and feed layer3 giving as result output3. That is way there exist a list-like layer which is a nn.Module. First of all, python lists are not registered in a nn.Module which will lead to issues. Layers in this snippet is a standard python list. Model.load_state_dict(model_zoo.load_url(model_urls)) Pretrained (bool): If True, returns a model pre-trained on ImageNet Layers.append(block(self.inplanes, planes))ĭef resnet18(pretrained=False, **kwargs): Layers.append(block(self.inplanes, planes, stride, downsample)) Nn.BatchNorm2d(planes * block.expansion), Kernel_size=1, stride=stride, bias=False), Nn.Conv2d(self.inplanes, planes * block.expansion, I am new to PyTorch/Deep learning and I am trying to understand the use of the following line to define a convolutional layer: self.layer1 nn.Sequential (nn.Conv1d (inputdim, nconvfilters, kernelsize7, padding0), nn.ReLU (), nn. If stride != 1 or self.inplanes != planes * block.expansion: Below is an example of computing the MAE and MSE between two vectors: 1. It is named as L1 because the computation of MAE is also called the L1-norm in mathematics. #self.inplanes为上个box_block的输出channel,planes为当前box_block块的输入channel In PyTorch, you can create MAE and MSE as loss functions using nn.L1Loss () and nn.MSELoss () respectively. n))ĭef _make_layer(self, block, planes, blocks, stride=1): N = m.kernel_size * m.kernel_size * m.out_channels Self.fc = nn.Linear(512 * block.expansion, num_classes) Self.layer4 = self._make_layer(block, 512, layers, stride=2) Self.layer3 = self._make_layer(block, 256, layers, stride=2) Self.layer2 = self._make_layer(block, 128, layers, stride=2) Self.layer1 = self._make_layer(block, 64, layers) Self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) nv1 = conv3x3(inplanes, planes, stride)ĭef _init_(self, block, layers, num_classes=10): Return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,ĭef _init_(self, inplanes, planes, stride=1, downsample=None): 定义resnet18ĭef conv3x3(in_planes, out_planes, stride=1): If this is the case, why not use return layers (I don’t know whether this is feasible)directly. I can’t understand the code,and I didn’t find a specific explanation in the reference books :ĭoes it mean that the last returned value is (layers),For example, if layers =, by executing this line of code, the final return value is still. I have a simple question about Resnet-18.(but I really don’t understand) The following is the network structure code of Resnet-18. Out = mlist(x) # this will cause an errorįor nn.Sequential, this is not the case: seqlist = nn.Sequential(nn.Linear(10, 10), nn.ReLU(), nn.I am a beginner. You can define a ModuleList, but you can not call this mlist with input, this will cause an error: import torch You can think nn.Sequential as a module and you can call it with a input like the normal module. On the other hand, nn.Sequential also contains a list of modules, but you need to make sure that output from current module can be fed into its next module, otherwise, you will get an error. ![]() So you can not call it like a normal module. Nn.ModuleList just stores a list nn.Modules and it does not have a forward() method. Both nn.ModuleList and nn.Sequential are containers that contains pytorch nn modules. ![]()
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