super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = ...
kernel_size=5, stride=2),nn.LeakyReLU(0.02),nn.BatchNorm2d(10),# 10 filters to 10 filtersnn.Conv2d(10, 10, kernel_size=3, stride=2),nn.LeakyReLU(0.02),nn.BatchNorm2d(10),View(250),nn.Linear(250, 10),nn.Sigmoid())
# 源代码 for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.relu(bn(conv(new_points))) # 更改后的代码 for conv, bn in zip(self.mlp_convs, self.mlp_bns): new_points = F.relu(bn(conv(new_points))) 1. 2. 3. 4. 5. 6. 7. 4.Previous re...
conv2(x))) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x # Initialize model model = TheModelClass() # Initialize optimizer optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # Print model's ...
3、 windows下在C:\Users\Administrator，linux系统在~/下新建一个.pypirc，里面写入：[distutils]index...
这个地方实现了在不写C的情况下直接执行forward,有一些自定义操作没有C,会直接调用python的版本。 这一步开始,调用了forward方法,首先会调用Net类的forward方法,然后会以此调用Conv2d的__call__()方法等。 当调用Conv2d()的forward方法,其forward方法写在了torch._C下: ...
self.conv1= nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1= nn.BatchNorm2d(64) 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]) ...
outputs = self.conv2d(x):对输入进行卷积处理,这一步output的形状是[batch,out_channels,p_w,p_h] outputs = outputs.view(x.size(0), -1, self.dim_caps):将4D的卷积输出变为3D的胶囊输出形式,output的形状为[batch,caps_num,dim_caps],其中caps_num为胶囊数量,可自动计算;dim_caps为胶囊长度,需要...
importtorchimporttorch.nnasnnimporttorch.nn.functionalasFclassMyModule(nn.Module):def__init__(self):super(MyModule,self).__init__()# torch.jit.trace produces a ScriptModule's conv1 and conv2 self.conv1=torch.jit.trace(nn.Conv2d(1,20,5),torch.rand(1,1,16,16))self.conv2=torch.jit...
ConvNeXt (CVPR'2022) MAE (CVPR'2022) PoolFormer (CVPR'2022) SegNeXt (NeurIPS'2022) 已支持的算法: FCN (CVPR'2015/TPAMI'2017) ERFNet (T-ITS'2017) UNet (MICCAI'2016/Nat. Methods'2019) PSPNet (CVPR'2017) DeepLabV3 (ArXiv'2017)