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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super(Net, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 32, 5) | |
| self.conv2 = nn.Conv2d(32, 64, 5) | |
| self.conv3 = nn.Conv2d(64, 128, 5) | |
| self.conv4 = nn.Conv2d(128, 256, 5) | |
| self.conv5 = nn.Conv2d(256, 512, 5) | |
| self.fc1 = None | |
| self.fc2 = nn.Linear(512, 128) | |
| self.fc3 = nn.Linear(128, 64) | |
| self.fc4 = nn.Linear(64, 2) | |
| def forward(self, x): | |
| x = x.float() | |
| """ x = F.relu(self.conv1(x)) | |
| x = F.relu(self.conv2(x)) | |
| x = F.max_pool2d(x, 2) | |
| x = F.relu(self.conv3(x)) | |
| x = F.relu(self.conv4(x)) | |
| x = F.max_pool2d(x, 2) | |
| x = F.relu(self.conv5(x)) | |
| x = F.max_pool2d(x, 2) """ | |
| x = F.max_pool2d(F.relu(self.conv1(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv2(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv3(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv4(x)), 2) | |
| x = F.max_pool2d(F.relu(self.conv5(x)), 2) | |
| #x = x.view(x.size(0), -1) | |
| x = torch.flatten(x, 1) | |
| if self.fc1 is None: | |
| self.fc1 = nn.Linear(x.shape[1], 512).to(x.device) | |
| x = F.relu(self.fc1(x)) | |
| x = F.relu(self.fc2(x)) | |
| x = F.relu(self.fc3(x)) | |
| x = self.fc4(x) | |
| return x |