(self)
| 19 | |
| 20 | class Light_Classifier(torch.nn.Module): |
| 21 | def __init__(self): |
| 22 | super(Light_Classifier, self).__init__() |
| 23 | self.conv1 = self.conv_block(c_in = 3, c_out = 15, kernel_size = 3, stride = 1, padding = 1) |
| 24 | self.conv2 = self.conv_block(c_in = 15, c_out = 12, kernel_size = 3, stride = 1, padding = 1) |
| 25 | self.conv3 = self.conv_block(c_in = 12, c_out = 3, kernel_size = 3, stride = 1, padding = 1) |
| 26 | # 32px --> 48 |
| 27 | # 100px --> 432 |
| 28 | # self.bigN = 432 |
| 29 | self.bigN = 48 |
| 30 | self.fc1 = nn.Linear(self.bigN, 32) |
| 31 | self.fc2 = nn.Linear(32, 16) |
| 32 | self.fc3 = nn.Linear(16, 1) |
| 33 | self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) |
| 34 | # self.prefc1 = nn.Linear(self.bigN, ) |
| 35 | |
| 36 | def conv_block(self, c_in, c_out, dropout=0.1, kernel_size=3, stride=1, **kwargs): |
| 37 | seq_block = nn.Sequential( |
nothing calls this directly
no test coverage detected