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| import torch | |
| import torch.nn as nn | |
| class CNN(nn.Module): | |
| """ | |
| **kwargs tous les autre args, sous forme de dict, | |
| couche de convolution, bias=False parce que l'on batchNorm (il a son propre biais), | |
| leaky relue: si x > 0 -> x, sinon -> 0.1 * x | |
| """ | |
| def __init__(self, in_channels, out_channels, **kwargs): | |
| super().__init__() | |
| self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) | |
| self.batchnorm = nn.BatchNorm2d(out_channels) | |
| self.leakyrelue = nn.LeakyReLU(0.1) | |
| def forward(self, x): | |
| return self.leakyrelue(self.batchnorm(self.conv(x))) | |
| class Yolo_V1(nn.Module): | |
| def __init__(self, in_channels=3, split_size=7, num_boxes=2, num_classes=20): | |
| super(Yolo_V1, self).__init__() | |
| # Darknet model, mais from scratch | |
| self.conv1 = CNN(in_channels, 64, kernel_size=7, stride=2, padding=3) | |
| self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.conv2 = CNN(64, 192, kernel_size=3, stride=1, padding=1) | |
| self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| self.conv3 = CNN(192, 128, kernel_size=1, stride=1, padding=0) | |
| self.conv4 = CNN(128, 256, kernel_size=3, stride=1, padding=1) | |
| self.conv5 = CNN(256, 256, kernel_size=1, stride=1, padding=0) | |
| self.conv6 = CNN(256, 512, kernel_size=3, stride=1, padding=1) | |
| self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| # Bloc répété 4 fois: (1x1 256) -> (3x3 512) | |
| self.conv7 = CNN(512, 256, kernel_size=1, stride=1, padding=0) | |
| self.conv8 = CNN(256, 512, kernel_size=3, stride=1, padding=1) | |
| self.conv9 = CNN(512, 256, kernel_size=1, stride=1, padding=0) | |
| self.conv10 = CNN(256, 512, kernel_size=3, stride=1, padding=1) | |
| self.conv11 = CNN(512, 256, kernel_size=1, stride=1, padding=0) | |
| self.conv12 = CNN(256, 512, kernel_size=3, stride=1, padding=1) | |
| self.conv13 = CNN(512, 256, kernel_size=1, stride=1, padding=0) | |
| self.conv14 = CNN(256, 512, kernel_size=3, stride=1, padding=1) | |
| self.conv15 = CNN(512, 512, kernel_size=1, stride=1, padding=0) | |
| self.conv16 = CNN(512, 1024, kernel_size=3, stride=1, padding=1) | |
| self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2) | |
| # Bloc répété 2 fois: (1x1 512) -> (3x3 1024) | |
| self.conv17 = CNN(1024, 512, kernel_size=1, stride=1, padding=0) | |
| self.conv18 = CNN(512, 1024, kernel_size=3, stride=1, padding=1) | |
| self.conv19 = CNN(1024, 512, kernel_size=1, stride=1, padding=0) | |
| self.conv20 = CNN(512, 1024, kernel_size=3, stride=1, padding=1) | |
| self.conv21 = CNN(1024, 1024, kernel_size=3, stride=1, padding=1) | |
| self.conv22 = CNN(1024, 1024, kernel_size=3, stride=2, padding=1) | |
| self.conv23 = CNN(1024, 1024, kernel_size=3, stride=1, padding=1) | |
| self.conv24 = CNN(1024, 1024, kernel_size=3, stride=1, padding=1) | |
| # Head du modele | |
| S, B, C = split_size, num_boxes, num_classes | |
| self.fc1 = nn.Linear(1024 * S * S, 496) | |
| self.dropout = nn.Dropout(0.0) | |
| self.leaky = nn.LeakyReLU(0.1) | |
| self.fc2 = nn.Linear(496, S * S * (C + B * 5)) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.maxpool1(x) | |
| x = self.conv2(x) | |
| x = self.maxpool2(x) | |
| x = self.conv3(x) | |
| x = self.conv4(x) | |
| x = self.conv5(x) | |
| x = self.conv6(x) | |
| x = self.maxpool3(x) | |
| x = self.conv7(x) | |
| x = self.conv8(x) | |
| x = self.conv9(x) | |
| x = self.conv10(x) | |
| x = self.conv11(x) | |
| x = self.conv12(x) | |
| x = self.conv13(x) | |
| x = self.conv14(x) | |
| x = self.conv15(x) | |
| x = self.conv16(x) | |
| x = self.maxpool4(x) | |
| x = self.conv17(x) | |
| x = self.conv18(x) | |
| x = self.conv19(x) | |
| x = self.conv20(x) | |
| x = self.conv21(x) | |
| x = self.conv22(x) | |
| x = self.conv23(x) | |
| x = self.conv24(x) | |
| x = torch.flatten(x, start_dim=1) | |
| x = self.fc1(x) | |
| x = self.dropout(x) | |
| x = self.leaky(x) | |
| x = self.fc2(x) | |
| return x |