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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 |