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6d8bf5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import torch.nn as nn
import torch
class MyModel(nn.Module):
def __init__(self, num_classes):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=4, stride=1, padding=0)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=1, padding=0)
self.bn2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(64, 128, kernel_size=4, stride=1, padding=0)
self.bn3 = nn.BatchNorm2d(128)
self.conv4 = nn.Conv2d(128, 128, kernel_size=4, stride=1, padding=0)
self.bn4 = nn.BatchNorm2d(128)
self.pool = nn.MaxPool2d(kernel_size=3, stride=3)
self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
self.fc1 = nn.Linear(6*6*128, 512)
self.fc2 = nn.Linear(512, num_classes)
self.flatten = nn.Flatten()
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.pool(x)
x = self.relu(self.bn2(self.conv2(x)))
x = self.pool(x)
x = self.relu(self.bn3(self.conv3(x)))
x = self.pool2(x)
x = self.relu(self.bn4(self.conv4(x)))
x = self.flatten(x)
x = self.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
def load_model(model_path, device):
model = MyModel(num_classes=5)
model.load_state_dict(torch.load(model_path, map_location=device, weights_only=True))
model.to(device)
model.eval()
return model
def get_gradcam_target_layer(model):
return model.bn4
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