DeepLaerning HW2

1.Envirnoment

The Python version we use is python 3.11.9, and other imortant packages like torch, torchvision, scikit-learn, we jsued use the pip install <packagename>to get the default.

2.Run the code

Download the dataset, and unzip them. You should keep the folder images, files train.txt, val.txt, test.txt with the .ipynb under the same path.
Download the dcheckpoints, still keep them with the .ipynb under the same path.
After putting the dataset and weights under the correct path, you run the .ipynb, should get the inference results immediately.

Note

Different .ipynbuse different checkpoints, we list as following:

1. HW2_ResNet34.ipynb--checkpoint.pth
2. Visualizedloss_epoch50.ipynb--checkpoint_cnn_rrdb.pth
3. HW2_Dynamic_RDBB--checkpoint_cnn_rrdb1.pth, checkpoint_cnn_rrdb1.pth
   (when use checkpoint_cnn_rrdb1.pth, you must keep the row self.rrdb1 = RRDB(64, 32), the RRDB channel is 32
   use checkpoint_cnn_rrdb2.pth, you must keep the row self.rrdb1 = RRDB(64, 64), the RRDB channel is 64)

# 定義CustomCNN_DynamicConv2d_RRDB模型
class CustomCNN_DynamicConv2d_RRDB(nn.Module):
    def __init__(self, num_classes):
        super(CustomCNN_DynamicConv2d_RRDB, self).__init__()
        self.dynamic_conv = DynamicConv2d(3, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.rrdb1 = RRDB(64, 32)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.dropout = nn.Dropout(0.5)
        self.fc = nn.Linear(128 * 32 * 32, num_classes)

    def forward(self, x):
        x = F.relu(self.bn1(self.dynamic_conv(x)))
        x = self.rrdb1(x)
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.dropout(x)
        x = x.reshape(x.size(0), -1)
        x = self.fc(x)
        return x

4. HW2_Dynamic(noRDBB).ipynb--checkpoint_cnn_rrdb3.pth
5. SpecialConV.ipynb--checkpoint_cnn_R.pth, checkpoint_cnn_RG.pth, checkpoint_cnn_RGB.pth