This is not an issue, more of a request. If you can please implement GRADCAM with pytorch.

[Subhajyotidas]

I have trained a VGG16 model with pytorch, and trying to implement GRADCAM based on that. The model works well, Even it is deployed in HuggingFace. I just need the Gradcam technique.

The definition is as follows:

class VGG16(nn.Module):

def __init__(self, num_classes=10):
    super(VGG16, self).__init__()
    self.layer1 = nn.Sequential(
        nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(64),
        nn.ReLU())
    self.layer2 = nn.Sequential(
        nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(64),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size = 2, stride = 2))
    self.layer3 = nn.Sequential(
        nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(128),
        nn.ReLU())
    self.layer4 = nn.Sequential(
        nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(128),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size = 2, stride = 2))
    self.layer5 = nn.Sequential(
        nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(256),
        nn.ReLU())
    self.layer6 = nn.Sequential(
        nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(256),
        nn.ReLU())
    self.layer7 = nn.Sequential(
        nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(256),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size = 2, stride = 2))
    self.layer8 = nn.Sequential(
        nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(512),
        nn.ReLU())
    self.layer9 = nn.Sequential(
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(512),
        nn.ReLU())
    self.layer10 = nn.Sequential(
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(512),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size = 2, stride = 2))
    self.layer11 = nn.Sequential(
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(512),
        nn.ReLU())
    self.layer12 = nn.Sequential(
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(512),
        nn.ReLU())
    self.layer13 = nn.Sequential(
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
        nn.BatchNorm2d(512),
        nn.ReLU(),
        nn.MaxPool2d(kernel_size = 2, stride = 2))
    self.fc = nn.Sequential(
        nn.Dropout(0.5),
        nn.Linear(7*7*512, 4096),
        nn.ReLU())
    self.fc1 = nn.Sequential(
        nn.Dropout(0.5),
        nn.Linear(4096, 4096),
        nn.ReLU())
    self.fc2= nn.Sequential(
        nn.Linear(4096, num_classes))

def forward(self, x):
    out = self.layer1(x)
    out = self.layer2(out)
    out = self.layer3(out)
    out = self.layer4(out)
    out = self.layer5(out)
    out = self.layer6(out)
    out = self.layer7(out)
    out = self.layer8(out)
    out = self.layer9(out)
    out = self.layer10(out)
    out = self.layer11(out)
    out = self.layer12(out)
    out = self.layer13(out)
    out = out.reshape(out.size(0), -1)
    out = self.fc(out)
    out = self.fc1(out)
    out = self.fc2(out)
    return out