we want to access a mac unit in a kernel of each layer of a pretrained network while prediction to perform changes in it. we are new to pytorch and are having issues in implementing the same. we also did post training quantization of weights and activation to 8bits. Our defined cnn is as follows:
class Simplenet(nn.Module):
def init(self):
super(Simplenet, self).init()
self.conv1 = nn.Conv2d(3, 6, 5)
self.relu_conv1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.relu_conv2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.relu_fc1 = nn.ReLU()
self.fc2 = nn.Linear(120, 84)
self.relu_fc2 = nn.ReLU()
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
    x = self.pool1(self.relu_conv1(self.conv1(x)))
    x = self.pool2(self.relu_conv2(self.conv2(x)))
    x = x.view(-1, 16 * 5 * 5)
    x = self.relu_fc1(self.fc1(x))
    x = self.relu_fc2(self.fc2(x))
    x = self.fc3(x)
    return F.log_softmax(x, dim=1)