Hi there,
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(3, 128, 3, 1, 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.backbone = Net()
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(128, 32)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.backbone(x)
x = self.avg_pool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
x = self.relu(x)
return x
If I want to attach observers (weight+activation) to backbone layers only, and keep the rest layers (self.avg_pool, self.fc, self.relu) unquantized, how should I define the qconfig_dict?