I want to invoke a pre-trained model like this:
for module in model.children():
x = module(x)
but sadly function model.children() does not return in order of the invoking in forward function.
Examples:
when I define my network like this:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 5,padding=(2,2))
self.conv2 = nn.Conv2d(6, 16, 5,padding=(2,2))
self.bn2 = nn.BatchNorm2d(16)
self.bn1 = nn.BatchNorm2d(6)
self.pool = nn.MaxPool2d(2, 2)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 7 * 7, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.relu = nn.ReLU()
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.pool(F.relu(x))
x = self.conv2(x)
x = self.bn2(x)
x = self.pool(F.relu(x))
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
x = torch.softmax(x,dim=1)
return x
function list(net.children()) output:
[Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)), Conv2d(6, 16, kernel_size=(5, 5),
stride=(1, 1), padding=(2, 2)), BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True,
track_running_stats=True), BatchNorm2d(6, eps=1e-05, momentum=0.1, affine=True,
track_running_stats=True), MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1,
ceil_mode=False), Linear(in_features=784, out_features=120, bias=True), Linear(in_features=120,
out_features=84, bias=True), Linear(in_features=84, out_features=10, bias=True), ReLU()]
not the order defined in function forward but function __init__.
After search in Google, a package torchsummary is a good idea for this question, but if I invoke F.relu in forward function,
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 6, 28, 28] 156
BatchNorm2d-2 [-1, 6, 28, 28] 12
MaxPool2d-3 [-1, 6, 14, 14] 0
Conv2d-4 [-1, 16, 14, 14] 2,416
BatchNorm2d-5 [-1, 16, 14, 14] 32
MaxPool2d-6 [-1, 16, 7, 7] 0
Linear-7 [-1, 120] 94,200
Linear-8 [-1, 84] 10,164
Linear-9 [-1, 10] 850
================================================================
There is no ReLU in results.
Any API about this function?