Hi, I noticed that when we define a pytorch model, we usually need to specify its components before applying
forward() function. An example is this:
class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = torch.flatten(x, 1) # flatten all dimensions except batch x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x
here we need to defined
self.conv2, etc… While in keras, we usually directly define these components as we build the computation graph, e.g.
encoder_input = keras.Input(shape=(28, 28, 1), name="img") x = layers.Conv2D(16, 3, activation="relu")(encoder_input) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.MaxPooling2D(3)(x) x = layers.Conv2D(32, 3, activation="relu")(x) x = layers.Conv2D(16, 3, activation="relu")(x)
Here we do not need to define several
model.Conv2D in advance, but instead define them as we build the graph.
I am wondering if there are similar way to do this in PyTorch? I am asking this since:
- sometimes it could be error-prone to keep track of what components I have defined in
__init__()and what components are being used in
- when the number of layers are large, it may not be quite convenient to label all layers with unique index.
If anyone has idea, please let me know, thanks!