Append() for nn.Sequential or directly converting nn.ModuleList to nn.Sequential

jingweiz (Jingweiz) 2017-09-06 09:16:37 UTC #1

Hi,
maybe I’m missing sth obvious but there does not seem to be an “append()” method for nn.Sequential, cos it would be handy when the layers of the sequential could not be added at once.
Or it would be equivalent if I first added all the layer I need into a ModuleList then there’s a method for directly converting all the modules in a ModuleList to a Sequential.
Thanks in advance!

allenye0119 (Allen Ye) 2017-09-06 09:35:53 UTC #2

You can first construct a python list of nn.Modules and unpack it into a nn.Sequential

import torch.nn as nn

modules = []
modules.append(nn.Linear(10, 10))
modules.append(nn.Linear(10, 10))

sequential = nn.Sequential(*modules)

jingweiz (Jingweiz) 2017-09-06 11:40:06 UTC #3

Ah that’s neat, thanks

QuantScientist (Solomon K ) 2017-09-06 13:24:24 UTC #4

Great example here:

github.com

vinhkhuc/PyTorch-Mini-Tutorials/blob/master/5_convolutional_net.py

import numpy as np

import torch
from torch.autograd import Variable
from torch import optim

from data_util import load_mnist


# We need to create two sequential models here since PyTorch doesn't have nn.View()
class ConvNet(torch.nn.Module):
    def __init__(self, output_dim):
        super(ConvNet, self).__init__()

        self.conv = torch.nn.Sequential()
        self.conv.add_module("conv_1", torch.nn.Conv2d(1, 10, kernel_size=5))
        self.conv.add_module("maxpool_1", torch.nn.MaxPool2d(kernel_size=2))
        self.conv.add_module("relu_1", torch.nn.ReLU())
        self.conv.add_module("conv_2", torch.nn.Conv2d(10, 20, kernel_size=5))
        self.conv.add_module("dropout_2", torch.nn.Dropout())

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jingweiz (Jingweiz) 2017-09-13 19:18:44 UTC #5

Thanks a lot! That’s really helpful!