Modify NASNet

Dear @Cadene, Thank you for your fantastic repository. I want to modify NASNet (i.e., apply some minor modifications on NASNet by remove Dropout, add sigmoid layer at the end of net and etc.). I have implemented below code:

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
from nasnet import nasnetalarge

num_classes = 10

# *** Define Modified NASNet Class:
class ModifiedNASNet(nn.Module):
    def __init__(self, num_classes):
        super(ModifiedNASNet, self).__init__()

        original_model = nasnetalarge(num_classes=1000, pretrained='imagenet')

        # Everything except the last classifier:
        self.features = nn.Sequential(*list(original_model.children())[:-4])

        # Set number of features:
        num_feats = 4032

        # Plug our linear layer:
        self.classifier = nn.Sequential(nn.Linear(num_feats, num_classes))

    def forward(self, x):
        f = self.features(x)
        out = F.relu(f, inplace=True)
        out = F.avg_pool2d(out, kernel_size=11, stride=1, padding=0, ceil_mode=False, count_include_pad=True)
        out = out.view(out.size(0), -1) # Flatten
        out = self.classifier(out)
        out = F.sigmoid(out)
        return out

# *** test above class:
model = ModifiedNASNet(num_classes).cuda()
# print(model)
x = Variable(torch.randn(1, 3, 331, 331), volatile=True).cuda()
out = model(x)
print(out)

However, the following error has occurred: TypeError: forward() missing 1 required positional argument: 'x_stem_0' Would you please kindly help me to address this problem?

[emited]

In the nn.Sequential container, a module’s output is taken as the next module’s input. But self.cell_stem1 takes input from both previous modules, self.conv0 and self.cell_stem_1. Try something like this:

import torch
from torch.autograd import Variable
import torch.nn.functional as F
from nasnet import NASNetALarge


class ModifiedNASNet(NASNetALarge):

    def classifier(self, x):
        out = F.relu(f, inplace=True)
        out = F.avg_pool2d(out, kernel_size=11, stride=1, padding=0, ceil_mode=False, count_include_pad=True)
        out = out.view(out.size(0), -1) # Flatten
        out = self.linear(out)
        out = F.sigmoid(out)
        return out        


# *** test above class:
model = ModifiedNASNet(num_classes).cuda()
# print(model)
x = Variable(torch.randn(1, 3, 331, 331), volatile=True).cuda()
out = model(x)
print(out)

[emited]

Hello @ClimbsRocks. It has been a long time since I used this library… @Cadene ?