##################################
image_testsets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in operators}
testloaders = {x: torch.utils.data.DataLoader(image_testsets[x], batch_size=10,
shuffle=True, num_workers=0)
for x in operators}
testset_sizes = {x: len(image_testsets[x]) for x in operators}
##################################
data = [objects, operators]
model = Model.AttrOpModel(data)
attr_params = [param for name, param in model.named_parameters() if 'attr_op' in name and param.requires_grad]
other_params = [param for name, param in model.named_parameters() if 'attr_op' not in name and param.requires_grad]
optim_params = [{'params':attr_params, 'lr':1e-05}, {'params':other_params}]
optimizer = optim.Adam(optim_params, lr=1e-04, weight_decay=5e-5)
feat_extractor = models.resnet18(pretrained=True)
feat_extractor.fc = nn.Sequential()
## Just Apple for now
inApple = []
while len(inApple) < 5:
inputs, classes = next(iter(dataloaders['Whole']))
for i in range(len(inputs)):
