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SingleDataset init and pytorch 1.0.1 compatible #138

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Dec 25, 2019
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SingleDataset init and pytorch 1.0.1 compatible #138

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2 changes: 1 addition & 1 deletion data/base_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ def get_transform(opt):
transform_list = []
if opt.resize_or_crop == 'resize_and_crop':
osize = [opt.loadSizeX, opt.loadSizeY]
transform_list.append(transforms.Scale(osize, Image.BICUBIC))
transform_list.append(transforms.Resize(osize, Image.BICUBIC))
transform_list.append(transforms.RandomCrop(opt.fineSize))
elif opt.resize_or_crop == 'crop':
transform_list.append(transforms.RandomCrop(opt.fineSize))
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1 change: 1 addition & 0 deletions data/custom_dataset_data_loader.py
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Expand Up @@ -13,6 +13,7 @@ def CreateDataset(opt):
elif opt.dataset_mode == 'single':
from data.single_dataset import SingleDataset
dataset = SingleDataset()
dataset.initialize(opt)
else:
raise ValueError("Dataset [%s] not recognized." % opt.dataset_mode)

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6 changes: 4 additions & 2 deletions models/test_model.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import torch
from torch.autograd import Variable
from collections import OrderedDict
import util.util as util
Expand Down Expand Up @@ -33,8 +34,9 @@ def set_input(self, input):
self.image_paths = input['A_paths']

def test(self):
self.real_A = Variable(self.input_A, volatile=True)
self.fake_B = self.netG.forward(self.real_A)
with torch.no_grad():
self.real_A = Variable(self.input_A)
self.fake_B = self.netG.forward(self.real_A)

# get image paths
def get_image_paths(self):
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