8f420a3b35944dcfa470dee958ef61dee221ce02,implementations/dualgan/datasets.py,MNISTM,__getitem__,#MNISTM#Any#,60
Before Change
// doing this so that it is consistent with all other datasets
// to return a PIL Image
img = Image.fromarray(img.squeeze().numpy(), mode="RGB")
if self.transform is not None:
img = self.transform(img)
After Change
half_w = int(w/2)
item_A = img_pair[:, :, :half_w]
item_B = img_pair[:, :, half_w:]
return {"A": item_A, "B": item_B}
def __len__(self):
In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 4
Instances
Project Name: eriklindernoren/PyTorch-GAN
Commit Name: 8f420a3b35944dcfa470dee958ef61dee221ce02
Time: 2018-04-23
Author: eriklindernoren@gmail.com
File Name: implementations/dualgan/datasets.py
Class Name: MNISTM
Method Name: __getitem__
Project Name: PavlosMelissinos/enet-keras
Commit Name: fd5fdd2507cb26d746324ae7bfd6c280580dd5e6
Time: 2017-05-04
Author: pmelissi@iti.gr
File Name: src/predict.py
Class Name:
Method Name: predict_pillow
Project Name: chainer/chainerrl
Commit Name: 3c6e08f518767aa4b9eddaa0350437e1e14a7e6d
Time: 2016-03-15
Author: muupan@gmail.com
File Name: ale.py
Class Name: ALE
Method Name: current_screen