add drop-last to avoid 1 batch.

Author: um1

train.py

@@ -171,7 +171,7 @@
                                           data_transforms['val'])
 
 dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
-                                             shuffle=True, num_workers=2, pin_memory=True,
+                                             shuffle=True, num_workers=2, pin_memory=True, drop_last=True,
                                              prefetch_factor=2, persistent_workers=True) # 8 workers may work faster
               for x in ['train', 'val']}
 # Use extra DG-Market Dataset for training. Please download it from https://github.com/NVlabs/DG-Net#dg-market.
@@ -184,7 +184,7 @@
     image_datasets['DG'] = DGFolder(os.path.join('../DG-Market' ),
                                           data_transforms['train'])
     dataloaders['DG'] = torch.utils.data.DataLoader(image_datasets['DG'], batch_size = max(8, opt.batchsize//2),
-                                             shuffle=True, num_workers=2, pin_memory=True)
+                                             shuffle=True, num_workers=2, drop_last=True, pin_memory=True)
     DGloader_iter = enumerate(dataloaders['DG'])
 
 dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}