Update references to torchvision

training/cifar/cifar10_deepspeed.py

@@ -6,7 +6,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision
-import torchvision.transforms as transforms
+from torchvision import transforms
 from deepspeed.accelerator import get_accelerator
 from deepspeed.moe.utils import split_params_into_different_moe_groups_for_optimizer
 

training/cifar/cifar10_tutorial.py

@@ -57,7 +57,7 @@
 """
 import torch
 import torchvision
-import torchvision.transforms as transforms
+from torchvision import transforms
 
 ########################################################################
 # The output of torchvision datasets are PILImage images of range [0, 1].

training/data_efficiency/vit_finetuning/main_imagenet.py

@@ -19,8 +19,8 @@
 import torch.multiprocessing as mp
 import torch.utils.data
 import torch.utils.data.distributed
-import torchvision.transforms as transforms
-import torchvision.datasets as datasets
+from torchvision import transforms
+from torchvision import datasets
 import torchvision.models as models
 from torch.utils.data import Subset
 import models
@@ -105,7 +105,7 @@ def _get_model(args):
     nchannels = 3
     model = models.__dict__[args.arch](num_classes=nclasses, nchannels=nchannels)
     return model
-    
+
 def _get_dist_model(gpu, args):
     ngpus_per_node = torch.cuida.device_count()
     if args.distributed:
@@ -149,9 +149,9 @@ def _get_dist_model(gpu, args):
         else:
             model = torch.nn.DataParallel(model).cuda()
     return model
-    
+
 def main():
-   
+
     args = parser.parse_args()
 
     if args.seed is not None:
@@ -190,7 +190,7 @@ def main():
 def main_worker(gpu, ngpus_per_node, args):
     global best_acc1
     global history
-    
+
     if args.deepspeed:
         gpu = args.local_rank
     args.gpu = gpu
@@ -205,7 +205,7 @@ def main_worker(gpu, ngpus_per_node, args):
         deepspeed.init_distributed()
     print(f'created model on gpu {gpu}')
     # exit ()
-    
+
     # define loss function (criterion), optimizer, and learning rate scheduler
     criterion = nn.CrossEntropyLoss().cuda(args.gpu)
 
@@ -284,14 +284,14 @@ def main_worker(gpu, ngpus_per_node, args):
         validate(val_loader, model, criterion, args)
         # return
     args.completed_step = 0
-    
+
     optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                 momentum=args.momentum,
                                 weight_decay=args.weight_decay)
-    
+
     """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
     scheduler = StepLR(optimizer, step_size=int(len(train_loader)*args.epochs//3), gamma=0.1)# None #
-    
+
 
     model, optimizer, _, scheduler  = deepspeed.initialize(
         model=model,
@@ -311,17 +311,17 @@ def main_worker(gpu, ngpus_per_node, args):
         time_epoch = time.time() - start_time
         # evaluate on validation set
         top5_val, top1_val, losses_val = validate(val_loader, model, criterion, args)
-        if args.gpu==0: 
+        if args.gpu==0:
             history["epoch"].append(epoch)
             history["val_loss"].append(losses_val)
-            history["val_acc1"].append(top1_val) 
-            history["val_acc5"].append(top5_val) 
+            history["val_acc1"].append(top1_val)
+            history["val_acc5"].append(top5_val)
             history["train_loss"].append(losses_train)
-            history["train_acc1"].append(top1_train) 
+            history["train_acc1"].append(top1_train)
             history["train_acc5"].append(top5_train)
-            torch.save(history,f"{args.out_dir}/stat.pt") 
+            torch.save(history,f"{args.out_dir}/stat.pt")
             try:
-                print (f'{epoch} epoch at time {time_epoch}s and learning rate {scheduler.get_last_lr()}') 
+                print (f'{epoch} epoch at time {time_epoch}s and learning rate {scheduler.get_last_lr()}')
             except:
                 print (f'{epoch} epoch at time {time_epoch}s and learning rate {args.lr}')
             print (f"finish epoch {epoch} or iteration {args.completed_step}, train_accuracy is {top1_train}, val_accuracy {top1_val}")
@@ -393,14 +393,14 @@ def train(scheduler, train_loader, model, criterion, optimizer, epoch, args):
             loss.backward()
             optimizer.step()
             scheduler.step()
-            
+
         # measure elapsed time
         batch_time.update(time.time() - end)
         end = time.time()
 
-        if i % args.print_freq == 0 and args.gpu==0: 
+        if i % args.print_freq == 0 and args.gpu==0:
             progress.display(i + 1)
-            
+
     if args.distributed:
         losses.all_reduce()
         top1.all_reduce()
@@ -432,7 +432,7 @@ def run_validate(loader, base_progress=0):
                 batch_time.update(time.time() - end)
                 end = time.time()
 
-                if i % args.print_freq == 0 and args.gpu==0: 
+                if i % args.print_freq == 0 and args.gpu==0:
                     progress.display(i + 1)
 
     batch_time = AverageMeter('Time', ':6.3f', Summary.NONE)
@@ -509,7 +509,7 @@ def all_reduce(self):
     def __str__(self):
         fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
         return fmtstr.format(**self.__dict__)
-    
+
     def summary(self):
         fmtstr = ''
         if self.summary_type is Summary.NONE:
@@ -522,7 +522,7 @@ def summary(self):
             fmtstr = '{name} {count:.3f}'
         else:
             raise ValueError('invalid summary type %r' % self.summary_type)
-        
+
         return fmtstr.format(**self.__dict__)
 
 
@@ -536,7 +536,7 @@ def display(self, batch):
         entries = [self.prefix + self.batch_fmtstr.format(batch)]
         entries += [str(meter) for meter in self.meters]
         print ('\t'.join(entries))
-        
+
     def display_summary(self):
         entries = [" *"]
         entries += [meter.summary() for meter in self.meters]

training/data_efficiency/vit_finetuning/utils/get_data.py

@@ -13,18 +13,18 @@
 # limitations under the License.
 import torch
 import os
-import torchvision.transforms as transforms
-import torchvision.datasets as datasets
+from torchvision import transforms
+from torchvision import datasets
 
 def get_dataset(dataset_name, data_dir, split, rand_fraction=None,clean=False, transform=None, imsize=None, bucket='pytorch-data', **kwargs):
 
   if dataset_name in [ 'cifar10', 'cifar100']:
-    dataset = globals()[f'get_{dataset_name}'](dataset_name, data_dir, split,  imsize=imsize, bucket=bucket, **kwargs)  
+    dataset = globals()[f'get_{dataset_name}'](dataset_name, data_dir, split,  imsize=imsize, bucket=bucket, **kwargs)
   elif dataset_name in [ 'cifar10vit224', 'cifar100vit224','cifar10vit384', 'cifar100vit384',]:
     imsize = int(dataset_name.split('vit')[-1])
     dataset_name = dataset_name.split('vit')[0]
     #print ('here')
-    dataset = globals()['get_cifar_vit'](dataset_name, data_dir, split, imsize=imsize, bucket=bucket, **kwargs)   
+    dataset = globals()['get_cifar_vit'](dataset_name, data_dir, split, imsize=imsize, bucket=bucket, **kwargs)
   else:
     assert 'cifar' in dataset_name
   print (dataset_name)
@@ -59,10 +59,10 @@ def get_transform(split, normalize=None, transform=None, imsize=None, aug='large
   if transform is None:
     if normalize is None:
         if aug == 'large':
-          
+
           normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
         else:
-          normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])  
+          normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])
     transform = transforms.Compose(get_aug(split, imsize=imsize, aug=aug)
                                    + [transforms.ToTensor(), normalize])
   return transform
@@ -71,7 +71,7 @@ def get_transform(split, normalize=None, transform=None, imsize=None, aug='large
 def get_cifar10(dataset_name, data_dir, split, transform=None, imsize=None, bucket='pytorch-data', **kwargs):
   if imsize==224:
     transform = get_transform(split, transform=transform, imsize=imsize, aug='large')
-  else:  
+  else:
     transform = get_transform(split, transform=transform, imsize=imsize, aug='small')
   return datasets.CIFAR10(data_dir, train=(split=='train'), transform=transform, download=True, **kwargs)
 
@@ -88,7 +88,7 @@ def get_cifar100N(dataset_name, data_dir, split, rand_fraction=None,transform=No
   if split=='train':
     return CIFAR100N(root=data_dir, train=(split=='train'), transform=transform, download=True, rand_fraction=rand_fraction)
   else:
-    return datasets.CIFAR100(data_dir, train=(split=='train'), transform=transform, download=True, **kwargs)        
+    return datasets.CIFAR100(data_dir, train=(split=='train'), transform=transform, download=True, **kwargs)
 
 def get_cifar_vit(dataset_name, data_dir, split, transform=None, imsize=None, bucket='pytorch-data', **kwargs):
     if imsize==224:
@@ -111,12 +111,12 @@ def get_cifar_vit(dataset_name, data_dir, split, transform=None, imsize=None, bu
       if dataset_name =='cifar10':
           return datasets.CIFAR10(data_dir, train=(split=='train'), transform=transform_data, download=True, **kwargs)
       elif dataset_name =='cifar100':
-      
+
           return datasets.CIFAR100(data_dir, train=(split=='train'), transform=transform_data, download=True, **kwargs)
       else:
           assert dataset_name in ['cifar10', 'cifar100']
     else:
-      
+
       if split=='train':
         transform_data = transforms.Compose([# transforms.ColorJitter(brightness= 0.4, contrast= 0.4, saturation= 0.4, hue= 0.1),
           transforms.Resize(imsize),
@@ -164,4 +164,4 @@ def get_imagenet_vit(dataset_name, data_dir, split, transform=None, imsize=None,
       #return torch.utils.data.distributed.DistributedSampler(train_dataset)
     else:
       return datasets.ImageFolder(valdir, transform_data)
-      #Ereturn torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)
+      #Ereturn torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)

training/gan/gan_baseline_train.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 import torch.utils.data
 import torchvision.datasets as dset
-import torchvision.transforms as transforms
+from torchvision import transforms
 import torchvision.utils as vutils
 from torch.utils.tensorboard import SummaryWriter
 from time import time

training/gan/gan_deepspeed_train.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 import torch.utils.data
 import torchvision.datasets as dset
-import torchvision.transforms as transforms
+from torchvision import transforms
 import torchvision.utils as vutils
 from torch.utils.tensorboard import SummaryWriter
 from time import time

training/imagenet/main.py

@@ -18,9 +18,9 @@
 import torch.optim
 import torch.utils.data
 import torch.utils.data.distributed
-import torchvision.datasets as datasets
 import torchvision.models as models
-import torchvision.transforms as transforms
+from torchvision import transforms
+from torchvision import datasets
 from torch.optim.lr_scheduler import StepLR
 from torch.utils.data import Subset
 
@@ -94,7 +94,7 @@ def main():
                       'which can slow down your training considerably! '
                       'You may see unexpected behavior when restarting '
                       'from checkpoints.')
-        
+
     if args.gpu is not None:
         warnings.warn('You have chosen a specific GPU. This will completely '
                       'disable data parallelism.')
@@ -112,7 +112,7 @@ def main():
     args.world_size = ngpus_per_node * args.world_size
     t_losses, t_acc1s = main_worker(args.gpu, ngpus_per_node, args)
     #dist.barrier()
-    
+
     # Write the losses to an excel file
     if dist.get_rank() ==0:
         all_losses = [torch.empty_like(t_losses) for _ in range(ngpus_per_node)]
@@ -278,7 +278,7 @@ def print_rank_0(msg):
         acc1s[epoch] = acc1
 
         scheduler.step()
-        
+
         # remember best acc@1 and save checkpoint
         is_best = acc1 > best_acc1
         best_acc1 = max(acc1, best_acc1)
@@ -449,7 +449,7 @@ def all_reduce(self):
     def __str__(self):
         fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
         return fmtstr.format(**self.__dict__)
-    
+
     def summary(self):
         fmtstr = ''
         if self.summary_type is Summary.NONE:
@@ -462,7 +462,7 @@ def summary(self):
             fmtstr = '{name} {count:.3f}'
         else:
             raise ValueError('invalid summary type %r' % self.summary_type)
-        
+
         return fmtstr.format(**self.__dict__)
 
 
@@ -476,7 +476,7 @@ def display(self, batch):
         entries = [self.prefix + self.batch_fmtstr.format(batch)]
         entries += [str(meter) for meter in self.meters]
         print('\t'.join(entries))
-        
+
     def display_summary(self):
         entries = [" *"]
         entries += [meter.summary() for meter in self.meters]

training/pipeline_parallelism/train.py

@@ -7,7 +7,7 @@
 import torch.distributed as dist
 
 import torchvision
-import torchvision.transforms as transforms
+from torchvision import transforms
 from torchvision.models import AlexNet
 from torchvision.models import vgg19