Apply TorchFix codemods to torchbenchmarks (#2287)

Summary: Pull Request resolved: #2287

Reviewed By: zou3519, xuzhao9

Differential Revision: D58301337

fbshipit-source-id: 2bdff7d9652060b5a2cba720cb54689aad66f18a

torchbenchmark/e2e_models/vision_resnet50/__init__.py

@@ -8,6 +8,7 @@
 from tqdm import tqdm
 
 from pathlib import Path
+from torchvision import models
 
 # setup environment variable
 CURRENT_DIR = Path(os.path.dirname(os.path.realpath(__file__)))
@@ -56,7 +57,7 @@ def __init__(self, test, batch_size=None, extra_args=[]):
  # by default, run 200 epochs
  self.num_epochs = 200
  # use random init model for train
- self.model = torchvision.models.resnet50().to(self.device)
+ self.model = models.resnet50().to(self.device)
  from .resnet import ResNet50
  self.model = ResNet50().to(self.device)
  self.model.train()
@@ -66,7 +67,7 @@ def __init__(self, test, batch_size=None, extra_args=[]):
  self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.T_max)
  else:
  # use pretrained model for eval
- self.model = torchvision.models.resnet50(pretrained=True).to(self.device)
+ self.model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1).to(self.device)
  self.model.eval()
 
  def get_optimizer(self):

torchbenchmark/models/alexnet/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/densenet121/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/functorch_dp_cifar10/__init__.py

@@ -1,7 +1,7 @@
 import torch
 import torch.optim as optim
 import torch.nn as nn
-import torchvision.models as models
+from torchvision import models
 from functorch import make_functional_with_buffers, vmap, grad
 from typing import Tuple
 

torchbenchmark/models/lennard_jones/__init__.py

@@ -5,7 +5,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from functorch import vmap, jacrev
+from torch.func import vmap, jacrev
 from typing import Tuple
 
 from ...util.model import BenchmarkModel

torchbenchmark/models/mnasnet1_0/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/mobilenet_v2/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/mobilenet_v2_quantized_qat/__init__.py

@@ -1,7 +1,7 @@
 # Generated by gen_torchvision_benchmark.py
 import torch
 import torch.optim as optim
-import torchvision.models as models
+from torchvision import models
 from torch.quantization import quantize_fx
 from torchbenchmark.tasks import COMPUTER_VISION
 from ...util.model import BenchmarkModel

torchbenchmark/models/mobilenet_v3_large/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/moco/__init__.py

@@ -10,7 +10,7 @@
 import torch.optim
 import torch.utils.data
 import torch.utils.data.distributed
-import torchvision.models as models
+from torchvision import models
 from typing import Tuple
 
 from .moco.builder import MoCo

torchbenchmark/models/moco/main_lincls.py

@@ -19,7 +19,7 @@
 import torch.utils.data.distributed
 import torchvision.transforms as transforms
 import torchvision.datasets as datasets
-import torchvision.models as models
+from torchvision import models
 
 model_names = sorted(name for name in models.__dict__
  if name.islower() and not name.startswith("__")

torchbenchmark/models/moco/main_moco.py

@@ -20,7 +20,7 @@
 import torch.utils.data.distributed
 import torchvision.transforms as transforms
 import torchvision.datasets as datasets
-import torchvision.models as models
+from torchvision import models
 
 from .moco.builder import MoCo
 

torchbenchmark/models/opacus_cifar10/__init__.py

@@ -2,7 +2,7 @@
 import torch.optim as optim
 import torch.nn as nn
 import torch.utils.data as data
-import torchvision.models as models
+from torchvision import models
 from opacus import PrivacyEngine
 from opacus.validators.module_validator import ModuleValidator
 from typing import Tuple

torchbenchmark/models/resnet152/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/resnet18/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/resnet50/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/resnet50_quantized_qat/__init__.py

@@ -2,7 +2,7 @@
 # Generated by gen_torchvision_benchmark.py
 import torch
 import torch.optim as optim
-import torchvision.models as models
+from torchvision import models
 from torch.quantization import quantize_fx
 from ...util.model import BenchmarkModel
 from torchbenchmark.tasks import COMPUTER_VISION

torchbenchmark/models/resnext50_32x4d/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/shufflenet_v2_x1_0/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/squeezenet1_1/__init__.py

@@ -2,7 +2,7 @@
 from torchbenchmark.tasks import COMPUTER_VISION
 import torch.optim as optim
 import torch
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/models/tacotron2/waveglow/glow.py

@@ -76,7 +76,7 @@ def __init__(self, c):
  )
 
  # Sample a random orthonormal matrix to initialize weights
- W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
+ W = torch.linalg.qr(torch.FloatTensor(c, c).normal_())[0]
 
  # Ensure determinant is 1.0 not -1.0
  if torch.det(W) < 0:

torchbenchmark/models/vgg16/__init__.py

@@ -1,6 +1,6 @@
 from torchbenchmark.util.framework.vision.model_factory import TorchVisionModel
 from torchbenchmark.tasks import COMPUTER_VISION
-import torchvision.models as models
+from torchvision import models
 
 class Model(TorchVisionModel):
  task = COMPUTER_VISION.CLASSIFICATION

torchbenchmark/util/framework/vision/model_factory.py

@@ -4,7 +4,7 @@
 
 import torch
 import torch.optim as optim
-import torchvision.models as models
+from torchvision import models
 from torchbenchmark.util.model import BenchmarkModel
 
 

userbenchmark/functorch/vmap_hessian_fc.py

@@ -1,6 +1,6 @@
 import torch
 import torch.nn as nn
-from functorch import vmap, jacfwd, jacrev
+from torch.func import vmap, jacfwd, jacrev
 from .util import BenchmarkCase
 
 # batched hessians of fully connected layers is a popular quantity