Changed deprecated argument "pretrained" to non-deprecated argument "weights" for torchvision models (#2263)

* changed depricated pretrained argument to weights
* Update intermediate_source/flask_rest_api_tutorial.py

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

---------

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Svetlana Karslioglu <svekars@fb.com>

Author: 3 people

beginner_source/basics/saveloadrun_tutorial.py

@@ -26,14 +26,14 @@
 # state dictionary, called ``state_dict``. These can be persisted via the ``torch.save``
 # method:
 
-model = models.vgg16(pretrained=True)
+model = models.vgg16(weights='IMAGENET1K_V1')
 torch.save(model.state_dict(), 'model_weights.pth')
 
 ##########################
 # To load model weights, you need to create an instance of the same model first, and then load the parameters
 # using ``load_state_dict()`` method.
 
-model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights
+model = models.vgg16() # we do not specify weights, i.e. create untrained model
 model.load_state_dict(torch.load('model_weights.pth'))
 model.eval()
 

beginner_source/introyt/captumyt.py

@@ -155,7 +155,7 @@
 # now.
 # 
 
-model = models.resnet101(pretrained=True)
+model = models.resnet101(weights='IMAGENET1K_V1')
 model = model.eval()
 
 

beginner_source/transfer_learning_tutorial.py

@@ -250,7 +250,7 @@ def visualize_model(model, num_images=6):
 # Load a pretrained model and reset final fully connected layer.
 #
 
-model_ft = models.resnet18(pretrained=True)
+model_ft = models.resnet18(weights='IMAGENET1K_V1')
 num_ftrs = model_ft.fc.in_features
 # Here the size of each output sample is set to 2.
 # Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
@@ -295,7 +295,7 @@ def visualize_model(model, num_images=6):
 # `here <https://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.
 #
 
-model_conv = torchvision.models.resnet18(pretrained=True)
+model_conv = torchvision.models.resnet18(weights='IMAGENET1K_V1')
 for param in model_conv.parameters():
     param.requires_grad = False
 

intermediate_source/flask_rest_api_tutorial.py

@@ -161,8 +161,8 @@ def transform_image(image_bytes):
 
 from torchvision import models
 
-# Make sure to pass `pretrained` as `True` to use the pretrained weights:
-model = models.densenet121(pretrained=True)
+# Make sure to set `weights` as `'IMAGENET1K_V1'` to use the pretrained weights:
+model = models.densenet121(weights='IMAGENET1K_V1')
 # Since we are using our model only for inference, switch to `eval` mode:
 model.eval()
 
@@ -269,7 +269,7 @@ def get_prediction(image_bytes):
 #
 #   app = Flask(__name__)
 #   imagenet_class_index = json.load(open('<PATH/TO/.json/FILE>/imagenet_class_index.json'))
-#   model = models.densenet121(pretrained=True)
+#   model = models.densenet121(weights='IMAGENET1K_V1')
 #   model.eval()
 #
 #

intermediate_source/tensorboard_profiler_tutorial.py

@@ -68,7 +68,7 @@
 # To run on GPU, move model and loss to GPU device.
 
 device = torch.device("cuda:0")
-model = torchvision.models.resnet18(pretrained=True).cuda(device)
+model = torchvision.models.resnet18(weights='IMAGENET1K_V1').cuda(device)
 criterion = torch.nn.CrossEntropyLoss().cuda(device)
 optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
 model.train()