Updating code on the article.

Author: datumbox

_posts/2021-12-22-introducing-torchvision-new-multi-weight-support-api.md

@@ -74,7 +74,7 @@ from torchvision.prototype import models as PM
 img = Image.open("test/assets/encode_jpeg/grace_hopper_517x606.jpg")
 
 # Step 1: Initialize model
-weights = PM.ResNet50_Weights.ImageNet1K_V1
+weights = PM.ResNet50_Weights.IMAGENET1K_V1
 model = PM.resnet50(weights=weights)
 model.eval()
 
@@ -96,21 +96,21 @@ As we can see the new API eliminates the aforementioned limitations. Let’s exp
 
 ### Multi-weight support
 
-At the heart of the new API, we have the ability to define multiple different weights for the same model variant. Each model building method (eg `resnet50`) has an associated Enum class (eg `ResNet50_Weights`) which has as many entries as the number of pre-trained weights available. Additionally, each Enum class has a `default` alias which points to the best available weights for the specific model. This allows the users who want to always use the best available weights to do so without modifying their code.
+At the heart of the new API, we have the ability to define multiple different weights for the same model variant. Each model building method (eg `resnet50`) has an associated Enum class (eg `ResNet50_Weights`) which has as many entries as the number of pre-trained weights available. Additionally, each Enum class has a `DEFAULT` alias which points to the best available weights for the specific model. This allows the users who want to always use the best available weights to do so without modifying their code.
 
 Here is an example of initializing models with different weights:
 
 ```python
 from torchvision.prototype.models import resnet50, ResNet50_Weights
 
 # Legacy weights with accuracy 76.130%
-model = resnet50(weights=ResNet50_Weights.ImageNet1K_V1)
+model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
 
-# New weights with accuracy 80.674%
-model = resnet50(weights=ResNet50_Weights.ImageNet1K_V2)
+# New weights with accuracy 80.858%
+model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
 
-# Best available weights (currently alias for ImageNet1K_V2)
-model = resnet50(weights=ResNet50_Weights.default)
+# Best available weights (currently alias for IMAGENET1K_V2)
+model = resnet50(weights=ResNet50_Weights.DEFAULT)
 
 # No weights - random initialization
 model = resnet50(weights=None)
@@ -124,10 +124,10 @@ The weights of each model are associated with meta-data. The type of information
 from torchvision.prototype.models import ResNet50_Weights
 
 # Accessing a single record
-size = ResNet50_Weights.ImageNet1K_V2.meta["size"]
+size = ResNet50_Weights.IMAGENET1K_V2.meta["size"]
 
 # Iterating the items of the meta-data dictionary
-for k, v in ResNet50_Weights.ImageNet1K_V2.meta.items():
+for k, v in ResNet50_Weights.IMAGENET1K_V2.meta.items():
     print(k, v)
 ```
 
@@ -137,10 +137,10 @@ Additionally, each weights entry is associated with the necessary preprocessing
 from torchvision.prototype.models import ResNet50_Weights
 
 # Initializing preprocessing at standard 224x224 resolution
-preprocess = ResNet50_Weights.ImageNet1K.transforms()
+preprocess = ResNet50_Weights.IMAGENET1K_V2.transforms()
 
 # Initializing preprocessing at 400x400 resolution
-preprocess = ResNet50_Weights.ImageNet1K.transforms(crop_size=400, resize_size=400)
+preprocess = ResNet50_Weights.IMAGENET1K_V2.transforms(crop_size=400, resize_size=400)
 
 # Once initialized the callable can accept the image data:
 # img_preprocessed = preprocess(img)
@@ -156,11 +156,11 @@ The ability to link directly the weights with their properties (meta data, prepr
 from torchvision.prototype.models import get_weight
 
 # Weights can be retrieved by name:
-assert get_weight("ResNet50_Weights.ImageNet1K_V1") == ResNet50_Weights.ImageNet1K_V1
-assert get_weight("ResNet50_Weights.ImageNet1K_V2") == ResNet50_Weights.ImageNet1K_V2
+assert get_weight("ResNet50_Weights.IMAGENET1K_V1") == ResNet50_Weights.IMAGENET1K_V1
+assert get_weight("ResNet50_Weights.IMAGENET1K_V2") == ResNet50_Weights.IMAGENET1K_V2
 
-# Including using the default alias:
-assert get_weight("ResNet50_Weights.default") == ResNet50_Weights.ImageNet1K_V2
+# Including using the DEFAULT alias:
+assert get_weight("ResNet50_Weights.DEFAULT") == ResNet50_Weights.IMAGENET1K_V2
 ```
 
 ## Deprecations
@@ -172,8 +172,8 @@ In the new API the boolean `pretrained` and `pretrained_backbone` parameters, wh
  UserWarning: The parameter 'pretrained' is deprecated, please use 'weights' instead.
 UserWarning: 
 Arguments other than a weight enum or `None` for 'weights' are deprecated. 
-The current behavior is equivalent to passing `weights=ResNet50_Weights.ImageNet1K_V1`. 
-You can also use `weights=ResNet50_Weights.default` to get the most up-to-date weights.
+The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. 
+You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
 ```
 
 Additionally the builder methods require using keyword parameters. The use of positional parameter is deprecated and using them emits the following warning:
@@ -191,7 +191,7 @@ Migrating to the new API is very straightforward. The following method calls bet
 
 ```
 # Using pretrained weights:
-torchvision.prototype.models.resnet50(weights=ResNet50_Weights.ImageNet1K_V1)
+torchvision.prototype.models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
 torchvision.models.resnet50(pretrained=True)
 torchvision.models.resnet50(True)
 
@@ -237,7 +237,7 @@ If you are still unconvinced about giving a try to the new API, here is one more
 |RegNet Y 8gf |80.032	|82.828	|
 |RegNet Y 16gf	|80.424	|82.89	|
 |RegNet Y 32gf	|80.878	|83.366	|
-|ResNet50	|76.13	|80.674	|
+|ResNet50	|76.13	|80.858	|
 |ResNet101	|77.374	|81.886	|
 |ResNet152	|78.312	|82.284	|
 |ResNeXt50 32x4d	|77.618	|81.198	|