Skip to content

Updating code of the article for TorchVision's Multi-Weight Support API #915

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 25, 2022
Merged

Updating code of the article for TorchVision's Multi-Weight Support API #915

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
38 changes: 19 additions & 19 deletions _posts/2021-12-22-introducing-torchvision-new-multi-weight-support-api.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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()

Expand All @@ -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)
Expand All @@ -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)
```

Expand All @@ -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)
Expand All @@ -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
Expand All @@ -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:
Expand All @@ -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)

Expand Down Expand Up @@ -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 |
Expand Down