composer_resnet

composer.models.composer_resnet(model_name, num_classes=1000, weights=None, pretrained=False, groups=1, width_per_group=64, initializers=None, loss_name='soft_cross_entropy')

Helper function to create a ComposerClassifier with a torchvision ResNet model.

From Deep Residual Learning for Image Recognition (He et al, 2015).

Parameters

• model_name (str) – Name of the ResNet model instance. Either ["resnet18", "resnet34", "resnet50", "resnet101", "resnet152"].

• num_classes (int, optional) – The number of classes. Needed for classification tasks. Default: 1000.

• weights (str, optional) – If provided, pretrained weights can be specified, such as with IMAGENET1K_V2. Default: None.

• pretrained (bool, optional) – If True, use ImageNet pretrained weights. Default: False. This parameter is deprecated and will soon be removed in favor of weights.

• groups (int, optional) – Number of filter groups for the 3x3 convolution layer in bottleneck blocks. Default: 1.

• width_per_group (int, optional) – Initial width for each convolution group. Width doubles after each stage. Default: 64.

• initializers (List[Initializer], optional) – Initializers for the model. None for no initialization. Default: None.

• loss_name (str, optional) – Loss function to use. E.g. ‘soft_cross_entropy’ or ‘binary_cross_entropy_with_logits’. Loss function must be in loss. Default: 'soft_cross_entropy'”.

Returns

ComposerModel – instance of ComposerClassifier with a torchvision ResNet model.

Example:

from composer.models import composer_resnet

model = composer_resnet(model_name='resnet18')  # creates a torchvision resnet18 for image classification