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Nov 11, 2021
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Default value docs for models using trainable_backbone_layers #4913

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11e363b
Default value docs for trainable_backbone_layers
ethanwhite Nov 11, 2021
f880d0f
Merge branch 'main' into trainable-backbone-docs
datumbox Nov 11, 2021
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9 changes: 6 additions & 3 deletions torchvision/models/detection/faster_rcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -379,7 +379,8 @@ def fasterrcnn_resnet50_fpn(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 3.
"""
trainable_backbone_layers = _validate_trainable_layers(
pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
Expand Down Expand Up @@ -464,7 +465,8 @@ def fasterrcnn_mobilenet_v3_large_320_fpn(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 3.
"""
weights_name = "fasterrcnn_mobilenet_v3_large_320_fpn_coco"
defaults = {
Expand Down Expand Up @@ -509,7 +511,8 @@ def fasterrcnn_mobilenet_v3_large_fpn(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 3.
"""
weights_name = "fasterrcnn_mobilenet_v3_large_fpn_coco"
defaults = {
Expand Down
3 changes: 2 additions & 1 deletion torchvision/models/detection/keypoint_rcnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -360,7 +360,8 @@ def keypointrcnn_resnet50_fpn(
num_keypoints (int): number of keypoints, default 17
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 3.
"""
trainable_backbone_layers = _validate_trainable_layers(
pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
Expand Down
3 changes: 2 additions & 1 deletion torchvision/models/detection/mask_rcnn.py
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Expand Up @@ -357,7 +357,8 @@ def maskrcnn_resnet50_fpn(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 3.
"""
trainable_backbone_layers = _validate_trainable_layers(
pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
Expand Down
3 changes: 2 additions & 1 deletion torchvision/models/detection/retinanet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -623,7 +623,8 @@ def retinanet_resnet50_fpn(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 3.
"""
trainable_backbone_layers = _validate_trainable_layers(
pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3
Expand Down
3 changes: 2 additions & 1 deletion torchvision/models/detection/ssd.py
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Expand Up @@ -587,7 +587,8 @@ def ssd300_vgg16(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 4.
"""
if "size" in kwargs:
warnings.warn("The size of the model is already fixed; ignoring the argument.")
Expand Down
3 changes: 2 additions & 1 deletion torchvision/models/detection/ssdlite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,7 +208,8 @@ def ssdlite320_mobilenet_v3_large(
num_classes (int): number of output classes of the model (including the background)
pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable. If ``None`` is
passed (the default) this value is set to 6.
norm_layer (callable, optional): Module specifying the normalization layer to use.
"""
if "size" in kwargs:
Expand Down