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Add memory_efficient arg to densenet docs #1090

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Jul 5, 2019
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Add memory_efficient arg to densenet docs #1090

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Add memory_efficient args to densenet docs
ekagra-ranjan Jul 4, 2019
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linting
ekagra-ranjan Jul 4, 2019
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12 changes: 10 additions & 2 deletions torchvision/models/densenet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,8 +98,8 @@ class DenseNet(nn.Module):
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
memory_efficient (bool) - set to True to use checkpointing. Much more memory efficient,
but slower. Default: *False*
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""

def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
Expand Down Expand Up @@ -192,6 +192,8 @@ def densenet121(pretrained=False, progress=True, **kwargs):
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
**kwargs)
Expand All @@ -204,6 +206,8 @@ def densenet161(pretrained=False, progress=True, **kwargs):
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
**kwargs)
Expand All @@ -216,6 +220,8 @@ def densenet169(pretrained=False, progress=True, **kwargs):
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
**kwargs)
Expand All @@ -228,6 +234,8 @@ def densenet201(pretrained=False, progress=True, **kwargs):
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
**kwargs)