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Fix network_type docstring formatting in PerceptualLoss #8670

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Fix network_type docstring formatting in PerceptualLoss #8670

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15 changes: 10 additions & 5 deletions monai/losses/perceptual.py
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Expand Up @@ -49,22 +49,27 @@ class PerceptualLoss(nn.Module):

Args:
spatial_dims: number of spatial dimensions.
network_type: {``"alex"``, ``"vgg"``, ``"squeeze"``, ``"radimagenet_resnet50"``,
``"medicalnet_resnet10_23datasets"``, ``"medicalnet_resnet50_23datasets"``, ``"resnet50"``}
Specifies the network architecture to use. Defaults to ``"alex"``.
network_type: type of network for perceptual loss. One of:
- "alex"
- "vgg"
- "squeeze"
- "radimagenet_resnet50"
- "medicalnet_resnet10_23datasets"
- "medicalnet_resnet50_23datasets"
- "resnet50"
is_fake_3d: if True use 2.5D approach for a 3D perceptual loss.
fake_3d_ratio: ratio of how many slices per axis are used in the 2.5D approach.
cache_dir: path to cache directory to save the pretrained network weights.
pretrained: whether to load pretrained weights. This argument only works when using networks from
LIPIS or Torchvision. Defaults to ``"True"``.
LIPIS or Torchvision. Defaults to ``True``.
pretrained_path: if `pretrained` is `True`, users can specify a weights file to be loaded
via using this argument. This argument only works when ``"network_type"`` is "resnet50".
Defaults to `None`.
pretrained_state_dict_key: if `pretrained_path` is not `None`, this argument is used to
extract the expected state dict. This argument only works when ``"network_type"`` is "resnet50".
Defaults to `None`.
channel_wise: if True, the loss is returned per channel. Otherwise the loss is averaged over the channels.
Defaults to ``False``.
Defaults to ``False``.
"""

def __init__(
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