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https://github.com/Wan-Video/Wan2.1.git
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35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# References:
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# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
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from torch import nn
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def drop_path(x, drop_prob: float = 0.0, training: bool = False):
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if drop_prob == 0.0 or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
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if keep_prob > 0.0:
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random_tensor.div_(keep_prob)
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output = x * random_tensor
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return output
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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