OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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85 lines
2.9 KiB
85 lines
2.9 KiB
# Copyright (c) OpenMMLab. All rights reserved. |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from mmcv.cnn import PLUGIN_LAYERS |
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eps = 1e-6 |
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@PLUGIN_LAYERS.register_module() |
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class DropBlock(nn.Module): |
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"""Randomly drop some regions of feature maps. |
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Please refer to the method proposed in `DropBlock |
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<https://arxiv.org/abs/1810.12890>`_ for details. |
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Args: |
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drop_prob (float): The probability of dropping each block. |
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block_size (int): The size of dropped blocks. |
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warmup_iters (int): The drop probability will linearly increase |
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from `0` to `drop_prob` during the first `warmup_iters` iterations. |
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Default: 2000. |
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""" |
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def __init__(self, drop_prob, block_size, warmup_iters=2000, **kwargs): |
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super(DropBlock, self).__init__() |
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assert block_size % 2 == 1 |
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assert 0 < drop_prob <= 1 |
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assert warmup_iters >= 0 |
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self.drop_prob = drop_prob |
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self.block_size = block_size |
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self.warmup_iters = warmup_iters |
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self.iter_cnt = 0 |
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def forward(self, x): |
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""" |
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Args: |
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x (Tensor): Input feature map on which some areas will be randomly |
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dropped. |
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Returns: |
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Tensor: The tensor after DropBlock layer. |
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""" |
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if not self.training: |
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return x |
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self.iter_cnt += 1 |
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N, C, H, W = list(x.shape) |
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gamma = self._compute_gamma((H, W)) |
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mask_shape = (N, C, H - self.block_size + 1, W - self.block_size + 1) |
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mask = torch.bernoulli(torch.full(mask_shape, gamma, device=x.device)) |
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mask = F.pad(mask, [self.block_size // 2] * 4, value=0) |
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mask = F.max_pool2d( |
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input=mask, |
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stride=(1, 1), |
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kernel_size=(self.block_size, self.block_size), |
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padding=self.block_size // 2) |
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mask = 1 - mask |
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x = x * mask * mask.numel() / (eps + mask.sum()) |
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return x |
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def _compute_gamma(self, feat_size): |
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"""Compute the value of gamma according to paper. gamma is the |
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parameter of bernoulli distribution, which controls the number of |
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features to drop. |
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gamma = (drop_prob * fm_area) / (drop_area * keep_area) |
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Args: |
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feat_size (tuple[int, int]): The height and width of feature map. |
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Returns: |
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float: The value of gamma. |
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""" |
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gamma = (self.drop_prob * feat_size[0] * feat_size[1]) |
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gamma /= ((feat_size[0] - self.block_size + 1) * |
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(feat_size[1] - self.block_size + 1)) |
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gamma /= (self.block_size**2) |
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factor = (1.0 if self.iter_cnt > self.warmup_iters else self.iter_cnt / |
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self.warmup_iters) |
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return gamma * factor |
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def extra_repr(self): |
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return (f'drop_prob={self.drop_prob}, block_size={self.block_size}, ' |
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f'warmup_iters={self.warmup_iters}')
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