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