|
|
|
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}') |
|
|
