""" Borrowed from https://github.com/rwightman/pytorch-image-models
DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl inspired by two Tensorflow impl that I liked:
- https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def drop_block_2d(
x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,
with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
runs with success, but needs further validation and possibly optimization for lower runtime impact.
"""
B, C, H, W = x.shape
total_size = W * H
clipped_block_size = min(block_size, min(W, H))
# seed_drop_rate, the gamma parameter
gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
(W - block_size + 1) * (H - block_size + 1))
# Forces the block to be inside the feature map.
w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
if batchwise:
# one mask for whole batch, quite a bit faster
uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
else:
uniform_noise = torch.rand_like(x)
block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
block_mask = -F.max_pool2d(
-block_mask,
kernel_size=clipped_block_size, # block_size,
stride=1,
padding=clipped_block_size // 2)
if with_noise:
normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
if inplace:
x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
else:
x = x * block_mask + normal_noise * (1 - block_mask)
else:
normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
if inplace:
x.mul_(block_mask * normalize_scale)
else:
x = x * block_mask * normalize_scale
return x
def drop_block_fast_2d(
x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
block mask at edges.
"""
B, C, H, W = x.shape
total_size = W * H
clipped_block_size = min(block_size, min(W, H))
gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
(W - block_size + 1) * (H - block_size + 1))
if batchwise:
# one mask for whole batch, quite a bit faster
block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
else:
# mask per batch element
block_mask = torch.rand_like(x) < gamma
block_mask = F.max_pool2d(
block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
if with_noise:
normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
if inplace:
x.mul_(1. - block_mask).add_(normal_noise * block_mask)
else:
x = x * (1. - block_mask) + normal_noise * block_mask
else:
block_mask = 1 - block_mask
normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype)
if inplace:
x.mul_(block_mask * normalize_scale)
else:
x = x * block_mask * normalize_scale
return x
class DropBlock2d(nn.Module):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
"""
def __init__(self,
drop_prob=0.1,
block_size=7,
gamma_scale=1.0,
with_noise=False,
inplace=False,
batchwise=False,
fast=True):
super(DropBlock2d, self).__init__()
self.drop_prob = drop_prob
self.gamma_scale = gamma_scale
self.block_size = block_size
self.with_noise = with_noise
self.inplace = inplace
self.batchwise = batchwise
self.fast = fast # FIXME finish comparisons of fast vs not
def forward(self, x):
if not self.training or not self.drop_prob:
return x
if self.fast:
return drop_block_fast_2d(
x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
else:
return drop_block_2d(
x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)