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drop.py
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drop.py
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""" 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
import paddle
import paddle.nn as nn
import paddle.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 = keep_prob + paddle.rand(shape, dtype=x.dtype)
random_tensor.floor_() # binarize
# output = x.div(keep_prob) * random_tensor
keep_prob = paddle.to_tensor(keep_prob)
output = paddle.divide(x, keep_prob) * random_tensor
return output
# class DropPath(nn.Module):
class DropPath(nn.Layer):
"""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)