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89 lines
3.1 KiB
89 lines
3.1 KiB
""" DropBlock, DropPath
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PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
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Hacked together by Ross Wightman
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"""
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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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import numpy as np
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import math
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def drop_block_2d(x, drop_prob=0.1, block_size=7, gamma_scale=1.0, drop_with_noise=False):
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""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
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"""
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_, _, height, width = x.shape
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total_size = width * height
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clipped_block_size = min(block_size, min(width, height))
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# seed_drop_rate, the gamma parameter
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seed_drop_rate = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
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(width - block_size + 1) *
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(height - block_size + 1))
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# Forces the block to be inside the feature map.
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w_i, h_i = torch.meshgrid(torch.arange(width).to(x.device), torch.arange(height).to(x.device))
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valid_block = ((w_i >= clipped_block_size // 2) & (w_i < width - (clipped_block_size - 1) // 2)) & \
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((h_i >= clipped_block_size // 2) & (h_i < height - (clipped_block_size - 1) // 2))
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valid_block = torch.reshape(valid_block, (1, 1, height, width)).float()
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uniform_noise = torch.rand_like(x, dtype=torch.float32)
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block_mask = ((2 - seed_drop_rate - valid_block + uniform_noise) >= 1).float()
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block_mask = -F.max_pool2d(
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-block_mask,
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kernel_size=clipped_block_size, # block_size, ???
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stride=1,
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padding=clipped_block_size // 2)
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if drop_with_noise:
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normal_noise = torch.randn_like(x)
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x = x * block_mask + normal_noise * (1 - block_mask)
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else:
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normalize_scale = block_mask.numel() / (torch.sum(block_mask) + 1e-7)
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x = x * block_mask * normalize_scale
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return x
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class DropBlock2d(nn.Module):
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""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
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"""
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def __init__(self,
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drop_prob=0.1,
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block_size=7,
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gamma_scale=1.0,
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with_noise=False):
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super(DropBlock2d, self).__init__()
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self.drop_prob = drop_prob
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self.gamma_scale = gamma_scale
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self.block_size = block_size
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self.with_noise = with_noise
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def forward(self, x):
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if not self.training or not self.drop_prob:
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return x
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return drop_block_2d(x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise)
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def drop_path(x, drop_prob=0.):
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"""Drop paths (Stochastic Depth) per sample (when applied in residual blocks).
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"""
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keep_prob = 1 - drop_prob
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random_tensor = keep_prob + torch.rand((x.size()[0], 1, 1, 1), dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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output = x.div(keep_prob) * random_tensor
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return output
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class DropPath(nn.ModuleDict):
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"""Drop paths (Stochastic Depth) per sample (when applied in residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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if not self.training or not self.drop_prob:
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return x
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return drop_path(x, self.drop_prob)
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