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pytorch-image-models/timm/models/nn_ops.py

78 lines
2.8 KiB

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