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

146 lines
6.0 KiB

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
## Assembled CNN Tensorflow Impl
#
# def _bernoulli(shape, mean, seed=None, dtype=tf.float32):
# return tf.nn.relu(tf.sign(mean - tf.random_uniform(shape, minval=0, maxval=1, dtype=dtype, seed=seed)))
#
#
# def dropblock(x, keep_prob, block_size, gamma_scale=1.0, seed=None, name=None,
# data_format='channels_last', is_training=True): # pylint: disable=invalid-name
# """
# Dropblock layer. For more details, refer to https://arxiv.org/abs/1810.12890
# :param x: A floating point tensor.
# :param keep_prob: A scalar Tensor with the same type as x. The probability that each element is kept.
# :param block_size: The block size to drop
# :param gamma_scale: The multiplier to gamma.
# :param seed: Python integer. Used to create random seeds.
# :param name: A name for this operation (optional)
# :param data_format: 'channels_last' or 'channels_first'
# :param is_training: If False, do nothing.
# :return: A Tensor of the same shape of x.
# """
# if not is_training:
# return x
#
# # Early return if nothing needs to be dropped.
# if (isinstance(keep_prob, float) and keep_prob == 1) or gamma_scale == 0:
# return x
#
# with tf.name_scope(name, "dropblock", [x]) as name:
# if not x.dtype.is_floating:
# raise ValueError("x has to be a floating point tensor since it's going to"
# " be scaled. Got a %s tensor instead." % x.dtype)
# if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
# raise ValueError("keep_prob must be a scalar tensor or a float in the "
# "range (0, 1], got %g" % keep_prob)
#
# br = (block_size - 1) // 2
# tl = (block_size - 1) - br
# if data_format == 'channels_last':
# _, h, w, c = x.shape.as_list()
# sampling_mask_shape = tf.stack([1, h - block_size + 1, w - block_size + 1, c])
# pad_shape = [[0, 0], [tl, br], [tl, br], [0, 0]]
# elif data_format == 'channels_first':
# _, c, h, w = x.shape.as_list()
# sampling_mask_shape = tf.stack([1, c, h - block_size + 1, w - block_size + 1])
# pad_shape = [[0, 0], [0, 0], [tl, br], [tl, br]]
# else:
# raise NotImplementedError
#
# gamma = (1. - keep_prob) * (w * h) / (block_size ** 2) / ((w - block_size + 1) * (h - block_size + 1))
# gamma = gamma_scale * gamma
# mask = _bernoulli(sampling_mask_shape, gamma, seed, tf.float32)
# mask = tf.pad(mask, pad_shape)
#
# xdtype_mask = tf.cast(mask, x.dtype)
# xdtype_mask = tf.layers.max_pooling2d(
# inputs=xdtype_mask, pool_size=block_size,
# strides=1, padding='SAME',
# data_format=data_format)
#
# xdtype_mask = 1 - xdtype_mask
# fp32_mask = tf.cast(xdtype_mask, tf.float32)
# ret = tf.multiply(x, xdtype_mask)
# float32_mask_size = tf.cast(tf.size(fp32_mask), tf.float32)
# float32_mask_reduce_sum = tf.reduce_sum(fp32_mask)
# normalize_factor = tf.cast(float32_mask_size / (float32_mask_reduce_sum + 1e-8), x.dtype)
# ret = ret * normalize_factor
# return ret
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).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 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, dtype=torch.float32) + 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)