Ross Wightman
4 years ago
parent
9f11b4e8a2
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3ff19079f9
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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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## Assembled CNN Tensorflow Impl
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#
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# def _bernoulli(shape, mean, seed=None, dtype=tf.float32):
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# return tf.nn.relu(tf.sign(mean - tf.random_uniform(shape, minval=0, maxval=1, dtype=dtype, seed=seed)))
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#
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#
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# def dropblock(x, keep_prob, block_size, gamma_scale=1.0, seed=None, name=None,
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# data_format='channels_last', is_training=True): # pylint: disable=invalid-name
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# """
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# Dropblock layer. For more details, refer to https://arxiv.org/abs/1810.12890
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# :param x: A floating point tensor.
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# :param keep_prob: A scalar Tensor with the same type as x. The probability that each element is kept.
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# :param block_size: The block size to drop
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# :param gamma_scale: The multiplier to gamma.
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# :param seed: Python integer. Used to create random seeds.
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# :param name: A name for this operation (optional)
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# :param data_format: 'channels_last' or 'channels_first'
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# :param is_training: If False, do nothing.
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# :return: A Tensor of the same shape of x.
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# """
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# if not is_training:
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# return x
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#
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# # Early return if nothing needs to be dropped.
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# if (isinstance(keep_prob, float) and keep_prob == 1) or gamma_scale == 0:
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# return x
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#
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# with tf.name_scope(name, "dropblock", [x]) as name:
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# if not x.dtype.is_floating:
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# raise ValueError("x has to be a floating point tensor since it's going to"
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# " be scaled. Got a %s tensor instead." % x.dtype)
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# if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
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# raise ValueError("keep_prob must be a scalar tensor or a float in the "
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# "range (0, 1], got %g" % keep_prob)
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#
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# br = (block_size - 1) // 2
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# tl = (block_size - 1) - br
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# if data_format == 'channels_last':
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# _, h, w, c = x.shape.as_list()
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# sampling_mask_shape = tf.stack([1, h - block_size + 1, w - block_size + 1, c])
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# pad_shape = [[0, 0], [tl, br], [tl, br], [0, 0]]
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# elif data_format == 'channels_first':
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# _, c, h, w = x.shape.as_list()
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# sampling_mask_shape = tf.stack([1, c, h - block_size + 1, w - block_size + 1])
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# pad_shape = [[0, 0], [0, 0], [tl, br], [tl, br]]
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# else:
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# raise NotImplementedError
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#
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# gamma = (1. - keep_prob) * (w * h) / (block_size ** 2) / ((w - block_size + 1) * (h - block_size + 1))
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# gamma = gamma_scale * gamma
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# mask = _bernoulli(sampling_mask_shape, gamma, seed, tf.float32)
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# mask = tf.pad(mask, pad_shape)
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#
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# xdtype_mask = tf.cast(mask, x.dtype)
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# xdtype_mask = tf.layers.max_pooling2d(
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# inputs=xdtype_mask, pool_size=block_size,
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# strides=1, padding='SAME',
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# data_format=data_format)
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#
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# xdtype_mask = 1 - xdtype_mask
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# fp32_mask = tf.cast(xdtype_mask, tf.float32)
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# ret = tf.multiply(x, xdtype_mask)
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# float32_mask_size = tf.cast(tf.size(fp32_mask), tf.float32)
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# float32_mask_reduce_sum = tf.reduce_sum(fp32_mask)
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# normalize_factor = tf.cast(float32_mask_size / (float32_mask_reduce_sum + 1e-8), x.dtype)
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# ret = ret * normalize_factor
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# return ret
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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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_, _, 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), torch.arange(height))
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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))
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valid_block = valid_block.to(x.dtype)
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uniform_noise = torch.rand_like(x)
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block_mask = ((2 - seed_drop_rate - valid_block + uniform_noise) >= 1).to(x.dtype)
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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, dtype=torch.float32) + 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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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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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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