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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import math
- import random
- import numpy as np
- from scipy.stats import beta
- def fftfreqnd(h, w=None, z=None):
- """ Get bin values for discrete fourier transform of size (h, w, z)
- :param h: Required, first dimension size
- :param w: Optional, second dimension size
- :param z: Optional, third dimension size
- """
- fz = fx = 0
- fy = np.fft.fftfreq(h)
- if w is not None:
- fy = np.expand_dims(fy, -1)
- if w % 2 == 1:
- fx = np.fft.fftfreq(w)[:w // 2 + 2]
- else:
- fx = np.fft.fftfreq(w)[:w // 2 + 1]
- if z is not None:
- fy = np.expand_dims(fy, -1)
- if z % 2 == 1:
- fz = np.fft.fftfreq(z)[:, None]
- else:
- fz = np.fft.fftfreq(z)[:, None]
- return np.sqrt(fx * fx + fy * fy + fz * fz)
- def get_spectrum(freqs, decay_power, ch, h, w=0, z=0):
- """ Samples a fourier image with given size and frequencies decayed by decay power
- :param freqs: Bin values for the discrete fourier transform
- :param decay_power: Decay power for frequency decay prop 1/f**d
- :param ch: Number of channels for the resulting mask
- :param h: Required, first dimension size
- :param w: Optional, second dimension size
- :param z: Optional, third dimension size
- """
- scale = np.ones(1) / (np.maximum(freqs, np.array([1. / max(w, h, z)]))
- **decay_power)
- param_size = [ch] + list(freqs.shape) + [2]
- param = np.random.randn(*param_size)
- scale = np.expand_dims(scale, -1)[None, :]
- return scale * param
- def make_low_freq_image(decay, shape, ch=1):
- """ Sample a low frequency image from fourier space
- :param decay_power: Decay power for frequency decay prop 1/f**d
- :param shape: Shape of desired mask, list up to 3 dims
- :param ch: Number of channels for desired mask
- """
- freqs = fftfreqnd(*shape)
- spectrum = get_spectrum(freqs, decay, ch,
- *shape) #.reshape((1, *shape[:-1], -1))
- spectrum = spectrum[:, 0] + 1j * spectrum[:, 1]
- mask = np.real(np.fft.irfftn(spectrum, shape))
- if len(shape) == 1:
- mask = mask[:1, :shape[0]]
- if len(shape) == 2:
- mask = mask[:1, :shape[0], :shape[1]]
- if len(shape) == 3:
- mask = mask[:1, :shape[0], :shape[1], :shape[2]]
- mask = mask
- mask = (mask - mask.min())
- mask = mask / mask.max()
- return mask
- def sample_lam(alpha, reformulate=False):
- """ Sample a lambda from symmetric beta distribution with given alpha
- :param alpha: Alpha value for beta distribution
- :param reformulate: If True, uses the reformulation of [1].
- """
- if reformulate:
- lam = beta.rvs(alpha + 1, alpha)
- else:
- lam = beta.rvs(alpha, alpha)
- return lam
- def binarise_mask(mask, lam, in_shape, max_soft=0.0):
- """ Binarises a given low frequency image such that it has mean lambda.
- :param mask: Low frequency image, usually the result of `make_low_freq_image`
- :param lam: Mean value of final mask
- :param in_shape: Shape of inputs
- :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.
- :return:
- """
- idx = mask.reshape(-1).argsort()[::-1]
- mask = mask.reshape(-1)
- num = math.ceil(lam * mask.size) if random.random() > 0.5 else math.floor(
- lam * mask.size)
- eff_soft = max_soft
- if max_soft > lam or max_soft > (1 - lam):
- eff_soft = min(lam, 1 - lam)
- soft = int(mask.size * eff_soft)
- num_low = int(num - soft)
- num_high = int(num + soft)
- mask[idx[:num_high]] = 1
- mask[idx[num_low:]] = 0
- mask[idx[num_low:num_high]] = np.linspace(1, 0, (num_high - num_low))
- mask = mask.reshape((1, 1, in_shape[0], in_shape[1]))
- return mask
- def sample_mask(alpha, decay_power, shape, max_soft=0.0, reformulate=False):
- """ Samples a mean lambda from beta distribution parametrised by alpha, creates a low frequency image and binarises
- it based on this lambda
- :param alpha: Alpha value for beta distribution from which to sample mean of mask
- :param decay_power: Decay power for frequency decay prop 1/f**d
- :param shape: Shape of desired mask, list up to 3 dims
- :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.
- :param reformulate: If True, uses the reformulation of [1].
- """
- if isinstance(shape, int):
- shape = (shape, )
- # Choose lambda
- lam = sample_lam(alpha, reformulate)
- # Make mask, get mean / std
- mask = make_low_freq_image(decay_power, shape)
- mask = binarise_mask(mask, lam, shape, max_soft)
- return float(lam), mask
- def sample_and_apply(x,
- alpha,
- decay_power,
- shape,
- max_soft=0.0,
- reformulate=False):
- """
- :param x: Image batch on which to apply fmix of shape [b, c, shape*]
- :param alpha: Alpha value for beta distribution from which to sample mean of mask
- :param decay_power: Decay power for frequency decay prop 1/f**d
- :param shape: Shape of desired mask, list up to 3 dims
- :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.
- :param reformulate: If True, uses the reformulation of [1].
- :return: mixed input, permutation indices, lambda value of mix,
- """
- lam, mask = sample_mask(alpha, decay_power, shape, max_soft, reformulate)
- index = np.random.permutation(x.shape[0])
- x1, x2 = x * mask, x[index] * (1 - mask)
- return x1 + x2, index, lam
- class FMixBase:
- """ FMix augmentation
- Args:
- decay_power (float): Decay power for frequency decay prop 1/f**d
- alpha (float): Alpha value for beta distribution from which to sample mean of mask
- size ([int] | [int, int] | [int, int, int]): Shape of desired mask, list up to 3 dims
- max_soft (float): Softening value between 0 and 0.5 which smooths hard edges in the mask.
- reformulate (bool): If True, uses the reformulation of [1].
- """
- def __init__(self,
- decay_power=3,
- alpha=1,
- size=(32, 32),
- max_soft=0.0,
- reformulate=False):
- super().__init__()
- self.decay_power = decay_power
- self.reformulate = reformulate
- self.size = size
- self.alpha = alpha
- self.max_soft = max_soft
- self.index = None
- self.lam = None
- def __call__(self, x):
- raise NotImplementedError
- def loss(self, *args, **kwargs):
- raise NotImplementedError
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