""" Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch
CutMix by timm: https://github.com/rwightman/pytorch-image-models/timm
"""
from typing import List, Union
import numpy as np
import torch
from super_gradients.training.exceptions.dataset_exceptions import IllegalDatasetParameterException
[docs]def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
x = x.long().view(-1, 1)
return torch.full((x.size()[0], num_classes), off_value, device=device).scatter_(1, x, on_value)
[docs]def mixup_target(target: torch.Tensor, num_classes: int, lam: float = 1., smoothing: float = 0.0, device: str = 'cuda'):
"""
generate a smooth target (label) two-hot tensor to support the mixed images with different labels
:param target: the targets tensor
:param num_classes: number of classes (to set the final tensor size)
:param lam: percentage of label a range [0, 1] in the mixing
:param smoothing: the smoothing multiplier
:param device: usable device ['cuda', 'cpu']
:return:
"""
off_value = smoothing / num_classes
on_value = 1. - smoothing + off_value
y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value, device=device)
y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value, device=device)
return y1 * lam + y2 * (1. - lam)
[docs]def rand_bbox(img_shape: tuple, lam: float, margin: float = 0., count: int = None):
""" Standard CutMix bounding-box
Generates a random square bbox based on lambda value. This impl includes
support for enforcing a border margin as percent of bbox dimensions.
:param img_shape: Image shape as tuple
:param lam: Cutmix lambda value
:param margin: Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
:param count: Number of bbox to generate
"""
ratio = np.sqrt(1 - lam)
img_h, img_w = img_shape[-2:]
cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
yl = np.clip(cy - cut_h // 2, 0, img_h)
yh = np.clip(cy + cut_h // 2, 0, img_h)
xl = np.clip(cx - cut_w // 2, 0, img_w)
xh = np.clip(cx + cut_w // 2, 0, img_w)
return yl, yh, xl, xh
[docs]def rand_bbox_minmax(img_shape: tuple, minmax: Union[tuple, list], count: int = None):
""" Min-Max CutMix bounding-box
Inspired by Darknet cutmix impl, generates a random rectangular bbox
based on min/max percent values applied to each dimension of the input image.
Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max.
:param img_shape: Image shape as tuple
:param minmax: Min and max bbox ratios (as percent of image size)
:param count: Number of bbox to generate
"""
assert len(minmax) == 2
img_h, img_w = img_shape[-2:]
cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
yl = np.random.randint(0, img_h - cut_h, size=count)
xl = np.random.randint(0, img_w - cut_w, size=count)
yu = yl + cut_h
xu = xl + cut_w
return yl, yu, xl, xu
[docs]def cutmix_bbox_and_lam(img_shape: tuple, lam: float, ratio_minmax: Union[tuple, list] = None, correct_lam: bool = True,
count: int = None):
"""
Generate bbox and apply lambda correction.
"""
if ratio_minmax is not None:
yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
else:
yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
if correct_lam or ratio_minmax is not None:
bbox_area = (yu - yl) * (xu - xl)
lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
return (yl, yu, xl, xu), lam
[docs]class CollateMixup:
"""
Collate with Mixup/Cutmix that applies different params to each element or whole batch
A Mixup impl that's performed while collating the batches.
"""
def __init__(self, mixup_alpha: float = 1., cutmix_alpha: float = 0., cutmix_minmax: List[float] = None,
prob: float = 1.0, switch_prob: float = 0.5,
mode: str = 'batch', correct_lam: bool = True, label_smoothing: float = 0.1, num_classes: int = 1000):
"""
Mixup/Cutmix that applies different params to each element or whole batch
:param mixup_alpha: mixup alpha value, mixup is active if > 0.
:param cutmix_alpha: cutmix alpha value, cutmix is active if > 0.
:param cutmix_minmax: cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
:param prob: probability of applying mixup or cutmix per batch or element
:param switch_prob: probability of switching to cutmix instead of mixup when both are active
:param mode: how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
:param correct_lam: apply lambda correction when cutmix bbox clipped by image borders
:param label_smoothing: apply label smoothing to the mixed target tensor
:param num_classes: number of classes for target
"""
self.mixup_alpha = mixup_alpha
self.cutmix_alpha = cutmix_alpha
self.cutmix_minmax = cutmix_minmax
if self.cutmix_minmax is not None:
assert len(self.cutmix_minmax) == 2
# force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
self.cutmix_alpha = 1.0
self.mix_prob = prob
self.switch_prob = switch_prob
self.label_smoothing = label_smoothing
self.num_classes = num_classes
self.mode = mode
self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix
self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)
def _params_per_elem(self, batch_size):
"""
generate two random masks to define which elements of the batch will be mixed and how (depending on the
self.mixup_enabled, self.mixup_alpha, self.cutmix_alpha parameters
:param batch_size:
:return: two tensors with shape=batch_size - the first contains the lambda value per batch element
and the second is a binary flag indicating use of cutmix per batch element
"""
lam = torch.ones(batch_size, dtype=torch.float32)
use_cutmix = torch.zeros(batch_size, dtype=torch.bool)
if self.mixup_enabled:
if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
use_cutmix = torch.rand(batch_size) < self.switch_prob
lam_mix = torch.where(
use_cutmix,
torch.distributions.beta.Beta(self.cutmix_alpha, self.cutmix_alpha).sample(sample_shape=batch_size),
torch.distributions.beta.Beta(self.mixup_alpha, self.mixup_alpha).sample(sample_shape=batch_size))
elif self.mixup_alpha > 0.:
lam_mix = torch.distributions.beta.Beta(self.mixup_alpha, self.mixup_alpha).sample(sample_shape=batch_size)
elif self.cutmix_alpha > 0.:
use_cutmix = torch.ones(batch_size, dtype=torch.bool)
lam_mix = torch.distributions.beta.Beta(self.cutmix_alpha, self.cutmix_alpha).sample(sample_shape=batch_size)
else:
raise IllegalDatasetParameterException("One of mixup_alpha > 0., cutmix_alpha > 0., "
"cutmix_minmax not None should be true.")
lam = torch.where(torch.rand(batch_size) < self.mix_prob, lam_mix.type(torch.float32), lam)
return lam, use_cutmix
def _params_per_batch(self):
"""
generate two random parameters to define if batch will be mixed and how (depending on the
self.mixup_enabled, self.mixup_alpha, self.cutmix_alpha parameters
:return: two parameters - the first contains the lambda value for the whole batch
and the second is a binary flag indicating use of cutmix for the batch
"""
lam = 1.
use_cutmix = False
if self.mixup_enabled and torch.rand(1) < self.mix_prob:
if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
use_cutmix = torch.rand(1) < self.switch_prob
lam_mix = torch.distributions.beta.Beta(self.cutmix_alpha, self.cutmix_alpha).sample() if use_cutmix else \
torch.distributions.beta.Beta(self.mixup_alpha, self.mixup_alpha).sample()
elif self.mixup_alpha > 0.:
lam_mix = torch.distributions.beta.Beta(self.mixup_alpha, self.mixup_alpha).sample()
elif self.cutmix_alpha > 0.:
use_cutmix = True
lam_mix = torch.distributions.beta.Beta(self.cutmix_alpha, self.cutmix_alpha).sample()
else:
raise IllegalDatasetParameterException("One of mixup_alpha > 0., cutmix_alpha > 0., "
"cutmix_minmax not None should be true.")
lam = float(lam_mix)
return lam, use_cutmix
def _mix_elem_collate(self, output: torch.Tensor, batch: list, half: bool = False):
"""
This is the implementation for 'elem' or 'half' modes
:param output: the output tensor to fill
:param batch: list of thr batch items
:return: a tensor containing the lambda values used for the mixing (this vector can be used for
mixing the labels as well)
"""
batch_size = len(batch)
num_elem = batch_size // 2 if half else batch_size
assert len(output) == num_elem
lam_batch, use_cutmix = self._params_per_elem(num_elem)
for i in range(num_elem):
j = batch_size - i - 1
lam = lam_batch[i]
mixed = batch[i][0]
if lam != 1.:
if use_cutmix[i]:
if not half:
mixed = torch.clone(mixed)
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
lam_batch[i] = lam
else:
mixed = mixed * lam + batch[j][0] * (1 - lam)
output[i] += mixed
if half:
lam_batch = torch.cat((lam_batch, torch.ones(num_elem)))
return torch.tensor(lam_batch).unsqueeze(1)
def _mix_pair_collate(self, output: torch.Tensor, batch: list):
"""
This is the implementation for 'pair' mode
:param output: the output tensor to fill
:param batch: list of thr batch items
:return: a tensor containing the lambda values used for the mixing (this vector can be used for
mixing the labels as well)
"""
batch_size = len(batch)
lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
for i in range(batch_size // 2):
j = batch_size - i - 1
lam = lam_batch[i]
mixed_i = batch[i][0]
mixed_j = batch[j][0]
assert 0 <= lam <= 1.0
if lam < 1.:
if use_cutmix[i]:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
patch_i = torch.clone(mixed_i[:, yl:yh, xl:xh])
mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]
mixed_j[:, yl:yh, xl:xh] = patch_i
lam_batch[i] = lam
else:
mixed_temp = mixed_i.type(torch.float32) * lam + mixed_j.type(torch.float32) * (1 - lam)
mixed_j = mixed_j.type(torch.float32) * lam + mixed_i.type(torch.float32) * (1 - lam)
mixed_i = mixed_temp
torch.rint(mixed_j, out=mixed_j)
torch.rint(mixed_i, out=mixed_i)
output[i] += mixed_i
output[j] += mixed_j
lam_batch = torch.cat((lam_batch, lam_batch[::-1]))
return torch.tensor(lam_batch).unsqueeze(1)
def _mix_batch_collate(self, output: torch.Tensor, batch: list):
"""
This is the implementation for 'batch' mode
:param output: the output tensor to fill
:param batch: list of thr batch items
:return: the lambda value used for the mixing
"""
batch_size = len(batch)
lam, use_cutmix = self._params_per_batch()
if use_cutmix:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
for i in range(batch_size):
j = batch_size - i - 1
mixed = batch[i][0]
if lam != 1.:
if use_cutmix:
mixed = torch.clone(mixed) # don't want to modify the original while iterating
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
else:
mixed = mixed * lam + batch[j][0] * (1 - lam)
output[i] += mixed
return lam
def __call__(self, batch, _=None):
batch_size = len(batch)
if batch_size % 2 != 0:
raise IllegalDatasetParameterException('Batch size should be even when using this')
half = 'half' in self.mode
if half:
batch_size //= 2
output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.float32)
if self.mode == 'elem' or self.mode == 'half':
lam = self._mix_elem_collate(output, batch, half=half)
elif self.mode == 'pair':
lam = self._mix_pair_collate(output, batch)
else:
lam = self._mix_batch_collate(output, batch)
target = torch.tensor([b[1] for b in batch], dtype=torch.int32)
target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
target = target[:batch_size]
return output, target