Source code for super_gradients.training.datasets.mixup

""" 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