diff --git a/timm/data/__init__.py b/timm/data/__init__.py index e1886fcc..15617859 100644 --- a/timm/data/__init__.py +++ b/timm/data/__init__.py @@ -4,7 +4,7 @@ from .dataset import Dataset, DatasetTar, AugMixDataset from .transforms import * from .loader import create_loader from .transforms_factory import create_transform -from .mixup import mix_batch, FastCollateMixup +from .mixup import Mixup, FastCollateMixup from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\ rand_augment_transform, auto_augment_transform from .real_labels import RealLabelsImagenet diff --git a/timm/data/mixup.py b/timm/data/mixup.py index a018ea07..63861bc7 100644 --- a/timm/data/mixup.py +++ b/timm/data/mixup.py @@ -10,11 +10,8 @@ CutMix: https://github.com/clovaai/CutMix-PyTorch Hacked together by / Copyright 2020 Ross Wightman """ - import numpy as np import torch -import math -import numbers def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'): @@ -30,20 +27,21 @@ def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'): return y1 * lam + y2 * (1. - lam) -def mixup_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False): - lam = 1. - if not disable: - lam = np.random.beta(alpha, alpha) - input = input.mul(lam).add_(1 - lam, input.flip(0)) - target = mixup_target(target, num_classes, lam, smoothing) - return input, target - +def rand_bbox(img_shape, lam, margin=0., count=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. -def rand_bbox(size, lam, border=0., count=None): - ratio = math.sqrt(1 - lam) - img_h, img_w = size[-2:] + Args: + img_shape (tuple): Image shape as tuple + lam (float): Cutmix lambda value + margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image) + count (int): 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(border * cut_h), int(border * cut_w) + 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) @@ -53,9 +51,20 @@ def rand_bbox(size, lam, border=0., count=None): return yl, yh, xl, xh -def rand_bbox_minmax(size, minmax, count=None): +def rand_bbox_minmax(img_shape, minmax, count=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. + + Args: + img_shape (tuple): Image shape as tuple + minmax (tuple or list): Min and max bbox ratios (as percent of image size) + count (int): Number of bbox to generate + """ assert len(minmax) == 2 - img_h, img_w = size[-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) @@ -66,6 +75,8 @@ def rand_bbox_minmax(size, minmax, count=None): def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=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: @@ -76,52 +87,22 @@ def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, cou return (yl, yu, xl, xu), lam -def cutmix_batch(input, target, alpha=0.2, num_classes=1000, smoothing=0.1, disable=False, correct_lam=False): - lam = 1. - if not disable: - lam = np.random.beta(alpha, alpha) - if lam != 1: - yl, yh, xl, xh = rand_bbox(input.size(), lam) - input[:, :, yl:yh, xl:xh] = input.flip(0)[:, :, yl:yh, xl:xh] - if correct_lam: - lam = 1. - (yh - yl) * (xh - xl) / float(input.shape[-2] * input.shape[-1]) - target = mixup_target(target, num_classes, lam, smoothing) - return input, target - - -def mix_batch( - input, target, mixup_alpha=0.2, cutmix_alpha=0., prob=1.0, switch_prob=.5, - num_classes=1000, smoothing=0.1, disable=False): - # FIXME test this version - if np.random.rand() > prob: - return input, target - use_cutmix = cutmix_alpha > 0. and np.random.rand() <= switch_prob - if use_cutmix: - return cutmix_batch(input, target, cutmix_alpha, num_classes, smoothing, disable) - else: - return mixup_batch(input, target, mixup_alpha, num_classes, smoothing, disable) - - -class FastCollateMixup: - """Fast Collate Mixup/Cutmix that applies different params to each element or whole batch - - NOTE once experiments are done, one of the three variants will remain with this class name +class Mixup: + """ Mixup/Cutmix that applies different params to each element or whole batch + Args: + mixup_alpha (float): mixup alpha value, mixup is active if > 0. + cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0. + cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None. + prob (float): probability of applying mixup or cutmix per batch or element + switch_prob (float): probability of switching to cutmix instead of mixup when both are active + elementwise (bool): apply mixup/cutmix params per batch element instead of per batch + correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders + label_smoothing (float): apply label smoothing to the mixed target tensor + num_classes (int): number of classes for target """ def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5, elementwise=False, correct_lam=True, label_smoothing=0.1, num_classes=1000): - """ - - Args: - mixup_alpha (float): mixup alpha value, mixup is active if > 0. - cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0. - cutmix_minmax (float): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None - prob (float): probability of applying mixup or cutmix per batch or element - switch_prob (float): probability of using cutmix instead of mixup when both active - elementwise (bool): apply mixup/cutmix params per batch element instead of per batch - label_smoothing (float): - num_classes (int): - """ self.mixup_alpha = mixup_alpha self.cutmix_alpha = cutmix_alpha self.cutmix_minmax = cutmix_minmax @@ -129,7 +110,7 @@ class FastCollateMixup: 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.prob = prob + self.mix_prob = prob self.switch_prob = switch_prob self.label_smoothing = label_smoothing self.num_classes = num_classes @@ -137,10 +118,9 @@ class FastCollateMixup: 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 _mix_elem(self, output, batch): - batch_size = len(batch) - lam_out = np.ones(batch_size, dtype=np.float32) - use_cutmix = np.zeros(batch_size).astype(np.bool) + def _params_per_elem(self, batch_size): + lam = np.ones(batch_size, dtype=np.float32) + use_cutmix = np.zeros(batch_size, dtype=np.bool) if self.mixup_enabled: if self.mixup_alpha > 0. and self.cutmix_alpha > 0.: use_cutmix = np.random.rand(batch_size) < self.switch_prob @@ -151,35 +131,17 @@ class FastCollateMixup: elif self.mixup_alpha > 0.: lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size) elif self.cutmix_alpha > 0.: - use_cutmix = np.ones(batch_size).astype(np.bool) + use_cutmix = np.ones(batch_size, dtype=np.bool) lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size) else: assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true." - lam_out = np.where(np.random.rand(batch_size) < self.prob, lam_mix.astype(np.float32), lam_out) - - for i in range(batch_size): - j = batch_size - i - 1 - lam = lam_out[i] - mixed = batch[i][0] - if lam != 1.: - if use_cutmix[i]: - mixed = mixed.copy() - (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_out[i] = lam - else: - mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam) - lam_out[i] = lam - np.round(mixed, out=mixed) - output[i] += torch.from_numpy(mixed.astype(np.uint8)) - return torch.tensor(lam_out).unsqueeze(1) + lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam) + return lam, use_cutmix - def _mix_batch(self, output, batch): - batch_size = len(batch) + def _params_per_batch(self): lam = 1. use_cutmix = False - if self.mixup_enabled and np.random.rand() < self.prob: + if self.mixup_enabled and np.random.rand() < self.mix_prob: if self.mixup_alpha > 0. and self.cutmix_alpha > 0.: use_cutmix = np.random.rand() < self.switch_prob lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \ @@ -192,17 +154,84 @@ class FastCollateMixup: else: assert False, "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(self, x): + batch_size = len(x) + lam_batch, use_cutmix = self._params_per_elem(batch_size) + x_orig = x.clone() # need to keep an unmodified original for mixing source + for i in range(batch_size): + j = batch_size - i - 1 + lam = lam_batch[i] + if lam != 1.: + if use_cutmix[i]: + (yl, yh, xl, xh), lam = cutmix_bbox_and_lam( + x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) + x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh] + lam_batch[i] = lam + else: + x[i] = x[i] * lam + x_orig[j] * (1 - lam) + return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1) + + def _mix_batch(self, x): + lam, use_cutmix = self._params_per_batch() + if lam == 1.: + return 1. 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) + x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam) + x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh] + else: + x_flipped = x.flip(0).mul_(1. - lam) + x.mul_(lam).add_(x_flipped) + return lam + + def __call__(self, x, target): + assert len(x) % 2 == 0, 'Batch size should be even when using this' + lam = self._mix_elem(x) if self.elementwise else self._mix_batch(x) + target = mixup_target(target, self.num_classes, lam, self.label_smoothing) + return x, target + + +class FastCollateMixup(Mixup): + """ Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch + A Mixup impl that's performed while collating the batches. + """ + + def _mix_elem_collate(self, output, batch): + batch_size = len(batch) + lam_batch, use_cutmix = self._params_per_elem(batch_size) for i in range(batch_size): j = batch_size - i - 1 + lam = lam_batch[i] mixed = batch[i][0] if lam != 1.: - if use_cutmix: + if use_cutmix[i]: mixed = mixed.copy() + (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.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam) + lam_batch[i] = lam + np.round(mixed, out=mixed) + output[i] += torch.from_numpy(mixed.astype(np.uint8)) + return torch.tensor(lam_batch).unsqueeze(1) + + def _mix_batch_collate(self, output, batch): + 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 = mixed.copy() # don't want to modify the original while iterating mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh] else: mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam) @@ -210,16 +239,15 @@ class FastCollateMixup: output[i] += torch.from_numpy(mixed.astype(np.uint8)) return lam - def __call__(self, batch): + def __call__(self, batch, _=None): batch_size = len(batch) assert batch_size % 2 == 0, 'Batch size should be even when using this' output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8) if self.elementwise: - lam = self._mix_elem(output, batch) + lam = self._mix_elem_collate(output, batch) else: - lam = self._mix_batch(output, batch) + lam = self._mix_batch_collate(output, batch) target = torch.tensor([b[1] for b in batch], dtype=torch.int64) target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu') - return output, target diff --git a/train.py b/train.py index 3b038d53..c28bd266 100755 --- a/train.py +++ b/train.py @@ -28,7 +28,7 @@ except ImportError: from torch.nn.parallel import DistributedDataParallel as DDP has_apex = False -from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mix_batch, AugMixDataset +from timm.data import Dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset from timm.models import create_model, resume_checkpoint, convert_splitbn_model from timm.utils import * from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy @@ -398,12 +398,18 @@ def main(): dataset_train = Dataset(train_dir) collate_fn = None - if args.prefetcher and (args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None): - assert not num_aug_splits # collate conflict (need to support deinterleaving in collate mixup) - collate_fn = FastCollateMixup( + mixup_fn = None + mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None + if mixup_active: + mixup_args = dict( mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax, prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, elementwise=args.mixup_elem, label_smoothing=args.smoothing, num_classes=args.num_classes) + if args.prefetcher: + assert not num_aug_splits # collate conflict (need to support deinterleaving in collate mixup) + collate_fn = FastCollateMixup(**mixup_args) + else: + mixup_fn = Mixup(**mixup_args) if num_aug_splits > 1: dataset_train = AugMixDataset(dataset_train, num_splits=num_aug_splits) @@ -465,17 +471,14 @@ def main(): if args.jsd: assert num_aug_splits > 1 # JSD only valid with aug splits set train_loss_fn = JsdCrossEntropy(num_splits=num_aug_splits, smoothing=args.smoothing).cuda() - validate_loss_fn = nn.CrossEntropyLoss().cuda() - elif args.mixup > 0.: - # smoothing is handled with mixup label transform + elif mixup_active: + # smoothing is handled with mixup target transform train_loss_fn = SoftTargetCrossEntropy().cuda() - validate_loss_fn = nn.CrossEntropyLoss().cuda() elif args.smoothing: train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing).cuda() - validate_loss_fn = nn.CrossEntropyLoss().cuda() else: train_loss_fn = nn.CrossEntropyLoss().cuda() - validate_loss_fn = train_loss_fn + validate_loss_fn = nn.CrossEntropyLoss().cuda() eval_metric = args.eval_metric best_metric = None @@ -503,7 +506,7 @@ def main(): train_metrics = train_epoch( epoch, model, loader_train, optimizer, train_loss_fn, args, lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir, - use_amp=use_amp, model_ema=model_ema) + use_amp=use_amp, model_ema=model_ema, mixup_fn=mixup_fn) if args.distributed and args.dist_bn in ('broadcast', 'reduce'): if args.local_rank == 0: @@ -543,11 +546,13 @@ def main(): def train_epoch( epoch, model, loader, optimizer, loss_fn, args, - lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None): + lr_scheduler=None, saver=None, output_dir='', use_amp=False, model_ema=None, mixup_fn=None): - if args.prefetcher and args.mixup > 0 and loader.mixup_enabled: - if args.mixup_off_epoch and epoch >= args.mixup_off_epoch: + if args.mixup_off_epoch and epoch >= args.mixup_off_epoch: + if args.prefetcher and loader.mixup_enabled: loader.mixup_enabled = False + elif mixup_fn is not None: + mixup_fn.mixup_enabled = False batch_time_m = AverageMeter() data_time_m = AverageMeter() @@ -563,12 +568,8 @@ def train_epoch( data_time_m.update(time.time() - end) if not args.prefetcher: input, target = input.cuda(), target.cuda() - if args.mixup > 0.: - input, target = mix_batch( - input, target, - mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, prob=args.mixup_prob, - switch_prob=args.mixup_switch_prob, num_classes=args.num_classes, smoothing=args.smoothing, - disable=args.mixup_off_epoch and epoch >= args.mixup_off_epoch) + if mixup_fn is not None: + input, target = mixup_fn(input, target) output = model(input)