mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Add per model crop pct, interpolation defaults, tie it all together
* create one resolve fn to pull together model defaults + cmd line args * update attribution comments in some models * test update train/validation/inference scripts
This commit is contained in:
Ross Wightman
parent
c328b155e9
commit
0562b91c38
@ -23,20 +23,20 @@ class PrefetchLoader:
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD):
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self.loader = loader
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self.stream = torch.cuda.Stream()
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self.mean = torch.tensor([x * 255 for x in mean]).cuda().view(1, 3, 1, 1)
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self.std = torch.tensor([x * 255 for x in std]).cuda().view(1, 3, 1, 1)
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if rand_erase_prob:
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if rand_erase_prob > 0.:
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self.random_erasing = RandomErasingTorch(
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probability=rand_erase_prob, per_pixel=rand_erase_pp)
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else:
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self.random_erasing = None
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def __iter__(self):
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stream = torch.cuda.Stream()
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first = True
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for next_input, next_target in self.loader:
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with torch.cuda.stream(self.stream):
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with torch.cuda.stream(stream):
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next_input = next_input.cuda(non_blocking=True)
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next_target = next_target.cuda(non_blocking=True)
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next_input = next_input.float().sub_(self.mean).div_(self.std)
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@ -48,7 +48,7 @@ class PrefetchLoader:
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else:
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first = False
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torch.cuda.current_stream().wait_stream(self.stream)
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torch.cuda.current_stream().wait_stream(stream)
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input = next_input
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target = next_target
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@ -64,28 +64,35 @@ class PrefetchLoader:
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def create_loader(
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dataset,
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img_size,
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input_size,
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batch_size,
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is_training=False,
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use_prefetcher=True,
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rand_erase_prob=0.,
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rand_erase_pp=False,
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interpolation='bilinear',
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD,
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num_workers=1,
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distributed=False,
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crop_pct=None,
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):
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if isinstance(input_size, tuple):
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img_size = input_size[-2:]
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else:
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img_size = input_size
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if is_training:
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transform = transforms_imagenet_train(
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img_size,
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interpolation=interpolation,
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use_prefetcher=use_prefetcher,
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mean=mean,
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std=std)
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else:
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transform = transforms_imagenet_eval(
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img_size,
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interpolation=interpolation,
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use_prefetcher=use_prefetcher,
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mean=mean,
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std=std,
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@ -15,28 +15,66 @@ IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
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IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
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def get_mean_and_std(model, args, num_chan=3):
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if hasattr(model, 'default_cfg'):
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mean = model.default_cfg['mean']
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std = model.default_cfg['std']
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def resolve_data_config(model, args, default_cfg={}, verbose=True):
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new_config = {}
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default_cfg = default_cfg
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if not default_cfg and hasattr(model, 'default_cfg'):
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default_cfg = model.default_cfg
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# Resolve input/image size
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# FIXME grayscale/chans arg to use different # channels?
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in_chans = 3
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input_size = (in_chans, 224, 224)
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if args.img_size is not None:
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# FIXME support passing img_size as tuple, non-square
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assert isinstance(args.img_size, int)
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input_size = (in_chans, args.img_size, args.img_size)
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elif 'input_size' in default_cfg:
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input_size = default_cfg['input_size']
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new_config['input_size'] = input_size
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# resolve interpolation method
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new_config['interpolation'] = 'bilinear'
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if args.interpolation:
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new_config['interpolation'] = args.interpolation
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elif 'interpolation' in default_cfg:
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new_config['interpolation'] = default_cfg['interpolation']
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# resolve dataset + model mean for normalization
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new_config['mean'] = get_mean_by_model(args.model)
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if args.mean is not None:
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mean = tuple(args.mean)
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if len(mean) == 1:
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mean = tuple(list(mean) * in_chans)
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else:
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assert len(mean) == in_chans
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new_config['mean'] = mean
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elif 'mean' in default_cfg:
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new_config['mean'] = default_cfg['mean']
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# resolve dataset + model std deviation for normalization
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new_config['std'] = get_std_by_model(args.model)
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if args.std is not None:
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std = tuple(args.std)
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if len(std) == 1:
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std = tuple(list(std) * in_chans)
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else:
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assert len(std) == in_chans
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new_config['std'] = std
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else:
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if args.mean is not None:
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mean = tuple(args.mean)
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if len(mean) == 1:
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mean = tuple(list(mean) * num_chan)
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else:
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assert len(mean) == num_chan
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else:
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mean = get_mean_by_model(args.model)
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if args.std is not None:
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std = tuple(args.std)
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if len(std) == 1:
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std = tuple(list(std) * num_chan)
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else:
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assert len(std) == num_chan
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else:
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std = get_std_by_model(args.model)
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return mean, std
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new_config['std'] = default_cfg['std']
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# resolve default crop percentage
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new_config['crop_pct'] = DEFAULT_CROP_PCT
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if 'crop_pct' in default_cfg:
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new_config['crop_pct'] = default_cfg['crop_pct']
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if verbose:
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print('Data processing configuration for current model + dataset:')
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for n, v in new_config.items():
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print('\t%s: %s' % (n, str(v)))
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return new_config
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def get_mean_by_name(name):
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@ -104,6 +142,7 @@ def transforms_imagenet_train(
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img_size=224,
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scale=(0.1, 1.0),
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color_jitter=(0.4, 0.4, 0.4),
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interpolation='bilinear',
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random_erasing=0.4,
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use_prefetcher=False,
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mean=IMAGENET_DEFAULT_MEAN,
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@ -112,7 +151,8 @@ def transforms_imagenet_train(
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tfl = [
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transforms.RandomResizedCrop(
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img_size, scale=scale, interpolation=Image.BICUBIC),
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img_size, scale=scale,
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interpolation=Image.BILINEAR if interpolation == 'bilinear' else Image.BICUBIC),
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transforms.RandomHorizontalFlip(),
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transforms.ColorJitter(*color_jitter),
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]
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@ -135,14 +175,24 @@ def transforms_imagenet_train(
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def transforms_imagenet_eval(
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img_size=224,
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crop_pct=None,
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interpolation='bilinear',
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use_prefetcher=False,
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mean=IMAGENET_DEFAULT_MEAN,
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std=IMAGENET_DEFAULT_STD):
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crop_pct = crop_pct or DEFAULT_CROP_PCT
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scale_size = int(math.floor(img_size / crop_pct))
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if isinstance(img_size, tuple):
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assert len(img_size) == 2
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if img_size[0] == img_size[1]:
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# fall-back to older behaviour so Resize scales to shortest edge if target is square
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scale_size = int(math.floor(img_size[0] / crop_pct))
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else:
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scale_size = tuple([int(x[0] / crop_pct) for x in img_size])
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else:
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scale_size = int(math.floor(img_size / crop_pct))
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tfl = [
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transforms.Resize(scale_size, Image.BICUBIC),
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transforms.Resize(scale_size, Image.BILINEAR if interpolation == 'bilinear' else Image.BICUBIC),
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transforms.CenterCrop(img_size),
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]
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if use_prefetcher:
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26
inference.py
26
inference.py
@ -12,7 +12,7 @@ import numpy as np
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import torch
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from models import create_model, apply_test_time_pool
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from data import Dataset, create_loader, get_mean_and_std
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from data import Dataset, create_loader, resolve_data_config
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from utils import AverageMeter
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torch.backends.cudnn.benchmark = True
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@ -30,6 +30,12 @@ parser.add_argument('-b', '--batch-size', default=256, type=int,
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metavar='N', help='mini-batch size (default: 256)')
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parser.add_argument('--img-size', default=224, type=int,
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metavar='N', help='Input image dimension')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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help='Override mean pixel value of dataset')
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parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
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help='Override std deviation of of dataset')
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parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
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help='Image resize interpolation type (overrides model)')
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parser.add_argument('--num-classes', type=int, default=1000,
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help='Number classes in dataset')
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parser.add_argument('--print-freq', '-p', default=10, type=int,
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@ -40,8 +46,8 @@ parser.add_argument('--pretrained', dest='pretrained', action='store_true',
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help='use pre-trained model')
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parser.add_argument('--num-gpu', type=int, default=1,
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help='Number of GPUS to use')
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parser.add_argument('--no-test-pool', dest='test_time_pool', action='store_false',
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help='use pre-trained model')
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parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
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help='disable test time pool')
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def main():
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@ -58,8 +64,8 @@ def main():
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print('Model %s created, param count: %d' %
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(args.model, sum([m.numel() for m in model.parameters()])))
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data_mean, data_std = get_mean_and_std(model, args)
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model, test_time_pool = apply_test_time_pool(model, args)
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config = resolve_data_config(model, args)
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model, test_time_pool = apply_test_time_pool(model, config, args)
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if args.num_gpu > 1:
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model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
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@ -68,12 +74,14 @@ def main():
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loader = create_loader(
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Dataset(args.data),
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img_size=args.img_size,
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input_size=config['input_size'],
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batch_size=args.batch_size,
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use_prefetcher=True,
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mean=data_mean,
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std=data_std,
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num_workers=args.workers)
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interpolation=config['interpolation'],
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mean=config['mean'],
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std=config['std'],
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num_workers=args.workers,
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crop_pct=1.0 if test_time_pool else config['crop_pct'])
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model.eval()
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@ -1,4 +1,4 @@
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"""Pytorch Densenet implementation tweaks
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"""Pytorch Densenet implementation w/ tweaks
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This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
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fixed kwargs passthrough and addition of dynamic global avg/max pool.
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"""
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@ -18,6 +18,7 @@ __all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161
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def _cfg(url=''):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 244), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'features.conv0', 'classifier': 'classifier',
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}
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@ -25,6 +25,7 @@ __all__ = ['DPN', 'dpn68', 'dpn92', 'dpn98', 'dpn131', 'dpn107']
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def _cfg(url=''):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_DPN_MEAN, 'std': IMAGENET_DPN_STD,
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'first_conv': 'features.conv1_1.conv', 'classifier': 'classifier',
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}
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@ -26,7 +26,6 @@ def load_checkpoint(model, checkpoint_path):
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def resume_checkpoint(model, checkpoint_path, start_epoch=None):
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start_epoch = 0 if start_epoch is None else start_epoch
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optimizer_state = None
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if os.path.isfile(checkpoint_path):
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print("=> loading checkpoint '{}'".format(checkpoint_path))
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@ -46,6 +45,7 @@ def resume_checkpoint(model, checkpoint_path, start_epoch=None):
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start_epoch = checkpoint['epoch'] if start_epoch is None else start_epoch
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else:
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model.load_state_dict(checkpoint)
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start_epoch = 0 if start_epoch is None else start_epoch
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return optimizer_state, start_epoch
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else:
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print("=> No checkpoint found at '{}'".format(checkpoint_path))
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@ -14,6 +14,7 @@ default_cfgs = {
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'inception_resnet_v2': {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
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'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
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'crop_pct': 0.8975, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'conv2d_1a.conv', 'classifier': 'last_linear',
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}
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@ -14,6 +14,7 @@ default_cfgs = {
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'inception_v4': {
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'url': 'http://webia.lip6.fr/~cadene/Downloads/inceptionv4-97ef9c30.pth',
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'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'features.0.conv', 'classifier': 'classif',
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}
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@ -1,3 +1,10 @@
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"""
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pnasnet5large implementation grabbed from Cadene's pretrained models
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Additional credit to https://github.com/creafz
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https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
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"""
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from __future__ import print_function, division, absolute_import
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from collections import OrderedDict
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@ -13,9 +20,10 @@ default_cfgs = {
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'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth',
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'input_size': (3, 331, 331),
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'pool_size': (11, 11),
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'crop_pct': 0.875,
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'interpolation': 'bicubic',
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'mean': (0.5, 0.5, 0.5),
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'std': (0.5, 0.5, 0.5),
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'crop_pct': 0.8975,
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'num_classes': 1001,
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'first_conv': 'conv_0.conv',
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'classifier': 'last_linear',
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@ -1,6 +1,8 @@
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"""Pytorch ResNet implementation tweaks
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"""Pytorch ResNet implementation w/ tweaks
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This file is a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
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additional dropout and dynamic global avg/max pool.
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ResNext additions added by Ross Wightman
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"""
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import torch
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import torch.nn as nn
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@ -18,7 +20,8 @@ def _cfg(url=''):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'crop_pct': 0.875,
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'conv1', 'classifier': 'fc',
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}
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@ -271,7 +274,7 @@ def resnet152(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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def resnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
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"""Constructs a ResNeXt50-32x4d model.
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"""
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default_cfg = default_cfgs['resnext50_32x4d2']
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default_cfg = default_cfgs['resnext50_32x4d']
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model = ResNet(
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Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
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num_classes=num_classes, in_chans=in_chans, **kwargs)
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@ -1,4 +1,10 @@
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"""
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SEResNet implementation from Cadene's pretrained models
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https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
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Additional credit to https://github.com/creafz
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Original model: https://github.com/hujie-frank/SENet
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ResNet code gently borrowed from
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https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
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"""
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@ -20,7 +26,8 @@ __all__ = ['SENet', 'senet154', 'seresnet50', 'seresnet101', 'seresnet152',
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def _cfg(url=''):
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return {
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 244), 'pool_size': (7, 7),
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'crop_pct': 0.875,
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'crop_pct': 0.875, 'interpolation': 'bilinear',
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
||||
'first_conv': 'layer0.conv1', 'classifier': 'last_linear',
|
||||
}
|
||||
|
||||
|
||||
@ -26,11 +26,13 @@ class TestTimePoolHead(nn.Module):
|
||||
return x.view(x.size(0), -1)
|
||||
|
||||
|
||||
def apply_test_time_pool(model, args):
|
||||
def apply_test_time_pool(model, config, args):
|
||||
test_time_pool = False
|
||||
if args.img_size > model.default_cfg['input_size'][-1] and not args.no_test_pool:
|
||||
print('Target input size (%d) > pretrained default (%d), using test time pooling' %
|
||||
(args.img_size, model.default_cfg['input_size'][-1]))
|
||||
if not args.no_test_pool and \
|
||||
config['input_size'][-1] > model.default_cfg['input_size'][-1] and \
|
||||
config['input_size'][-2] > model.default_cfg['input_size'][-2]:
|
||||
print('Target input size (%s) > pretrained default (%s), using test time pooling' %
|
||||
(str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:])))
|
||||
model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
|
||||
test_time_pool = True
|
||||
return model, test_time_pool
|
||||
|
||||
@ -37,10 +37,11 @@ default_cfgs = {
|
||||
'xception': {
|
||||
'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
|
||||
'input_size': (3, 299, 299),
|
||||
'crop_pct': 0.8975,
|
||||
'interpolation': 'bicubic',
|
||||
'mean': (0.5, 0.5, 0.5),
|
||||
'std': (0.5, 0.5, 0.5),
|
||||
'num_classes': 1000,
|
||||
'crop_pct': 0.8975,
|
||||
'first_conv': 'conv1',
|
||||
'classifier': 'fc'
|
||||
# The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
|
||||
|
||||
24
train.py
24
train.py
@ -43,6 +43,12 @@ parser.add_argument('--pretrained', action='store_true', default=False,
|
||||
help='Start with pretrained version of specified network (if avail)')
|
||||
parser.add_argument('--img-size', type=int, default=224, metavar='N',
|
||||
help='Image patch size (default: 224)')
|
||||
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
|
||||
help='Override mean pixel value of dataset')
|
||||
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
|
||||
help='Override std deviation of of dataset')
|
||||
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
|
||||
help='Image resize interpolation type (overrides model)')
|
||||
parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
|
||||
help='input batch size for training (default: 32)')
|
||||
parser.add_argument('-s', '--initial-batch-size', type=int, default=0, metavar='N',
|
||||
@ -150,13 +156,13 @@ def main():
|
||||
global_pool=args.gp,
|
||||
checkpoint_path=args.initial_checkpoint)
|
||||
|
||||
data_mean, data_std = get_mean_and_std(model, args)
|
||||
data_config = resolve_data_config(model, args, verbose=args.local_rank == 0)
|
||||
|
||||
# optionally resume from a checkpoint
|
||||
start_epoch = 0
|
||||
optimizer_state = None
|
||||
if args.resume:
|
||||
start_epoch, optimizer_state = resume_checkpoint(model, args.resume, args.start_epoch)
|
||||
optimizer_state, start_epoch = resume_checkpoint(model, args.resume, args.start_epoch)
|
||||
|
||||
if args.num_gpu > 1:
|
||||
if args.amp:
|
||||
@ -196,14 +202,15 @@ def main():
|
||||
|
||||
loader_train = create_loader(
|
||||
dataset_train,
|
||||
img_size=args.img_size,
|
||||
input_size=data_config['input_size'],
|
||||
batch_size=args.batch_size,
|
||||
is_training=True,
|
||||
use_prefetcher=True,
|
||||
rand_erase_prob=args.reprob,
|
||||
rand_erase_pp=args.repp,
|
||||
mean=data_mean,
|
||||
std=data_std,
|
||||
interpolation=data_config['interpolation'],
|
||||
mean=data_config['mean'],
|
||||
std=data_config['std'],
|
||||
num_workers=args.workers,
|
||||
distributed=args.distributed,
|
||||
)
|
||||
@ -216,12 +223,13 @@ def main():
|
||||
|
||||
loader_eval = create_loader(
|
||||
dataset_eval,
|
||||
img_size=args.img_size,
|
||||
input_size=data_config['input_size'],
|
||||
batch_size=4 * args.batch_size,
|
||||
is_training=False,
|
||||
use_prefetcher=True,
|
||||
mean=data_mean,
|
||||
std=data_std,
|
||||
interpolation=data_config['interpolation'],
|
||||
mean=data_config['mean'],
|
||||
std=data_config['std'],
|
||||
num_workers=args.workers,
|
||||
distributed=args.distributed,
|
||||
)
|
||||
|
||||
30
validate.py
30
validate.py
@ -10,7 +10,7 @@ import torch.nn as nn
|
||||
import torch.nn.parallel
|
||||
|
||||
from models import create_model, apply_test_time_pool
|
||||
from data import Dataset, create_loader, get_mean_and_std
|
||||
from data import Dataset, create_loader, resolve_data_config
|
||||
from utils import accuracy, AverageMeter
|
||||
|
||||
torch.backends.cudnn.benchmark = True
|
||||
@ -24,8 +24,14 @@ parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
|
||||
help='number of data loading workers (default: 2)')
|
||||
parser.add_argument('-b', '--batch-size', default=256, type=int,
|
||||
metavar='N', help='mini-batch size (default: 256)')
|
||||
parser.add_argument('--img-size', default=224, type=int,
|
||||
metavar='N', help='Input image dimension')
|
||||
parser.add_argument('--img-size', default=None, type=int,
|
||||
metavar='N', help='Input image dimension, uses model default if empty')
|
||||
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
|
||||
help='Override mean pixel value of dataset')
|
||||
parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
|
||||
help='Override std deviation of of dataset')
|
||||
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
|
||||
help='Image resize interpolation type (overrides model)')
|
||||
parser.add_argument('--num-classes', type=int, default=1000,
|
||||
help='Number classes in dataset')
|
||||
parser.add_argument('--print-freq', '-p', default=10, type=int,
|
||||
@ -37,7 +43,7 @@ parser.add_argument('--pretrained', dest='pretrained', action='store_true',
|
||||
parser.add_argument('--num-gpu', type=int, default=1,
|
||||
help='Number of GPUS to use')
|
||||
parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
|
||||
help='disable test time pool for DPN models')
|
||||
help='disable test time pool')
|
||||
|
||||
|
||||
def main():
|
||||
@ -54,9 +60,8 @@ def main():
|
||||
print('Model %s created, param count: %d' %
|
||||
(args.model, sum([m.numel() for m in model.parameters()])))
|
||||
|
||||
data_mean, data_std = get_mean_and_std(model, args)
|
||||
|
||||
model, test_time_pool = apply_test_time_pool(model, args)
|
||||
data_config = resolve_data_config(model, args)
|
||||
model, test_time_pool = apply_test_time_pool(model, data_config, args)
|
||||
|
||||
if args.num_gpu > 1:
|
||||
model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
|
||||
@ -68,13 +73,14 @@ def main():
|
||||
|
||||
loader = create_loader(
|
||||
Dataset(args.data),
|
||||
img_size=args.img_size,
|
||||
input_size=data_config['input_size'],
|
||||
batch_size=args.batch_size,
|
||||
use_prefetcher=False,
|
||||
mean=data_mean,
|
||||
std=data_std,
|
||||
use_prefetcher=True,
|
||||
interpolation=data_config['interpolation'],
|
||||
mean=data_config['mean'],
|
||||
std=data_config['std'],
|
||||
num_workers=args.workers,
|
||||
crop_pct=1.0 if test_time_pool else None)
|
||||
crop_pct=1.0 if test_time_pool else data_config['crop_pct'])
|
||||
|
||||
batch_time = AverageMeter()
|
||||
losses = AverageMeter()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user