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

data/loader.py

@@ -23,20 +23,20 @@ def __init__(self,
             mean=IMAGENET_DEFAULT_MEAN,
             std=IMAGENET_DEFAULT_STD):
         self.loader = loader
-        self.stream = torch.cuda.Stream()
         self.mean = torch.tensor([x * 255 for x in mean]).cuda().view(1, 3, 1, 1)
         self.std = torch.tensor([x * 255 for x in std]).cuda().view(1, 3, 1, 1)
-        if rand_erase_prob:
+        if rand_erase_prob > 0.:
             self.random_erasing = RandomErasingTorch(
                 probability=rand_erase_prob, per_pixel=rand_erase_pp)
         else:
             self.random_erasing = None
 
     def __iter__(self):
+        stream = torch.cuda.Stream()
         first = True
 
         for next_input, next_target in self.loader:
-            with torch.cuda.stream(self.stream):
+            with torch.cuda.stream(stream):
                 next_input = next_input.cuda(non_blocking=True)
                 next_target = next_target.cuda(non_blocking=True)
                 next_input = next_input.float().sub_(self.mean).div_(self.std)
@@ -48,7 +48,7 @@ def __iter__(self):
             else:
                 first = False
 
-            torch.cuda.current_stream().wait_stream(self.stream)
+            torch.cuda.current_stream().wait_stream(stream)
             input = next_input
             target = next_target
 
@@ -64,28 +64,35 @@ def sampler(self):
 
 def create_loader(
         dataset,
-        img_size,
+        input_size,
         batch_size,
         is_training=False,
         use_prefetcher=True,
         rand_erase_prob=0.,
         rand_erase_pp=False,
+        interpolation='bilinear',
         mean=IMAGENET_DEFAULT_MEAN,
         std=IMAGENET_DEFAULT_STD,
         num_workers=1,
         distributed=False,
         crop_pct=None,
 ):
+    if isinstance(input_size, tuple):
+        img_size = input_size[-2:]
+    else:
+        img_size = input_size
 
     if is_training:
         transform = transforms_imagenet_train(
             img_size,
+            interpolation=interpolation,
             use_prefetcher=use_prefetcher,
             mean=mean,
             std=std)
     else:
         transform = transforms_imagenet_eval(
             img_size,
+            interpolation=interpolation,
             use_prefetcher=use_prefetcher,
             mean=mean,
             std=std,

data/transforms.py

@@ -15,28 +15,66 @@
 IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
 
 
-def get_mean_and_std(model, args, num_chan=3):
-    if hasattr(model, 'default_cfg'):
-        mean = model.default_cfg['mean']
-        std = model.default_cfg['std']
-    else:
-        if args.mean is not None:
-            mean = tuple(args.mean)
-            if len(mean) == 1:
-                mean = tuple(list(mean) * num_chan)
-            else:
-                assert len(mean) == num_chan
+def resolve_data_config(model, args, default_cfg={}, verbose=True):
+    new_config = {}
+    default_cfg = default_cfg
+    if not default_cfg and hasattr(model, 'default_cfg'):
+        default_cfg = model.default_cfg
+
+    # Resolve input/image size
+    # FIXME grayscale/chans arg to use different # channels?
+    in_chans = 3
+    input_size = (in_chans, 224, 224)
+    if args.img_size is not None:
+        # FIXME support passing img_size as tuple, non-square
+        assert isinstance(args.img_size, int)
+        input_size = (in_chans, args.img_size, args.img_size)
+    elif 'input_size' in default_cfg:
+        input_size = default_cfg['input_size']
+    new_config['input_size'] = input_size
+
+    # resolve interpolation method
+    new_config['interpolation'] = 'bilinear'
+    if args.interpolation:
+        new_config['interpolation'] = args.interpolation
+    elif 'interpolation' in default_cfg:
+        new_config['interpolation'] = default_cfg['interpolation']
+
+    # resolve dataset + model mean for normalization
+    new_config['mean'] = get_mean_by_model(args.model)
+    if args.mean is not None:
+        mean = tuple(args.mean)
+        if len(mean) == 1:
+            mean = tuple(list(mean) * in_chans)
         else:
-            mean = get_mean_by_model(args.model)
-        if args.std is not None:
-            std = tuple(args.std)
-            if len(std) == 1:
-                std = tuple(list(std) * num_chan)
-            else:
-                assert len(std) == num_chan
+            assert len(mean) == in_chans
+        new_config['mean'] = mean
+    elif 'mean' in default_cfg:
+        new_config['mean'] = default_cfg['mean']
+
+    # resolve dataset + model std deviation for normalization
+    new_config['std'] = get_std_by_model(args.model)
+    if args.std is not None:
+        std = tuple(args.std)
+        if len(std) == 1:
+            std = tuple(list(std) * in_chans)
         else:
-            std = get_std_by_model(args.model)
-    return mean, std
+            assert len(std) == in_chans
+        new_config['std'] = std
+    else:
+        new_config['std'] = default_cfg['std']
+
+    # resolve default crop percentage
+    new_config['crop_pct'] = DEFAULT_CROP_PCT
+    if 'crop_pct' in default_cfg:
+        new_config['crop_pct'] = default_cfg['crop_pct']
+
+    if verbose:
+        print('Data processing configuration for current model + dataset:')
+        for n, v in new_config.items():
+            print('\t%s: %s' % (n, str(v)))
+
+    return new_config
 
 
 def get_mean_by_name(name):
@@ -104,6 +142,7 @@ def transforms_imagenet_train(
         img_size=224,
         scale=(0.1, 1.0),
         color_jitter=(0.4, 0.4, 0.4),
+        interpolation='bilinear',
         random_erasing=0.4,
         use_prefetcher=False,
         mean=IMAGENET_DEFAULT_MEAN,
@@ -112,7 +151,8 @@ def transforms_imagenet_train(
 
     tfl = [
         transforms.RandomResizedCrop(
-            img_size, scale=scale, interpolation=Image.BICUBIC),
+            img_size, scale=scale,
+            interpolation=Image.BILINEAR if interpolation == 'bilinear' else Image.BICUBIC),
         transforms.RandomHorizontalFlip(),
         transforms.ColorJitter(*color_jitter),
     ]
@@ -135,14 +175,24 @@ def transforms_imagenet_train(
 def transforms_imagenet_eval(
         img_size=224,
         crop_pct=None,
+        interpolation='bilinear',
         use_prefetcher=False,
         mean=IMAGENET_DEFAULT_MEAN,
         std=IMAGENET_DEFAULT_STD):
     crop_pct = crop_pct or DEFAULT_CROP_PCT
-    scale_size = int(math.floor(img_size / crop_pct))
+
+    if isinstance(img_size, tuple):
+        assert len(img_size) == 2
+        if img_size[0] == img_size[1]:
+            # fall-back to older behaviour so Resize scales to shortest edge if target is square
+            scale_size = int(math.floor(img_size[0] / crop_pct))
+        else:
+            scale_size = tuple([int(x[0] / crop_pct) for x in img_size])
+    else:
+        scale_size = int(math.floor(img_size / crop_pct))
 
     tfl = [
-        transforms.Resize(scale_size, Image.BICUBIC),
+        transforms.Resize(scale_size, Image.BILINEAR if interpolation == 'bilinear' else Image.BICUBIC),
         transforms.CenterCrop(img_size),
     ]
     if use_prefetcher:

inference.py

@@ -12,7 +12,7 @@
 import torch
 
 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 AverageMeter
 
 torch.backends.cudnn.benchmark = True
@@ -30,6 +30,12 @@
                     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('--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,
@@ -40,8 +46,8 @@
                     help='use pre-trained model')
 parser.add_argument('--num-gpu', type=int, default=1,
                     help='Number of GPUS to use')
-parser.add_argument('--no-test-pool', dest='test_time_pool', action='store_false',
-                    help='use pre-trained model')
+parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
+                    help='disable test time pool')
 
 
 def main():
@@ -58,8 +64,8 @@ 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)
+    config = resolve_data_config(model, args)
+    model, test_time_pool = apply_test_time_pool(model, config, args)
 
     if args.num_gpu > 1:
         model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
@@ -68,12 +74,14 @@ def main():
 
     loader = create_loader(
         Dataset(args.data),
-        img_size=args.img_size,
+        input_size=config['input_size'],
         batch_size=args.batch_size,
         use_prefetcher=True,
-        mean=data_mean,
-        std=data_std,
-        num_workers=args.workers)
+        interpolation=config['interpolation'],
+        mean=config['mean'],
+        std=config['std'],
+        num_workers=args.workers,
+        crop_pct=1.0 if test_time_pool else config['crop_pct'])
 
     model.eval()
 

models/densenet.py

@@ -1,4 +1,4 @@
-"""Pytorch Densenet implementation tweaks
+"""Pytorch Densenet implementation w/ tweaks
 This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
 fixed kwargs passthrough and addition of dynamic global avg/max pool.
 """
@@ -18,6 +18,7 @@
 def _cfg(url=''):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 244), 'pool_size': (7, 7),
+        'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'features.conv0', 'classifier': 'classifier',
     }

models/dpn.py

@@ -25,6 +25,7 @@
 def _cfg(url=''):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
+        'crop_pct': 0.875, 'interpolation': 'bicubic',
         'mean': IMAGENET_DPN_MEAN, 'std': IMAGENET_DPN_STD,
         'first_conv': 'features.conv1_1.conv', 'classifier': 'classifier',
     }

models/helpers.py

@@ -26,7 +26,6 @@ def load_checkpoint(model, checkpoint_path):
 
 
 def resume_checkpoint(model, checkpoint_path, start_epoch=None):
-    start_epoch = 0 if start_epoch is None else start_epoch
     optimizer_state = None
     if os.path.isfile(checkpoint_path):
         print("=> loading checkpoint '{}'".format(checkpoint_path))
@@ -46,6 +45,7 @@ def resume_checkpoint(model, checkpoint_path, start_epoch=None):
             start_epoch = checkpoint['epoch'] if start_epoch is None else start_epoch
         else:
             model.load_state_dict(checkpoint)
+            start_epoch = 0 if start_epoch is None else start_epoch
         return optimizer_state, start_epoch
     else:
         print("=> No checkpoint found at '{}'".format(checkpoint_path))

models/inception_resnet_v2.py

@@ -14,6 +14,7 @@
     'inception_resnet_v2': {
         'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
         'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
+        'crop_pct': 0.8975, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
         'first_conv': 'conv2d_1a.conv', 'classifier': 'last_linear',
     }

models/inception_v4.py

@@ -14,6 +14,7 @@
     'inception_v4': {
         'url': 'http://webia.lip6.fr/~cadene/Downloads/inceptionv4-97ef9c30.pth',
         'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
+        'crop_pct': 0.875, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
         'first_conv': 'features.0.conv', 'classifier': 'classif',
     }

models/pnasnet.py

@@ -1,3 +1,10 @@
+"""
+ pnasnet5large implementation grabbed from Cadene's pretrained models
+ Additional credit to https://github.com/creafz
+
+ https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
+
+"""
 from __future__ import print_function, division, absolute_import
 from collections import OrderedDict
 
@@ -13,9 +20,10 @@
         'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth',
         'input_size': (3, 331, 331),
         'pool_size': (11, 11),
+        'crop_pct': 0.875,
+        'interpolation': 'bicubic',
         'mean': (0.5, 0.5, 0.5),
         'std': (0.5, 0.5, 0.5),
-        'crop_pct': 0.8975,
         'num_classes': 1001,
         'first_conv': 'conv_0.conv',
         'classifier': 'last_linear',

models/resnet.py

@@ -1,6 +1,8 @@
-"""Pytorch ResNet implementation tweaks
+"""Pytorch ResNet implementation w/ tweaks
 This file is a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
 additional dropout and dynamic global avg/max pool.
+
+ResNext additions added by Ross Wightman
 """
 import torch
 import torch.nn as nn
@@ -18,7 +20,8 @@ def _cfg(url=''):
     return {
         'url': url,
         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'crop_pct': 0.875,
+        'crop_pct': 0.875, 'interpolation': 'bilinear',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'conv1', 'classifier': 'fc',
     }
 
@@ -271,7 +274,7 @@ def resnet152(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
 def resnext50_32x4d(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
     """Constructs a ResNeXt50-32x4d model.
     """
-    default_cfg = default_cfgs['resnext50_32x4d2']
+    default_cfg = default_cfgs['resnext50_32x4d']
     model = ResNet(
         Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4,
         num_classes=num_classes, in_chans=in_chans, **kwargs)

models/senet.py

@@ -1,4 +1,10 @@
 """
+SEResNet implementation from Cadene's pretrained models
+https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/senet.py
+Additional credit to https://github.com/creafz
+
+Original model: https://github.com/hujie-frank/SENet
+
 ResNet code gently borrowed from
 https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
 """
@@ -20,7 +26,8 @@
 def _cfg(url=''):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 244), 'pool_size': (7, 7),
-        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'crop_pct': 0.875,
+        'crop_pct': 0.875, 'interpolation': 'bilinear',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'layer0.conv1', 'classifier': 'last_linear',
     }