Merge pull request #964 from rwightman/more_datasets

Dataset additions

timm/data/dataset.py

@@ -23,15 +23,17 @@ class ImageDataset(data.Dataset):
             self,
             root,
             parser=None,
-            class_map='',
+            class_map=None,
             load_bytes=False,
             transform=None,
+            target_transform=None,
     ):
         if parser is None or isinstance(parser, str):
             parser = create_parser(parser or '', root=root, class_map=class_map)
         self.parser = parser
         self.load_bytes = load_bytes
         self.transform = transform
+        self.target_transform = target_transform
         self._consecutive_errors = 0
 
     def __getitem__(self, index):
@@ -49,7 +51,9 @@ class ImageDataset(data.Dataset):
         if self.transform is not None:
             img = self.transform(img)
         if target is None:
-            target = torch.tensor(-1, dtype=torch.long)
+            target = -1
+        elif self.target_transform is not None:
+            target = self.target_transform(target)
         return img, target
 
     def __len__(self):
@@ -71,26 +75,28 @@ class IterableImageDataset(data.IterableDataset):
             split='train',
             is_training=False,
             batch_size=None,
-            class_map='',
-            load_bytes=False,
             repeats=0,
+            download=False,
             transform=None,
+            target_transform=None,
     ):
         assert parser is not None
         if isinstance(parser, str):
             self.parser = create_parser(
-                parser, root=root, split=split, is_training=is_training, batch_size=batch_size, repeats=repeats)
+                parser, root=root, split=split, is_training=is_training,
+                batch_size=batch_size, repeats=repeats, download=download)
         else:
             self.parser = parser
         self.transform = transform
+        self.target_transform = target_transform
         self._consecutive_errors = 0
 
     def __iter__(self):
         for img, target in self.parser:
             if self.transform is not None:
                 img = self.transform(img)
-            if target is None:
-                target = torch.tensor(-1, dtype=torch.long)
+            if self.target_transform is not None:
+                target = self.target_transform(target)
             yield img, target
 
     def __len__(self):

timm/data/dataset_factory.py

@@ -1,7 +1,26 @@
 import os
 
+from torchvision.datasets import CIFAR100, CIFAR10, MNIST, QMNIST, KMNIST, FashionMNIST,\
+    Places365, ImageNet, ImageFolder
+try:
+    from torchvision.datasets import INaturalist
+    has_inaturalist = True
+except ImportError:
+    has_inaturalist = False
+
 from .dataset import IterableImageDataset, ImageDataset
 
+_TORCH_BASIC_DS = dict(
+    cifar10=CIFAR10,
+    cifar100=CIFAR100,
+    mnist=MNIST,
+    qmist=QMNIST,
+    kmnist=KMNIST,
+    fashion_mnist=FashionMNIST,
+)
+_TRAIN_SYNONYM = {'train', 'training'}
+_EVAL_SYNONYM = {'val', 'valid', 'validation', 'eval', 'evaluation'}
+
 
 def _search_split(root, split):
     # look for sub-folder with name of split in root and use that if it exists
@@ -9,22 +28,107 @@ def _search_split(root, split):
     try_root = os.path.join(root, split_name)
     if os.path.exists(try_root):
         return try_root
-    if split_name == 'validation':
-        try_root = os.path.join(root, 'val')
-        if os.path.exists(try_root):
-            return try_root
+
+    def _try(syn):
+        for s in syn:
+            try_root = os.path.join(root, s)
+            if os.path.exists(try_root):
+                return try_root
+        return root
+    if split_name in _TRAIN_SYNONYM:
+        root = _try(_TRAIN_SYNONYM)
+    elif split_name in _EVAL_SYNONYM:
+        root = _try(_EVAL_SYNONYM)
     return root
 
 
-def create_dataset(name, root, split='validation', search_split=True, is_training=False, batch_size=None, **kwargs):
+def create_dataset(
+        name,
+        root,
+        split='validation',
+        search_split=True,
+        class_map=None,
+        load_bytes=False,
+        is_training=False,
+        download=False,
+        batch_size=None,
+        repeats=0,
+        **kwargs
+):
+    """ Dataset factory method
+
+    In parenthesis after each arg are the type of dataset supported for each arg, one of:
+      * folder - default, timm folder (or tar) based ImageDataset
+      * torch - torchvision based datasets
+      * TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
+      * all - any of the above
+
+    Args:
+        name: dataset name, empty is okay for folder based datasets
+        root: root folder of dataset (all)
+        split: dataset split (all)
+        search_split: search for split specific child fold from root so one can specify
+            `imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (folder, torch/folder)
+        class_map: specify class -> index mapping via text file or dict (folder)
+        load_bytes: load data, return images as undecoded bytes (folder)
+        download: download dataset if not present and supported (TFDS, torch)
+        is_training: create dataset in train mode, this is different from the split.
+            For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS)
+        batch_size: batch size hint for (TFDS)
+        repeats: dataset repeats per iteration i.e. epoch (TFDS)
+        **kwargs: other args to pass to dataset
+
+    Returns:
+        Dataset object
+    """
     name = name.lower()
-    if name.startswith('tfds'):
+    if name.startswith('torch/'):
+        name = name.split('/', 2)[-1]
+        torch_kwargs = dict(root=root, download=download, **kwargs)
+        if name in _TORCH_BASIC_DS:
+            ds_class = _TORCH_BASIC_DS[name]
+            use_train = split in _TRAIN_SYNONYM
+            ds = ds_class(train=use_train, **torch_kwargs)
+        elif name == 'inaturalist' or name == 'inat':
+            assert has_inaturalist, 'Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalist'
+            target_type = 'full'
+            split_split = split.split('/')
+            if len(split_split) > 1:
+                target_type = split_split[0].split('_')
+                if len(target_type) == 1:
+                    target_type = target_type[0]
+                split = split_split[-1]
+            if split in _TRAIN_SYNONYM:
+                split = '2021_train'
+            elif split in _EVAL_SYNONYM:
+                split = '2021_valid'
+            ds = INaturalist(version=split, target_type=target_type, **torch_kwargs)
+        elif name == 'places365':
+            if split in _TRAIN_SYNONYM:
+                split = 'train-standard'
+            elif split in _EVAL_SYNONYM:
+                split = 'val'
+            ds = Places365(split=split, **torch_kwargs)
+        elif name == 'imagenet':
+            if split in _EVAL_SYNONYM:
+                split = 'val'
+            ds = ImageNet(split=split, **torch_kwargs)
+        elif name == 'image_folder' or name == 'folder':
+            # in case torchvision ImageFolder is preferred over timm ImageDataset for some reason
+            if search_split and os.path.isdir(root):
+                # look for split specific sub-folder in root
+                root = _search_split(root, split)
+            ds = ImageFolder(root, **kwargs)
+        else:
+            assert False, f"Unknown torchvision dataset {name}"
+    elif name.startswith('tfds/'):
         ds = IterableImageDataset(
-            root, parser=name, split=split, is_training=is_training, batch_size=batch_size, **kwargs)
+            root, parser=name, split=split, is_training=is_training,
+            download=download, batch_size=batch_size, repeats=repeats, **kwargs)
     else:
         # FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
-        kwargs.pop('repeats', 0)  # FIXME currently only Iterable dataset support the repeat multiplier
         if search_split and os.path.isdir(root):
+            # look for split specific sub-folder in root
             root = _search_split(root, split)
-        ds = ImageDataset(root, parser=name, **kwargs)
+        ds = ImageDataset(root, parser=name, class_map=class_map, load_bytes=load_bytes, **kwargs)
     return ds

timm/data/parsers/class_map.py

@@ -1,16 +1,19 @@
 import os
 
 
-def load_class_map(filename, root=''):
-    class_map_path = filename
+def load_class_map(map_or_filename, root=''):
+    if isinstance(map_or_filename, dict):
+        assert dict, 'class_map dict must be non-empty'
+        return map_or_filename
+    class_map_path = map_or_filename
     if not os.path.exists(class_map_path):
-        class_map_path = os.path.join(root, filename)
-        assert os.path.exists(class_map_path), 'Cannot locate specified class map file (%s)' % filename
-    class_map_ext = os.path.splitext(filename)[-1].lower()
+        class_map_path = os.path.join(root, class_map_path)
+        assert os.path.exists(class_map_path), 'Cannot locate specified class map file (%s)' % map_or_filename
+    class_map_ext = os.path.splitext(map_or_filename)[-1].lower()
     if class_map_ext == '.txt':
         with open(class_map_path) as f:
             class_to_idx = {v.strip(): k for k, v in enumerate(f)}
     else:
-        assert False, 'Unsupported class map extension'
+        assert False, f'Unsupported class map file extension ({class_map_ext}).'
     return class_to_idx
 

timm/data/parsers/parser_factory.py

@@ -17,7 +17,7 @@ def create_parser(name, root, split='train', **kwargs):
     # explicitly select other options shortly
     if prefix == 'tfds':
         from .parser_tfds import ParserTfds  # defer tensorflow import
-        parser = ParserTfds(root, name, split=split, shuffle=kwargs.pop('shuffle', False), **kwargs)
+        parser = ParserTfds(root, name, split=split, **kwargs)
     else:
         assert os.path.exists(root)
         # default fallback path (backwards compat), use image tar if root is a .tar file, otherwise image folder

timm/data/parsers/parser_tfds.py

@@ -6,8 +6,6 @@ https://www.tensorflow.org/datasets/catalog/overview#image_classification
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
-import os
-import io
 import math
 import torch
 import torch.distributed as dist
@@ -17,6 +15,13 @@ try:
     import tensorflow as tf
     tf.config.set_visible_devices([], 'GPU')  # Hands off my GPU! (or pip install tensorflow-cpu)
     import tensorflow_datasets as tfds
+    try:
+        tfds.even_splits('', 1, drop_remainder=False)  # non-buggy even_splits has drop_remainder arg
+        has_buggy_even_splits = False
+    except TypeError:
+        print("Warning: This version of tfds doesn't have the latest even_splits impl. "
+              "Please update or use tfds-nightly for better fine-grained split behaviour.")
+        has_buggy_even_splits = True
 except ImportError as e:
     print(e)
     print("Please install tensorflow_datasets package `pip install tensorflow-datasets`.")
@@ -25,7 +30,7 @@ from .parser import Parser
 
 
 MAX_TP_SIZE = 8  # maximum TF threadpool size, only doing jpeg decodes and queuing activities
-SHUFFLE_SIZE = 20480  # samples to shuffle in DS queue
+SHUFFLE_SIZE = 16384  # samples to shuffle in DS queue
 PREFETCH_SIZE = 2048  # samples to prefetch
 
 
@@ -57,32 +62,71 @@ class ParserTfds(Parser):
         components.
 
     """
-    def __init__(self, root, name, split='train', shuffle=False, is_training=False, batch_size=None, repeats=0):
+    def __init__(
+            self,
+            root,
+            name,
+            split='train',
+            is_training=False,
+            batch_size=None,
+            download=False,
+            repeats=0,
+            seed=42,
+            prefetch_size=None,
+            shuffle_size=None,
+            max_threadpool_size=None
+    ):
+        """ Tensorflow-datasets Wrapper
+
+        Args:
+            root: root data dir (ie your TFDS_DATA_DIR. not dataset specific sub-dir)
+            name: tfds dataset name (eg `imagenet2012`)
+            split: tfds dataset split (can use all TFDS split strings eg `train[:10%]`)
+            is_training: training mode, shuffle enabled, dataset len rounded by batch_size
+            batch_size: batch_size to use to unsure total samples % batch_size == 0 in training across all dis nodes
+            download: download and build TFDS dataset if set, otherwise must use tfds CLI
+            repeats: iterate through (repeat) the dataset this many times per iteration (once if 0 or 1)
+            seed: common seed for shard shuffle across all distributed/worker instances
+            prefetch_size: override default tf.data prefetch buffer size
+            shuffle_size: override default tf.data shuffle buffer size
+            max_threadpool_size: override default threadpool size for tf.data
+        """
         super().__init__()
         self.root = root
         self.split = split
-        self.shuffle = shuffle
         self.is_training = is_training
         if self.is_training:
             assert batch_size is not None,\
                 "Must specify batch_size in training mode for reasonable behaviour w/ TFDS wrapper"
         self.batch_size = batch_size
         self.repeats = repeats
-        self.subsplit = None
+        self.common_seed = seed  # a seed that's fixed across all worker / distributed instances
+        self.prefetch_size = prefetch_size or PREFETCH_SIZE
+        self.shuffle_size = shuffle_size or SHUFFLE_SIZE
+        self.max_threadpool_size = max_threadpool_size or MAX_TP_SIZE
 
+        # TFDS builder and split information
         self.builder = tfds.builder(name, data_dir=root)
-        # NOTE: please use tfds command line app to download & prepare datasets, I don't want to call
-        # download_and_prepare() by default here as it's caused issues generating unwanted paths.
-        self.num_samples = self.builder.info.splits[split].num_examples
-        self.ds = None  # initialized lazily on each dataloader worker process
+        # NOTE: the tfds command line app can be used download & prepare datasets if you don't enable download flag
+        if download:
+            self.builder.download_and_prepare()
+        self.split_info = self.builder.info.splits[split]
+        self.num_samples = self.split_info.num_examples
 
-        self.worker_info = None
+        # Distributed world state
         self.dist_rank = 0
         self.dist_num_replicas = 1
         if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1:
             self.dist_rank = dist.get_rank()
             self.dist_num_replicas = dist.get_world_size()
 
+        # Attributes that are updated in _lazy_init, including the tf.data pipeline itself
+        self.global_num_workers = 1
+        self.worker_info = None
+        self.worker_seed = 0  # seed unique to each work instance
+        self.subsplit = None  # set when data is distributed across workers using sub-splits
+        self.ds = None  # initialized lazily on each dataloader worker process
+
     def _lazy_init(self):
         """ Lazily initialize the dataset.
 
@@ -97,78 +141,83 @@ class ParserTfds(Parser):
         worker_info = torch.utils.data.get_worker_info()
 
         # setup input context to split dataset across distributed processes
-        split = self.split
         num_workers = 1
+        global_worker_id = 0
         if worker_info is not None:
             self.worker_info = worker_info
+            self.worker_seed = worker_info.seed
             num_workers = worker_info.num_workers
-            global_num_workers = self.dist_num_replicas * num_workers
-            worker_id = worker_info.id
+            self.global_num_workers = self.dist_num_replicas * num_workers
+            global_worker_id = self.dist_rank * num_workers + worker_info.id
 
-            # FIXME I need to spend more time figuring out the best way to distribute/split data across
-            # combo of distributed replicas + dataloader worker processes
-            """
+            """ Data sharding
             InputContext will assign subset of underlying TFRecord files to each 'pipeline' if used.
             My understanding is that using split, the underling TFRecord files will shuffle (shuffle_files=True)
             between the splits each iteration, but that understanding could be wrong.
-            Possible split options include:
-              * InputContext for both distributed & worker processes (current)
-              * InputContext for distributed and sub-splits for worker processes
-              * sub-splits for both
+            
+            I am currently using a mix of InputContext shard assignment and fine-grained sub-splits for distributing
+            the data across workers. For training InputContext is used to assign shards to nodes unless num_shards
+            in dataset < total number of workers. Otherwise sub-split API is used for datasets without enough shards or
+            for validation where we can't drop samples and need to avoid minimize uneven splits to avoid padding.
             """
-            # split_size = self.num_samples // num_workers
-            # start = worker_id * split_size
-            # if worker_id == num_workers - 1:
-            #     split = split + '[{}:]'.format(start)
-            # else:
-            #     split = split + '[{}:{}]'.format(start, start + split_size)
-            if not self.is_training and '[' not in self.split:
-                # If not training, and split doesn't define a subsplit, manually split the dataset
-                # for more even samples / worker
-                self.subsplit = even_split_indices(self.split, global_num_workers, self.num_samples)[
-                    self.dist_rank * num_workers + worker_id]
-
-        if self.subsplit is None:
+            should_subsplit = self.global_num_workers > 1 and (
+                    self.split_info.num_shards < self.global_num_workers or not self.is_training)
+            if should_subsplit:
+                # split the dataset w/o using sharding for more even samples / worker, can result in less optimal
+                # read patterns for distributed training (overlap across shards) so better to use InputContext there
+                if has_buggy_even_splits:
+                    # my even_split workaround doesn't work on subsplits, upgrade tfds!
+                    if not isinstance(self.split_info, tfds.core.splits.SubSplitInfo):
+                        subsplits = even_split_indices(self.split, self.global_num_workers, self.num_samples)
+                        self.subsplit = subsplits[global_worker_id]
+                else:
+                    subsplits = tfds.even_splits(self.split, self.global_num_workers)
+                    self.subsplit = subsplits[global_worker_id]
+
+        input_context = None
+        if self.global_num_workers > 1 and self.subsplit is None:
+            # set input context to divide shards among distributed replicas
             input_context = tf.distribute.InputContext(
-                num_input_pipelines=self.dist_num_replicas * num_workers,
-                input_pipeline_id=self.dist_rank * num_workers + worker_id,
+                num_input_pipelines=self.global_num_workers,
+                input_pipeline_id=global_worker_id,
                 num_replicas_in_sync=self.dist_num_replicas  # FIXME does this arg have any impact?
             )
-        else:
-            input_context = None
-
         read_config = tfds.ReadConfig(
-            shuffle_seed=42,
+            shuffle_seed=self.common_seed,
             shuffle_reshuffle_each_iteration=True,
             input_context=input_context)
         ds = self.builder.as_dataset(
-            split=self.subsplit or self.split, shuffle_files=self.shuffle, read_config=read_config)
-        # avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
+            split=self.subsplit or self.split, shuffle_files=self.is_training, read_config=read_config)
+        # avoid overloading threading w/ combo of TF ds threads + PyTorch workers
         options = tf.data.Options()
-        options.experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
-        options.experimental_threading.max_intra_op_parallelism = 1
+        thread_member = 'threading' if hasattr(options, 'threading') else 'experimental_threading'
+        getattr(options, thread_member).private_threadpool_size = max(1, self.max_threadpool_size // num_workers)
+        getattr(options, thread_member).max_intra_op_parallelism = 1
         ds = ds.with_options(options)
         if self.is_training or self.repeats > 1:
             # to prevent excessive drop_last batch behaviour w/ IterableDatasets
             # see warnings at https://pytorch.org/docs/stable/data.html#multi-process-data-loading
             ds = ds.repeat()  # allow wrap around and break iteration manually
-        if self.shuffle:
-            ds = ds.shuffle(min(self.num_samples, SHUFFLE_SIZE) // self._num_pipelines, seed=0)
-        ds = ds.prefetch(min(self.num_samples // self._num_pipelines, PREFETCH_SIZE))
+        if self.is_training:
+            ds = ds.shuffle(min(self.num_samples, self.shuffle_size) // self.global_num_workers, seed=self.worker_seed)
+        ds = ds.prefetch(min(self.num_samples // self.global_num_workers, self.prefetch_size))
         self.ds = tfds.as_numpy(ds)
 
     def __iter__(self):
         if self.ds is None:
             self._lazy_init()
-        # compute a rounded up sample count that is used to:
+
+        # Compute a rounded up sample count that is used to:
         #   1. make batches even cross workers & replicas in distributed validation.
         #     This adds extra samples and will slightly alter validation results.
         #   2. determine loop ending condition in training w/ repeat enabled so that only full batch_size
         #     batches are produced (underlying tfds iter wraps around)
-        target_sample_count = math.ceil(max(1, self.repeats) * self.num_samples / self._num_pipelines)
+        target_sample_count = math.ceil(max(1, self.repeats) * self.num_samples / self.global_num_workers)
         if self.is_training:
             # round up to nearest batch_size per worker-replica
             target_sample_count = math.ceil(target_sample_count / self.batch_size) * self.batch_size
+
+        # Iterate until exhausted or sample count hits target when training (ds.repeat enabled)
         sample_count = 0
         for sample in self.ds:
             img = Image.fromarray(sample['image'], mode='RGB')
@@ -179,21 +228,17 @@ class ParserTfds(Parser):
                 # this results in extra samples per epoch but seems more desirable than dropping
                 # up to N*J batches per epoch (where N = num distributed processes, and J = num worker processes)
                 break
-        if not self.is_training and self.dist_num_replicas and 0 < sample_count < target_sample_count:
+
+        # Pad across distributed nodes (make counts equal by adding samples)
+        if not self.is_training and self.dist_num_replicas > 1 and self.subsplit is not None and \
+                0 < sample_count < target_sample_count:
             # Validation batch padding only done for distributed training where results are reduced across nodes.
             # For single process case, it won't matter if workers return different batch sizes.
-            # FIXME if using input_context or % based subsplits, sample count can vary by more than +/- 1 and this
-            # approach is not optimal
-            yield img, sample['label']  # yield prev sample again
-            sample_count += 1
-
-    @property
-    def _num_workers(self):
-        return 1 if self.worker_info is None else self.worker_info.num_workers
-
-    @property
-    def _num_pipelines(self):
-        return self._num_workers * self.dist_num_replicas
+            # If using input_context or % based splits, sample count can vary significantly across workers and this
+            # approach should not be used (hence disabled if self.subsplit isn't set).
+            while sample_count < target_sample_count:
+                yield img, sample['label']  # yield prev sample again
+                sample_count += 1
 
     def __len__(self):
         # this is just an estimate and does not factor in extra samples added to pad batches based on
@@ -201,7 +246,7 @@ class ParserTfds(Parser):
         return math.ceil(max(1, self.repeats) * self.num_samples / self.dist_num_replicas)
 
     def _filename(self, index, basename=False, absolute=False):
-        assert False, "Not supported" # no random access to samples
+        assert False, "Not supported"  # no random access to samples
 
     def filenames(self, basename=False, absolute=False):
         """ Return all filenames in dataset, overrides base"""

train.py

@@ -70,7 +70,7 @@ parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
 
 parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
 
-# Dataset / Model parameters
+# Dataset parameters
 parser.add_argument('data_dir', metavar='DIR',
                     help='path to dataset')
 parser.add_argument('--dataset', '-d', metavar='NAME', default='',
@@ -79,6 +79,12 @@ parser.add_argument('--train-split', metavar='NAME', default='train',
                     help='dataset train split (default: train)')
 parser.add_argument('--val-split', metavar='NAME', default='validation',
                     help='dataset validation split (default: validation)')
+parser.add_argument('--dataset-download', action='store_true', default=False,
+                    help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
+parser.add_argument('--class-map', default='', type=str, metavar='FILENAME',
+                    help='path to class to idx mapping file (default: "")')
+
+# Model parameters
 parser.add_argument('--model', default='resnet50', type=str, metavar='MODEL',
                     help='Name of model to train (default: "resnet50"')
 parser.add_argument('--pretrained', action='store_true', default=False,
@@ -484,11 +490,16 @@ def main():
 
     # create the train and eval datasets
     dataset_train = create_dataset(
-        args.dataset,
-        root=args.data_dir, split=args.train_split, is_training=True,
-        batch_size=args.batch_size, repeats=args.epoch_repeats)
+        args.dataset, root=args.data_dir, split=args.train_split, is_training=True,
+        class_map=args.class_map,
+        download=args.dataset_download,
+        batch_size=args.batch_size,
+        repeats=args.epoch_repeats)
     dataset_eval = create_dataset(
-        args.dataset, root=args.data_dir, split=args.val_split, is_training=False, batch_size=args.batch_size)
+        args.dataset, root=args.data_dir, split=args.val_split, is_training=False,
+        class_map=args.class_map,
+        download=args.dataset_download,
+        batch_size=args.batch_size)
 
     # setup mixup / cutmix
     collate_fn = None

validate.py

@@ -48,6 +48,8 @@ parser.add_argument('--dataset', '-d', metavar='NAME', default='',
                     help='dataset type (default: ImageFolder/ImageTar if empty)')
 parser.add_argument('--split', metavar='NAME', default='validation',
                     help='dataset split (default: validation)')
+parser.add_argument('--dataset-download', action='store_true', default=False,
+                    help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
 parser.add_argument('--model', '-m', metavar='NAME', default='dpn92',
                     help='model architecture (default: dpn92)')
 parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
@@ -175,7 +177,7 @@ def validate(args):
 
     dataset = create_dataset(
         root=args.data, name=args.dataset, split=args.split,
-        load_bytes=args.tf_preprocessing, class_map=args.class_map)
+        download=args.dataset_download, load_bytes=args.tf_preprocessing, class_map=args.class_map)
 
     if args.valid_labels:
         with open(args.valid_labels, 'r') as f: