Matching two bits_and_tpu changes for TFDs wrapper

* change 'samples' -> 'examples' for tfds wrapper to match tfds naming
* add class_to_idx for image classification datasets in tfds wrapper

timm/data/parsers/parser_tfds.py

@@ -30,38 +30,47 @@ from .parser import Parser
 
 
 MAX_TP_SIZE = 8  # maximum TF threadpool size, only doing jpeg decodes and queuing activities
-SHUFFLE_SIZE = 16384  # samples to shuffle in DS queue
-PREFETCH_SIZE = 2048  # samples to prefetch
+SHUFFLE_SIZE = 8192  # examples to shuffle in DS queue
+PREFETCH_SIZE = 2048  # examples to prefetch
 
 
-def even_split_indices(split, n, num_samples):
-    partitions = [round(i * num_samples / n) for i in range(n + 1)]
-    return [f"{split}[{partitions[i]}:{partitions[i+1]}]" for i in range(n)]
+def even_split_indices(split, n, num_examples):
+    partitions = [round(i * num_examples / n) for i in range(n + 1)]
+    return [f"{split}[{partitions[i]}:{partitions[i + 1]}]" for i in range(n)]
+
+
+def get_class_labels(info):
+    if 'label' not in info.features:
+        return {}
+    class_label = info.features['label']
+    class_to_idx = {n: class_label.str2int(n) for n in class_label.names}
+    return class_to_idx
 
 
 class ParserTfds(Parser):
     """ Wrap Tensorflow Datasets for use in PyTorch
 
     There several things to be aware of:
-      * To prevent excessive samples being dropped per epoch w/ distributed training or multiplicity of
+      * To prevent excessive examples being dropped per epoch w/ distributed training or multiplicity of
          dataloader workers, the train iterator wraps to avoid returning partial batches that trigger drop_last
          https://github.com/pytorch/pytorch/issues/33413
       * With PyTorch IterableDatasets, each worker in each replica operates in isolation, the final batch
         from each worker could be a different size. For training this is worked around by option above, for
-        validation extra samples are inserted iff distributed mode is enabled so that the batches being reduced
+        validation extra examples are inserted iff distributed mode is enabled so that the batches being reduced
         across replicas are of same size. This will slightly alter the results, distributed validation will not be
         100% correct. This is similar to common handling in DistributedSampler for normal Datasets but a bit worse
-        since there are up to N * J extra samples with IterableDatasets.
+        since there are up to N * J extra examples with IterableDatasets.
       * The sharding (splitting of dataset into TFRecord) files imposes limitations on the number of
         replicas and dataloader workers you can use. For really small datasets that only contain a few shards
         you may have to train non-distributed w/ 1-2 dataloader workers. This is likely not a huge concern as the
         benefit of distributed training or fast dataloading should be much less for small datasets.
-      * This wrapper is currently configured to return individual, decompressed image samples from the TFDS
+      * This wrapper is currently configured to return individual, decompressed image examples from the TFDS
         dataset. The augmentation (transforms) and batching is still done in PyTorch. It would be possible
         to specify TF augmentation fn and return augmented batches w/ some modifications to other downstream
         components.
 
     """
+
     def __init__(
             self,
             root,
@@ -72,6 +81,10 @@ class ParserTfds(Parser):
             download=False,
             repeats=0,
             seed=42,
+            input_name='image',
+            input_image='RGB',
+            target_name='label',
+            target_image='',
             prefetch_size=None,
             shuffle_size=None,
             max_threadpool_size=None
@@ -83,10 +96,14 @@ class ParserTfds(Parser):
             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
+            batch_size: batch_size to use to unsure total examples % 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
+            input_name: name of Feature to return as data (input)
+            input_image: image mode if input is an image (currently PIL mode string)
+            target_name: name of Feature to return as target (label)
+            target_image: image mode if target is an image (currently PIL mode string)
             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
@@ -96,22 +113,29 @@ class ParserTfds(Parser):
         self.split = split
         self.is_training = is_training
         if self.is_training:
-            assert batch_size is not None,\
+            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.common_seed = seed  # a seed that's fixed across all worker / distributed instances
+
+        # performance settings
         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.input_name = input_name  # FIXME support tuples / lists of inputs and targets and full range of Feature
+        self.input_image = input_image
+        self.target_name = target_name
+        self.target_image = target_image
         self.builder = tfds.builder(name, data_dir=root)
         # 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.class_to_idx = get_class_labels(self.builder.info) if self.target_name == 'label' else {}
         self.split_info = self.builder.info.splits[split]
-        self.num_samples = self.split_info.num_examples
+        self.num_examples = self.split_info.num_examples
 
         # Distributed world state
         self.dist_rank = 0
@@ -158,17 +182,17 @@ class ParserTfds(Parser):
             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.
+            for validation where we can't drop examples and need to avoid minimize uneven splits to avoid padding.
             """
             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
+                # split the dataset w/o using sharding for more even examples / 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)
+                        subsplits = even_split_indices(self.split, self.global_num_workers, self.num_examples)
                         self.subsplit = subsplits[global_worker_id]
                 else:
                     subsplits = tfds.even_splits(self.split, self.global_num_workers)
@@ -199,8 +223,8 @@ class ParserTfds(Parser):
             # 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.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))
+            ds = ds.shuffle(min(self.num_examples, self.shuffle_size) // self.global_num_workers, seed=self.worker_seed)
+        ds = ds.prefetch(min(self.num_examples // self.global_num_workers, self.prefetch_size))
         self.ds = tfds.as_numpy(ds)
 
     def __iter__(self):
@@ -209,44 +233,49 @@ class ParserTfds(Parser):
 
         # 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.
+        #     This adds extra examples 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.global_num_workers)
+        target_example_count = math.ceil(max(1, self.repeats) * self.num_examples / 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
+            target_example_count = math.ceil(target_example_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')
-            yield img, sample['label']
-            sample_count += 1
-            if self.is_training and sample_count >= target_sample_count:
+        example_count = 0
+        for example in self.ds:
+            input_data = example[self.input_name]
+            if self.input_image:
+                input_data = Image.fromarray(input_data, mode=self.input_image)
+            target_data = example[self.target_name]
+            if self.target_image:
+                target_data = Image.fromarray(target_data, mode=self.target_image)
+            yield input_data, target_data
+            example_count += 1
+            if self.is_training and example_count >= target_example_count:
                 # Need to break out of loop when repeat() is enabled for training w/ oversampling
-                # this results in extra samples per epoch but seems more desirable than dropping
+                # this results in extra examples 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
 
-        # Pad across distributed nodes (make counts equal by adding samples)
+        # Pad across distributed nodes (make counts equal by adding examples)
         if not self.is_training and self.dist_num_replicas > 1 and self.subsplit is not None and \
-                0 < sample_count < target_sample_count:
+                0 < example_count < target_example_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.
             # 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
+            while example_count < target_example_count:
+                yield input_data, target_data  # yield prev sample again
+                example_count += 1
 
     def __len__(self):
-        # this is just an estimate and does not factor in extra samples added to pad batches based on
+        # this is just an estimate and does not factor in extra examples added to pad batches based on
         # complete worker & replica info (not available until init in dataloader).
-        return math.ceil(max(1, self.repeats) * self.num_samples / self.dist_num_replicas)
+        return math.ceil(max(1, self.repeats) * self.num_examples / 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 examples
 
     def filenames(self, basename=False, absolute=False):
         """ Return all filenames in dataset, overrides base"""
@@ -254,7 +283,7 @@ class ParserTfds(Parser):
             self._lazy_init()
         names = []
         for sample in self.ds:
-            if len(names) > self.num_samples:
+            if len(names) > self.num_examples:
                 break  # safety for ds.repeat() case
             if 'file_name' in sample:
                 name = sample['file_name']