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Matching two bits_and_tpu changes for TFDs wrapper

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* change 'samples' -> 'examples' for tfds wrapper to match tfds naming
* add class_to_idx for image classification datasets in tfds wrapper
pull/989/head
Ross Wightman 3 years ago
parent 65419f60cc
commit cfa414cad2
1 changed files with 66 additions and 37 deletions
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103
timm/data/parsers/parser_tfds.py
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@ -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
@ -154,21 +178,21 @@ class ParserTfds(Parser):
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.
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']

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