You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
203 lines
10 KiB
203 lines
10 KiB
""" Dataset parser interface that wraps TFDS datasets
|
|
|
|
Wraps many (most?) TFDS image-classification datasets
|
|
from https://github.com/tensorflow/datasets
|
|
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
|
|
from PIL import Image
|
|
|
|
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
|
|
except ImportError as e:
|
|
print(e)
|
|
print("Please install tensorflow_datasets package `pip install tensorflow-datasets`.")
|
|
exit(1)
|
|
from .parser import Parser
|
|
|
|
|
|
MAX_TP_SIZE = 8 # maximum TF threadpool size, only doing jpeg decodes and queuing activities
|
|
SHUFFLE_SIZE = 16834 # samples to shuffle in DS queue
|
|
PREFETCH_SIZE = 4096 # samples to prefetch
|
|
|
|
|
|
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
|
|
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
|
|
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.
|
|
* 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
|
|
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, name, split='train', shuffle=False, is_training=False, batch_size=None, repeats=0):
|
|
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.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
|
|
|
|
self.worker_info = None
|
|
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()
|
|
|
|
def _lazy_init(self):
|
|
""" Lazily initialize the dataset.
|
|
|
|
This is necessary to init the Tensorflow dataset pipeline in the (dataloader) process that
|
|
will be using the dataset instance. The __init__ method is called on the main process,
|
|
this will be called in a dataloader worker process.
|
|
|
|
NOTE: There will be problems if you try to re-use this dataset across different loader/worker
|
|
instances once it has been initialized. Do not call any dataset methods that can call _lazy_init
|
|
before it is passed to dataloader.
|
|
"""
|
|
worker_info = torch.utils.data.get_worker_info()
|
|
|
|
# setup input context to split dataset across distributed processes
|
|
split = self.split
|
|
num_workers = 1
|
|
if worker_info is not None:
|
|
self.worker_info = worker_info
|
|
num_workers = worker_info.num_workers
|
|
worker_id = 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
|
|
"""
|
|
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
|
|
"""
|
|
# 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)
|
|
|
|
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_replicas_in_sync=self.dist_num_replicas # FIXME does this have any impact?
|
|
)
|
|
|
|
read_config = tfds.ReadConfig(input_context=input_context)
|
|
ds = self.builder.as_dataset(split=split, shuffle_files=self.shuffle, read_config=read_config)
|
|
# avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
|
|
ds.options().experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
|
|
ds.options().experimental_threading.max_intra_op_parallelism = 1
|
|
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 // self._num_pipelines, SHUFFLE_SIZE), seed=0)
|
|
ds = ds.prefetch(min(self.num_samples // self._num_pipelines, 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:
|
|
# 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)
|
|
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
|
|
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:
|
|
# 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
|
|
# 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:
|
|
# 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 this needs more testing, possible for sharding / split api to cause differences of > 1?
|
|
assert target_sample_count - sample_count == 1 # should only be off by 1 or sharding 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
|
|
|
|
def __len__(self):
|
|
# this is just an estimate and does not factor in extra samples 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)
|
|
|
|
def _filename(self, index, basename=False, absolute=False):
|
|
assert False, "Not supported" # no random access to samples
|
|
|
|
def filenames(self, basename=False, absolute=False):
|
|
""" Return all filenames in dataset, overrides base"""
|
|
if self.ds is None:
|
|
self._lazy_init()
|
|
names = []
|
|
for sample in self.ds:
|
|
if len(names) > self.num_samples:
|
|
break # safety for ds.repeat() case
|
|
if 'file_name' in sample:
|
|
name = sample['file_name']
|
|
elif 'filename' in sample:
|
|
name = sample['filename']
|
|
elif 'id' in sample:
|
|
name = sample['id']
|
|
else:
|
|
assert False, "No supported name field present"
|
|
names.append(name)
|
|
return names
|