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228 lines
11 KiB
228 lines
11 KiB
""" Dataset parser interface that wraps TFDS datasets
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Wraps many (most?) TFDS image-classification datasets
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from https://github.com/tensorflow/datasets
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https://www.tensorflow.org/datasets/catalog/overview#image_classification
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import os
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import io
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import math
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import torch
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import torch.distributed as dist
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from PIL import Image
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try:
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import tensorflow as tf
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tf.config.set_visible_devices([], 'GPU') # Hands off my GPU! (or pip install tensorflow-cpu)
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import tensorflow_datasets as tfds
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except ImportError as e:
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print(e)
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print("Please install tensorflow_datasets package `pip install tensorflow-datasets`.")
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exit(1)
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from .parser import Parser
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from timm.bits import get_device
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MAX_TP_SIZE = 8 # maximum TF threadpool size, only doing jpeg decodes and queuing activities
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SHUFFLE_SIZE = 16834 # samples to shuffle in DS queue
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PREFETCH_SIZE = 4096 # samples to prefetch
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def even_split_indices(split, n, num_samples):
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partitions = [round(i * num_samples / n) for i in range(n + 1)]
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return [f"{split}[{partitions[i]}:{partitions[i+1]}]" for i in range(n)]
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class ParserTfds(Parser):
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""" Wrap Tensorflow Datasets for use in PyTorch
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There several things to be aware of:
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* To prevent excessive samples being dropped per epoch w/ distributed training or multiplicity of
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dataloader workers, the train iterator wraps to avoid returning partial batches that trigger drop_last
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https://github.com/pytorch/pytorch/issues/33413
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* With PyTorch IterableDatasets, each worker in each replica operates in isolation, the final batch
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from each worker could be a different size. For training this is worked around by option above, for
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validation extra samples are inserted iff distributed mode is enabled so that the batches being reduced
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across replicas are of same size. This will slightly alter the results, distributed validation will not be
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100% correct. This is similar to common handling in DistributedSampler for normal Datasets but a bit worse
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since there are up to N * J extra samples with IterableDatasets.
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* The sharding (splitting of dataset into TFRecord) files imposes limitations on the number of
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replicas and dataloader workers you can use. For really small datasets that only contain a few shards
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you may have to train non-distributed w/ 1-2 dataloader workers. This is likely not a huge concern as the
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benefit of distributed training or fast dataloading should be much less for small datasets.
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* This wrapper is currently configured to return individual, decompressed image samples from the TFDS
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dataset. The augmentation (transforms) and batching is still done in PyTorch. It would be possible
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to specify TF augmentation fn and return augmented batches w/ some modifications to other downstream
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components.
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"""
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def __init__(self, root, name, split='train', shuffle=False, is_training=False, batch_size=None, repeats=0):
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super().__init__()
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self.root = root
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self.split = split
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self.shuffle = shuffle
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self.is_training = is_training
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if self.is_training:
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assert batch_size is not None,\
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"Must specify batch_size in training mode for reasonable behaviour w/ TFDS wrapper"
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self.batch_size = batch_size
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self.repeats = repeats
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self.subsplit = None
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self.builder = tfds.builder(name, data_dir=root)
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# NOTE: please use tfds command line app to download & prepare datasets, I don't want to call
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# download_and_prepare() by default here as it's caused issues generating unwanted paths.
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self.num_samples = self.builder.info.splits[split].num_examples
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self.ds = None # initialized lazily on each dataloader worker process
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self.worker_info = None
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self.dist_rank = 0
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self.dist_num_replicas = 1
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dev_env = get_device()
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# FIXME allow to work without devenv usage?
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if dev_env.is_distributed and dev_env.world_size > 1:
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self.dist_rank = dev_env.global_rank
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self.dist_num_replicas = dev_env.world_size
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# if dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1:
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# self.dist_rank = dist.get_rank()
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# self.dist_num_replicas = dist.get_world_size()
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def _lazy_init(self):
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""" Lazily initialize the dataset.
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This is necessary to init the Tensorflow dataset pipeline in the (dataloader) process that
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will be using the dataset instance. The __init__ method is called on the main process,
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this will be called in a dataloader worker process.
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NOTE: There will be problems if you try to re-use this dataset across different loader/worker
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instances once it has been initialized. Do not call any dataset methods that can call _lazy_init
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before it is passed to dataloader.
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"""
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worker_info = torch.utils.data.get_worker_info()
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# setup input context to split dataset across distributed processes
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split = self.split
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num_workers = 1
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if worker_info is not None:
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self.worker_info = worker_info
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num_workers = worker_info.num_workers
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global_num_workers = self.dist_num_replicas * num_workers
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worker_id = worker_info.id
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# FIXME I need to spend more time figuring out the best way to distribute/split data across
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# combo of distributed replicas + dataloader worker processes
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"""
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InputContext will assign subset of underlying TFRecord files to each 'pipeline' if used.
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My understanding is that using split, the underling TFRecord files will shuffle (shuffle_files=True)
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between the splits each iteration, but that understanding could be wrong.
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Possible split options include:
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* InputContext for both distributed & worker processes (current)
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* InputContext for distributed and sub-splits for worker processes
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* sub-splits for both
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"""
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# split_size = self.num_samples // num_workers
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# start = worker_id * split_size
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# if worker_id == num_workers - 1:
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# split = split + '[{}:]'.format(start)
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# else:
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# split = split + '[{}:{}]'.format(start, start + split_size)
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if not self.is_training and '[' not in self.split:
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# If not training, and split doesn't define a subsplit, manually split the dataset
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# for more even samples / worker
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self.subsplit = even_split_indices(self.split, global_num_workers, self.num_samples)[
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self.dist_rank * num_workers + worker_id]
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if self.subsplit is None:
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input_context = tf.distribute.InputContext(
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num_input_pipelines=self.dist_num_replicas * num_workers,
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input_pipeline_id=self.dist_rank * num_workers + worker_id,
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num_replicas_in_sync=self.dist_num_replicas # FIXME does this arg have any impact?
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)
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else:
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input_context = None
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read_config = tfds.ReadConfig(
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shuffle_seed=42,
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shuffle_reshuffle_each_iteration=True,
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input_context=input_context)
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ds = self.builder.as_dataset(
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split=self.subsplit or self.split, shuffle_files=self.shuffle, read_config=read_config)
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# avoid overloading threading w/ combo fo TF ds threads + PyTorch workers
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ds.options().experimental_threading.private_threadpool_size = max(1, MAX_TP_SIZE // num_workers)
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ds.options().experimental_threading.max_intra_op_parallelism = 1
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if self.is_training or self.repeats > 1:
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# to prevent excessive drop_last batch behaviour w/ IterableDatasets
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# see warnings at https://pytorch.org/docs/stable/data.html#multi-process-data-loading
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ds = ds.repeat() # allow wrap around and break iteration manually
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if self.shuffle:
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ds = ds.shuffle(min(self.num_samples // self._num_pipelines, SHUFFLE_SIZE), seed=0)
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ds = ds.prefetch(min(self.num_samples // self._num_pipelines, PREFETCH_SIZE))
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self.ds = tfds.as_numpy(ds)
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def __iter__(self):
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if self.ds is None:
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self._lazy_init()
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# compute a rounded up sample count that is used to:
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# 1. make batches even cross workers & replicas in distributed validation.
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# This adds extra samples and will slightly alter validation results.
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# 2. determine loop ending condition in training w/ repeat enabled so that only full batch_size
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# batches are produced (underlying tfds iter wraps around)
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target_sample_count = math.ceil(max(1, self.repeats) * self.num_samples / self._num_pipelines)
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if self.is_training:
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# round up to nearest batch_size per worker-replica
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target_sample_count = math.ceil(target_sample_count / self.batch_size) * self.batch_size
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sample_count = 0
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for sample in self.ds:
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img = Image.fromarray(sample['image'], mode='RGB')
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yield img, sample['label']
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sample_count += 1
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if self.is_training and sample_count >= target_sample_count:
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# Need to break out of loop when repeat() is enabled for training w/ oversampling
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# this results in extra samples per epoch but seems more desirable than dropping
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# up to N*J batches per epoch (where N = num distributed processes, and J = num worker processes)
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break
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if not self.is_training and self.dist_num_replicas and 0 < sample_count < target_sample_count:
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# Validation batch padding only done for distributed training where results are reduced across nodes.
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# For single process case, it won't matter if workers return different batch sizes.
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# FIXME if using input_context or % based subsplits, sample count can vary by more than +/- 1 and this
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# approach is not optimal
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yield img, sample['label'] # yield prev sample again
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sample_count += 1
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@property
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def _num_workers(self):
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return 1 if self.worker_info is None else self.worker_info.num_workers
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@property
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def _num_pipelines(self):
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return self._num_workers * self.dist_num_replicas
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def __len__(self):
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# this is just an estimate and does not factor in extra samples added to pad batches based on
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# complete worker & replica info (not available until init in dataloader).
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return math.ceil(max(1, self.repeats) * self.num_samples / self.dist_num_replicas)
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def _filename(self, index, basename=False, absolute=False):
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assert False, "Not supported" # no random access to samples
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def filenames(self, basename=False, absolute=False):
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""" Return all filenames in dataset, overrides base"""
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if self.ds is None:
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self._lazy_init()
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names = []
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for sample in self.ds:
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if len(names) > self.num_samples:
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break # safety for ds.repeat() case
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if 'file_name' in sample:
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name = sample['file_name']
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elif 'filename' in sample:
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name = sample['filename']
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elif 'id' in sample:
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name = sample['id']
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else:
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assert False, "No supported name field present"
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names.append(name)
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return names
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