Add webdataset (WDS) support, update TFDS to make some naming in parsers more similar. Fix workers=0 compatibility. Add ImageNet22k/12k synset defs.
Ross Wightman
3 years ago
parent
3fce010ca8
commit
da2796ae82
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""" Dataset parser interface for webdataset
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Hacked together by / Copyright 2022 Ross Wightman
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"""
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import math
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import os
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import io
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import json
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import yaml
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import random
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from dataclasses import dataclass
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from itertools import islice
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from functools import partial
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from typing import Dict, Tuple
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import torch
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from PIL import Image
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try:
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import webdataset as wds
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from webdataset.shardlists import expand_urls
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except ImportError:
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wds = None
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expand_urls = None
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from .parser import Parser
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from timm.bits import get_global_device, is_global_device
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SHUFFLE_SIZE = 8192
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def _load_info(root, basename='info'):
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info_json = os.path.join(root, basename + '.json')
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info_yaml = os.path.join(root, basename + '.yaml')
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info_dict = {}
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if os.path.exists(info_json):
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with open(info_json, 'r') as f:
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info_dict = json.load(f)
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elif os.path.exists(info_yaml):
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with open(info_yaml, 'r') as f:
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info_dict = yaml.safe_load(f)
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return info_dict
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@dataclass
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class SplitInfo:
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num_samples: int
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filenames: Tuple[str]
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shard_lengths: Tuple[int] = ()
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name: str = ''
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def _parse_split_info(split: str, info: Dict):
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def _info_convert(dict_info):
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return SplitInfo(
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num_samples=dict_info['num_samples'],
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filenames=tuple(dict_info['filenames']),
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shard_lengths=tuple(dict_info['shard_lengths']),
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name=dict_info['name'],
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)
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if 'tar' in split or '..' in split:
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# split in WDS string braceexpand format, sample count can be included with a | separator
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# ex: `dataset-split-{0000..9999}.tar|100000` for 9999 shards, covering 100,000 samples
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split = split.split('|')
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num_samples = 0
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split_name = ''
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if len(split) > 1:
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num_samples = int(split[1])
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split = split[0]
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if '::' not in split:
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split_parts = split.split('-', 3)
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split_idx = len(split_parts) - 1
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if split_idx and 'splits' in info and split_parts[split_idx] in info['splits']:
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split_name = split_parts[split_idx]
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split_filenames = expand_urls(split)
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if split_name:
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split_info = info['splits'][split_name]
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if not num_samples:
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_fc = {f: c for f, c in zip(split_info['filenames'], split_info['shard_lengths'])}
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num_samples = sum(_fc[f] for f in split_filenames)
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split_info['filenames'] = tuple(_fc.keys())
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split_info['shard_lengths'] = tuple(_fc.values())
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split_info['num_samples'] = num_samples
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split_info = _info_convert(split_info)
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else:
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split_info = SplitInfo(
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name=split_name,
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num_samples=num_samples,
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filenames=split_filenames,
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)
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else:
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if split not in info['splits']:
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raise RuntimeError(f"split {split} not found in info ({info['splits'].keys()})")
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split = split
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split_info = info['splits'][split]
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split_info = _info_convert(split_info)
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return split_info
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def _decode(sample, image_key='jpg', image_format='RGB', target_key='cls'):
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""" Custom sample decode
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* decode and convert PIL Image
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* cls byte string label to int
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* pass through JSON byte string (if it exists) without parse
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"""
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with io.BytesIO(sample[image_key]) as b:
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img = Image.open(b)
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img.load()
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if image_format:
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img = img.convert(image_format)
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return dict(jpg=img, cls=int(sample[target_key]), json=sample.get('json', None))
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class ParserWebdataset(Parser):
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def __init__(
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self,
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root,
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name,
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split,
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is_training=False,
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batch_size=None,
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repeats=0,
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seed=42,
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input_name='image',
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input_image='RGB',
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target_name=None,
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target_image='',
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prefetch_size=None,
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shuffle_size=None,
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):
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super().__init__()
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self.root = root
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self.is_training = is_training
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self.batch_size = batch_size
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self.repeats = repeats
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self.common_seed = seed # a seed that's fixed across all worker / distributed instances
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self.shard_shuffle_size = 500
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self.sample_shuffle_size = shuffle_size or SHUFFLE_SIZE
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self.image_key = 'jpg'
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self.image_format = input_image
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self.target_key = 'cls'
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self.filename_key = 'filename'
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self.key_ext = '.JPEG' # extension to add to key for original filenames (DS specific, default ImageNet)
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self.info = _load_info(self.root)
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self.split_info = _parse_split_info(split, self.info)
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self.num_samples = self.split_info.num_samples
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if not self.num_samples:
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raise RuntimeError(f'Invalid split definition, no samples found.')
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# Distributed world state
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self.dist_rank = 0
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self.dist_num_replicas = 1
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if is_global_device():
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dev_env = get_global_device()
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if dev_env.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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else:
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# FIXME warn if we fallback to torch distributed?
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import torch.distributed as dist
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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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# Attributes that are updated in _lazy_init
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self.worker_id = 0
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self.worker_seed = seed # seed unique to each worker instance
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self.num_workers = 1
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self.global_worker_id = 0
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self.global_num_workers = 1
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self.init_count = 0
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# DataPipeline is lazy init, majority of WDS DataPipeline could be init here, BUT, shuffle seed
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# is not handled in manner where it can be deterministic for each worker AND initialized up front
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self.ds = None
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def _lazy_init(self):
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""" Lazily initialize worker (in worker processes)
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"""
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worker_info = torch.utils.data.get_worker_info()
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if worker_info is not None:
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self.worker_id = worker_info.id
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self.worker_seed = worker_info.seed
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self.num_workers = worker_info.num_workers
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self.global_num_workers = self.dist_num_replicas * self.num_workers
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self.global_worker_id = self.dist_rank * self.num_workers + self.worker_id
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# init data pipeline
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abs_shard_filenames = [os.path.join(self.root, f) for f in self.split_info.filenames]
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pipeline = [wds.SimpleShardList(abs_shard_filenames)]
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# at this point we have an iterator over all the shards
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if self.is_training:
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pipeline.extend([
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wds.detshuffle(self.shard_shuffle_size, seed=self.common_seed),
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self._split_by_node_and_worker,
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# at this point, we have an iterator over the shards assigned to each worker
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wds.tarfile_to_samples(),
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wds.shuffle(
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self.sample_shuffle_size,
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rng=random.Random(self.worker_seed)), # this is why we lazy-init whole DataPipeline
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])
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else:
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pipeline.extend([
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self._split_by_node_and_worker,
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# at this point, we have an iterator over the shards assigned to each worker
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wds.tarfile_to_samples(),
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])
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pipeline.extend([
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wds.map(partial(_decode, image_key=self.image_key, image_format=self.image_format))
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])
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self.ds = wds.DataPipeline(*pipeline)
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self.init_count += 1
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def _split_by_node_and_worker(self, src):
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if self.global_num_workers > 1:
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for s in islice(src, self.global_worker_id, self.global_num_workers):
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yield s
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else:
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for s in src:
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yield s
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def __iter__(self):
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if not self.init_count:
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self._lazy_init()
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i = 0
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num_worker_samples = math.ceil(self.num_samples / self.global_num_workers)
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if self.is_training and self.batch_size is not None:
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num_worker_samples = (num_worker_samples // self.batch_size) * self.batch_size
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ds = self.ds.with_epoch(num_worker_samples)
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for sample in ds:
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yield sample[self.image_key], sample[self.target_key]
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i += 1
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print('end', i) # FIXME debug
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def __len__(self):
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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 examples
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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 not self.init_count:
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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 self.filename_key in sample:
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name = sample[self.filename_key]
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elif '__key__' in sample:
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name = sample['__key__'] + self.key_ext
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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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if len(names) >= self.num_samples:
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break # safety for ds.repeat() case
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return names
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