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# Copyright 2024 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=line-too-long
r"""Pre-training ViT-S/16 on ILSVRC-2012 following https://arxiv.org/abs/2205.01580.
This should take 6-7h to finish 90ep on a TPU-v3-8 and reach 76.5%,
see the tech report for more details.
Command to run:
big_vision.train \
--config big_vision/configs/vit_s16_i1k.py \
--workdir gs://[your_bucket]/big_vision/`date '+%m-%d_%H%M'`
To run for 300ep, add `--config.total_epochs 300` to the command.
"""
import ml_collections as mlc
def get_config():
"""Config for training."""
config = mlc.ConfigDict()
config.seed = 0
config.total_epochs = 90
config.num_classes = 1000
config.loss = 'softmax_xent'
config.input = {}
config.input.data = dict(
name='imagenet2012',
split='train[:99%]',
)
config.input.batch_size = 1024
config.input.cache_raw = True # Needs up to 120GB of RAM!
config.input.shuffle_buffer_size = 250_000
pp_common = (
'|value_range(-1, 1)'
'|onehot(1000, key="{lbl}", key_result="labels")'
'|keep("image", "labels")'
)
config.input.pp = (
'decode_jpeg_and_inception_crop(224)|flip_lr|randaug(2,10)' +
pp_common.format(lbl='label')
)
pp_eval = 'decode|resize_small(256)|central_crop(224)' + pp_common
# To continue using the near-defunct randaug op.
config.pp_modules = ['ops_general', 'ops_image', 'ops_text', 'archive.randaug']
config.log_training_steps = 50
config.ckpt_steps = 1000
# Model section
config.model_name = 'vit'
config.model = dict(
variant='S/16',
rep_size=True,
pool_type='gap',
posemb='sincos2d',
)
# Optimizer section
config.grad_clip_norm = 1.0
config.optax_name = 'scale_by_adam'
config.optax = dict(mu_dtype='bfloat16')
config.lr = 0.001
config.wd = 0.0001
config.schedule = dict(warmup_steps=10_000, decay_type='cosine')
config.mixup = dict(p=0.2, fold_in=None)
# Eval section
def get_eval(split, dataset='imagenet2012'):
return dict(
type='classification',
data=dict(name=dataset, split=split),
pp_fn=pp_eval.format(lbl='label'),
loss_name=config.loss,
log_steps=2500, # Very fast O(seconds) so it's fine to run it often.
)
config.evals = {}
config.evals.train = get_eval('train[:2%]')
config.evals.minival = get_eval('train[99%:]')
config.evals.val = get_eval('validation')
config.evals.v2 = get_eval('test', dataset='imagenet_v2')
config.evals.real = get_eval('validation', dataset='imagenet2012_real')
config.evals.real.pp_fn = pp_eval.format(lbl='real_label')
return config
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