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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 BiT on ILSVRC-2012 as in https://arxiv.org/abs/1912.11370
Run training of a BiT-ResNet-50x1 variant, which takes ~32min on v3-128:
big_vision.train \
--config big_vision/configs/bit_i1k.py \
--workdir gs://[your_bucket]/big_vision/`date '+%m-%d_%H%M'` \
--config.model.depth 50 --config.model.width 1
"""
# from big_vision.configs.common_fewshot import get_fewshot_lsr
import ml_collections as mlc
def get_config(runlocal=False):
"""Config for training on ImageNet-1k."""
config = mlc.ConfigDict()
config.seed = 0
config.total_epochs = 90
config.num_classes = 1000
config.loss = 'softmax_xent'
config.input = dict()
config.input.data = dict(
name='imagenet2012',
split='train[:99%]',
)
config.input.batch_size = 4096
config.input.cache_raw = True # Needs up to 120GB of RAM!
config.input.shuffle_buffer_size = 250_000 # Per host.
pp_common = '|onehot(1000, key="{lbl}", key_result="labels")'
pp_common += '|value_range(-1, 1)|keep("image", "labels")'
config.input.pp = 'decode_jpeg_and_inception_crop(224)|flip_lr' + pp_common.format(lbl='label')
pp_eval = 'decode|resize_small(256)|central_crop(224)' + pp_common
config.log_training_steps = 50
config.ckpt_steps = 1000
# Model section
config.model_name = 'bit'
config.model = dict(
depth=50, # You can also pass e.g. [3, 5, 10, 2]
width=1.0,
)
# Optimizer section
config.optax_name = 'big_vision.momentum_hp'
config.grad_clip_norm = 1.0
# linear scaling rule. Don't forget to sweep if sweeping batch_size.
config.wd = (1e-4 / 256) * config.input.batch_size
config.lr = (0.1 / 256) * config.input.batch_size
config.schedule = dict(decay_type='cosine', warmup_steps=1000)
# 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=1000, # Very fast O(seconds) so it's fine to run it often.
cache='final_data',
)
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')
# config.evals.fewshot = get_fewshot_lsr(runlocal=runlocal)
# config.evals.fewshot.log_steps = 1000
if runlocal:
config.input.batch_size = 32
config.input.cache_raw = False
config.input.shuffle_buffer_size = 100
local_eval = config.evals.val
config.evals = {'val': local_eval}
config.evals.val.cache = 'none'
return config |