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# Copyright 2022 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"""A config for training a UViM stage I model for the panoptic task.
This config is expected to reproduce the paper's result and achieve
approximately 75.7 PQ points on the COCO holdout data.
We also provide a low-resource variant of this config, which can be enabled
by adding `:singlehost` postfix to the config name. This one is expected to
achieve 67.8 PQ points on the COCO holdout data.
"""
import itertools
import big_vision.configs.common as bvcc
import ml_collections as mlc
def get_config(arg='res=512,patch_size=16'):
"""Config for training label compression on COCO-panoptic."""
arg = bvcc.parse_arg(arg, res=512, patch_size=16,
runlocal=False, singlehost=False)
config = mlc.ConfigDict()
config.task = 'proj.uvim.panoptic_task'
config.input = {}
config.input.data = dict(name='coco/2017_panoptic', split='train[4096:]')
config.input.batch_size = 1024
config.input.shuffle_buffer_size = 25_000
config.total_epochs = 1000
config.input.pp = (
f'decode|coco_panoptic|concat(["semantics","instances"], "labels")|'
f'randu("fliplr")|det_fliplr(key="image")|det_fliplr(key="labels")|'
f'inception_box|crop_box(key="image")|crop_box(key="labels")|'
f'resize({arg.res})|resize({arg.res},key="labels",method="nearest")|'
f'value_range(-1, 1)|make_canonical|keep("image","labels")'
)
pp_eval = (
f'decode|coco_panoptic|concat(["semantics","instances"], "labels")|'
f'resize({arg.res})|resize({arg.res},key="labels",method="nearest")|'
f'value_range(-1, 1)|make_canonical|keep("image","labels")'
)
config.log_training_steps = 50
config.ckpt_steps = 1000
config.keep_ckpt_steps = 20_000
# Model section
config.model_name = 'proj.uvim.vit'
config.model = mlc.ConfigDict()
config.model.input_size = (arg.res, arg.res)
config.model.patch_size = (arg.patch_size, arg.patch_size)
config.model.code_len = 256
config.model.width = 768
config.model.enc_depth = 6
config.model.dec_depth = 12
config.model.mlp_dim = 3072
config.model.num_heads = 12
config.model.dict_size = 4096 # Number of words in dict.
config.model.codeword_dim = 768
config.model.dict_momentum = 0.995 # Momentum for dict. learning.
config.model.with_encoder_ctx = True
config.model.with_decoder_ctx = True
config.model.code_dropout = 'random'
config.model.bottleneck_resize = True
config.model.inputs = {
'semantics': (133 + 1, arg.patch_size**2), # +1 for void label
'instances': (100, arg.patch_size**2), # COCO: actually 98 train/78 validation.
}
config.model.outputs = config.model.inputs
# VQVAE-specific params.
config.freeze_dict = False # Will freeze a dict. inside VQ-VAE model.
config.w_commitment = 0.0
# Optimizer section
config.optax_name = 'big_vision.scale_by_adafactor'
config.optax = dict(beta2_cap=0.95)
config.lr = 4e-4
config.wd = 4e-5
config.schedule = dict(decay_type='cosine', warmup_steps=4_000)
config.grad_clip_norm = 1.0
# Evaluation section
config.evals = {}
config.evals.val = mlc.ConfigDict()
config.evals.val.type = 'proj.uvim.compute_mean'
config.evals.val.pred = 'validation'
config.evals.val.data = {**config.input.data}
config.evals.val.data.split = 'train[:4096]'
config.evals.val.pp_fn = pp_eval
config.evals.val.log_steps = 250
base = {
'type': 'proj.uvim.coco_panoptic',
'pp_fn': pp_eval.replace('decode|', ''),
'log_steps': 10_000,
# Filters objects that occupy less than 0.03^2 fraction of all pixels.
# 'predict_kwargs': {'min_fraction': 0.03 ** 2},
}
config.evals.coco_panoptic_train = dict(**base, split='train[4096:8192]')
config.evals.coco_panoptic_holdout = dict(**base, split='train[:4096]')
config.evals.coco_panoptic = dict(**base, split='validation')
# config.evals.save_pred = dict(type='proj.uvim.save_predictions')
# config.evals.save_pred.pp = pp_eval.replace('decode|', '')
# config.evals.save_pred.log_steps = 100_000
# config.evals.save_pred.dataset = config.dataset
# config.evals.save_pred.split = 'validation[:1024]'
# config.evals.save_pred.outfile = 'inference.npz'
config.seed = 0
if arg.singlehost:
config.input.batch_size = 128
config.num_epochs = 100
elif arg.runlocal:
config.input.batch_size = 16
config.input.shuffle_buffer_size = 10
config.log_training_steps = 5
config.model.enc_depth = 1
config.model.dec_depth = 1
config.evals.val.data.split = 'validation[:16]'
config.evals.val.log_steps = 20
return config |