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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 depth task.
"""
import itertools
import big_vision.configs.common as bvcc
import ml_collections as mlc
QUANTIZATION_BINS = 256
# Depths outside of this range will not be evaluated.
MIN_DEPTH = 1e-3
MAX_DEPTH = 10
def get_config(arg='res=512,patch_size=16'):
"""Config for training label compression on NYU depth v2."""
arg = bvcc.parse_arg(arg, res=512, patch_size=16,
runlocal=False, singlehost=False)
config = mlc.ConfigDict()
config.task = 'proj.uvim.depth_task'
config.input = {}
config.input.data = dict(name='nyu_depth_v2', split='train)
config.input.batch_size = 1024
config.input.shuffle_buffer_size = 25_000
config.total_epochs = 200
config.input.pp = (
f'decode|nyu_depth|'
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)|keep("image","labels")'
)
pp_eval = (
f'decode|nyu_depth|nyu_eval_crop|'
f'resize({arg.res})|resize({arg.res},key="labels",method="nearest")|'
f'value_range(-1, 1)|keep("image","labels")'
)
# There are no image IDs in TFDS, so hand through the ground truth for eval.
pp_pred = (
f'nyu_depth|nyu_eval_crop|copy("labels","ground_truth")|'
f'resize({arg.res})|resize({arg.res},key="labels",method="nearest")|'
f'value_range(-1, 1)|'
f'keep("image","labels","ground_truth")'
)
config.log_training_steps = 50
config.ckpt_steps = 1000
config.keep_ckpt_steps = 20_000
# Model section
config.min_depth = MIN_DEPTH
config.max_depth = MAX_DEPTH
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 = {
'depth': (QUANTIZATION_BINS, arg.patch_size**2),
}
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 = 1e-3
config.wd = 1e-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 = 'validation'
config.evals.val.pp_fn = pp_eval
config.evals.val.log_steps = 250
base = {
'type': 'proj.uvim.nyu_depth',
'dataset': config.input.data.name,
'pp_fn': pp_pred,
'log_steps': 2000,
'min_depth': MIN_DEPTH,
'max_depth': MAX_DEPTH,
}
config.evals.nyu_depth_val = dict(**base, split='validation')
config.seed = 0
if arg.singlehost:
config.input.batch_size = 128
config.total_epochs = 50
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 |