File size: 5,229 Bytes
74e8f2f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
# 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"""Train VAE on NYU depth data for GIVT-based UViM.
"""
import big_vision.configs.common as bvcc
import ml_collections as mlc
QUANTIZATION_BINS = 256
MIN_DEPTH = 0.001
MAX_DEPTH = 10.0
def get_config(arg='res=512,patch_size=16'):
"""Config for training label compression on NYU depth."""
arg = bvcc.parse_arg(arg, res=512, patch_size=16,
runlocal=False, singlehost=False)
config = mlc.ConfigDict()
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'bin_nyu_depth(min_depth={MIN_DEPTH}, max_depth={MAX_DEPTH}, num_bins={QUANTIZATION_BINS})|'
f'value_range(-1, 1)|copy("labels", "image")|keep("image")'
)
pp_eval = (
f'decode|nyu_depth|nyu_eval_crop|'
f'resize({arg.res})|resize({arg.res},key="labels",method="nearest")|'
f'bin_nyu_depth(min_depth={MIN_DEPTH}, max_depth={MAX_DEPTH}, num_bins={QUANTIZATION_BINS})|'
f'value_range(-1, 1)|copy("labels", "image")|keep("image")'
)
pp_pred = (
f'decode|nyu_depth|nyu_eval_crop|copy("labels","ground_truth")|'
f'resize({arg.res})|resize({arg.res},key="labels",method="nearest")|'
f'bin_nyu_depth(min_depth={MIN_DEPTH}, max_depth={MAX_DEPTH}, num_bins={QUANTIZATION_BINS})|'
f'value_range(-1, 1)|copy("labels", "image")|'
f'keep("image", "ground_truth")'
)
config.log_training_steps = 50
config.ckpt_steps = 1000
config.keep_ckpt_steps = None
# Model section
config.min_depth = MIN_DEPTH
config.max_depth = MAX_DEPTH
config.model_name = 'proj.givt.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.codeword_dim = 16
config.model.code_dropout = 'none'
config.model.bottleneck_resize = True
config.model.scan = True
config.model.remat_policy = 'nothing_saveable'
config.model_init = ''
config.rec_loss_fn = 'xent' # xent, l2
config.mask_zero_target = True
# values: (index in source image, number of classes)
config.model.inout_specs = {
'depth': (0, QUANTIZATION_BINS),
}
config.beta = 2e-4
config.beta_percept = 0.0
# Optimizer section
config.optax_name = 'scale_by_adam'
config.optax = dict(b2=0.95)
# FSDP training by default
config.sharding_strategy = [('.*', 'fsdp(axis="data")')]
config.sharding_rules = [('act_batch', ('data',))]
config.lr = 1e-3
config.wd = 1e-4
config.schedule = dict(decay_type='cosine', warmup_steps=0.1)
config.grad_clip_norm = 1.0
# Evaluation section
config.evals = {}
config.evals.val = mlc.ConfigDict()
config.evals.val.type = '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.givt.nyu_depth',
'data': {**config.input.data},
'pp_fn': pp_pred,
'pred': 'predict_depth',
'log_steps': 2000,
'min_depth': MIN_DEPTH,
'max_depth': MAX_DEPTH,
}
config.evals.nyu_depth_val = {**base}
config.evals.nyu_depth_val.data.split = 'validation'
# ### Uses a lot of memory
# config.evals.save_pred = dict(type='proj.givt.save_predictions')
# config.evals.save_pred.pp_fn = pp_eval
# config.evals.save_pred.log_steps = 100_000
# config.evals.save_pred.data = {**config.input.data}
# config.evals.save_pred.data.split = 'validation[:64]'
# config.evals.save_pred.batch_size = 64
# config.evals.save_pred.outfile = 'inference.npz'
config.eval_only = False
config.seed = 0
if arg.singlehost:
config.input.batch_size = 128
config.num_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
config.evals.nyu_depth_val.data.split = 'validation[:16]'
return config |