File size: 5,690 Bytes
24f9881
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
# MIT License

# Copyright (c) 2022 Intelligent Systems Lab Org

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# File author: Shariq Farooq Bhat

from zoedepth.utils.misc import count_parameters, parallelize
from zoedepth.utils.config import get_config
from zoedepth.utils.arg_utils import parse_unknown
from zoedepth.trainers.builder import get_trainer
from zoedepth.models.builder import build_model
from zoedepth.data.data_mono import MixedNYUKITTI
import torch.utils.data.distributed
import torch.multiprocessing as mp
import torch
import numpy as np
from pprint import pprint
import argparse
import os

os.environ["PYOPENGL_PLATFORM"] = "egl"
os.environ["WANDB_START_METHOD"] = "thread"


def fix_random_seed(seed: int):
    """
    Fix random seed for reproducibility

    Args:
        seed (int): random seed
    """
    import random

    import numpy
    import torch

    random.seed(seed)
    numpy.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


def load_ckpt(config, model, checkpoint_dir="./checkpoints", ckpt_type="best"):
    import glob
    import os

    from zoedepth.models.model_io import load_wts

    if hasattr(config, "checkpoint"):
        checkpoint = config.checkpoint
    elif hasattr(config, "ckpt_pattern"):
        pattern = config.ckpt_pattern
        matches = glob.glob(os.path.join(
            checkpoint_dir, f"*{pattern}*{ckpt_type}*"))
        if not (len(matches) > 0):
            raise ValueError(f"No matches found for the pattern {pattern}")

        checkpoint = matches[0]

    else:
        return model
    model = load_wts(model, checkpoint)
    print("Loaded weights from {0}".format(checkpoint))
    return model


def main_worker(gpu, ngpus_per_node, config):
    try:
        fix_random_seed(43)

        config.gpu = gpu

        model = build_model(config)
        model = load_ckpt(config, model)
        model = parallelize(config, model)

        total_params = f"{round(count_parameters(model)/1e6,2)}M"
        config.total_params = total_params
        print(f"Total parameters : {total_params}")

        train_loader = MixedNYUKITTI(config, "train").data
        test_loader = MixedNYUKITTI(config, "online_eval").data

        trainer = get_trainer(config)(
            config, model, train_loader, test_loader, device=config.gpu)

        trainer.train()
    finally:
        import wandb
        wandb.finish()


if __name__ == '__main__':
    mp.set_start_method('forkserver')

    parser = argparse.ArgumentParser()
    parser.add_argument("-m", "--model", type=str, default="synunet")
    parser.add_argument("-d", "--dataset", type=str, default='mix')
    parser.add_argument("--trainer", type=str, default=None)

    args, unknown_args = parser.parse_known_args()
    overwrite_kwargs = parse_unknown(unknown_args)

    overwrite_kwargs["model"] = args.model
    if args.trainer is not None:
        overwrite_kwargs["trainer"] = args.trainer

    config = get_config(args.model, "train", args.dataset, **overwrite_kwargs)
    # git_commit()
    if config.use_shared_dict:
        shared_dict = mp.Manager().dict()
    else:
        shared_dict = None
    config.shared_dict = shared_dict

    config.batch_size = config.bs
    config.mode = 'train'
    if config.root != "." and not os.path.isdir(config.root):
        os.makedirs(config.root)

    try:
        node_str = os.environ['SLURM_JOB_NODELIST'].replace(
            '[', '').replace(']', '')
        nodes = node_str.split(',')

        config.world_size = len(nodes)
        config.rank = int(os.environ['SLURM_PROCID'])
        # config.save_dir = "/ibex/scratch/bhatsf/videodepth/checkpoints"

    except KeyError as e:
        # We are NOT using SLURM
        config.world_size = 1
        config.rank = 0
        nodes = ["127.0.0.1"]

    if config.distributed:

        print(config.rank)
        port = np.random.randint(15000, 15025)
        config.dist_url = 'tcp://{}:{}'.format(nodes[0], port)
        print(config.dist_url)
        config.dist_backend = 'nccl'
        config.gpu = None

    ngpus_per_node = torch.cuda.device_count()
    config.num_workers = config.workers
    config.ngpus_per_node = ngpus_per_node
    print("Config:")
    pprint(config)
    if config.distributed:
        config.world_size = ngpus_per_node * config.world_size
        mp.spawn(main_worker, nprocs=ngpus_per_node,
                 args=(ngpus_per_node, config))
    else:
        if ngpus_per_node == 1:
            config.gpu = 0
        main_worker(config.gpu, ngpus_per_node, config)