forked from TencentARC/InstantMesh
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
286 lines (248 loc) · 8.47 KB
/
train.py
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
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
import os, sys
import argparse
import shutil
import subprocess
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.strategies import DDPStrategy
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities import rank_zero_only
from src.utils.train_util import instantiate_from_config
@rank_zero_only
def rank_zero_print(*args):
print(*args)
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
"-r",
"--resume",
type=str,
default=None,
help="resume from checkpoint",
)
parser.add_argument(
"--resume_weights_only",
action="store_true",
help="only resume model weights",
)
parser.add_argument(
"-b",
"--base",
type=str,
default="base_config.yaml",
help="path to base configs",
)
parser.add_argument(
"-n",
"--name",
type=str,
default="",
help="experiment name",
)
parser.add_argument(
"--num_nodes",
type=int,
default=1,
help="number of nodes to use",
)
parser.add_argument(
"--gpus",
type=str,
default="0,",
help="gpu ids to use",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=42,
help="seed for seed_everything",
)
parser.add_argument(
"-l",
"--logdir",
type=str,
default="logs",
help="directory for logging data",
)
return parser
class SetupCallback(Callback):
def __init__(self, resume, logdir, ckptdir, cfgdir, config):
super().__init__()
self.resume = resume
self.logdir = logdir
self.ckptdir = ckptdir
self.cfgdir = cfgdir
self.config = config
def on_fit_start(self, trainer, pl_module):
if trainer.global_rank == 0:
# Create logdirs and save configs
os.makedirs(self.logdir, exist_ok=True)
os.makedirs(self.ckptdir, exist_ok=True)
os.makedirs(self.cfgdir, exist_ok=True)
rank_zero_print("Project config")
rank_zero_print(OmegaConf.to_yaml(self.config))
OmegaConf.save(self.config,
os.path.join(self.cfgdir, "project.yaml"))
class CodeSnapshot(Callback):
"""
Modified from https://github.com/threestudio-project/threestudio/blob/main/threestudio/utils/callbacks.py#L60
"""
def __init__(self, savedir):
self.savedir = savedir
def get_file_list(self):
return [
b.decode()
for b in set(
subprocess.check_output(
'git ls-files -- ":!:configs/*"', shell=True
).splitlines()
)
| set( # hard code, TODO: use config to exclude folders or files
subprocess.check_output(
"git ls-files --others --exclude-standard", shell=True
).splitlines()
)
]
@rank_zero_only
def save_code_snapshot(self):
os.makedirs(self.savedir, exist_ok=True)
for f in self.get_file_list():
if not os.path.exists(f) or os.path.isdir(f):
continue
os.makedirs(os.path.join(self.savedir, os.path.dirname(f)), exist_ok=True)
shutil.copyfile(f, os.path.join(self.savedir, f))
def on_fit_start(self, trainer, pl_module):
try:
self.save_code_snapshot()
except:
rank_zero_warn(
"Code snapshot is not saved. Please make sure you have git installed and are in a git repository."
)
if __name__ == "__main__":
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
sys.path.append(os.getcwd())
parser = get_parser()
opt, unknown = parser.parse_known_args()
cfg_fname = os.path.split(opt.base)[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
exp_name = "-" + opt.name if opt.name != "" else ""
logdir = os.path.join(opt.logdir, cfg_name+exp_name)
ckptdir = os.path.join(logdir, "checkpoints")
cfgdir = os.path.join(logdir, "configs")
codedir = os.path.join(logdir, "code")
seed_everything(opt.seed)
# init configs
config = OmegaConf.load(opt.base)
lightning_config = config.lightning
trainer_config = lightning_config.trainer
trainer_config["accelerator"] = "gpu"
rank_zero_print(f"Running on GPUs {opt.gpus}")
ngpu = len(opt.gpus.strip(",").split(','))
trainer_config['devices'] = ngpu
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
# model
model = instantiate_from_config(config.model)
if opt.resume and opt.resume_weights_only:
model = model.__class__.load_from_checkpoint(opt.resume, **config.model.params)
model.logdir = logdir
# trainer and callbacks
trainer_kwargs = dict()
# logger
default_logger_cfg = {
"target": "pytorch_lightning.loggers.TensorBoardLogger",
"params": {
"name": "tensorboard",
"save_dir": logdir,
"version": "0",
}
}
logger_cfg = OmegaConf.merge(default_logger_cfg)
trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
# model checkpoint
default_modelckpt_cfg = {
"target": "pytorch_lightning.callbacks.ModelCheckpoint",
"params": {
"dirpath": ckptdir,
"filename": "{step:08}",
"verbose": True,
"save_last": True,
"every_n_train_steps": 5000,
"save_top_k": -1, # save all checkpoints
}
}
if "modelcheckpoint" in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
# callbacks
default_callbacks_cfg = {
"setup_callback": {
"target": "train.SetupCallback",
"params": {
"resume": opt.resume,
"logdir": logdir,
"ckptdir": ckptdir,
"cfgdir": cfgdir,
"config": config,
}
},
"learning_rate_logger": {
"target": "pytorch_lightning.callbacks.LearningRateMonitor",
"params": {
"logging_interval": "step",
}
},
"code_snapshot": {
"target": "train.CodeSnapshot",
"params": {
"savedir": codedir,
}
},
}
default_callbacks_cfg["checkpoint_callback"] = modelckpt_cfg
if "callbacks" in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
trainer_kwargs["callbacks"] = [
instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
trainer_kwargs['precision'] = '32-true'
trainer_kwargs["strategy"] = DDPStrategy(find_unused_parameters=True)
# trainer
trainer = Trainer(**trainer_config, **trainer_kwargs, num_nodes=opt.num_nodes)
trainer.logdir = logdir
# data
data = instantiate_from_config(config.data)
data.prepare_data()
data.setup("fit")
# configure learning rate
base_lr = config.model.base_learning_rate
if 'accumulate_grad_batches' in lightning_config.trainer:
accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
else:
accumulate_grad_batches = 1
rank_zero_print(f"accumulate_grad_batches = {accumulate_grad_batches}")
lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
model.learning_rate = base_lr
rank_zero_print("++++ NOT USING LR SCALING ++++")
rank_zero_print(f"Setting learning rate to {model.learning_rate:.2e}")
# run training loop
if opt.resume and not opt.resume_weights_only:
trainer.fit(model, data, ckpt_path=opt.resume)
else:
trainer.fit(model, data)