File size: 9,834 Bytes
f1586f7 |
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 |
import os
import configparser
import argparse
import logging
from functools import partial
from typing import Any, Dict, Optional, Union
import lightning as L
from lightning.pytorch import seed_everything
from lightning.pytorch.loggers import WandbLogger
from lightning.pytorch.callbacks import ModelCheckpoint, LearningRateMonitor, TQDMProgressBar
import torch
from torch.utils.data import DataLoader
from data.kubric_data import KubricData
from models.locotrack_model import LocoTrack
import model_utils
from data.evaluation_datasets import get_eval_dataset
class LocoTrackModel(L.LightningModule):
def __init__(
self,
model_kwargs: Optional[Dict[str, Any]] = None,
model_forward_kwargs: Optional[Dict[str, Any]] = None,
loss_name: Optional[str] = 'tapir_loss',
loss_kwargs: Optional[Dict[str, Any]] = None,
query_first: Optional[bool] = False,
optimizer_name: Optional[str] = 'Adam',
optimizer_kwargs: Optional[Dict[str, Any]] = None,
scheduler_name: Optional[str] = 'OneCycleLR',
scheduler_kwargs: Optional[Dict[str, Any]] = None,
):
super().__init__()
self.model = LocoTrack(**(model_kwargs or {}))
self.model_forward_kwargs = model_forward_kwargs or {}
self.loss = partial(model_utils.__dict__[loss_name], **(loss_kwargs or {}))
self.query_first = query_first
self.optimizer_name = optimizer_name
self.optimizer_kwargs = optimizer_kwargs or {'lr': 2e-3}
self.scheduler_name = scheduler_name
self.scheduler_kwargs = scheduler_kwargs or {'max_lr': 2e-3, 'pct_start': 0.05, 'total_steps': 300000}
def training_step(self, batch, batch_idx):
output = self.model(batch['video'], batch['query_points'], **self.model_forward_kwargs)
loss, loss_scalars = self.loss(batch, output)
self.log_dict(
{f'train/{k}': v.item() for k, v in loss_scalars.items()},
logger=True,
on_step=True,
sync_dist=True,
)
return loss
def validation_step(self, batch, batch_idx, dataloader_idx=None):
output = self.model(batch['video'], batch['query_points'], **self.model_forward_kwargs)
loss, loss_scalars = self.loss(batch, output)
metrics = model_utils.eval_batch(batch, output, query_first=self.query_first)
if self.trainer.global_rank == 0:
log_prefix = 'val/'
if dataloader_idx is not None:
log_prefix = f'val/data_{dataloader_idx}/'
self.log_dict(
{log_prefix + k: v for k, v in loss_scalars.items()},
logger=True,
rank_zero_only=True,
)
self.log_dict(
{log_prefix + k: v.item() for k, v in metrics.items()},
logger=True,
rank_zero_only=True,
)
logging.info(f"Batch {batch_idx}: {metrics}")
def test_step(self, batch, batch_idx, dataloader_idx=None):
output = self.model(batch['video'], batch['query_points'], **self.model_forward_kwargs)
loss, loss_scalars = self.loss(batch, output)
metrics = model_utils.eval_batch(batch, output, query_first=self.query_first)
if self.trainer.global_rank == 0:
log_prefix = 'test/'
if dataloader_idx is not None:
log_prefix = f'test/data_{dataloader_idx}/'
self.log_dict(
{log_prefix + k: v for k, v in loss_scalars.items()},
logger=True,
rank_zero_only=True,
)
self.log_dict(
{log_prefix + k: v.item() for k, v in metrics.items()},
logger=True,
rank_zero_only=True,
)
logging.info(f"Batch {batch_idx}: {metrics}")
def configure_optimizers(self):
weights = [p for n, p in self.named_parameters() if 'bias' not in n]
bias = [p for n, p in self.named_parameters() if 'bias' in n]
optimizer = torch.optim.__dict__[self.optimizer_name](
[
{'params': weights, **self.optimizer_kwargs},
{'params': bias, **self.optimizer_kwargs, 'weight_decay': 0.}
]
)
scheduler = torch.optim.lr_scheduler.__dict__[self.scheduler_name](optimizer, **self.scheduler_kwargs)
return [optimizer], [{"scheduler": scheduler, "interval": "step"}]
def train(
mode: str,
save_path: str,
val_dataset_path: str,
ckpt_path: str = None,
kubric_dir: str = '',
precision: str = '32',
batch_size: int = 1,
val_check_interval: Union[int, float] = 5000,
log_every_n_steps: int = 10,
gradient_clip_val: float = 1.0,
max_steps: int = 300_000,
model_kwargs: Optional[Dict[str, Any]] = None,
model_forward_kwargs: Optional[Dict[str, Any]] = None,
loss_name: str = 'tapir_loss',
loss_kwargs: Optional[Dict[str, Any]] = None,
optimizer_name: str = 'Adam',
optimizer_kwargs: Optional[Dict[str, Any]] = None,
scheduler_name: str = 'OneCycleLR',
scheduler_kwargs: Optional[Dict[str, Any]] = None,
# query_first: bool = False,
):
"""Train the LocoTrack model with specified configurations."""
seed_everything(42, workers=True)
model = LocoTrackModel(
model_kwargs=model_kwargs,
model_forward_kwargs=model_forward_kwargs,
loss_name=loss_name,
loss_kwargs=loss_kwargs,
query_first='q_first' in mode,
optimizer_name=optimizer_name,
optimizer_kwargs=optimizer_kwargs,
scheduler_name=scheduler_name,
scheduler_kwargs=scheduler_kwargs,
)
if ckpt_path is not None and 'train' in mode:
model.load_state_dict(torch.load(ckpt_path)['state_dict'])
logger = WandbLogger(project='LocoTrack_Pytorch', save_dir=save_path, id=os.path.basename(save_path))
lr_monitor = LearningRateMonitor(logging_interval='step')
checkpoint_callback = ModelCheckpoint(
dirpath=save_path,
save_last=True,
save_top_k=3,
mode="max",
monitor="val/average_pts_within_thresh",
auto_insert_metric_name=True,
save_on_train_epoch_end=False,
)
eval_dataset = get_eval_dataset(
mode=mode,
path=val_dataset_path,
)
eval_dataloder = {
k: DataLoader(
v,
batch_size=1,
shuffle=False,
) for k, v in eval_dataset.items()
}
if 'train' in mode:
trainer = L.Trainer(
strategy='ddp',
logger=logger,
precision=precision,
val_check_interval=val_check_interval,
log_every_n_steps=log_every_n_steps,
gradient_clip_val=gradient_clip_val,
max_steps=max_steps,
sync_batchnorm=True,
callbacks=[checkpoint_callback, lr_monitor],
)
train_dataloader = KubricData(
global_rank=trainer.global_rank,
data_dir=kubric_dir,
batch_size=batch_size * trainer.world_size,
)
trainer.fit(model, train_dataloader, eval_dataloder, ckpt_path=ckpt_path)
elif 'eval' in mode:
trainer = L.Trainer(strategy='ddp', logger=logger, precision=precision)
trainer.test(model, eval_dataloder, ckpt_path=ckpt_path)
else:
raise ValueError(f"Invalid mode: {mode}")
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Train or evaluate the LocoTrack model.")
parser.add_argument('--config', type=str, default='config.ini', help="Path to the configuration file.")
parser.add_argument('--mode', type=str, required=True, help="Mode to run: 'train' or 'eval' with optional 'q_first' and the name of evaluation dataset.")
parser.add_argument('--ckpt_path', type=str, default=None, help="Path to the checkpoint file")
parser.add_argument('--save_path', type=str, default='snapshots', help="Path to save the logs and checkpoints.")
args = parser.parse_args()
config = configparser.ConfigParser()
config.read(args.config)
# Extract parameters from the config file
train_params = {
'mode': args.mode,
'ckpt_path': args.ckpt_path,
'save_path': args.save_path,
'val_dataset_path': eval(config.get('TRAINING', 'val_dataset_path', fallback='{}')),
'kubric_dir': config.get('TRAINING', 'kubric_dir', fallback=''),
'precision': config.get('TRAINING', 'precision', fallback='32'),
'batch_size': config.getint('TRAINING', 'batch_size', fallback=1),
'val_check_interval': config.getfloat('TRAINING', 'val_check_interval', fallback=5000),
'log_every_n_steps': config.getint('TRAINING', 'log_every_n_steps', fallback=10),
'gradient_clip_val': config.getfloat('TRAINING', 'gradient_clip_val', fallback=1.0),
'max_steps': config.getint('TRAINING', 'max_steps', fallback=300000),
'model_kwargs': eval(config.get('MODEL', 'model_kwargs', fallback='{}')),
'model_forward_kwargs': eval(config.get('MODEL', 'model_forward_kwargs', fallback='{}')),
'loss_name': config.get('LOSS', 'loss_name', fallback='tapir_loss'),
'loss_kwargs': eval(config.get('LOSS', 'loss_kwargs', fallback='{}')),
'optimizer_name': config.get('OPTIMIZER', 'optimizer_name', fallback='Adam'),
'optimizer_kwargs': eval(config.get('OPTIMIZER', 'optimizer_kwargs', fallback='{"lr": 2e-3}')),
'scheduler_name': config.get('SCHEDULER', 'scheduler_name', fallback='OneCycleLR'),
'scheduler_kwargs': eval(config.get('SCHEDULER', 'scheduler_kwargs', fallback='{"max_lr": 2e-3, "pct_start": 0.05, "total_steps": 300000}')),
}
train(**train_params)
|