Spaces:
Running
Running
File size: 4,309 Bytes
5769ee4 |
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 |
import os
import shutil
from mmcv import Config
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.utilities.seed import seed_everything
import wandb
from risk_biased.utils.callbacks import SwitchTrainingModeCallback
from risk_biased.utils.callbacks import (
HistogramCallback,
PlotTrajCallback,
DrawCallbackParams,
)
from risk_biased.utils.load_model import load_from_config
from scripts.scripts_utils.load_utils import get_config
def create_log_dir():
working_dir = os.path.dirname(os.path.realpath(__file__))
log_dir = os.path.join(working_dir, "logs")
if not os.path.exists(log_dir):
os.mkdir(log_dir)
return log_dir
def save_log_config(cfg: Config, predictor):
# Save and log the config (not only a copy of the config file because settings may have been overwritten by argparse)
log_config_path = os.path.join(wandb.run.dir, "learning_config.py")
cfg.dump(log_config_path)
wandb.save(log_config_path)
# Save files listed in the current wandb log dir
for file_name in cfg.files_to_log:
dest_path = os.path.join(wandb.run.dir, os.path.basename(file_name))
shutil.copy(file_name, dest_path)
wandb.save(dest_path)
if cfg.log_weights_and_grads:
wandb.watch(predictor, log="all", log_freq=100)
def create_callbacks(cfg: Config, log_dir: str, is_interaction: bool) -> list:
# Save checkpoint of last model in a specific directory
last_run_checkpoint_callback = ModelCheckpoint(
monitor="val/minfde/prior",
mode="min",
filename="epoch={epoch:02d}-step={step}-val_minfde_prior={val/minfde/prior:.2f}",
auto_insert_metric_name=False,
dirpath=os.path.join(log_dir, "checkpoints_last_run"),
save_last=True,
)
# Save checkpoints of current run in current wandb log dir
checkpoint_callback = ModelCheckpoint(
monitor="val/minfde/prior",
mode="min",
filename="epoch={epoch:02d}-step={step}-val_minfde_prior={val/minfde/prior:.2f}",
auto_insert_metric_name=False,
dirpath=wandb.run.dir,
save_last=True,
)
callbacks = [
last_run_checkpoint_callback,
checkpoint_callback,
]
if not is_interaction:
histogram_callback = HistogramCallback(
params=DrawCallbackParams.from_config(cfg),
n_samples=1000,
)
plot_callback = PlotTrajCallback(
params=DrawCallbackParams.from_config(cfg), n_samples=10
)
callbacks.append(histogram_callback)
callbacks.append(plot_callback)
if cfg.early_stopping:
early_stopping_callback = EarlyStopping(
monitor="val/minfde/prior",
min_delta=-0.2,
patience=5,
verbose=False,
mode="min",
)
callbacks.append(early_stopping_callback)
switch_mode_callback = SwitchTrainingModeCallback(
switch_at_epoch=cfg.num_epochs_cvae
)
callbacks.append(switch_mode_callback)
return callbacks
def get_trainer(cfg: Config, logger: WandbLogger, callbacks: list) -> Trainer:
num_epochs = cfg.num_epochs_cvae + cfg.num_epochs_bias
return Trainer(
gpus=cfg.gpus,
max_epochs=num_epochs,
logger=logger,
val_check_interval=float(cfg.val_check_interval_epoch),
accumulate_grad_batches=cfg.accumulate_grad_batches,
callbacks=callbacks,
)
def main(is_interaction: bool = False):
log_dir = create_log_dir()
cfg = get_config(log_dir, is_interaction)
predictor, dataloaders, cfg = load_from_config(cfg)
if cfg.seed is not None:
seed_everything(cfg.seed)
save_log_config(cfg, predictor)
logger = WandbLogger(
project=cfg.project, log_model=True, save_dir=log_dir, id=wandb.run.id
)
callbacks = create_callbacks(cfg, log_dir, is_interaction)
trainer = get_trainer(cfg, logger, callbacks)
trainer.fit(
predictor,
train_dataloaders=dataloaders.train_dataloader(),
val_dataloaders=dataloaders.val_dataloader(),
)
wandb.finish()
if __name__ == "__main__":
main(is_interaction=True)
|