138 lines
5.3 KiB
Python
138 lines
5.3 KiB
Python
# Imports
|
|
# =============================================================================
|
|
from pathlib import Path
|
|
|
|
from tqdm import tqdm
|
|
import warnings
|
|
|
|
import torch
|
|
from pytorch_lightning import Trainer
|
|
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
|
|
|
|
from ml_lib.modules.utils import LightningBaseModule
|
|
from ml_lib.utils.logging import Logger
|
|
|
|
# Project Specific Logger SubClasses
|
|
from util.config import MConfig
|
|
|
|
|
|
warnings.filterwarnings('ignore', category=FutureWarning)
|
|
warnings.filterwarnings('ignore', category=UserWarning)
|
|
|
|
|
|
def fix_all_random_seeds(config_obj):
|
|
import numpy as np
|
|
import torch
|
|
import random
|
|
np.random.seed(config.main.seed)
|
|
torch.manual_seed(config.main.seed)
|
|
random.seed(config.main.seed)
|
|
|
|
|
|
def run_lightning_loop(config_obj):
|
|
|
|
# Logging
|
|
# ================================================================================
|
|
# Logger
|
|
with Logger(config_obj) as logger:
|
|
# Callbacks
|
|
# =============================================================================
|
|
# Checkpoint Saving
|
|
checkpoint_callback = ModelCheckpoint(
|
|
monitor='uar_score',
|
|
filepath=str(logger.log_dir / 'ckpt_weights'),
|
|
verbose=False,
|
|
save_top_k=5,
|
|
)
|
|
|
|
# Early Stopping
|
|
# TODO: For This to work, set a validation step and End Eval and Score
|
|
early_stopping_callback = EarlyStopping(
|
|
monitor='uar_score',
|
|
min_delta=0.01,
|
|
patience=10,
|
|
)
|
|
|
|
# Trainer
|
|
# =============================================================================
|
|
trainer = Trainer(max_epochs=config_obj.train.epochs,
|
|
show_progress_bar=True,
|
|
weights_save_path=logger.log_dir,
|
|
gpus=[0] if torch.cuda.is_available() else None,
|
|
check_val_every_n_epoch=10,
|
|
# num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
|
|
# row_log_interval=(model.n_train_batches * 0.1), # TODO: Better Value / Setting
|
|
# log_save_interval=(model.n_train_batches * 0.2), # TODO: Better Value / Setting
|
|
checkpoint_callback=checkpoint_callback,
|
|
logger=logger,
|
|
fast_dev_run=config_obj.main.debug,
|
|
early_stop_callback=None
|
|
)
|
|
|
|
# Model
|
|
# =============================================================================
|
|
# Build and Init its Weights
|
|
model: LightningBaseModule = config_obj.build_and_init_model()
|
|
# Log paramters
|
|
pytorch_total_params = sum(p.numel() for p in model.parameters())
|
|
logger.log_text('n_parameters', pytorch_total_params)
|
|
|
|
# Train It
|
|
if config_obj.model.type.lower() != 'ensemble':
|
|
trainer.fit(model)
|
|
|
|
# Save the last state & all parameters
|
|
trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
|
|
model.save_to_disk(logger.log_dir)
|
|
|
|
# Evaluate It
|
|
if config_obj.main.eval:
|
|
with torch.no_grad():
|
|
model.eval()
|
|
if torch.cuda.is_available():
|
|
model.cuda()
|
|
outputs = []
|
|
from tqdm import tqdm
|
|
for idx, batch in enumerate(tqdm(model.val_dataloader()[0])):
|
|
batch_x, label = batch
|
|
batch_x = batch_x.to(device='cuda' if model.on_gpu else 'cpu')
|
|
label = label.to(device='cuda' if model.on_gpu else 'cpu')
|
|
outputs.append(
|
|
model.validation_step((batch_x, label), idx, 1)
|
|
)
|
|
summary_dict = model.validation_epoch_end([outputs])
|
|
print(summary_dict['log']['uar_score'])
|
|
|
|
# trainer.test()
|
|
outpath = Path(config_obj.train.outpath)
|
|
model_type = config_obj.model.type
|
|
parameters = logger.name
|
|
version = f'version_{logger.version}'
|
|
inference_out = f'{parameters}_test_out.csv'
|
|
|
|
from main_inference import prepare_dataloader
|
|
test_dataloader = prepare_dataloader(config)
|
|
|
|
with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile:
|
|
outfile.write(f'file_name,prediction\n')
|
|
|
|
from tqdm import tqdm
|
|
for batch in tqdm(test_dataloader, total=len(test_dataloader)):
|
|
batch_x, file_name = batch
|
|
batch_x = batch_x.unsqueeze(0).to(device='cuda' if model.on_gpu else 'cpu')
|
|
y = model(batch_x).main_out
|
|
prediction = (y.squeeze() >= 0.5).int().item()
|
|
import variables as V
|
|
prediction = 'clear' if prediction == V.CLEAR else 'mask'
|
|
outfile.write(f'{file_name},{prediction}\n')
|
|
return model
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
from _paramters import main_arg_parser
|
|
|
|
config = MConfig.read_argparser(main_arg_parser)
|
|
fix_all_random_seeds(config)
|
|
trained_model = run_lightning_loop(config)
|