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upstream.py
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upstream.py
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import os
import argparse
import torch
import numpy as np
import random
import src.config
import src.datasets
import src.models
import pytorch_lightning as pl
import wandb
from datetime import datetime
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
def upstream(config, ds_path=None):
"""Starts upstream training with the given config and dataset path
The upstream task is defined in the given config
"""
# Set all seeds:
torch.manual_seed(config.SEED)
torch.cuda.manual_seed(config.SEED)
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(config.SEED)
random.seed(config.SEED)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
ds_path = config.TRAIN_DATA if ds_path is None else ds_path
# Iterate over all model configs if given
for args in src.utils.grid_search(config.ALGORITHM_ARGS):
for ds_args in src.utils.grid_search(config.DATASET_ARGS):
print(f'Dataset arguments: {ds_args}', flush=True)
######### Train with given args ##########
print(f'Evaluating arguments: {args}', flush=True)
# Create the datasets
valid_dataset = src.datasets.get_dataset(
dataset_name=config.DATASET,
dataset_args=ds_args,
root_dir=ds_path,
config_path=config.CONFIG_PATH,
test_mode=False,
valid_mode=True,
skip_files=[]
)
test_dataset = src.datasets.get_dataset(
dataset_name=config.DATASET,
dataset_args=ds_args,
root_dir=ds_path,
config_path=config.CONFIG_PATH,
test_mode=True, valid_mode=False,
skip_files=valid_dataset.used_files
)
skip_files = valid_dataset.used_files+test_dataset.used_files
train_dataset = src.datasets.get_dataset(
dataset_name=config.DATASET,
dataset_args=ds_args,
root_dir=ds_path,
config_path=config.CONFIG_PATH,
test_mode=False, valid_mode=False,
skip_files=skip_files
)
# Create the dataloaders
collate_fn = train_dataset.collate_fn if hasattr(train_dataset, 'collate_fn') else None
valid_dl = torch.utils.data.DataLoader(
dataset=valid_dataset,
batch_size=args['batch_size'],
shuffle=False,
num_workers=config.NUM_WORKERS,
collate_fn=collate_fn
)
train_dl = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=args['batch_size'],
shuffle=True,
num_workers=config.NUM_WORKERS,
collate_fn=collate_fn
)
test_dl = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=args['batch_size'],
shuffle=False,
num_workers=1,
collate_fn=collate_fn
)
_epochs = args['epochs']
total_step_count = len(train_dl)*_epochs
val_after_nth_step = len(train_dl)/4 # 4 times valid per epoch
val_check_interval = val_after_nth_step/len(train_dl)
if val_check_interval <= 1:
check_val_every_n_epoch = 1
else:
check_val_every_n_epoch = int(val_check_interval)
val_check_interval = 1.0
print(f'Epochs: {_epochs}, ',
f'Steps: {total_step_count}, ',
f'val_check_interval: {val_check_interval}',
f'check_val_every_n_epoch: {check_val_every_n_epoch}'
)
#######################
args.update({'input_dim': train_dataset.input_shape,
'output_dim': train_dataset.output_shapes,
'total_step_count': total_step_count,
'_epochs': _epochs})
print('Create the model')
model = src.models.get_model(
algorithm_name=config.ALGORITHM,
algorithm_args=args
)
loggers = []
if config.WANDB:
if type(ds_path)==list:
ds_name = 'Combined'
else:
ds_name = os.path.realpath(ds_path).split('/')[-1]
proj_name = 'harth_plus_dl_upstream_'+config.PROJ_NAME+'_'+ds_name
wandb_logger = WandbLogger(project=proj_name)
wandb_logger.watch(model, log_graph=False)
loggers.append(wandb_logger)
callbacks = []
cp_path = config.CONFIG_PATH
cp_name = str(
datetime.today()
).replace(':','_').replace(' ','__').replace('.','_')
checkpoint_callback = ModelCheckpoint(
monitor='val_loss' if len(valid_dataset)!=0 else None,
dirpath=cp_path,
filename=cp_name,
verbose=True
)
callbacks.append(checkpoint_callback)
early_stopping = EarlyStopping(
monitor='val_loss',
min_delta=0.0,
patience=total_step_count*0.10, # 10% of total steps
verbose=True
)
callbacks.append(early_stopping)
trainer = pl.Trainer(
gpus=config.NUM_GPUS,
logger=loggers,
callbacks=callbacks,
max_epochs=_epochs,
num_sanity_val_steps=1,
log_every_n_steps=1,
check_val_every_n_epoch=check_val_every_n_epoch,
val_check_interval=val_check_interval,
accelerator="gpu",
strategy='ddp'
)
trainer.fit(model, train_dl, valid_dl)
##### Final testing #####
if len(test_dataset) != 0:
model_cls = src.models.get_model_class(config.ALGORITHM)
best_model = model_cls.load_from_checkpoint(
os.path.join(cp_path,cp_name+'.ckpt')
)
best_model.eval() # eval mode
trainer.test(best_model, dataloaders=test_dl)
if config.WANDB: wandb.finish()
torch.cuda.empty_cache()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Start Upstream training.')
parser.add_argument('-p', '--params_path', required=False, type=str,
help='params path with config.yml file',
default='/param/config.yml')
parser.add_argument('-d', '--dataset_path', required=False, type=str,
help='path to dataset.', default=None)
args = parser.parse_args()
config_path = args.params_path
# Read config
config = src.config.UpstreamConfig(config_path)
ds_path = args.dataset_path
upstream(config, ds_path)