You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
116 lines
4.3 KiB
116 lines
4.3 KiB
#!/usr/bin/env python |
|
|
|
import os |
|
import random |
|
|
|
import numpy as np |
|
import paddle |
|
import paddlers |
|
from paddlers import transforms as T |
|
|
|
from config_utils import parse_args, build_objects, CfgNode |
|
|
|
|
|
def format_cfg(cfg, indent=0): |
|
s = '' |
|
if isinstance(cfg, dict): |
|
for i, (k, v) in enumerate(sorted(cfg.items())): |
|
s += ' ' * indent + str(k) + ': ' |
|
if isinstance(v, (dict, list, CfgNode)): |
|
s += '\n' + format_cfg(v, indent=indent + 1) |
|
else: |
|
s += str(v) |
|
if i != len(cfg) - 1: |
|
s += '\n' |
|
elif isinstance(cfg, list): |
|
for i, v in enumerate(cfg): |
|
s += ' ' * indent + '- ' |
|
if isinstance(v, (dict, list, CfgNode)): |
|
s += '\n' + format_cfg(v, indent=indent + 1) |
|
else: |
|
s += str(v) |
|
if i != len(cfg) - 1: |
|
s += '\n' |
|
elif isinstance(cfg, CfgNode): |
|
s += ' ' * indent + f"type: {cfg.type}" + '\n' |
|
s += ' ' * indent + f"module: {cfg.module}" + '\n' |
|
s += ' ' * indent + 'args: \n' + format_cfg(cfg.args, indent + 1) |
|
return s |
|
|
|
|
|
if __name__ == '__main__': |
|
CfgNode.set_context(globals()) |
|
|
|
cfg = parse_args() |
|
print(format_cfg(cfg)) |
|
|
|
if cfg['seed'] is not None: |
|
random.seed(cfg['seed']) |
|
np.random.seed(cfg['seed']) |
|
paddle.seed(cfg['seed']) |
|
|
|
# Automatically download data |
|
if cfg['download_on']: |
|
paddlers.utils.download_and_decompress( |
|
cfg['download_url'], path=cfg['download_path']) |
|
|
|
if not isinstance(cfg['datasets']['eval'].args, dict): |
|
raise ValueError("args of eval dataset must be a dict!") |
|
if cfg['datasets']['eval'].args.get('transforms', None) is not None: |
|
raise ValueError( |
|
"Found key 'transforms' in args of eval dataset and the value is not None." |
|
) |
|
eval_transforms = T.Compose(build_objects(cfg['transforms']['eval'], mod=T)) |
|
# Inplace modification |
|
cfg['datasets']['eval'].args['transforms'] = eval_transforms |
|
eval_dataset = build_objects(cfg['datasets']['eval'], mod=paddlers.datasets) |
|
|
|
if cfg['cmd'] == 'train': |
|
if not isinstance(cfg['datasets']['train'].args, dict): |
|
raise ValueError("args of train dataset must be a dict!") |
|
if cfg['datasets']['train'].args.get('transforms', None) is not None: |
|
raise ValueError( |
|
"Found key 'transforms' in args of train dataset and the value is not None." |
|
) |
|
train_transforms = T.Compose( |
|
build_objects( |
|
cfg['transforms']['train'], mod=T)) |
|
# Inplace modification |
|
cfg['datasets']['train'].args['transforms'] = train_transforms |
|
train_dataset = build_objects( |
|
cfg['datasets']['train'], mod=paddlers.datasets) |
|
model = build_objects( |
|
cfg['model'], mod=getattr(paddlers.tasks, cfg['task'])) |
|
if cfg['optimizer']: |
|
if len(cfg['optimizer'].args) == 0: |
|
cfg['optimizer'].args = {} |
|
if not isinstance(cfg['optimizer'].args, dict): |
|
raise TypeError("args of optimizer must be a dict!") |
|
if cfg['optimizer'].args.get('parameters', None) is not None: |
|
raise ValueError( |
|
"Found key 'parameters' in args of optimizer and the value is not None." |
|
) |
|
cfg['optimizer'].args['parameters'] = model.net.parameters() |
|
optimizer = build_objects(cfg['optimizer'], mod=paddle.optimizer) |
|
else: |
|
optimizer = None |
|
|
|
model.train( |
|
num_epochs=cfg['num_epochs'], |
|
train_dataset=train_dataset, |
|
train_batch_size=cfg['train_batch_size'], |
|
eval_dataset=eval_dataset, |
|
optimizer=optimizer, |
|
save_interval_epochs=cfg['save_interval_epochs'], |
|
log_interval_steps=cfg['log_interval_steps'], |
|
save_dir=cfg['save_dir'], |
|
learning_rate=cfg['learning_rate'], |
|
early_stop=cfg['early_stop'], |
|
early_stop_patience=cfg['early_stop_patience'], |
|
use_vdl=cfg['use_vdl'], |
|
resume_checkpoint=cfg['resume_checkpoint'] or None, |
|
**cfg['train']) |
|
elif cfg['cmd'] == 'eval': |
|
model = paddlers.tasks.load_model(cfg['resume_checkpoint']) |
|
res = model.evaluate(eval_dataset) |
|
print(res)
|
|
|