def _save_fitable(self, run: Run, fitable: Model):
"""
:param run: sacred.Run object. see sacred documentation for more details on utility.
:param fitable: tensorflow.keras.Model object.
"""
path = self.exp_config["run_config"]["model_path"]
if self.exp_config["run_config"]["save_verbosity"] > 0:
fitable.summary()
fitable.save(self.exp_config["run_config"]["model_path"])
run.add_artifact(path)
def train(train_corpus: str,
dev_corpus: str,
c1: float = 0.0,
c2: float = 0.0,
algorithm: str = 'lbfgs',
max_iterations: int = 100,
all_possible_transitions: bool = False,
window_size: int = 1,
model_filename: str = None,
_run: Run = None,
_log: logger = None):
"""
running crf experiment
"""
_run.add_resource(train_corpus)
_run.add_resource(dev_corpus)
train_sents, _ = get_tagged_sents_and_words(train_corpus)
dev_sents, _ = get_tagged_sents_and_words(dev_corpus)
X_train = [sent2features(s, window_size) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_dev = [sent2features(s, window_size) for s in dev_sents]
y_dev = [sent2labels(s) for s in dev_sents]
crf = sklearn_crfsuite.CRF(
algorithm=algorithm,
c1=c1,
c2=c2,
max_iterations=max_iterations,
all_possible_transitions=all_possible_transitions,
model_filename=model_filename,
)
crf.fit(X_train, y_train)
y_pred = crf.predict(X_dev)
overall, by_type = evaluate(y_dev, y_pred)
_run.info[f'overall_f1'] = overall.f1_score
_run.log_scalar('overall_f1', overall.f1_score)
_run.info[f'overall_precision'] = overall.precision
_run.log_scalar('overall_precision', overall.precision)
_run.info[f'overall_recall'] = overall.recall
_run.log_scalar('overall_recall', overall.recall)
_log.info(f'Overall F1 score: {overall.f1_score}')
for _, key in enumerate(sorted(by_type.keys())):
for metric_key in by_type[key]._fields:
metric_val = getattr(by_type[key], metric_key)
_run.info[f'{key}-{metric_key}'] = metric_val
_run.log_scalar(f'{key}-{metric_key}', metric_val)
_log.info(f'{key}-{metric_key}: {metric_val}')
if model_filename is not None:
_log.info(f'saving to: {model_filename}.pkl')
joblib.dump(crf, f'{model_filename}.pkl')
_run.add_artifact(f'{model_filename}.pkl')
def train(train_corpus: str,
dev_corpus: str,
pacrf: str,
model_filename: str,
labels: List,
c1: float = 0.0,
c2: float = 1.0,
algorithm: str = 'lbfgs',
max_iterations: int = None,
all_possible_transitions: bool = False,
window_size: int = 0,
_run: Run = None,
_log: logger = None):
"""
running crf experiment
"""
_run.add_resource(train_corpus)
_run.add_resource(dev_corpus)
train_sents, _ = get_tagged_sents_and_words(train_corpus)
dev_sents, _ = get_tagged_sents_and_words(dev_corpus)
tmp_train = tempfile.NamedTemporaryFile(mode='w+')
# temp_train_corpus = open(f'{model_filename}-{train_corpus}.feature', mode='w+')
print_corpus(train_sents, labels, tmp_train, window_size=window_size)
# X_dev = [sent2features(s, window_size) for s in dev_sents]
y_dev = [sent2labels_colmap(s, col=1) for s in dev_sents]
tmp_dev = tempfile.NamedTemporaryFile(mode='w+')
# temp_test_corpus = open(f'{model_filename}-{test_corpus}.feature', mode='w+')
print_corpus(dev_sents, labels, tmp_dev, window_size=window_size)
# to call partial-crf via Popen command
# command = f'{pacrf} learn -m {model_filename} -a {algorithm} {temp_train_corpus}'
# call([pacrf, "--help"])
crfsuire_proc = Popen([pacrf, "learn", "-m", model_filename, "-a", algorithm, \
"-p", f"c1={c1}", "-p", f"c2={c2}", tmp_train.name])
out, err = crfsuire_proc.communicate()
print(out)
print(err)
# os.system(f'{pacrf} learn -m {model_filename} -a {algorithm} {tmp_train.name}')
tmp_train.close()
tmp_pred = tempfile.NamedTemporaryFile(mode='w+')
# cmd_out([pacrf, "tag", "-m", model_filename, tmp_dev.name, ">", tmp_pred.name])
_run.add_artifact(model_filename)
# TODO modified this to call partial-crf via Popen command
# y_pred = crf.predict(X_dev)
y_pred = get_tagged_sents_and_words(tmp_pred.name)
print(y_pred)
y_pred = [sent2labels_colmap(s, 0) for s in y_pred]
# TODO modified this to read partial-crf via tempfile
overall, by_type = evaluate(y_dev, y_pred)
tmp_pred.close()
tmp_dev.close()
_run.info[f'overall_f1'] = overall.f1_score
_run.log_scalar('overall_f1', overall.f1_score)
_run.info[f'overall_precision'] = overall.precision
_run.log_scalar('overall_precision', overall.precision)
_run.info[f'overall_recall'] = overall.recall
_run.log_scalar('overall_recall', overall.recall)
_log.info(f'Overall F1 score: {overall.f1_score}')
for _, key in enumerate(sorted(by_type.keys())):
for metric_key in by_type[key]._fields:
metric_val = getattr(by_type[key], metric_key)
_run.info[f'{key}-{metric_key}'] = metric_val
_run.log_scalar(f'{key}-{metric_key}', metric_val)
_log.info(f'{key}-{metric_key}: {metric_val}')
def sacred_main(_run: Run, seed, showoff, out_dir, batch_size, epochs, tags,
model_desc, experiment_id, weights, train_examples,
val_examples, deterministic, train_datasets, val_datasets, lr,
lr_milestones, lr_gamma, optim_algorithm):
seed_all(seed)
init_algorithms(deterministic=deterministic)
exp_out_dir = None
if out_dir:
exp_out_dir = path.join(out_dir, experiment_id)
makedirs(exp_out_dir, exist_ok=True)
print(f'Experiment ID: {experiment_id}')
####
# Model
####
if weights is None:
model = create_model(model_desc)
else:
details = torch.load(weights)
model_desc = details['model_desc']
model = create_model(model_desc)
model.load_state_dict(details['state_dict'])
model.to(global_opts['device'])
print(json.dumps(model_desc, sort_keys=True, indent=2))
####
# Data
####
train_loader = create_train_dataloader(train_datasets, model.data_specs,
batch_size, train_examples)
if len(val_datasets) > 0:
val_loader = create_val_dataloader(val_datasets, model.data_specs,
batch_size, val_examples)
else:
val_loader = None
####
# Reporting
####
reporter = Reporter(with_val=(val_loader is not None))
reporter.setup_console_output()
reporter.setup_sacred_output(_run)
notebook = None
if showoff:
title = '3D pose model ({}@{})'.format(model_desc['type'],
model_desc['version'])
notebook = create_showoff_notebook(title, tags)
reporter.setup_showoff_output(notebook)
def set_progress(value):
if notebook is not None:
notebook.set_progress(value)
tel = reporter.telemetry
tel['config'].set_value(_run.config)
tel['host_info'].set_value(get_host_info())
####
# Optimiser
####
if optim_algorithm == '1cycle':
from torch import optim
optimiser = optim.SGD(model.parameters(), lr=0)
scheduler = make_1cycle(optimiser,
epochs * len(train_loader),
lr_max=lr,
momentum=0.9)
else:
scheduler = learning_schedule(model.parameters(), optim_algorithm, lr,
lr_milestones, lr_gamma)
####
# Training
####
model_file = None
if exp_out_dir:
model_file = path.join(exp_out_dir, 'model-latest.pth')
with open(path.join(exp_out_dir, 'config.json'), 'w') as f:
json.dump(tel['config'].value(), f, sort_keys=True, indent=2)
for epoch in range(epochs):
tel['epoch'].set_value(epoch)
print('> Epoch {:3d}/{:3d}'.format(epoch + 1, epochs))
def on_train_progress(samples_processed):
so_far = epoch * len(train_loader.dataset) + samples_processed
total = epochs * len(train_loader.dataset)
set_progress(so_far / total)
do_training_pass(epoch, model, tel, train_loader, scheduler,
on_train_progress)
if val_loader:
do_validation_pass(epoch, model, tel, val_loader)
_run.result = tel['train_pck'].value()[0]
if model_file is not None:
state = {
'state_dict': model.state_dict(),
'model_desc': model_desc,
'train_datasets': train_datasets,
'optimizer': scheduler.optimizer.state_dict(),
'epoch': epoch + 1,
}
torch.save(state, model_file)
tel.step()
# Add the final model as a Sacred artifact
if model_file is not None and path.isfile(model_file):
_run.add_artifact(model_file)
set_progress(1.0)
return _run.result
