def test(context, model_snapshot, test_path, use_cuda, use_tensorboard, field_names):
"""Test a trained model snapshot.
If model-snapshot is provided, the models and configuration will then be
loaded from the snapshot rather than any passed config file.
Otherwise, a config file will be loaded.
"""
model_snapshot, use_cuda, use_tensorboard = _get_model_snapshot(
context, model_snapshot, use_cuda, use_tensorboard
)
print("\n=== Starting testing...")
metric_channels = []
if use_tensorboard:
metric_channels.append(TensorBoardChannel())
try:
test_model_from_snapshot_path(
model_snapshot,
use_cuda,
test_path,
metric_channels,
field_names=field_names,
)
finally:
for mc in metric_channels:
mc.close()
def test(context, model_snapshot, test_path, use_cuda, use_tensorboard):
"""Test a trained model snapshot.
If model-snapshot is provided, the models and configuration will then be
loaded from the snapshot rather than any passed config file.
Otherwise, a config file will be loaded.
"""
if model_snapshot:
print(f"Loading model snapshot and config from {model_snapshot}")
if use_cuda is None:
raise Exception(
"if --model-snapshot is set --use-cuda/--no-cuda must be set")
else:
print(f"No model snapshot provided, loading from config")
config = context.obj.load_config()
model_snapshot = config.save_snapshot_path
use_cuda = config.use_cuda_if_available
print(f"Configured model snapshot {model_snapshot}")
print("\n=== Starting testing...")
metric_channels = []
if config.use_tensorboard:
metric_channels.append(TensorBoardChannel())
try:
test_model_from_snapshot_path(model_snapshot, use_cuda, test_path,
metric_channels)
finally:
for mc in metric_channels:
mc.close()
def test(context, model_snapshot, test_path, use_cuda, use_tensorboard):
"""Test a trained model snapshot.
If model-snapshot is provided, the models and configuration will then be
loaded from the snapshot rather than any passed config file.
Otherwise, a config file will be loaded.
"""
summary_writer = SummaryWriter() if use_tensorboard else None
if model_snapshot:
print(f"Loading model snapshot and config from {model_snapshot}")
if use_cuda is None:
raise Exception(
"if --model-snapshot is set --use-cuda/--no-cuda must be set"
)
else:
print(f"No model snapshot provided, loading from config")
config = context.obj.load_config()
model_snapshot = config.save_snapshot_path
use_cuda = config.use_cuda_if_available
print(f"Configured model snapshot {model_snapshot}")
print("\n=== Starting testing...")
try:
test_model_from_snapshot_path(
model_snapshot, use_cuda, test_path, summary_writer
)
finally:
if summary_writer is not None:
summary_writer.close()
def train(context):
"""Train a model and save the best snapshot."""
config = parse_config(context.obj.load_config())
print("\n===Starting training...")
if config.distributed_world_size == 1:
train_model(config)
else:
train_model_distributed(config)
print("\n=== Starting testing...")
test_model_from_snapshot_path(
config.save_snapshot_path,
config.use_cuda_if_available,
config.task.data_handler.test_path,
)
def test(context, model_snapshot, test_path, use_cuda):
"""Test a trained model snapshot.
If model-snapshot is provided, the models and configuration will then be loaded from
the snapshot rather than any passed config file.
Otherwise, a config file will be loaded.
"""
if model_snapshot:
print(f"Loading model snapshot and config from {model_snapshot}")
if use_cuda is None:
raise Exception(
"if --model-snapshot is set --use-cuda/--no-cuda must be set")
else:
print(f"No model snapshot provided, loading from config")
config = parse_config(context.obj.load_config())
model_snapshot = config.save_snapshot_path
use_cuda = config.use_cuda_if_available
print(f"Configured model snapshot {model_snapshot}")
print("\n=== Starting testing...")
test_model_from_snapshot_path(model_snapshot, use_cuda, test_path)
def train(context):
"""Train a model and save the best snapshot."""
config = context.obj.load_config()
print("\n===Starting training...")
summary_writer = SummaryWriter() if config.use_tensorboard else None
try:
if config.distributed_world_size == 1:
train_model(config, summary_writer=summary_writer)
else:
train_model_distributed(config, summary_writer)
print("\n=== Starting testing...")
test_model_from_snapshot_path(
config.save_snapshot_path,
config.use_cuda_if_available,
config.task.data_handler.test_path,
summary_writer,
)
finally:
if summary_writer is not None:
summary_writer.close()
def train(context):
"""Train a model and save the best snapshot."""
config = context.obj.load_config()
print("\n===Starting training...")
metric_channels = []
if config.use_tensorboard:
metric_channels.append(TensorBoardChannel())
try:
if config.distributed_world_size == 1:
train_model(config, metric_channels=metric_channels)
else:
train_model_distributed(config, metric_channels)
print("\n=== Starting testing...")
test_model_from_snapshot_path(
config.save_snapshot_path,
config.use_cuda_if_available,
test_path=None,
metric_channels=metric_channels,
)
finally:
for mc in metric_channels:
mc.close()
