def test(ckpt_file, cfg_file, half):
"""
Monocular depth estimation test script.
Parameters
----------
ckpt_file : str
Checkpoint path for a pretrained model
cfg_file : str
Configuration file
half: bool
use half precision (fp16)
"""
# Initialize horovod
hvd_init()
# Parse arguments
config, state_dict = parse_test_file(ckpt_file, cfg_file)
# Set debug if requested
set_debug(config.debug)
# Initialize monodepth model from checkpoint arguments
model_wrapper = ModelWrapper(config)
# Restore model state
model_wrapper.load_state_dict(state_dict)
# change to half precision for evaluation if requested
config.arch["dtype"] = torch.float16 if half else None
# Create trainer with args.arch parameters
trainer = HorovodTrainer(**config.arch)
# Test model
trainer.test(model_wrapper)
def train(file):
"""
Monocular depth estimation training script.
Parameters
----------
file : str
Filepath, can be either a
**.yaml** for a yacs configuration file or a
**.ckpt** for a pre-trained checkpoint file.
"""
# Initialize horovod
hvd_init()
# Produce configuration and checkpoint from filename
config, ckpt = parse_train_file(file)
# Set debug if requested
set_debug(config.debug)
# model checkpoint
checkpoint = (None if config.checkpoint.filepath is "" or rank() > 0 else
filter_args_create(ModelCheckpoint, config.checkpoint))
# Initialize model wrapper
model_wrapper = ModelWrapper(config, resume=ckpt)
# Create trainer with args.arch parameters
trainer = HorovodTrainer(**config.arch, checkpoint=checkpoint)
# Train model
trainer.fit(model_wrapper)
def train(file):
"""
Monocular depth estimation training script.
Parameters
----------
file : str
Filepath, can be either a
**.yaml** for a yacs configuration file or a
**.ckpt** for a pre-trained checkpoint file.
"""
# Produce configuration and checkpoint from filename
config, ckpt = parse_train_file(file)
# config.arch.max_epochs=50
# Initialize horovod
if hasattr(config, "gpu"):
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
str(x) for x in config.gpu) # "0,1"
hvd_init()
# Set debug if requested
set_debug(config.debug)
# Wandb Logger
logger = None # if config.wandb.dry_run or rank() > 0 \
# else filter_args_create(WandbLogger, config.wandb)
# model checkpoint
checkpoint = None if config.checkpoint.filepath is '' or rank() > 0 else \
filter_args_create(ModelCheckpoint, config.checkpoint)
# Initialize model wrapper
model_wrapper = ModelWrapper(config,
resume=ckpt,
logger=logger,
use_horovod=HAS_HOROVOD)
# Create trainer with args.arch parameters
if HAS_HOROVOD:
from packnet_sfm.trainers.horovod_trainer import HorovodTrainer
trainer = HorovodTrainer(checkpoint=checkpoint, **config.arch)
else:
trainer = PytorchTrainer(checkpoint=checkpoint, **config.arch)
# Train model
trainer.fit(model_wrapper)
def train(file):
"""
Monocular depth estimation training script.
Parameters
----------
file : str
Filepath, can be either a
**.yaml** for a yacs configuration file or a
**.ckpt** for a pre-trained checkpoint file.
"""
# Initialize horovod
hvd_init()
# Produce configuration and checkpoint from filename
config, ckpt = parse_train_file(file)
# Set debug if requested
set_debug(config.debug)
# Wandb Logger
logger = None if config.wandb.dry_run or rank() > 0 \
else filter_args_create(WandbLogger, config.wandb)
# model checkpoint
checkpoint = None if config.checkpoint.filepath is '' or rank() > 0 else \
filter_args_create(ModelCheckpoint, config.checkpoint)
# Initialize model wrapper
model_wrapper = ModelWrapper(config, resume=ckpt, logger=logger)
print("Depth Net - %d parameters" % sum(p.numel() for p in model_wrapper.depth_net.parameters()))
print("Pose Net - %d parameters" % sum(p.numel() for p in model_wrapper.pose_net.parameters()))
# Create trainer with args.arch parameters
trainer = HorovodTrainer(**config.arch, checkpoint=checkpoint)
# Train model
trainer.fit(model_wrapper)