def main(config):
# Uncomment to see device placement
# tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
# Prepare directories for saving data
prepare_dirs(config)
# Load data on CPU
with tf.device("/cpu:0"):
data_loader = DataLoader(config)
dataset = data_loader.load()
smpl_loader = data_loader.get_smpl_loader()
val_dataset = data_loader.load_val_dataset()
trainer = Trainer(config, dataset, smpl_loader, val_dataset)
save_config(config)
trainer.train()
def main(config):
# Uncomment to see device placement
# tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
# Load data on CPU
with tf.device("/cpu:0"):
data_loader = DataLoader(config)
val_dataset = data_loader.load_val_dataset()
# Make sure that everything is validated both on mr and kp loss
config.use_mesh_repro_loss = True
config.use_kp_loss = True
trainer = Trainer(config, None, None, val_dataset, validation_only=True)
trainer.validate_checkpoint()
