def test_error(key):
"""Measure the error of a single model."""
reset_cfg()
cfg.merge_from_file(model_zoo.get_config_file(key))
cfg.TEST.WEIGHTS = model_zoo.get_weights_file(key)
cfg.OUT_DIR, cfg.LOG_DEST = tempfile.mkdtemp(), "file"
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.test_model)
log_file = os.path.join(cfg.OUT_DIR, "stdout.log")
data = logging.sort_log_data(logging.load_log_data(log_file))["test_epoch"]
data = {"top1_err": data["top1_err"][-1], "top5_err": data["top5_err"][-1]}
shutil.rmtree(cfg.OUT_DIR)
return data
def main():
# Load config options
config.load_cfg_fom_args("Train a classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
# Ensure that the output dir exists
os.makedirs(cfg.OUT_DIR, exist_ok=True)
# Save the config
config.dump_cfg()
# Perform training
if cfg.NUM_GPUS > 1:
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=train_model)
else:
train_model()
def main():
"""Execute operation (train, test, time, etc.)."""
args = parse_args()
mode = args.mode
config.load_cfg(args.cfg)
cfg.merge_from_list(args.opts)
config.assert_cfg()
cfg.freeze()
if mode == "info":
print(builders.get_model()())
print("complexity:", net.complexity(builders.get_model()))
elif mode == "train":
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.train_model)
elif mode == "test":
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.test_model)
elif mode == "time":
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.time_model)
elif mode == "scale":
cfg.defrost()
cx_orig = net.complexity(builders.get_model())
scaler.scale_model()
cx_scaled = net.complexity(builders.get_model())
cfg_file = config.dump_cfg()
print("Scaled config dumped to:", cfg_file)
print("Original model complexity:", cx_orig)
print("Scaled model complexity:", cx_scaled)
def main():
corruptions, levels = config.load_cfg_fom_args(
"Train a classification model.")
config.assert_and_infer_cfg()
# Perform training
results = dist.multi_proc_run(
num_proc=cfg.NUM_GPUS,
fun=lambda: trainer.test_ftta_model(corruptions, levels))
# plot the results table
for index, level in enumerate(levels):
console = Console()
table = Table(show_header=True, header_style="cyan")
table.add_column('level')
for corruption in corruptions:
table.add_column(corruptions_concise[corruption])
res = list(map(lambda x: str(x), results[index]))
res = [str(level)] + res
table.add_row(*res)
console.print(table)
def main():
config.load_cfg_fom_args("Train a classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.train_model)
def main():
config.load_cfg_fom_args("Compute model and loader timings.")
config.assert_and_infer_cfg()
cfg.freeze()
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.time_model)
def main():
config.load_cfg_fom_args("Compute precise time for a model on 1 GPU.")
config.assert_and_infer_cfg()
cfg.freeze()
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.time_model)