def main():
config.load_cfg_fom_args("Train a classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
# Perform training
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.train_model)
def main():
config.load_cfg_fom_args("Scale a model.")
config.assert_and_infer_cfg()
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():
# Load config options
config.load_cfg_fom_args("Test a trained classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
# Perform evaluation
if cfg.NUM_GPUS > 1:
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=test_model)
else:
test_model()
def main():
config.load_cfg_fom_args("Train a classification model.")
config.assert_and_infer_cfg()
D2Utils.cfg_merge_from_easydict(cfg, global_cfg)
cfg.freeze()
trainer_module = cfg.get('trainer_module', 'pycls.core.trainer')
trainer_module = importlib.import_module(trainer_module)
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer_module.train_model)
def main():
config.load_cfg_fom_args("Train a classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
print("building model {}".format(cfg.MODEL.TYPE))
model = build_model()
model.eval()
x = torch.randn(1, 3, 224, 224)
y = model(x)
print(y.shape)
model_complex = complexity(model)
print(model_complex)
def main():
# Parse cmd line args
args = parse_args()
# Load config options
cfg.merge_from_file(args.cfg_file)
cfg.merge_from_list(args.opts)
assert_and_infer_cfg()
cfg.freeze()
# Perform evaluation
if cfg.NUM_GPUS > 1:
mpu.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=single_proc_test)
else:
single_proc_test()
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():
# Parse cmd line args
args = parse_args()
# Load config options
cfg.merge_from_file(args.cfg_file)
cfg.merge_from_list(args.opts)
assert_and_infer_cfg()
cfg.freeze()
# Ensure that the output dir exists
os.makedirs(cfg.OUT_DIR, exist_ok=True)
# Save the config
dump_cfg()
# Perform training
if cfg.NUM_GPUS > 1:
mpu.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=single_proc_train)
else:
single_proc_train()
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("Test a trained classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
test()
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("Test a trained classification model.")
config.assert_and_infer_cfg()
cfg.freeze()
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.test_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)
SPACE = 'darts'
DATASET = 'cifar10'
SOURCE_TASK = 'cls'
TARGET_TASK = 'psd50000'
file_list = np.load(
f'configs/sample_based/{SPACE}/{DATASET}/selected_files.npy',
allow_pickle=True)
input_dir = f'configs/sample_based/{SPACE}/{DATASET}/{SOURCE_TASK}/'
output_dir = f'configs/sample_based/{SPACE}/{DATASET}/{TARGET_TASK}/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for f in file_list:
f_name = f.split('.')[0]
source_config = f'{input_dir}/{f}'
target_config = f'{output_dir}/{f}'
config.load_cfg(input_dir, f)
config.assert_and_infer_cfg()
config.cfg.TRAIN.PSD_LABEL_SPLIT = 50000
config.cfg.TRAIN.PSD_UNLABEL_BATCH_SIZE = 50
config.cfg.TRAIN.PSD_LABEL_BATCH_SIZE = 32
config.cfg.TRAIN.PSD_THRESHOLD = 0.95
config.cfg.LOG_PERIOD = 100
config.cfg.TASK = 'psd'
config.cfg.OPTIM.MAX_EPOCH = 50
config.cfg.OPTIM.BASE_LR = 0.1
config.cfg.OPTIM.MOMENTUM = 0.9
config.dump_cfg_to_file(target_config)
print(source_config, target_config)