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():
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 get_model_data(name, timings, errors):
"""Get model data for a single model."""
# Load model config
reset_cfg()
cfg.merge_from_file(model_zoo.get_config_file(name))
config_url, _, model_id, _, weight_url_full = model_zoo.get_model_info(
name)
# Get model complexity
cx = net.complexity(builders.get_model())
# Inference time is measured in ms with a reference batch_size and num_gpus
batch_size, num_gpus = 64, 1
reference = batch_size / cfg.TEST.BATCH_SIZE * cfg.NUM_GPUS / num_gpus
infer_time = timings[name]["test_fw_time"] * reference * 1000
# Training time is measured in hours for 100 epochs over the ImageNet train set
iterations = 1281167 / cfg.TRAIN.BATCH_SIZE * 100
train_time = timings[name]["train_fw_bw_time"] * iterations / 3600
# Gather all data about the model
return {
"config_url": "configs/" + config_url,
"flops": round(cx["flops"] / 1e9, 1),
"params": round(cx["params"] / 1e6, 1),
"acts": round(cx["acts"] / 1e6, 1),
"batch_size": cfg.TRAIN.BATCH_SIZE,
"infer_time": round(infer_time),
"train_time": round(train_time, 1),
"error": round(errors[name]["top1_err"], 1),
"model_id": model_id,
"weight_url": weight_url_full,
}
def test_complexity(self, cfg_file, cx_expected):
"""Test complexity of a single model with the specified config."""
cfg_init = cfg.clone()
cfg.merge_from_file(cfg_file)
cx = net.complexity(builders.get_model())
cfg.merge_from_other_cfg(cfg_init)
self.assertEqual(cx_expected, cx)
def check_complexity_constraints(constraints):
"""Checks complexity constraints."""
cx, valid = None, True
for p, v in constraints.CX.items():
p, min_v, max_v = p.lower(), v[0], v[1]
if min_v != 0 or max_v != 0:
cx = cx if cx else net.complexity(builders.get_model())
min_v = cx[p] if min_v == 0 else min_v
max_v = cx[p] if max_v == 0 else max_v
valid = valid and (min_v <= cx[p] <= max_v)
return valid
def dump_complexity():
"""Measure the complexity of every model in the configs/ directory."""
complexity = {"date-created": str(datetime.datetime.now())}
cfg_files = [os.path.join(r, f) for r, _, fs in os.walk("configs/") for f in fs]
cfg_files = sorted(f for f in cfg_files if ".yaml" in f)
for cfg_file in cfg_files:
cfg_init = cfg.clone()
cfg.merge_from_file(cfg_file)
complexity[cfg_file] = net.complexity(builders.get_model())
cfg.merge_from_other_cfg(cfg_init)
with open(_COMPLEXITY_FILE, "w") as file:
json.dump(complexity, file, sort_keys=True, indent=4)
def test_complexity(key):
"""Measure the complexity of a single model."""
reset_cfg()
cfg_file = os.path.join(_PYCLS_DIR, key)
merge_from_file(cfg_file)
return net.complexity(builders.get_model())
