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_timing(key):
"""Measure the timing of a single model."""
reset_cfg()
merge_from_file(model_zoo.get_config_file(key))
cfg.PREC_TIME.WARMUP_ITER, cfg.PREC_TIME.NUM_ITER = 5, 50
cfg.OUT_DIR, cfg.LOG_DEST = tempfile.mkdtemp(), "file"
dist.multi_proc_run(num_proc=cfg.NUM_GPUS, fun=trainer.time_model)
log_file = os.path.join(cfg.OUT_DIR, "stdout.log")
data = logging.sort_log_data(logging.load_log_data(log_file))["iter_times"]
shutil.rmtree(cfg.OUT_DIR)
return data
def test_error(key):
"""Measure the error of a single model."""
reset_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 build_model(name, pretrained=False, cfg_list=()):
"""Constructs a predefined model (note: loads global config as well)."""
# Load the config
reset_cfg()
config_file = get_config_file(name)
cfg.merge_from_file(config_file)
cfg.merge_from_list(cfg_list)
# Construct model
model = builders.build_model()
# Load pretrained weights
if pretrained:
weights_file = get_weights_file(name)
cp.load_checkpoint(weights_file, model)
return model
def sample_cfgs(seed):
"""Samples chunk configs and return those that are unique and valid."""
# Fix RNG seed (every call to this function should use a unique seed)
np.random.seed(seed)
setup_cfg = sweep_cfg.SETUP
cfgs = {}
for _ in range(setup_cfg.CHUNK_SIZE):
# Sample parameters [key, val, ...] list based on the samplers
params = samplers.sample_parameters(setup_cfg.SAMPLERS)
# Check if config is unique, if not continue
key = zip(params[0::2], params[1::2])
key = " ".join(["{} {}".format(k, v) for k, v in key])
if key in cfgs:
continue
# Generate config from parameters
reset_cfg()
cfg.merge_from_other_cfg(setup_cfg.BASE_CFG)
cfg.merge_from_list(params)
# Check if config is valid, if not continue
is_valid = samplers.check_regnet_constraints(setup_cfg.CONSTRAINTS)
if not is_valid:
continue
# Special logic for dealing w model scaling (side effect is to standardize cfg)
if cfg.MODEL.TYPE in ["anynet", "effnet", "regnet"]:
scaler.scale_model()
# Check if config is valid, if not continue
is_valid = samplers.check_complexity_constraints(setup_cfg.CONSTRAINTS)
if not is_valid:
continue
# Set config description to key
cfg.DESC = key
# Store copy of config if unique and valid
cfgs[key] = cfg.clone()
# Stop sampling if already reached quota
if len(cfgs) == setup_cfg.NUM_CONFIGS:
break
return cfgs
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())