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# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import numpy as np
from collections import Counter
import tqdm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader
from detectron2.engine import default_argument_parser
from detectron2.modeling import build_model
from detectron2.utils.analysis import (
activation_count_operators,
flop_count_operators,
parameter_count_table,
)
from detectron2.utils.logger import setup_logger
logger = logging.getLogger("detectron2")
def setup(args):
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.DATALOADER.NUM_WORKERS = 0
cfg.merge_from_list(args.opts)
cfg.freeze()
setup_logger()
return cfg
def do_flop(cfg):
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
model = build_model(cfg)
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
model.eval()
counts = Counter()
total_flops = []
for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa
count = flop_count_operators(model, data)
counts += count
total_flops.append(sum(count.values()))
logger.info(
"(G)Flops for Each Type of Operators:\n" + str([(k, v / idx) for k, v in counts.items()])
)
logger.info("Total (G)Flops: {}±{}".format(np.mean(total_flops), np.std(total_flops)))
def do_activation(cfg):
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
model = build_model(cfg)
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
model.eval()
counts = Counter()
total_activations = []
for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa
count = activation_count_operators(model, data)
counts += count
total_activations.append(sum(count.values()))
logger.info(
"(Million) Activations for Each Type of Operators:\n"
+ str([(k, v / idx) for k, v in counts.items()])
)
logger.info(
"Total (Million) Activations: {}±{}".format(
np.mean(total_activations), np.std(total_activations)
)
)
def do_parameter(cfg):
model = build_model(cfg)
logger.info("Parameter Count:\n" + parameter_count_table(model, max_depth=5))
def do_structure(cfg):
model = build_model(cfg)
logger.info("Model Structure:\n" + str(model))
if __name__ == "__main__":
parser = default_argument_parser(
epilog="""
Examples:
To show parameters of a model:
$ ./analyze_model.py --tasks parameter \\
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
Flops and activations are data-dependent, therefore inputs and model weights
are needed to count them:
$ ./analyze_model.py --num-inputs 100 --tasks flop \\
--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\
MODEL.WEIGHTS /path/to/model.pkl
"""
)
parser.add_argument(
"--tasks",
choices=["flop", "activation", "parameter", "structure"],
required=True,
nargs="+",
)
parser.add_argument(
"--num-inputs",
default=100,
type=int,
help="number of inputs used to compute statistics for flops/activations, "
"both are data dependent.",
)
args = parser.parse_args()
assert not args.eval_only
assert args.num_gpus == 1
cfg = setup(args)
for task in args.tasks:
{
"flop": do_flop,
"activation": do_activation,
"parameter": do_parameter,
"structure": do_structure,
}[task](cfg)
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