DeepLearningExamples/PyTorch/Segmentation/MaskRCNN/pytorch/tools/test_net.py
2021-10-26 17:00:32 +00:00

157 lines
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Python
Executable file

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.data import make_data_loader
from maskrcnn_benchmark.engine.inference import inference
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark.utils.collect_env import collect_env_info
from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process
from maskrcnn_benchmark.utils.logger import setup_logger
from maskrcnn_benchmark.utils.miscellaneous import mkdir
from maskrcnn_benchmark.utils.logger import format_step
import dllogger
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default="/workspace/object_detection/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=os.getenv('LOCAL_RANK', 0))
parser.add_argument("--json-summary",
help="Out file for DLLogger",
default="dllogger_inference.out",
type=str)
parser.add_argument(
"--skip-eval",
dest="skip_eval",
help="Do not eval the predictions",
action="store_true",
)
parser.add_argument(
"--fp16",
help="Mixed precision training",
action="store_true",
)
parser.add_argument(
"--amp",
help="Mixed precision training",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
args.fp16 = args.fp16 or args.amp
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
distributed = num_gpus > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
synchronize()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
if is_main_process():
dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
filename=args.json_summary),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
else:
dllogger.init(backends=[])
save_dir = ""
dllogger.log(step="PARAMETER", data={"config":cfg})
dllogger.log(step="PARAMETER", data={"gpu_count": num_gpus})
# dllogger.log(step="PARAMETER", data={"env_info": collect_env_info()})
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
# Initialize mixed-precision
if args.fp16:
use_mixed_precision = True
else:
use_mixed_precision = cfg.DTYPE == "float16"
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
iou_types = ("bbox",)
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm",)
output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
results = []
for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
if use_mixed_precision:
with torch.cuda.amp.autocast():
result = inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
skip_eval=args.skip_eval,
dllogger=dllogger,
)
else:
result = inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
skip_eval=args.skip_eval,
dllogger=dllogger,
)
synchronize()
results.append(result)
if is_main_process() and not args.skip_eval:
map_results, raw_results = results[0]
bbox_map = map_results.results["bbox"]['AP']
segm_map = map_results.results["segm"]['AP']
dllogger.log(step=tuple(), data={"BBOX_mAP": bbox_map, "MASK_mAP": segm_map})
if __name__ == "__main__":
main()
dllogger.log(step=tuple(), data={})