def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default=
"/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
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("atss_core", save_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info("cfg:\n", cfg)
logger.info("Collecting env info ( It might take some time)")
logger.info("\n" + collect_env_info())
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
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", )
if cfg.MODEL.KEYPOINT_ON:
iou_types = iou_types + ("keypoints", )
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)
for output_folder, dataset_name, data_loader_val in zip(
output_folders, dataset_names, data_loaders_val):
inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=False if cfg.MODEL.ATSS_ON or cfg.MODEL.FCOS_ON
or cfg.MODEL.RETINANET_ON else 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,
)
synchronize()
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
synchronize()
# define what is "cfg" #
# here has change something #
try:
cfg.merge_from_file(args.config_file)
except Exception as e:
print(e)
# cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("atss_core", output_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info("args", args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
# train #
model = train(cfg, args.local_rank, args.distributed)
if not args.skip_test:
run_test(cfg, model, args.distributed)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Webcam Demo")
parser.add_argument(
"--config-file",
default=
"../configs/atss/wei_score/atss_dcnv2_X_101_64x4d_FPN_2x.yaml", # <-----模型配置文件
metavar="FILE",
help="path to config file",
)
parser.add_argument(
"--weights",
default=
"../checkpoint/atss_dcnv2_X_101_64x4d_FPN_2x/model_0010000.pth", #<-----------训练模型地址
metavar="FILE",
help="path to the trained model",
)
parser.add_argument(
"--images-dir",
# default="../datasets/myData/val", #<------------测试图像的路径
default="../datasets/test", #<------------测试图像的路径
metavar="DIR",
help="path to demo images directory",
)
parser.add_argument(
"--min-image-size",
type=int,
default=800,
help="Smallest size of the image to feed to the model. "
"Model was trained with 800, which gives best results",
)
parser.add_argument(
"opts",
help="Modify model config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
# load config from file and command-line arguments
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.MODEL.WEIGHT = args.weights
cfg.freeze()
# The following per-class thresholds are computed by maximizing
# per-class f-measure in their precision-recall curve.
# Please see compute_thresholds_for_classes() in coco_eval.py for details.
# thresholds_for_classes = [
# 0.4923645853996277, 0.4928510785102844, 0.5040897727012634,
# 0.4912887513637543, 0.5016880631446838, 0.5278812646865845,
# 0.5351834893226624, 0.5003424882888794, 0.4955945909023285,
# 0.43564629554748535, 0.6089804172515869, 0.666087806224823,
# 0.5932040214538574, 0.48406165838241577, 0.4062422513961792,
# 0.5571075081825256, 0.5671307444572449, 0.5268378257751465,
# 0.5112953186035156, 0.4647842049598694, 0.5324517488479614,
# 0.5795850157737732, 0.5152440071105957, 0.5280804634094238,
# 0.4791383445262909, 0.5261335372924805, 0.4906163215637207,
# 0.523737907409668, 0.47027698159217834, 0.5103300213813782,
# 0.4645252823829651, 0.5384289026260376, 0.47796186804771423,
# 0.4403403103351593, 0.5101461410522461, 0.5535093545913696,
# 0.48472103476524353, 0.5006796717643738, 0.5485560894012451,
# 0.4863888621330261, 0.5061569809913635, 0.5235867500305176,
# 0.4745445251464844, 0.4652363359928131, 0.4162440598011017,
# 0.5252017974853516, 0.42710989713668823, 0.4550687372684479,
# 0.4943239390850067, 0.4810051918029785, 0.47629663348197937,
# 0.46629616618156433, 0.4662836790084839, 0.4854755401611328,
# 0.4156557023525238, 0.4763634502887726, 0.4724511504173279,
# 0.4915047585964203, 0.5006274580955505, 0.5124194622039795,
# 0.47004589438438416, 0.5374764204025269, 0.5876904129981995,
# 0.49395060539245605, 0.5102297067642212, 0.46571290493011475,
# 0.5164387822151184, 0.540651798248291, 0.5323763489723206,
# 0.5048757195472717, 0.5302401781082153, 0.48333442211151123,
# 0.5109739303588867, 0.4077408015727997, 0.5764586925506592,
# 0.5109297037124634, 0.4685552418231964, 0.5148998498916626,
# 0.4224434792995453, 0.4998510777950287
# ]
thresholds_for_classes = [
0.5,
0.5,
0.5,
]
demo_im_names = os.listdir(args.images_dir)
# prepare object that handles inference plus adds predictions on top of image
coco_demo = COCODemo(
cfg,
confidence_thresholds_for_classes=thresholds_for_classes,
min_image_size=args.min_image_size)
for im_name in demo_im_names:
img = cv2.imread(os.path.join(args.images_dir, im_name))
if img is None:
continue
start_time = time.time()
composite = coco_demo.run_on_opencv_image(img)
print("{}\tinference time: {:.2f}s".format(im_name,
time.time() - start_time))
cv2.imwrite("../result_1/" + im_name, composite)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Webcam Demo")
parser.add_argument(
"--config-file",
default="../configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument(
"--confidence-threshold",
type=float,
default=0.7,
help="Minimum score for the prediction to be shown",
)
parser.add_argument(
"--min-image-size",
type=int,
default=224,
help="Smallest size of the image to feed to the model. "
"Model was trained with 800, which gives best results",
)
parser.add_argument(
"--show-mask-heatmaps",
dest="show_mask_heatmaps",
help="Show a heatmap probability for the top masks-per-dim masks",
action="store_true",
)
parser.add_argument(
"--masks-per-dim",
type=int,
default=2,
help="Number of heatmaps per dimension to show",
)
parser.add_argument(
"opts",
help="Modify model config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
# load config from file and command-line arguments
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
# prepare object that handles inference plus adds predictions on top of image
coco_demo = COCODemo(
cfg,
confidence_threshold=args.confidence_threshold,
show_mask_heatmaps=args.show_mask_heatmaps,
masks_per_dim=args.masks_per_dim,
min_image_size=args.min_image_size,
)
cam = cv2.VideoCapture(0)
while True:
start_time = time.time()
ret_val, img = cam.read()
composite = coco_demo.run_on_opencv_image(img)
print("Time: {:.2f} s / img".format(time.time() - start_time))
cv2.imshow("COCO detections", composite)
if cv2.waitKey(1) == 27:
break # esc to quit
cv2.destroyAllWindows()