def main():
parser = argparse.ArgumentParser(description="SSD Demo.")
parser.add_argument(
"config_file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--ckpt",
type=str,
default=None,
help="Trained weights.")
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
detections = get_detections(cfg=cfg, ckpt=args.ckpt)
json_path = pathlib.Path(cfg.OUTPUT_DIR, "test_detected_boxes.json")
dump_detections(cfg, detections, json_path)
def main():
parser = argparse.ArgumentParser(description="SSD Demo.")
parser.add_argument(
"config_file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"video_path", type=str, metavar="FILE",
help="Path to source video")
parser.add_argument(
"output_path", type=str,
help="Output path to save video with detections")
parser.add_argument("--ckpt", type=str, default=None, help="Trained weights.")
parser.add_argument("--score_threshold", type=float, default=0.7)
parser.add_argument("--dataset_type", default="tdt4265", type=str, help='Specify dataset type. Currently support voc and coco.')
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.freeze()
print("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
print(config_str)
print("Running with config:\n{}".format(cfg))
infer_video(cfg=cfg,
ckpt=args.ckpt,
score_threshold=args.score_threshold,
video_path=args.video_path,
output_path=args.output_path,
dataset_type=args.dataset_type)
def main():
parser = argparse.ArgumentParser(description="SSD Demo.")
parser.add_argument(
"config_file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--ckpt",
type=str,
default=None,
help="Trained weights.")
parser.add_argument("--score_threshold", type=float, default=0.7)
parser.add_argument("--images_dir",
default='demo/voc',
type=str,
help='Specify a image dir to do prediction.')
parser.add_argument(
"--dataset_type",
default="voc",
type=str,
help='Specify dataset type. Currently support voc and coco.')
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
print("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
print(config_str)
print("Running with config:\n{}".format(cfg))
run_demo(cfg=cfg,
ckpt=args.ckpt,
score_threshold=args.score_threshold,
images_dir=pathlib.Path(args.images_dir),
output_dir=pathlib.Path(args.images_dir, "result"),
dataset_type=args.dataset_type)
def main():
parser = argparse.ArgumentParser(
description='SSD Evaluation on VOC and COCO dataset.')
parser.add_argument(
"config_file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"--ckpt",
help=
"The path to the checkpoint for test, default is the latest checkpoint.",
default=None,
type=str,
)
parser.add_argument("--N_images",
default=100,
type=int,
help="The number of images to check runtime with.")
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.TEST.BATCH_SIZE = 1
cfg.freeze()
logger = setup_logger("SSD", cfg.OUTPUT_DIR)
logger.info(args)
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))
evaluation(cfg, ckpt=args.ckpt, N_images=args.N_images)
def main():
args = get_parser().parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = pathlib.Path(cfg.OUTPUT_DIR)
output_dir.mkdir(exist_ok=True, parents=True)
logger = setup_logger("SSD", output_dir)
logger.info(args)
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))
model = start_train(cfg)
logger.info('Start evaluating...')
torch.cuda.empty_cache() # speed up evaluating after training finished
do_evaluation(cfg, model)
def main():
parser = argparse.ArgumentParser(
description='Single Shot MultiBox Detector Training With PyTorch')
parser.add_argument(
"config_file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = pathlib.Path(cfg.OUTPUT_DIR)
output_dir.mkdir(exist_ok=True, parents=True)
logger = setup_logger("SSD", output_dir)
logger.info(args)
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))
model = start_train(cfg)
logger.info('Start evaluating...')
torch.cuda.empty_cache() # speed up evaluating after training finished
do_evaluation(cfg, model)
import numpy as np
import matplotlib.pyplot as plt
from train import get_parser
from ssd.config.defaults import cfg
from ssd.data.build import make_data_loader
from vizer.draw import draw_boxes
from ssd.modeling.box_head.prior_box import PriorBox
from ssd.utils import box_utils
args = get_parser().parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
data_loader = make_data_loader(cfg, is_train=True)
mean = np.array([cfg.INPUT.PIXEL_MEAN]).reshape(1, 1, -1)
std = np.array([cfg.INPUT.PIXEL_STD])
priors = PriorBox(cfg)()
if isinstance(data_loader, list):
data_loader = data_loader[0]
for img, batch, *_ in data_loader:
boxes = batch["boxes"]
# SSD Target transform transfers target boxes into prior locations
# Have to revert the transformation
boxes = box_utils.convert_locations_to_boxes(boxes, priors,
cfg.MODEL.CENTER_VARIANCE,
cfg.MODEL.SIZE_VARIANCE)
boxes = box_utils.center_form_to_corner_form(boxes)
