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 pathlib
import torch
import os
from ssd.config.defaults import cfg
from train import get_parser
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# Change this
MODEL_FILE = "model_021000.pth"
checkpoint = pathlib.Path(cfg.OUTPUT_DIR, MODEL_FILE)
assert checkpoint.is_file()
# Create a new directory for new training run
new_dir = checkpoint.parent.parent
new_dir = pathlib.Path(
checkpoint.parent.parent,
checkpoint.parent.stem.replace("_waymo", "") + "_tdt4265")
# Copy new checkpoint
new_dir.mkdir()
new_checkpoint_path = new_dir.joinpath("waymo_model.pth")
# Read last checkpoint written
with open(new_dir.joinpath("last_checkpoint.txt"), "w") as fp:
fp.write(f"{new_checkpoint_path}")
# Load last checkpoint and only transfer learn parameters from the model (not optimizer etc)
new_checkpoint = {}
cfg.INPUT.IMAGE_SIZE[1],
fill=False,
edgecolor="black"))
do_plot = False
# Only plot prior boxes at middle, for visability
indice_to_plot = len(indices_to_visualize) / 2
for i, idx in enumerate(indices_to_visualize):
prior = priors_as_location[idx]
color = colors[i]
if i >= (indice_to_plot - len(aspect_ratio_indices) // 2) and i < (
indice_to_plot + len(aspect_ratio_indices) // 2):
do_plot = True
else:
do_plot = False
plot_bbox(ax, prior, color, do_plot, PLOT_CIRCLE)
plt.show()
if __name__ == "__main__":
config_path = "configs/train_rdd2020.yaml"
cfg.merge_from_file(config_path)
cfg.freeze()
visualize_validation_set(cfg)
#visualize_training_set(cfg)
#visualize_prior_boxes(cfg, layer=3)