def extract_feat_faster_start(args, cfg):
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
num_gpus = len(args.gpu_id.split(','))
MIN_BOXES = cfg.MODEL.BUA.EXTRACTOR.MIN_BOXES
MAX_BOXES = cfg.MODEL.BUA.EXTRACTOR.MAX_BOXES
CONF_THRESH = cfg.MODEL.BUA.EXTRACTOR.CONF_THRESH
# Extract features.
imglist = os.listdir(args.image_dir)
num_images = len(imglist)
print('Number of images: {}.'.format(num_images))
if args.num_cpus != 0:
ray.init(num_cpus=args.num_cpus)
else:
ray.init()
img_lists = [imglist[i::num_gpus] for i in range(num_gpus)]
pb = ProgressBar(len(imglist))
actor = pb.actor
print('Number of GPUs: {}.'.format(num_gpus))
extract_feat_list = []
for i in range(num_gpus):
extract_feat_list.append(
extract_feat_faster.remote(i, img_lists[i], cfg, args, actor))
pb.print_until_done()
ray.get(extract_feat_list)
ray.get(actor.get_counter.remote())
def extract_image_feature(args):
# Change configs to be used
check_dirs(args.output_dir)
cfg = setup(args)
cfg.defrost()
cfg.MODEL.WEIGHTS = args.image_model
# Not this problem. The logger path is set up in default_setup(cfg, args) in setup(args.)
# cfg.OUTPUT_DIR = args.image_log
cfg.freeze()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
num_gpus = len(args.gpu_id.split(','))
MIN_BOXES = cfg.MODEL.BUA.EXTRACTOR.MIN_BOXES
MAX_BOXES = cfg.MODEL.BUA.EXTRACTOR.MAX_BOXES
CONF_THRESH = cfg.MODEL.BUA.EXTRACTOR.CONF_THRESH
# Extract features.
imglist = os.listdir(args.image_dir)
num_images = len(imglist)
print('Number of images: {}.'.format(num_images))
if args.num_cpus != 0:
ray.init(num_cpus=args.num_cpus)
else:
ray.init()
img_lists = [imglist[i::num_gpus] for i in range(num_gpus)]
pb = ProgressBar(len(imglist))
actor = pb.actor
print('Number of GPUs: {}.'.format(num_gpus))
extract_feat_list = []
for i in range(num_gpus):
extract_feat_list.append(extract_feat.remote(i, img_lists[i], cfg, args, actor))
pb.print_until_done()
ray.get(extract_feat_list)
ray.get(actor.get_counter.remote())
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection2 Inference")
parser.add_argument(
"--config-file",
default="configs/bua-caffe/extract-bua-caffe-r101.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument('--num-cpus',
default=1,
type=int,
help='number of cpus to use for ray, 0 means no limit')
parser.add_argument('--gpus',
dest='gpu_id',
help='GPU id(s) to use',
default='0',
type=str)
parser.add_argument("--mode",
default="caffe",
type=str,
help="bua_caffe, ...")
parser.add_argument(
'--extract-mode',
default='roi_feats',
type=str,
help="'roi_feats', 'bboxes' and 'bbox_feats' indicates \
'extract roi features directly', 'extract bboxes only' and \
'extract roi features with pre-computed bboxes' respectively"
)
parser.add_argument('--min-max-boxes',
default='min_max_default',
type=str,
help='the number of min-max boxes of extractor')
parser.add_argument('--out-dir',
dest='output_dir',
help='output directory for features',
default="features")
parser.add_argument('--image-dir',
dest='image_dir',
help='directory with images',
default="image")
parser.add_argument('--bbox-dir',
dest='bbox_dir',
help='directory with bbox',
default="bbox")
parser.add_argument(
"--resume",
action="store_true",
help="whether to attempt to resume from the checkpoint directory",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg = setup(args)
os.makedirs(args.output_dir, exist_ok=True)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
num_gpus = len(args.gpu_id.split(','))
MIN_BOXES = cfg.MODEL.BUA.EXTRACTOR.MIN_BOXES
MAX_BOXES = cfg.MODEL.BUA.EXTRACTOR.MAX_BOXES
CONF_THRESH = cfg.MODEL.BUA.EXTRACTOR.CONF_THRESH
# Extract features.
imglist = os.listdir(args.image_dir)
num_images = len(imglist)
print('Number of images: {}.'.format(num_images))
if args.num_cpus != 0:
ray.init(num_cpus=args.num_cpus)
else:
ray.init()
img_lists = [imglist[i::num_gpus] for i in range(num_gpus)]
pb = ProgressBar(len(imglist))
actor = pb.actor
print('Number of GPUs: {}.'.format(num_gpus))
extract_feat_list = []
for i in range(num_gpus):
extract_feat_list.append(
extract_feat.remote(i, img_lists[i], cfg, args, actor))
pb.print_until_done()
ray.get(extract_feat_list)
ray.get(actor.get_counter.remote())
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection2 Inference")
parser.add_argument(
"--config-file",
default="configs/bua-caffe/extract-bua-caffe-r101.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument('--num-cpus', default=1, type=int,
help='number of cpus to use for ray, 0 means no limit')
parser.add_argument('--gpus', dest='gpu_id', help='GPU id(s) to use',
default='0', type=str)
parser.add_argument("--mode", default="caffe", type=str, help="bua_caffe, ...")
parser.add_argument('--extract-mode', default='roi_feats', type=str,
help="'roi_feats', 'bboxes' and 'bbox_feats' indicates \
'extract roi features directly', 'extract bboxes only' and \
'extract roi features with pre-computed bboxes' respectively")
parser.add_argument('--min-max-boxes', default='min_max_default', type=str,
help='the number of min-max boxes of extractor')
parser.add_argument('--out-dir', dest='output_dir',
help='output directory for features',
default="features")
parser.add_argument('--image-dir', dest='image_dir',
help='directory with images',
default="image")
parser.add_argument('--bbox-dir', dest='bbox_dir',
help='directory with bbox',
default="bbox")
parser.add_argument(
"--resume",
action="store_true",
help="whether to attempt to resume from the checkpoint directory",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg = setup(args)
os.makedirs(args.output_dir, exist_ok=True)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
num_gpus = len(args.gpu_id.split(','))
# Load classes
classes = [] # ['__background__']
with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "evaluation", 'objects_vocab.txt')) as f:
for object in f.readlines():
classes.append(object.split(',')[0].lower().strip())
# Load attributes
attributes = [] # ['__no_attribute__']
with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "evaluation", 'attributes_vocab.txt')) as f:
for att in f.readlines():
attributes.append(att.split(',')[0].lower().strip())
# Save class and attribute names to file
np.savez_compressed(
os.path.join(args.output_dir, "info.npz"),
classes=classes, attributes=attributes, cfg=cfg, args=args) #info={'cfg':cfg, 'args':args})
# Extract features.
imglist = os.listdir(args.image_dir)
num_images = len(imglist)
print('Number of images: {}.'.format(num_images))
if args.num_cpus != 0:
ray.init(num_cpus=args.num_cpus)
else:
ray.init()
img_lists = [imglist[i::num_gpus] for i in range(num_gpus)]
pb = ProgressBar(len(imglist))
actor = pb.actor
print('Number of GPUs: {}.'.format(num_gpus))
extract_feat_list = []
for i in range(num_gpus):
extract_feat_list.append(extract_feat.remote(i, img_lists[i], cfg, args, actor))
pb.print_until_done()
ray.get(extract_feat_list)
ray.get(actor.get_counter.remote())
