def main():
start = time.time()
# init args
args = parse_args()
assert args.config_file is not None, 'a config file must be provided!'
# init and load retrieval pipeline settings
cfg = get_defaults_cfg()
cfg = setup_cfg(cfg, args.config_file, args.opts)
# load features
query_fea, query_info, _ = feature_loader.load(cfg.index.query_fea_dir,
cfg.index.feature_names)
gallery_fea, gallery_info, _ = feature_loader.load(
cfg.index.gallery_fea_dir, cfg.index.feature_names)
print("using init_load time: {:6f}s".format(time.time() - start))
# build helper and index features
index_helper = build_index_helper(cfg.index)
index_result_info, query_fea, gallery_fea = index_helper.do_index(
query_fea, query_info, gallery_fea)
# # build helper and evaluate results
# evaluate_helper = build_evaluate_helper(cfg.evaluate)
# mAP, recall_at_k = evaluate_helper.do_eval(index_result_info, gallery_info)
# # show results
# evaluate_helper.show_results(mAP, recall_at_k)
save_result(args.save_file, index_result_info, query_info, gallery_info)
save_result_n(r"./result_tmp/submission_30.csv", index_result_info,
query_info, gallery_info)
print("using total time: {:6f}s".format(time.time() - start))
def main():
# init args
args = parse_args()
assert args.config_file is not None, 'a config file must be provided!'
# init and load retrieval pipeline settings
cfg = get_defaults_cfg()
cfg = setup_cfg(cfg, args.config_file, args.opts)
# load features
query_fea, query_info, _ = feature_loader.load(cfg.index.query_fea_dir,
cfg.index.feature_names)
gallery_fea, gallery_info, _ = feature_loader.load(
cfg.index.gallery_fea_dir, cfg.index.feature_names)
# build helper and index features
index_helper = build_index_helper(cfg.index)
index_result_info, query_fea, gallery_fea = index_helper.do_index(
query_fea, query_info, gallery_fea)
# build helper and evaluate results
evaluate_helper = build_evaluate_helper(cfg.evaluate)
mAP, recall_at_k = evaluate_helper.do_eval(index_result_info, gallery_info)
# show results
evaluate_helper.show_results(mAP, recall_at_k)
def main():
# init args
args = parse_args()
assert args.config_file is not "", 'a config file must be provided!'
assert os.path.exists(args.config_file), 'the config file must be existed!'
# init and load retrieval pipeline settings
cfg = get_defaults_cfg()
cfg = setup_cfg(cfg, args.config_file, args.opts)
# set path for single image
path = '/data/caltech101/query/airplanes/image_0004.jpg'
# build transformers
transformers = build_transformers(cfg.datasets.transformers)
# build model
model = build_model(cfg.model)
# read image and convert it to tensor
img = Image.open(path).convert("RGB")
img_tensor = transformers(img)
# build helper and extract feature for single image
extract_helper = build_extract_helper(model, cfg.extract)
img_fea_info = extract_helper.do_single_extract(img_tensor)
stacked_feature = list()
for name in cfg.index.feature_names:
assert name in img_fea_info[
0], "invalid feature name: {} not in {}!".format(
name, img_fea_info[0].keys())
stacked_feature.append(img_fea_info[0][name].cpu())
img_fea = np.concatenate(stacked_feature, axis=1)
# load gallery features
gallery_fea, gallery_info, _ = feature_loader.load(
cfg.index.gallery_fea_dir, cfg.index.feature_names)
# build helper and single index feature
index_helper = build_index_helper(cfg.index)
index_result_info, query_fea, gallery_fea = index_helper.do_index(
img_fea, img_fea_info, gallery_fea)
index_helper.save_topk_retrieved_images('retrieved_images/',
index_result_info[0], 5,
gallery_info)
print('single index have done!')
def main():
# init args
args = parse_args()
assert args.data_json is not None, 'the dataset json must be provided!'
assert args.save_path is not None, 'the save path must be provided!'
assert args.config_file is not None, 'a config file must be provided!'
# init and load retrieval pipeline settings
cfg = get_defaults_cfg()
cfg = setup_cfg(cfg, args.config_file, args.opts)
# build dataset and dataloader
dataset = build_folder(args.data_json, cfg.datasets)
dataloader = build_loader(dataset, cfg.datasets)
# build model
model = build_model(cfg.model)
# build helper and extract features
extract_helper = build_extract_helper(model, cfg.extract)
extract_helper.do_extract(dataloader, args.save_path, args.save_interval)