def main():
# init args
args = parse_args()
assert args.save_path is not None, 'the save path must be provided!'
assert args.search_modules is not None, 'the search modules must be provided!'
# init retrieval pipeline settings
cfg = get_defaults_cfg()
# load search space
datasets = load_datasets()
pre_processes = importlib.import_module("{}.pre_process_dict".format(
args.search_modules)).pre_processes
models = importlib.import_module("{}.extract_dict".format(
args.search_modules)).models
extracts = importlib.import_module("{}.extract_dict".format(
args.search_modules)).extracts
# search in an exhaustive way
for data_name, data_args in datasets.items():
for pre_proc_name, pre_proc_args in pre_processes.items():
for model_name, model_args in models.items():
feature_full_name = data_name + "_" + pre_proc_name + "_" + model_name
print(feature_full_name)
if os.path.exists(
os.path.join(args.save_path, feature_full_name)):
print("[Search Extract]: config exists...")
continue
# load retrieval pipeline settings
cfg.datasets.merge_from_other_cfg(pre_proc_args)
cfg.model.merge_from_other_cfg(model_args)
cfg.extract.merge_from_other_cfg(extracts[model_name])
# build dataset and dataloader
dataset = build_folder(data_args, 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,
save_path=os.path.join(
args.save_path,
feature_full_name))
def main():
# init args
args = parse_args()
# init retrieval pipeline settings
cfg = get_defaults_cfg()
# load search space
datasets = load_datasets()
pre_processes = importlib.import_module("{}.pre_process_dict".format(args.search_modules)).pre_processes
models = importlib.import_module("{}.extract_dict".format(args.search_modules)).models
extracts = importlib.import_module("{}.extract_dict".format(args.search_modules)).extracts
for data_name, data_args in datasets.items():
for pre_proc_name, pre_proc_args in pre_processes.items():
for model_name, model_args in models.items():
feature_full_name = data_name + "_" + pre_proc_name + "_" + model_name
print(feature_full_name)
if os.path.exists(os.path.join(args.save_path, feature_full_name)):
print("[Search Extract]: config exists...")
continue
# load retrieval pipeline settings
cfg.datasets.merge_from_other_cfg(pre_proc_args)
cfg.model.merge_from_other_cfg(model_args)
cfg.extract.merge_from_other_cfg(extracts[model_name])
# set train feature path for pwa
pwa_train_fea_dir = os.path.join("/data/features/test_gap_gmp_gem_crow_spoc", feature_full_name)
if "query" in pwa_train_fea_dir:
pwa_train_fea_dir.replace("query", "gallery")
elif "paris" in pwa_train_fea_dir:
pwa_train_fea_dir.replace("paris", "oxford_gallery")
print("[PWA Extractor]: train feature: {}".format(pwa_train_fea_dir))
cfg.extract.aggregators.PWA.train_fea_dir = pwa_train_fea_dir
# build dataset and dataloader
dataset = build_folder(data_args, 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, save_path=os.path.join(args.save_path, feature_full_name))
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)