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():
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.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)
def main():
# init args
args = parse_args()
assert args.fea_dir is not None, 'the feature directory must be provided!'
assert args.search_modules is not None, 'the search modules must be provided!'
assert args.save_path is not None, 'the save path must be provided!'
# init retrieval pipeline settings
cfg = get_defaults_cfg()
# load search space
datasets = load_datasets()
indexes = importlib.import_module("{}.index_dict".format(
args.search_modules)).indexes
evaluates = importlib.import_module("{}.index_dict".format(
args.search_modules)).evaluates
if os.path.exists(args.save_path):
with open(args.save_path, "r") as f:
results = json.load(f)
else:
results = list()
for dir in os.listdir(args.fea_dir):
for data_name, data_args in datasets.items():
for index_name, index_args in indexes.items():
if data_name in dir:
print(dir)
# get dirs
gallery_fea_dir, query_fea_dir, train_fea_dir = get_dir(
args.fea_dir, dir, data_args)
# get evaluate setting
evaluate_args = evaluates["reid_overall"]
for dim_proc in index_args.dim_processors.names:
if dim_proc in ["PartPCA", "PartSVD", "PCA", "SVD"]:
index_args.dim_processors[
dim_proc].train_fea_dir = train_fea_dir
for fea_name in fea_names:
result_dict = get_default_result_dict(
dir, data_name, index_name, fea_name)
if check_result_exist(result_dict, results):
print("[Search Query]: config exists...")
continue
# load retrieval pipeline settings
index_args.feature_names = [fea_name]
cfg.index.merge_from_other_cfg(index_args)
cfg.evaluate.merge_from_other_cfg(evaluate_args)
# load features
query_fea, query_info, _ = feature_loader.load(
query_fea_dir, [fea_name])
gallery_fea, gallery_info, _ = feature_loader.load(
gallery_fea_dir, [fea_name])
# build helper and index features
index_helper = build_index_helper(cfg.index)
index_result_info, _, _ = 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)
# record results
to_save_recall = dict()
for k in recall_at_k:
to_save_recall[str(k)] = recall_at_k[k]
result_dict["mAP"] = float(mAP)
result_dict["recall_at_k"] = to_save_recall
results.append(result_dict)
# save results
with open(args.save_path, "w") as f:
json.dump(results, f)
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
pre_processes = SearchModules()
pre_processes.add(
"Direct256128", {
"batch_size": 32,
"folder": {
"name": "Folder"
},
"collate_fn": {
"name": "CollateFn"
},
"transformers": {
"names": ["DirectResize", "TwoFlip", "ToTensor", "Normalize"],
"DirectResize": {
"size": (256, 128),
"interpolation": 3
},
"Normalize": {
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225]
}
}
})
cfg = get_defaults_cfg()
pre_processes.check_valid(cfg["datasets"])
