From 06afa9b644beb3ee2ab58b0104a8a962f20dcf76 Mon Sep 17 00:00:00 2001
From: hubenyi <867320491@qq.com>
Date: Fri, 17 Apr 2020 20:31:26 +0800
Subject: [PATCH] upload
---
docs/GETTING_STARTED.md | 27 ++-
docs/INSTALL.md | 2 +-
docs/MODEL_ZOO.md | 2 +-
.../dim_processors_impl/part_pca.py | 4 +-
.../dim_processors_impl/part_svd.py | 14 +-
search/reid_search_extract.py | 13 +-
...d_search_query.py => reid_search_index.py} | 29 ++-
search/reid_search_modules/extract_dict.py | 4 +-
.../{query_dict.py => index_dict.py} | 44 ++---
...ta_process_dict.py => pre_process_dict.py} | 6 +-
search/reid_search_query.py | 149 ----------------
search/search_extract.py | 18 +-
.../{new_search_query.py => search_index.py} | 44 +++--
.../{query_dict.py => index_dict.py} | 40 +++--
...ta_process_dict.py => pre_process_dict.py} | 14 +-
search/search_pwa_extract.py | 25 +--
.../{query_dict.py => index_dict.py} | 64 +++----
.../pre_process_dict.py} | 14 +-
search/search_query.py | 167 ------------------
.../{query_dict.py => index_dict.py} | 64 +++----
.../pre_process_dict.py} | 14 +-
search/show_search_results.py | 61 ++-----
search/utils/misc.py | 69 +++++++-
23 files changed, 325 insertions(+), 563 deletions(-)
rename search/{new_reid_search_query.py => reid_search_index.py} (81%)
rename search/reid_search_modules/{query_dict.py => index_dict.py} (81%)
rename search/reid_search_modules/{data_process_dict.py => pre_process_dict.py} (87%)
delete mode 100644 search/reid_search_query.py
rename search/{new_search_query.py => search_index.py} (72%)
rename search/search_modules/{query_dict.py => index_dict.py} (76%)
rename search/search_modules/{data_process_dict.py => pre_process_dict.py} (87%)
rename search/search_pwa_modules/{query_dict.py => index_dict.py} (65%)
rename search/{search_rmac_modules/data_process_dict.py => search_pwa_modules/pre_process_dict.py} (87%)
delete mode 100644 search/search_query.py
rename search/search_rmac_modules/{query_dict.py => index_dict.py} (65%)
rename search/{search_pwa_modules/data_process_dict.py => search_rmac_modules/pre_process_dict.py} (87%)
diff --git a/docs/GETTING_STARTED.md b/docs/GETTING_STARTED.md
index ba01afe..9d8ba86 100644
--- a/docs/GETTING_STARTED.md
+++ b/docs/GETTING_STARTED.md
@@ -67,7 +67,7 @@ Examples:
# for dataset collecting images with the same label in one directory
python3 main/make_data_json.py -d /data/caltech101/gallery/ -sp data_jsons/caltech_gallery.json -t general
-python3 main/make_data_json.py -d /data/caltech101/query/ -sp data_jsons/caltech_query.json -t feneral
+python3 main/make_data_json.py -d /data/caltech101/query/ -sp data_jsons/caltech_query.json -t general
# for oxford/paris dataset
python3 main/make_data_json.py -d /data/cbir/oxford/gallery/ -sp data_jsons/oxford_gallery.json -t oxford -gt /data/cbir/oxford/gt/
@@ -229,10 +229,10 @@ cd search/
### Define Search Space
-We decompose the search space into three sub search spaces: data_process, extract and index, each of which corresponds to a specified file. Search space is defined by adding methods with hyper-parameters to a specified dict. You can add a search operator as follows:
+We decompose the search space into three sub search spaces: pre_process, extract and index, each of which corresponds to a specified file. Search space is defined by adding methods with hyper-parameters to a specified dict. You can add a search operator as follows:
```shell
-data_processes.add(
+pre_processes.add(
"PadResize224",
{
"batch_size": 32,
@@ -257,7 +257,7 @@ data_processes.add(
)
```
-By doing this, a data_process operator named "PadResize224" is added to the data_process sub search space and will be searched in the following process.
+By doing this, a pre_process operator named "PadResize224" is added to the data_process sub search space and will be searched in the following process.
### Search
@@ -287,7 +287,7 @@ python3 search_extract.py -sp /data/features/gap_gmp_gem_crow_spoc/ -sm search_m
Search for the indexing combinations by:
```shell
-python3 search_query.py [-fd ${fea_dir}] [-sm ${search_modules}] [-sp ${save_path}]
+python3 search_index.py [-fd ${fea_dir}] [-sm ${search_modules}] [-sp ${save_path}]
```
Arguments:
@@ -299,8 +299,23 @@ Arguments:
Examples:
```shell
-python3 search_query.py -fd /data/features/gap_gmp_gem_crow_spoc/ -sm search_modules -sp /data/features/gap_gmp_gem_crow_spoc_result.json
+python3 search_index.py -fd /data/features/gap_gmp_gem_crow_spoc/ -sm search_modules -sp /data/features/gap_gmp_gem_crow_spoc_result.json
```
+#### show search results
+We provide two ways to show the search results. One is save all the search results in a csv format file, which can be used for further analyses. The other is showing the search results according to the given keywords. You can define the keywords as follows:
+
+```sh
+keywords = {
+ 'data_name': ['market'],
+ 'pre_process_name': list(),
+ 'model_name': list(),
+ 'feature_map_name': list(),
+ 'aggregator_name': list(),
+ 'post_process_name': ['no_fea_process', 'l2_normalize', 'pca_whiten', 'pca_wo_whiten'],
+ }
+```
+
+See show_search_results.py for more details.
diff --git a/docs/INSTALL.md b/docs/INSTALL.md
index 37b3eed..e412344 100644
--- a/docs/INSTALL.md
+++ b/docs/INSTALL.md
@@ -35,7 +35,7 @@ cd pyretri
3. Install PyRetri.
```shell
-python setup.py install
+python3 setup.py install
```
## Prepare Datasets
diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md
index 4454ca9..832c6fa 100644
--- a/docs/MODEL_ZOO.md
+++ b/docs/MODEL_ZOO.md
@@ -35,7 +35,7 @@ Choosing the implementations mentioned above as baselines and adding some tricks
For person re-identification, we use the model provided by [Person_reID_baseline](https://github.com/layumi/Person_reID_baseline_pytorch) and reproduce its resutls. In addition, we train a model on DukeMTMC-reID through the open source code for further experiments.
-###pre-trained models
+### pre-trained models
| Training Set | Backbone | for Short | Download |
| :-----------: | :-------: | :-------: | :------: |
diff --git a/pyretri/index/dim_processor/dim_processors_impl/part_pca.py b/pyretri/index/dim_processor/dim_processors_impl/part_pca.py
index 33b3824..95d8797 100644
--- a/pyretri/index/dim_processor/dim_processors_impl/part_pca.py
+++ b/pyretri/index/dim_processor/dim_processors_impl/part_pca.py
@@ -84,7 +84,9 @@ class PartPCA(DimProcessorBase):
pca = self.pcas[fea_name]["pca"]
ori_fea = fea[:, st_idx: ed_idx]
- proj_fea = pca.transform(ori_fea)
+ proj_fea = normalize(ori_fea, norm='l2')
+ proj_fea = pca.transform(proj_fea)
+ proj_fea = normalize(proj_fea, norm='l2')
ret.append(proj_fea)
diff --git a/pyretri/index/dim_processor/dim_processors_impl/part_svd.py b/pyretri/index/dim_processor/dim_processors_impl/part_svd.py
index 79bbb7d..0494ce4 100644
--- a/pyretri/index/dim_processor/dim_processors_impl/part_svd.py
+++ b/pyretri/index/dim_processor/dim_processors_impl/part_svd.py
@@ -65,6 +65,7 @@ class PartSVD(DimProcessorBase):
else:
proj_part_dim = self._hyper_params["proj_dim"] - already_proj_dim
assert proj_part_dim < ori_part_dim, "reduction dimension can not be distributed to each part!"
+ already_proj_dim += proj_part_dim
svd = SKSVD(n_components=proj_part_dim)
train_fea = fea[:, st_idx: ed_idx]
@@ -79,22 +80,23 @@ class PartSVD(DimProcessorBase):
}
def __call__(self, fea: np.ndarray) -> np.ndarray:
- if self._hyper_params["proj_dim"] != 0:
- ret = np.zeros(shape=(fea.shape[0], self._hyper_params["proj_dim"]))
- else:
- ret = np.zeros(shape=(fea.shape[0], fea.shape[1] - len(self.svds)))
+ fea_names = np.sort(list(self.svds.keys()))
+ ret = list()
- for fea_name in self.svds:
+ for fea_name in fea_names:
st_idx, ed_idx = self.svds[fea_name]["pos"][0], self.svds[fea_name]["pos"][1]
svd = self.svds[fea_name]["svd"]
proj_fea = fea[:, st_idx: ed_idx]
+ proj_fea = normalize(proj_fea, norm='l2')
proj_fea = svd.transform(proj_fea)
if self._hyper_params["whiten"]:
proj_fea = proj_fea / (self.svds[fea_name]["std"] + 1e-6)
+ proj_fea = normalize(proj_fea, norm='l2')
- ret[:, st_idx: ed_idx] = proj_fea
+ ret.append(proj_fea)
+ ret = np.concatenate(ret, axis=1)
return ret
diff --git a/search/reid_search_extract.py b/search/reid_search_extract.py
index 291ea11..c7ac13f 100644
--- a/search/reid_search_extract.py
+++ b/search/reid_search_extract.py
@@ -49,24 +49,27 @@ def main():
# load search space
datasets = load_datasets()
- data_processes = importlib.import_module("{}.data_process_dict".format(args.search_modules)).data_processes
+ 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 data_proc_name, data_proc_args in data_processes.items():
-
+ for pre_proc_name, pre_proc_args in pre_processes.items():
if 'market' in data_name:
model_name = 'market_res50'
elif 'duke' in data_name:
model_name = 'duke_res50'
- feature_full_name = data_name + "_" + data_proc_name + "_" + model_name
+ 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(data_proc_args)
+ cfg.datasets.merge_from_other_cfg(pre_proc_args)
cfg.model.merge_from_other_cfg(models[model_name])
cfg.extract.merge_from_other_cfg(extracts[model_name])
diff --git a/search/new_reid_search_query.py b/search/reid_search_index.py
similarity index 81%
rename from search/new_reid_search_query.py
rename to search/reid_search_index.py
index 737aab0..bf814e2 100644
--- a/search/new_reid_search_query.py
+++ b/search/reid_search_index.py
@@ -5,7 +5,7 @@ import importlib
import os
import argparse
-from .utils.misc import check_exist, get_dir
+from utils.misc import check_result_exist, get_dir, get_default_result_dict
from pyretri.config import get_defaults_cfg
from pyretri.index import build_index_helper, feature_loader
@@ -53,8 +53,8 @@ def main():
# load search space
datasets = load_datasets()
- queries = importlib.import_module("{}.query_dict".format(args.search_modules)).queries
- evaluates = importlib.import_module("{}.query_dict".format(args.search_modules)).evaluates
+ 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:
@@ -64,8 +64,9 @@ def main():
for dir in os.listdir(args.fea_dir):
for data_name, data_args in datasets.items():
- for query_name, query_args in queries.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)
@@ -73,21 +74,19 @@ def main():
# get evaluate setting
evaluate_args = evaluates["reid_overall"]
- for dim_proc in query_args.dim_processors.names:
+ for dim_proc in index_args.dim_processors.names:
if dim_proc in ["PartPCA", "PartSVD", "PCA", "SVD"]:
- query_args.dim_processors[dim_proc].train_fea_dir = train_fea_dir
+ 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, query_name, fea_name)
- if check_exist(result_dict, results):
+ 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
- print(data_name + '_' + fea_name + '_' + query_name)
# load retrieval pipeline settings
- query_args.feature_names = [fea_name]
- cfg.index.merge_from_other_cfg(query_args)
+ index_args.feature_names = [fea_name]
+ cfg.index.merge_from_other_cfg(index_args)
cfg.evaluate.merge_from_other_cfg(evaluate_args)
# load features
@@ -95,12 +94,12 @@ def main():
gallery_fea, gallery_info, _ = feature_loader.load(gallery_fea_dir, [fea_name])
# build helper and index features
- query_helper = build_index_helper(cfg.index)
- query_result_info, _, _ = query_helper.do_index(query_fea, query_info, gallery_fea)
+ 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(query_result_info, gallery_info)
+ mAP, recall_at_k = evaluate_helper.do_eval(index_result_info, gallery_info)
# record results
to_save_recall = dict()
diff --git a/search/reid_search_modules/extract_dict.py b/search/reid_search_modules/extract_dict.py
index 6545552..c9d1cc0 100644
--- a/search/reid_search_modules/extract_dict.py
+++ b/search/reid_search_modules/extract_dict.py
@@ -19,6 +19,7 @@ models.add(
extracts.add(
"market_res50",
{
+ "assemble": 1,
"extractor": {
"name": "ReIDSeries",
"ReIDSeries": {
@@ -39,13 +40,14 @@ models.add(
{
"name": "ft_net",
"ft_net": {
- "load_checkpoint": "/home/songrenjie/projects/reID_baseline/model/ft_ResNet50/net_59.pth"
+ "load_checkpoint": "/home/songrenjie/projects/reID_baseline/model/ft_ResNet50/res50_duke.pth"
}
}
)
extracts.add(
"duke_res50",
{
+ "assemble": 1,
"extractor": {
"name": "ReIDSeries",
"ReIDSeries": {
diff --git a/search/reid_search_modules/query_dict.py b/search/reid_search_modules/index_dict.py
similarity index 81%
rename from search/reid_search_modules/query_dict.py
rename to search/reid_search_modules/index_dict.py
index d1c386b..51ba809 100644
--- a/search/reid_search_modules/query_dict.py
+++ b/search/reid_search_modules/index_dict.py
@@ -3,10 +3,10 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-queries = SearchModules()
+indexes = SearchModules()
evaluates = SearchModules()
-queries.add(
+indexes.add(
"no_fea_process",
{
"gallery_fea_dir": "",
@@ -32,7 +32,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"l2_normalize",
{
"gallery_fea_dir": "",
@@ -58,7 +58,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"pca_wo_whiten",
{
"gallery_fea_dir": "",
@@ -67,11 +67,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartPCA"],
- "PartPCA": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
@@ -89,7 +90,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"pca_whiten",
{
"gallery_fea_dir": "",
@@ -98,11 +99,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartPCA"],
- "PartPCA": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
@@ -120,7 +122,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"svd_wo_whiten",
{
"gallery_fea_dir": "",
@@ -129,11 +131,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartSVD"],
- "PartSVD": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
@@ -151,7 +154,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"svd_whiten",
{
"gallery_fea_dir": "",
@@ -160,11 +163,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartSVD"],
- "PartSVD": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
@@ -211,5 +215,5 @@ evaluates.add(
cfg = get_defaults_cfg()
-queries.check_valid(cfg["index"])
+indexes.check_valid(cfg["index"])
evaluates.check_valid(cfg["evaluate"])
diff --git a/search/reid_search_modules/data_process_dict.py b/search/reid_search_modules/pre_process_dict.py
similarity index 87%
rename from search/reid_search_modules/data_process_dict.py
rename to search/reid_search_modules/pre_process_dict.py
index ba33d4f..5556ab1 100644
--- a/search/reid_search_modules/data_process_dict.py
+++ b/search/reid_search_modules/pre_process_dict.py
@@ -3,9 +3,9 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-data_processes = SearchModules()
+pre_processes = SearchModules()
-data_processes.add(
+pre_processes.add(
"Direct256128",
{
"batch_size": 32,
@@ -31,4 +31,4 @@ data_processes.add(
cfg = get_defaults_cfg()
-data_processes.check_valid(cfg["datasets"])
+pre_processes.check_valid(cfg["datasets"])
diff --git a/search/reid_search_query.py b/search/reid_search_query.py
deleted file mode 100644
index 36f6267..0000000
--- a/search/reid_search_query.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import json
-import importlib
-import os
-import argparse
-
-from pyretri.config import get_defaults_cfg
-from pyretri.query import build_query_helper
-from pyretri.evaluate import build_evaluate_helper
-from pyretri.query.utils import feature_loader
-
-fea_names = ["output"]
-
-task_mapping = {
- "market_gallery": {
- "gallery": "market_gallery",
- "query": "market_query",
- "train_fea_dir": "market_gallery"
- },
- "duke_gallery": {
- "gallery": "duke_gallery",
- "query": "duke_query",
- "train_fea_dir": "duke_gallery"
- },
-}
-
-
-def check_exist(now_res, exist_results):
- for e_r in exist_results:
- totoal_equal = True
- for key in now_res:
- if now_res[key] != e_r[key]:
- totoal_equal = False
- break
- if totoal_equal:
- return True
- return False
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='A tool box for deep learning-based image retrieval')
- parser.add_argument('--fea_dir', '-f', default=None, type=str, help="path of feature directory", required=True)
- parser.add_argument(
- "--search_modules",
- "-m",
- default="",
- help="name of search module's directory",
- type=str,
- required=True
- )
- parser.add_argument("--save_path", "-s", default=None, type=str, required=True)
- args = parser.parse_args()
-
- return args
-
-
-def get_default_result_dict(dir, task_name, query_name, fea_name):
-
- result_dict = {
- "task_name": task_name.split("_")[0],
- "dataprocess": dir.split("_")[0],
- "model_name": "_".join(dir.split("_")[-2:]),
- "feature_map_name": fea_name.split("_")[0],
- "fea_process_name": query_name
- }
- if fea_name == "output":
- result_dict["aggregator_name"] = "none"
- else:
- result_dict["aggregator_name"] = fea_name.split("_")[1]
-
- return result_dict
-
-
-def main():
-
- args = parse_args()
-
- cfg = get_defaults_cfg()
-
- queries = importlib.import_module("{}.query_dict".format(args.search_modules)).queries
- evaluates = importlib.import_module("{}.query_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()
-
- q_cnt = 0
- for dir in os.listdir(args.fea_dir):
- q_cnt += 1
- print("Processing {} / {} queries...".format(q_cnt, len(queries)))
- for task_name in task_mapping:
- for query_name, query_args in queries.items():
- if task_name in dir:
- for fea_name in fea_names:
-
- gallery_fea_dir = os.path.join(args.fea_dir, dir)
- query_fea_dir = gallery_fea_dir.replace(task_name, task_mapping[task_name]["query"])
- train_fea_dir = gallery_fea_dir.replace(task_name, task_mapping[task_name]["train_fea_dir"])
-
- assert os.path.exists(gallery_fea_dir), gallery_fea_dir
- assert os.path.exists(query_fea_dir), query_fea_dir
- assert os.path.exists(train_fea_dir), train_fea_dir
-
- for post_proc in ["PartPCA", "PartSVD"]:
- if post_proc in query_args.post_processors.names:
- query_args.post_processors[post_proc].train_fea_dir = train_fea_dir
- query_args.gallery_fea_dir, query_args.query_fea_dir = gallery_fea_dir, query_fea_dir
- query_args.feature_names = [fea_name]
- eval_args = evaluates["reid_overall"]
-
- result_dict = get_default_result_dict(dir, task_name, query_name, fea_name)
-
- if check_exist(result_dict, results):
- print("[Search Query]: config exists...")
- continue
-
- cfg.query.merge_from_other_cfg(query_args)
- cfg.evaluate.merge_from_other_cfg(eval_args)
-
- query_helper = build_query_helper(cfg.query)
- evaluate_helper = build_evaluate_helper(cfg.evaluate)
-
- query_fea, query_info_dicts, _ = feature_loader.load(cfg.query.query_fea_dir,
- cfg.query.feature_names)
- gallery_fea, gallery_info_dicts, _ = feature_loader.load(cfg.query.gallery_fea_dir,
- cfg.query.feature_names)
-
- query_result_info_dicts, _, _ = query_helper.do_query(query_fea, query_info_dicts, gallery_fea)
- mAP, recall_at_k = evaluate_helper.do_eval(query_result_info_dicts, gallery_info_dicts)
-
- to_save_dict = dict()
- for k in recall_at_k:
- to_save_dict[str(k)] = recall_at_k[k]
-
- result_dict["mAP"] = float(mAP)
- result_dict["recall_at_k"] = to_save_dict
- print(result_dict)
- assert False
-
- results.append(result_dict)
- with open(args.save_path, "w") as f:
- json.dump(results, f)
-
-
-if __name__ == '__main__':
- main()
diff --git a/search/search_extract.py b/search/search_extract.py
index 0d4e64d..3c92639 100644
--- a/search/search_extract.py
+++ b/search/search_extract.py
@@ -30,17 +30,11 @@ def load_datasets():
return datasets
-def check_exist(save_path, full_name):
- if os.path.exists(os.path.join(save_path, full_name)):
- return True
- return False
-
-
def parse_args():
parser = argparse.ArgumentParser(description='A tool box for deep learning-based image retrieval')
parser.add_argument('opts', default=None, nargs=argparse.REMAINDER)
parser.add_argument('--save_path', '-sp', default=None, type=str, help="save path for feature")
- parser.add_argument("--search_modules", "-sm", default="", type=str, help="name of search module's directory")
+ parser.add_argument("--search_modules", "-sm", default=None, type=str, help="name of search module's directory")
args = parser.parse_args()
return args
@@ -58,24 +52,24 @@ def main():
# load search space
datasets = load_datasets()
- data_processes = importlib.import_module("{}.data_process_dict".format(args.search_modules)).data_processes
+ 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 data_proc_name, data_proc_args in data_processes.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 + "_" + data_proc_name + "_" + model_name
+ feature_full_name = data_name + "_" + pre_proc_name + "_" + model_name
print(feature_full_name)
- if check_exist(args.save_path, 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(data_proc_args)
+ 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])
diff --git a/search/new_search_query.py b/search/search_index.py
similarity index 72%
rename from search/new_search_query.py
rename to search/search_index.py
index ae01d7b..c51eb24 100644
--- a/search/new_search_query.py
+++ b/search/search_index.py
@@ -5,13 +5,26 @@ import importlib
import os
import argparse
-from .utils.misc import check_exist, get_dir
+from utils.misc import check_result_exist, get_dir, get_default_result_dict
from pyretri.config import get_defaults_cfg
from pyretri.index import build_index_helper, feature_loader
from pyretri.evaluate import build_evaluate_helper
+# # gap, gmp, gem, spoc, crow
+# vgg_fea = ["pool4_GAP", "pool4_GMP", "pool4_GeM", "pool4_SPoC", "pool4_Crow",
+# "pool5_GAP", "pool5_GMP", "pool5_GeM", "pool5_SPoC", "pool5_Crow",
+# "fc"]
+# res_fea = ["pool3_GAP", "pool3_GMP", "pool3_GeM", "pool3_SPoC", "pool4_Crow",
+# "pool4_GAP", "pool4_GMP", "pool4_GeM", "pool4_SPoC", "pool4_Crow",
+# "pool5_GAP", "pool5_GMP", "pool5_GeM", "pool5_SPoC", "pool5_Crow"]
+
+# # scda, rmca
+# vgg_fea = ["pool5_SCDA", "pool5_RMAC"]
+# res_fea = ["pool5_SCDA", "pool5_RMAC"]
+
+# pwa
vgg_fea = ["pool5_PWA"]
res_fea = ["pool5_PWA"]
@@ -80,8 +93,8 @@ def main():
# load search space
datasets = load_datasets()
- queries = importlib.import_module("{}.query_dict".format(args.search_modules)).queries
- evaluates = importlib.import_module("{}.query_dict".format(args.search_modules)).evaluates
+ 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:
@@ -91,8 +104,10 @@ def main():
for dir in os.listdir(args.fea_dir):
for data_name, data_args in datasets.items():
- for query_name, query_args in queries.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)
@@ -102,19 +117,20 @@ def main():
# get feature names
fea_names = get_fea_names(gallery_fea_dir)
- for post_proc in query_args.post_processors.names:
- if post_proc in ["PartPCA", "PartSVD", "PCA", "SVD"]:
- query_args.post_processors[post_proc].train_fea_dir = train_fea_dir
+ # set train feature path for dimension reduction processes
+ 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, query_name, fea_name)
- if check_exist(result_dict, results):
+ 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
- query_args.feature_names = [fea_name]
- cfg.index.merge_from_other_cfg(query_args)
+ index_args.feature_names = [fea_name]
+ cfg.index.merge_from_other_cfg(index_args)
cfg.evaluate.merge_from_other_cfg(evaluate_args)
# load features
@@ -122,12 +138,12 @@ def main():
gallery_fea, gallery_info, _ = feature_loader.load(gallery_fea_dir, [fea_name])
# build helper and index features
- query_helper = build_query_helper(cfg.query)
- query_result_info, _, _ = query_helper.do_query(query_fea, query_info, gallery_fea)
+ 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(query_result_info, gallery_info)
+ mAP, recall_at_k = evaluate_helper.do_eval(index_result_info, gallery_info)
# record results
to_save_recall = dict()
diff --git a/search/search_modules/query_dict.py b/search/search_modules/index_dict.py
similarity index 76%
rename from search/search_modules/query_dict.py
rename to search/search_modules/index_dict.py
index 1ff4bad..b932295 100644
--- a/search/search_modules/query_dict.py
+++ b/search/search_modules/index_dict.py
@@ -3,10 +3,10 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-queries = SearchModules()
+indexes = SearchModules()
evaluates = SearchModules()
-queries.add(
+indexes.add(
"pca_wo_whiten",
{
"gallery_fea_dir": "",
@@ -15,11 +15,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartPCA"],
- "PartPCA": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
@@ -37,7 +38,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"pca_whiten",
{
"gallery_fea_dir": "",
@@ -46,11 +47,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartPCA"],
- "PartPCA": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
@@ -68,7 +70,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"svd_wo_whiten",
{
"gallery_fea_dir": "",
@@ -77,11 +79,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartSVD"],
- "PartSVD": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
@@ -99,7 +102,7 @@ queries.add(
}
)
-queries.add(
+indexes.add(
"svd_whiten",
{
"gallery_fea_dir": "",
@@ -108,11 +111,12 @@ queries.add(
"feature_names": [],
"dim_processors": {
- "names": ["PartSVD"],
- "PartSVD": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
@@ -150,5 +154,5 @@ evaluates.add(
cfg = get_defaults_cfg()
-queries.check_valid(cfg["index"])
+indexes.check_valid(cfg["index"])
evaluates.check_valid(cfg["evaluate"])
diff --git a/search/search_modules/data_process_dict.py b/search/search_modules/pre_process_dict.py
similarity index 87%
rename from search/search_modules/data_process_dict.py
rename to search/search_modules/pre_process_dict.py
index 63bbc1c..7b68194 100644
--- a/search/search_modules/data_process_dict.py
+++ b/search/search_modules/pre_process_dict.py
@@ -3,9 +3,9 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-data_processes = SearchModules()
+pre_processes = SearchModules()
-data_processes.add(
+pre_processes.add(
"Shorter256Center224",
{
"batch_size": 32,
@@ -16,8 +16,8 @@ data_processes.add(
"name": "CollateFn"
},
"transformers": {
- "names": ["ResizeShorter", "CenterCrop", "ToTensor", "Normalize"],
- "ResizeShorter": {
+ "names": ["ShorterResize", "CenterCrop", "ToTensor", "Normalize"],
+ "ShorterResize": {
"size": 256
},
"CenterCrop": {
@@ -31,7 +31,7 @@ data_processes.add(
}
)
-data_processes.add(
+pre_processes.add(
"Direct224",
{
"batch_size": 32,
@@ -54,7 +54,7 @@ data_processes.add(
}
)
-data_processes.add(
+pre_processes.add(
"PadResize224",
{
"batch_size": 32,
@@ -80,4 +80,4 @@ data_processes.add(
cfg = get_defaults_cfg()
-data_processes.check_valid(cfg["datasets"])
+pre_processes.check_valid(cfg["datasets"])
diff --git a/search/search_pwa_extract.py b/search/search_pwa_extract.py
index a19abfd..86413d3 100644
--- a/search/search_pwa_extract.py
+++ b/search/search_pwa_extract.py
@@ -21,9 +21,9 @@ def load_datasets():
"cub_query": os.path.join(data_json_dir, "cub_query.json"),
"indoor_gallery": os.path.join(data_json_dir, "indoor_gallery.json"),
"indoor_query": os.path.join(data_json_dir, "indoor_query.json"),
- "caltech101_gallery": os.path.join(data_json_dir, "caltech101_gallery.json"),
- "caltech101_query": os.path.join(data_json_dir, "caltech101_query.json"),
- "paris": os.path.join(data_json_dir, "paris.json"),
+ "caltech_gallery": os.path.join(data_json_dir, "caltech_gallery.json"),
+ "caltech_query": os.path.join(data_json_dir, "caltech_query.json"),
+ "paris_all": os.path.join(data_json_dir, "paris.json"),
}
for data_path in datasets.values():
assert os.path.exists(data_path), "non-exist dataset path {}".format(data_path)
@@ -48,25 +48,30 @@ def main():
# init retrieval pipeline settings
cfg = get_defaults_cfg()
- data_processes = importlib.import_module("{}.data_process_dict".format(args.search_modules)).data_processes
+ # 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
- datasets = load_datasets()
-
for data_name, data_args in datasets.items():
- for data_proc_name, data_proc_args in data_processes.items():
+ for pre_proc_name, pre_proc_args in pre_processes.items():
for model_name, model_args in models.items():
- feature_full_name = data_process_name + "_" + dataset_name + "_" + model_name
+ 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(data_proc_args)
+ 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])
- pwa_train_fea_dir = os.path.join("/data/my_features/gap_gmp_gem_crow_spoc", feature_full_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:
diff --git a/search/search_pwa_modules/query_dict.py b/search/search_pwa_modules/index_dict.py
similarity index 65%
rename from search/search_pwa_modules/query_dict.py
rename to search/search_pwa_modules/index_dict.py
index da9324f..b932295 100644
--- a/search/search_pwa_modules/query_dict.py
+++ b/search/search_pwa_modules/index_dict.py
@@ -3,10 +3,10 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-queries = SearchModules()
+indexes = SearchModules()
evaluates = SearchModules()
-queries.add(
+indexes.add(
"pca_wo_whiten",
{
"gallery_fea_dir": "",
@@ -14,16 +14,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartPCA",
- "PartPCA": {
+ "dim_processors": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -31,13 +32,13 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
)
-queries.add(
+indexes.add(
"pca_whiten",
{
"gallery_fea_dir": "",
@@ -45,16 +46,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartPCA",
- "PartPCA": {
+ "dim_processors": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -62,13 +64,13 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
)
-queries.add(
+indexes.add(
"svd_wo_whiten",
{
"gallery_fea_dir": "",
@@ -76,16 +78,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartSVD",
- "PartSVD": {
+ "dim_processors": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -93,13 +96,13 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
)
-queries.add(
+indexes.add(
"svd_whiten",
{
"gallery_fea_dir": "",
@@ -107,16 +110,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartSVD",
- "PartSVD": {
+ "dim_processors": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -124,7 +128,7 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
@@ -150,5 +154,5 @@ evaluates.add(
cfg = get_defaults_cfg()
-queries.check_valid(cfg["query"])
+indexes.check_valid(cfg["index"])
evaluates.check_valid(cfg["evaluate"])
diff --git a/search/search_rmac_modules/data_process_dict.py b/search/search_pwa_modules/pre_process_dict.py
similarity index 87%
rename from search/search_rmac_modules/data_process_dict.py
rename to search/search_pwa_modules/pre_process_dict.py
index 63bbc1c..7b68194 100644
--- a/search/search_rmac_modules/data_process_dict.py
+++ b/search/search_pwa_modules/pre_process_dict.py
@@ -3,9 +3,9 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-data_processes = SearchModules()
+pre_processes = SearchModules()
-data_processes.add(
+pre_processes.add(
"Shorter256Center224",
{
"batch_size": 32,
@@ -16,8 +16,8 @@ data_processes.add(
"name": "CollateFn"
},
"transformers": {
- "names": ["ResizeShorter", "CenterCrop", "ToTensor", "Normalize"],
- "ResizeShorter": {
+ "names": ["ShorterResize", "CenterCrop", "ToTensor", "Normalize"],
+ "ShorterResize": {
"size": 256
},
"CenterCrop": {
@@ -31,7 +31,7 @@ data_processes.add(
}
)
-data_processes.add(
+pre_processes.add(
"Direct224",
{
"batch_size": 32,
@@ -54,7 +54,7 @@ data_processes.add(
}
)
-data_processes.add(
+pre_processes.add(
"PadResize224",
{
"batch_size": 32,
@@ -80,4 +80,4 @@ data_processes.add(
cfg = get_defaults_cfg()
-data_processes.check_valid(cfg["datasets"])
+pre_processes.check_valid(cfg["datasets"])
diff --git a/search/search_query.py b/search/search_query.py
deleted file mode 100644
index d5944c8..0000000
--- a/search/search_query.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import json
-import importlib
-import os
-import argparse
-
-from pyretri.config import get_defaults_cfg
-from pyretri.query import build_query_helper
-from pyretri.evaluate import build_evaluate_helper
-from pyretri.index import feature_loader
-
-
-vgg_fea = ["pool5_PWA"]
-res_fea = ["pool5_PWA"]
-
-task_mapping = {
- "oxford_gallery": {
- "gallery": "oxford_gallery",
- "query": "oxford_query",
- "train_fea_dir": "paris"
- },
- "cub_gallery": {
- "gallery": "cub_gallery",
- "query": "cub_query",
- "train_fea_dir": "cub_gallery"
- },
- "indoor_gallery": {
- "gallery": "indoor_gallery",
- "query": "indoor_query",
- "train_fea_dir": "indoor_gallery"
- },
- "caltech101_gallery": {
- "gallery": "caltech101_gallery",
- "query": "caltech101_query",
- "train_fea_dir": "caltech101_gallery"
- }
-}
-
-
-def check_exist(now_res, exist_results):
- for e_r in exist_results:
- totoal_equal = True
- for key in now_res:
- if now_res[key] != e_r[key]:
- totoal_equal = False
- break
- if totoal_equal:
- return True
- return False
-
-
-def get_default_result_dict(dir, task_name, query_name, fea_name):
- result_dict = {
- "task_name": task_name.split("_")[0],
- "dataprocess": dir.split("_")[0],
- "model_name": "_".join(dir.split("_")[-2:]),
- "feature_map_name": fea_name.split("_")[0],
- "fea_process_name": query_name
- }
-
- if fea_name == "fc":
- result_dict["aggregator_name"] = "none"
- else:
- result_dict["aggregator_name"] = fea_name.split("_")[1]
-
- return result_dict
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='A tool box for deep learning-based image retrieval')
- parser.add_argument('--fea_dir', '-fd', default=None, type=str, help="path of feature dirs", required=True)
- parser.add_argument("--search_modules", "-sm", default=None, type=str, help="name of search module's directory")
- parser.add_argument("--save_path", "-sp", default=None, type=str, help="path for saving results")
- args = parser.parse_args()
-
- return args
-
-
-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
- queries = importlib.import_module("{}.query_dict".format(args.search_modules)).queries
- evaluates = importlib.import_module("{}.query_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()
-
- q_cnt = 0
- for dir in os.listdir(args.fea_dir):
- q_cnt += 1
- print("Processing {} / {} queries...".format(q_cnt, len(queries)))
- for query_name, query_args in queries.items():
- for task_name in task_mapping:
- if task_name in dir:
-
- if "vgg" in gallery_fea_dir:
- fea_names = vgg_fea
- else:
- fea_names = res_fea
-
- for fea_name in fea_names:
- gallery_fea_dir = os.path.join(args.fea_dir, dir)
- query_fea_dir = gallery_fea_dir.replace(task_name, task_mapping[task_name]["query"])
- train_fea_dir = gallery_fea_dir.replace(task_name, task_mapping[task_name]["train_fea_dir"])
-
- for post_proc in ["PartPCA", "PartSVD"]:
- if post_proc in query_args.post_processors.names:
- query_args.post_processors[post_proc].train_fea_dir = train_fea_dir
-
- query.gallery_fea_dir, query.query_fea_dir = gallery_fea_dir, query_fea_dir
-
- query.feature_names = [fea_name]
- if task_name == "oxford_base":
- evaluate = evaluates["oxford_overall"]
- else:
- evaluate = evaluates["overall"]
-
- result_dict = get_default_result_dict(dir, task_name, query_name, fea_name)
-
- if check_exist(result_dict, results):
- print("[Search Query]: config exists...")
- continue
-
- # load retrieval pipeline settings
- cfg.query.merge_from_other_cfg(query)
- cfg.evaluate.merge_from_other_cfg(evaluate)
-
- # load features
- query_fea, query_info, _ = feature_loader.load(cfg.query.query_fea_dir, cfg.query.feature_names)
- gallery_fea, gallery_info, _ = feature_loader.load(cfg.query.gallery_fea_dir,
- cfg.query.feature_names)
-
- # build helper and index features
- query_helper = build_query_helper(cfg.query)
- query_result_info, _, _ = query_helper.do_query(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(query_result_info, gallery_info)
-
- # save results
- to_save_dict = dict()
- for k in recall_at_k:
- to_save_dict[str(k)] = recall_at_k[k]
- result_dict["mAP"] = float(mAP)
- result_dict["recall_at_k"] = to_save_dict
-
- results.append(result_dict)
- with open(args.save_path, "w") as f:
- json.dump(results, f)
-
-
-if __name__ == '__main__':
- main()
diff --git a/search/search_rmac_modules/query_dict.py b/search/search_rmac_modules/index_dict.py
similarity index 65%
rename from search/search_rmac_modules/query_dict.py
rename to search/search_rmac_modules/index_dict.py
index da9324f..b932295 100644
--- a/search/search_rmac_modules/query_dict.py
+++ b/search/search_rmac_modules/index_dict.py
@@ -3,10 +3,10 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-queries = SearchModules()
+indexes = SearchModules()
evaluates = SearchModules()
-queries.add(
+indexes.add(
"pca_wo_whiten",
{
"gallery_fea_dir": "",
@@ -14,16 +14,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartPCA",
- "PartPCA": {
+ "dim_processors": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -31,13 +32,13 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
)
-queries.add(
+indexes.add(
"pca_whiten",
{
"gallery_fea_dir": "",
@@ -45,16 +46,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartPCA",
- "PartPCA": {
+ "dim_processors": {
+ "names": ["L2Normalize", "PCA", "L2Normalize"],
+ "PCA": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 512
+ "proj_dim": 512,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -62,13 +64,13 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
)
-queries.add(
+indexes.add(
"svd_wo_whiten",
{
"gallery_fea_dir": "",
@@ -76,16 +78,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartSVD",
- "PartSVD": {
+ "dim_processors": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": False,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -93,13 +96,13 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
)
-queries.add(
+indexes.add(
"svd_whiten",
{
"gallery_fea_dir": "",
@@ -107,16 +110,17 @@ queries.add(
"feature_names": [],
- "post_processor": {
- "name": "PartSVD",
- "PartSVD": {
+ "dim_processors": {
+ "names": ["L2Normalize", "SVD", "L2Normalize"],
+ "SVD": {
"whiten": True,
"train_fea_dir": "",
- "proj_dim": 511
+ "proj_dim": 511,
+ "l2": True,
}
},
- "database_enhance": {
+ "feature_enhancer": {
"name": "Identity"
},
@@ -124,7 +128,7 @@ queries.add(
"name": "KNN"
},
- "re_rank": {
+ "re_ranker": {
"name": "Identity"
}
}
@@ -150,5 +154,5 @@ evaluates.add(
cfg = get_defaults_cfg()
-queries.check_valid(cfg["query"])
+indexes.check_valid(cfg["index"])
evaluates.check_valid(cfg["evaluate"])
diff --git a/search/search_pwa_modules/data_process_dict.py b/search/search_rmac_modules/pre_process_dict.py
similarity index 87%
rename from search/search_pwa_modules/data_process_dict.py
rename to search/search_rmac_modules/pre_process_dict.py
index 63bbc1c..7b68194 100644
--- a/search/search_pwa_modules/data_process_dict.py
+++ b/search/search_rmac_modules/pre_process_dict.py
@@ -3,9 +3,9 @@
from utils.search_modules import SearchModules
from pyretri.config import get_defaults_cfg
-data_processes = SearchModules()
+pre_processes = SearchModules()
-data_processes.add(
+pre_processes.add(
"Shorter256Center224",
{
"batch_size": 32,
@@ -16,8 +16,8 @@ data_processes.add(
"name": "CollateFn"
},
"transformers": {
- "names": ["ResizeShorter", "CenterCrop", "ToTensor", "Normalize"],
- "ResizeShorter": {
+ "names": ["ShorterResize", "CenterCrop", "ToTensor", "Normalize"],
+ "ShorterResize": {
"size": 256
},
"CenterCrop": {
@@ -31,7 +31,7 @@ data_processes.add(
}
)
-data_processes.add(
+pre_processes.add(
"Direct224",
{
"batch_size": 32,
@@ -54,7 +54,7 @@ data_processes.add(
}
)
-data_processes.add(
+pre_processes.add(
"PadResize224",
{
"batch_size": 32,
@@ -80,4 +80,4 @@ data_processes.add(
cfg = get_defaults_cfg()
-data_processes.check_valid(cfg["datasets"])
+pre_processes.check_valid(cfg["datasets"])
diff --git a/search/show_search_results.py b/search/show_search_results.py
index 6627693..5057f47 100644
--- a/search/show_search_results.py
+++ b/search/show_search_results.py
@@ -4,9 +4,10 @@ import os
import argparse
import json
-import csv
import codecs
+from utils.misc import save_to_csv, filter_by_keywords
+
def parse_args():
parser = argparse.ArgumentParser(description='A tool box for deep learning-based image retrieval')
@@ -22,62 +23,30 @@ def show_results(results):
print(results[i])
-def save_to_csv(results, csv_path):
- start = []
- col_num = 12
- if not os.path.exists(csv_path):
- start = ["data_process", "model", "feature", "fea_process", "market_mAP", "market_mAP_re", "market_R1",
- "market_R1_re", "duke_mAP", "duke_mAP_re", "duke_R1", "duke_R1_re"]
- with open(csv_path, 'a+') as f:
- csv_write = csv.writer(f)
- if len(start) > 0:
- csv_write.writerow(start)
- for i in range(len(results)):
- data_row = [0 for x in range(col_num)]
- data_row[0] = results[i]["dataprocess"]
- data_row[1] = results[i]["model_name"]
- data_row[2] = results[i]["feature_map_name"]
- data_row[3] = results[i]["fea_process_name"]
- if results[i]["task_name"] == 'market':
- data_row[4] = results[i]["mAP"]
- data_row[6] = results[i]["recall_at_k"]['1']
- elif results[i]["task_name"] == 'duke':
- data_row[8] = results[i]["mAP"]
- data_row[10] = results[i]["recall_at_k"]['1']
- csv_write.writerow(data_row)
-
-
def main():
-
+ # init args
args = parse_args()
assert os.path.exists(args.results_json_path), 'the config file must be existed!'
with open(args.results_json_path, "r") as f:
results = json.load(f)
- key_words = {
- 'task_name': ['market'],
- 'dataprocess': list(),
- 'model_name': list(),
- 'feature_map_name': list(),
- 'aggregator_name': list(),
- 'fea_process_name': ['no_fea_process', 'l2_normalize', 'pca_whiten', 'pca_wo_whiten'],
- }
-
+ # save the search results in a csv format file.
csv_path = '/home/songrenjie/projects/RetrievalToolBox/test.csv'
save_to_csv(results, csv_path)
- for key in key_words:
- no_match = []
- if len(key_words[key]) == 0:
- continue
- else:
- for i in range(len(results)):
- if not results[i][key] in key_words[key]:
- no_match.append(i)
- for num in no_match[::-1]:
- results.pop(num)
+ # define the keywords to be selected
+ keywords = {
+ 'data_name': ['market'],
+ 'pre_process_name': list(),
+ 'model_name': list(),
+ 'feature_map_name': list(),
+ 'aggregator_name': list(),
+ 'post_process_name': ['no_fea_process', 'l2_normalize', 'pca_whiten', 'pca_wo_whiten'],
+ }
+ # show search results according to the given keywords
+ results = filter_by_keywords(results, keywords)
show_results(results)
diff --git a/search/utils/misc.py b/search/utils/misc.py
index 5f0af0f..7c82ad7 100644
--- a/search/utils/misc.py
+++ b/search/utils/misc.py
@@ -1,9 +1,10 @@
# -*- coding: utf-8 -*-
+import os
from typing import Dict, List
+import csv
-
-def check_exist(now_res: Dict, exist_results: List) -> bool:
+def check_result_exist(now_res: Dict, exist_results: List) -> bool:
"""
Check if the config exists.
@@ -45,14 +46,14 @@ def get_dir(root_path: str, dir: str, dataset: Dict) -> (str, str, str):
return gallery_fea_dir, query_fea_dir, train_fea_dir
-def get_default_result_dict(dir, data_name, query_name, fea_name) -> Dict:
+def get_default_result_dict(dir: str, data_name: str, index_name: str, fea_name: str) -> Dict:
"""
Get the default result dict based on the experimental factors.
Args:
dir (str): the path of one single extracted feature directory.
data_name (str): the name of the dataset.
- query_name (str): the name of query process.
+ index_name (str): the name of query process.
fea_name (str): the name of the features to be loaded.
Returns:
@@ -60,16 +61,70 @@ def get_default_result_dict(dir, data_name, query_name, fea_name) -> Dict:
"""
result_dict = {
"data_name": data_name.split("_")[0],
- "dataprocess": dir.split("_")[0],
+ "pre_process_name": dir.split("_")[2],
"model_name": "_".join(dir.split("_")[-2:]),
"feature_map_name": fea_name.split("_")[0],
- "fea_process_name": query_name
+ "post_process_name": index_name
}
- if fea_name == "fc":
+ if len(fea_name.split("_")) == 1:
result_dict["aggregator_name"] = "none"
else:
result_dict["aggregator_name"] = fea_name.split("_")[1]
return result_dict
+
+def save_to_csv(results: List[Dict], csv_path: str) -> None:
+ """
+ Save the search results in a csv format file.
+
+ Args:
+ results (List): a list of retrieval results.
+ csv_path (str): the path for saving the csv file.
+ """
+ start = ["data", "pre_process", "model", "feature_map", "aggregator", "post_process"]
+ for i in range(len(start)):
+ results = sorted(results, key=lambda result: result[start[len(start) - i - 1] + "_name"])
+ start.append('mAP')
+ start.append('Recall@1')
+
+ with open(csv_path, 'w') as f:
+ csv_write = csv.writer(f)
+ if len(start) > 0:
+ csv_write.writerow(start)
+ for i in range(len(results)):
+ data_row = [0 for x in range(len(start))]
+ data_row[0] = results[i]["data_name"]
+ data_row[1] = results[i]["pre_process_name"]
+ data_row[2] = results[i]["model_name"]
+ data_row[3] = results[i]["feature_map_name"]
+ data_row[4] = results[i]["aggregator_name"]
+ data_row[5] = results[i]["post_process_name"]
+ data_row[6] = results[i]["mAP"]
+ data_row[7] = results[i]["recall_at_k"]['1']
+ csv_write.writerow(data_row)
+
+
+def filter_by_keywords(results: List[Dict], keywords: Dict) -> List[Dict]:
+ """
+ Filter the search results according to the given keywords
+
+ Args:
+ results (List): a list of retrieval results.
+ keywords (Dict): a dict containing keywords to be selected.
+
+ Returns:
+
+ """
+ for key in keywords:
+ no_match = []
+ if len(keywords[key]) == 0:
+ continue
+ else:
+ for i in range(len(results)):
+ if not results[i][key] in keywords[key]:
+ no_match.append(i)
+ for num in no_match[::-1]:
+ results.pop(num)
+ return results