From 711119890dba5939adf05f8ff124305aab913db3 Mon Sep 17 00:00:00 2001 From: xiaomoguhzz <179760844@qq.com> Date: Fri, 16 Jun 2023 11:30:21 +0800 Subject: [PATCH] add unknown task process Signed-off-by: xiaomoguhzz <179760844@qq.com> --- benchmarking.py | 69 +++ .../singletask_learning_active_boost.py | 147 +++++ .../singletask_learning_tta.py | 98 ++++ examples/yaoba/__init__.py | 0 .../benchmarkingjob.yaml | 70 +++ .../readme/README.md | 153 ++++++ .../readme/diagram1.jpg | Bin 0 -> 194888 bytes .../readme/diagram2.jpg | Bin 0 -> 321049 bytes .../readme/diagram3.jpg | Bin 0 -> 48881 bytes .../readme/requirements.txt | 7 + .../resource/FPN_model_config.py | 222 ++++++++ .../resource/__init__.py | 0 .../resource/base_config.py | 208 +++++++ .../resource/fpn_component/__init__.py | 0 .../convfc_bbox_head_weightv4.py | 188 +++++++ .../resource/fpn_component/tradboost_coco.py | 69 +++ .../resource/fpn_component/tradboost_pipe.py | 46 ++ .../weighted_standard_roi_head.py | 74 +++ .../resource/json/NG_labeled.json | 1 + .../resource/json/NG_test.json | 1 + .../resource/json/known_train.json | 1 + .../resource/train.py | 242 ++++++++ .../resource/utils/F1-score.py | 137 +++++ .../resource/utils/TTA_augs_xyxy_cv2.py | 513 +++++++++++++++++ .../resource/utils/infer_and_error.py | 202 +++++++ .../resource/utils/random_select.py | 25 + .../resource/utils/transform_unkonwn.py | 167 ++++++ .../utils/txt2json_yaoba_change_id.py | 81 +++ .../testalgorithms/algorithm.yaml | 37 ++ .../testalgorithms/basemodel.py | 231 ++++++++ .../singletask_learning_boost/testenv/map.py | 35 ++ .../testenv/testenv.yaml | 24 + .../benchmarkingjob.yaml | 70 +++ .../dataset/test.json | 1 + .../dataset/train.json | 1 + .../dataset/val.json | 1 + .../readme/README.md | 166 ++++++ .../readme/diagram1.png | Bin 0 -> 148615 bytes .../readme/diagram2.png | Bin 0 -> 25654 bytes .../readme/diagram3.jpg | Bin 0 -> 111194 bytes .../readme/diagram4.jpg | Bin 0 -> 48881 bytes .../resource/utils/TTA_augs_xyxy_cv2.py | 517 ++++++++++++++++++ .../resource/utils/TTA_strategy.py | 199 +++++++ .../resource/utils/__init__.py | 0 .../resource/utils/demo.py | 41 ++ .../resource/utils/general_TTA_v4.py | 205 +++++++ .../resource/utils/general_TTA_v5.py | 183 +++++++ .../resource/yolox_s_8x8_300e_yaoba.py | 215 ++++++++ .../testalgorithms/algorithm.yaml | 30 + .../testalgorithms/basemodel.py | 184 +++++++ .../testenv/map.py | 35 ++ .../testenv/testenv.yaml | 21 + 52 files changed, 4917 insertions(+) create mode 100644 benchmarking.py create mode 100644 core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_active_boost.py create mode 100644 core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_tta.py create mode 100644 examples/yaoba/__init__.py create mode 100644 examples/yaoba/singletask_learning_boost/benchmarkingjob.yaml create mode 100644 examples/yaoba/singletask_learning_boost/readme/README.md create mode 100644 examples/yaoba/singletask_learning_boost/readme/diagram1.jpg create mode 100644 examples/yaoba/singletask_learning_boost/readme/diagram2.jpg create mode 100644 examples/yaoba/singletask_learning_boost/readme/diagram3.jpg create mode 100644 examples/yaoba/singletask_learning_boost/readme/requirements.txt create mode 100644 examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/__init__.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/base_config.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/fpn_component/__init__.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/fpn_component/convfc_bbox_head_weightv4.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_coco.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_pipe.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/fpn_component/weighted_standard_roi_head.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/json/NG_labeled.json create mode 100644 examples/yaoba/singletask_learning_boost/resource/json/NG_test.json create mode 100644 examples/yaoba/singletask_learning_boost/resource/json/known_train.json create mode 100644 examples/yaoba/singletask_learning_boost/resource/train.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/utils/F1-score.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/utils/TTA_augs_xyxy_cv2.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/utils/infer_and_error.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/utils/random_select.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/utils/transform_unkonwn.py create mode 100644 examples/yaoba/singletask_learning_boost/resource/utils/txt2json_yaoba_change_id.py create mode 100644 examples/yaoba/singletask_learning_boost/testalgorithms/algorithm.yaml create mode 100644 examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py create mode 100644 examples/yaoba/singletask_learning_boost/testenv/map.py create mode 100644 examples/yaoba/singletask_learning_boost/testenv/testenv.yaml create mode 100644 examples/yaoba/singletask_learning_yolox_tta/benchmarkingjob.yaml create mode 100644 examples/yaoba/singletask_learning_yolox_tta/dataset/test.json create mode 100644 examples/yaoba/singletask_learning_yolox_tta/dataset/train.json create mode 100644 examples/yaoba/singletask_learning_yolox_tta/dataset/val.json create mode 100644 examples/yaoba/singletask_learning_yolox_tta/readme/README.md create mode 100644 examples/yaoba/singletask_learning_yolox_tta/readme/diagram1.png create mode 100644 examples/yaoba/singletask_learning_yolox_tta/readme/diagram2.png create mode 100644 examples/yaoba/singletask_learning_yolox_tta/readme/diagram3.jpg create mode 100644 examples/yaoba/singletask_learning_yolox_tta/readme/diagram4.jpg create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_augs_xyxy_cv2.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_strategy.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/utils/__init__.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/utils/demo.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v4.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v5.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/testalgorithms/algorithm.yaml create mode 100644 examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/testenv/map.py create mode 100644 examples/yaoba/singletask_learning_yolox_tta/testenv/testenv.yaml diff --git a/benchmarking.py b/benchmarking.py new file mode 100644 index 00000000..ba8c9315 --- /dev/null +++ b/benchmarking.py @@ -0,0 +1,69 @@ +# Copyright 2022 The KubeEdge Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""main""" + +import sys +import argparse + +from core.common.log import LOGGER +from core.common import utils +from core.cmd.obj import BenchmarkingJob +from core.__version__ import __version__ + + +def main(): + """ main command-line interface to ianvs""" + try: + parser = _generate_parser() + args = parser.parse_args() + config_file = args.benchmarking_config_file + if not utils.is_local_file(config_file): + raise SystemExit(f"not found benchmarking config({config_file}) file in local") + + config = utils.yaml2dict(args.benchmarking_config_file) + job = BenchmarkingJob(config[str.lower(BenchmarkingJob.__name__)]) + job.run() + + LOGGER.info("benchmarkingjob runs successfully.") + except Exception as err: + raise RuntimeError(f"benchmarkingjob runs failed, error: {err}.") from err + + +def _generate_parser(): + parser = argparse.ArgumentParser(description='AI Benchmarking Tool') + parser.prog = "ianvs" + + parser.add_argument("-f", + "--benchmarking_config_file", + nargs="?", + type=str, + help="run a benchmarking job, " + "and the benchmarking config file must be yaml/yml file.") + + parser.add_argument('-v', + '--version', + action='version', + version=__version__, + help='show program version info and exit.') + + if len(sys.argv) == 1: + parser.print_help(sys.stderr) + sys.exit(1) + + return parser + + +if __name__ == '__main__': + main() diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_active_boost.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_active_boost.py new file mode 100644 index 00000000..7277f60a --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_active_boost.py @@ -0,0 +1,147 @@ +import json +import os +from core.common.constant import ParadigmType +from examples.yaoba.singletask_learning_boost.resource.utils.infer_and_error import infer_anno, merge_predict_results, \ + compute_error, gen_txt_according_json, get_new_train_json +from examples.yaoba.singletask_learning_boost.resource.utils.transform_unkonwn import aug_image_bboxes +from .singletask_learning import SingleTaskLearning +import os.path as osp + + +class SingleTaskLearningACBoost(SingleTaskLearning): + + def __init__(self, workspace, **kwargs): + super(SingleTaskLearningACBoost, self).__init__(workspace, **kwargs) + + def run(self): + job = self.build_paradigm_job(str(ParadigmType.SINGLE_TASK_LEARNING.value)) + known_dataset_json, unknown_dataset_json, img_path = self._prepare_for_calculate_weights() + base_config_path = osp.join(job.resource_dir, "base_config.py") + train_script_path = osp.join(job.resource_dir, "train.py") + ac_boost_training_json, aug_img_folder = self._calculate_weights_for_training( + base_config=base_config_path, + known_json_path=known_dataset_json, + unknown_json_path=unknown_dataset_json, + img_path=img_path, + tmp_path=os.path.join(job.work_dir, "tmp_folder"), + train_script_path=train_script_path + ) + trained_model = self._ac_boost_train(job, ac_boost_training_json, aug_img_folder) + inference_result = self._inference(job, trained_model) + self.system_metric_info['use_raw'] = True + return inference_result, self.system_metric_info + + def _ac_boost_train(self, job, training_anno, training_img_folder): + train_output_model_path = job.train((training_img_folder, training_anno)) + trained_model_path = job.save(train_output_model_path) + return trained_model_path + + def _inference(self, job, trained_model): + # Load test set data + img_prefix = self.dataset.image_folder_url + ann_file_path = self.dataset.test_url + ann_file = json.load(open(ann_file_path, mode="r", encoding="utf-8")) + test_set = [] + for i in ann_file['images']: + test_set.append(os.path.join(img_prefix, i['file_name'])) + + job.load(trained_model) + infer_res = job.predict(test_set) + return infer_res + + def _prepare_for_calculate_weights(self): + known_dataset_json = self.dataset.known_dataset_url + unknown_dataset_json = self.dataset.unknown_dataset_url + img_path = self.dataset.image_folder_url + return known_dataset_json, unknown_dataset_json, img_path + + def _calculate_weights_for_training(self, + base_config, + known_json_path, + unknown_json_path, + img_path, + tmp_path, + train_script_path): + r"""Generate instance weights required for unknown task training. In object detection, + an instance means a bounding box, i.e., generating training weights for each bounding box. + Args: + base_config (str): path of config file for training known/unknown model + known_json_path (str): path of JSON file for training known model + unknown_json_path (str): path of JSON file for training unknown model + img_path (str): image path of training, validation, and test set. + tmp_path (str): path to save temporary files, including augmented images, training JSON files, etc. + train_script_path (str): path of mmdet training script + Return: + new_training_weight (str): JSON file with instance weights for unknown task training, + which contains both the known and unknown training sets. + aug_img_folder (str): the image paths required for training the model using the JSON file with instance weights. + """ + if not os.path.exists(tmp_path): + os.mkdir(tmp_path) + # Define necessary path + aug_img_folder = osp.join(tmp_path, "aug_img_folder") # The directory for saving augmented images + known_model_folder = osp.join(tmp_path, "known_model") # The directory for saving known model training results + unknown_model_folder = osp.join(tmp_path, "unknown_model") # The directory for saving unknown model training results + aug_unknown_json = osp.join(tmp_path, 'aug_unknown.json') # The JSON file path of the unknown data after augmentation + + # Augmenting the unknown data and returning the paths of the augmented images + aug_image_bboxes( + anno=unknown_json_path, + augs=[('flip', 1), ('brightness', 0.6), ('flip', -1)], + image_path=img_path, + out_path=tmp_path + ) + + # Train the known model + known_model_training_task = f"python {train_script_path} " \ + f"{base_config} --seed 1 --deterministic --cfg-options " \ + f"data.train.ann_file={known_json_path} " \ + f"data.train.img_prefix={img_path} " \ + f"work_dir={known_model_folder}" + os.system(known_model_training_task) + + # Train the unknown model + unknown_model_training_task = f"python {train_script_path} " \ + f"{base_config} --seed 1 --deterministic --cfg-options " \ + f"data.train.ann_file={aug_unknown_json} " \ + f"data.train.img_prefix={aug_img_folder} " \ + f"work_dir={unknown_model_folder}" + os.system(unknown_model_training_task) + + # using above known model to infer unknown data + infer_anno( + config_file=base_config, + checkpoint_file=osp.join(known_model_folder, 'latest.pth'), + img_path=aug_img_folder, + anno_path=aug_unknown_json, + out_path=osp.join(tmp_path, 'unknown_infer_results.json') + ) + + # using above unknown model to infer known data + infer_anno( + config_file=base_config, + checkpoint_file=osp.join(unknown_model_folder, 'latest.pth'), + img_path=aug_img_folder, + anno_path=known_json_path, + out_path=osp.join(tmp_path, 'known_infer_results.json') + ) + + # merging the prediction results and computing error + merge_predict_results( + result1=osp.join(tmp_path, 'unknown_infer_results.json'), + result2=osp.join(tmp_path, 'known_infer_results.json'), + out_dir=osp.join(tmp_path, "merge_predict_result.json") + ) + new_json = compute_error(osp.join(tmp_path, "merge_predict_result.json")) + + # generating the weights of the overall training sample based on the prediction error. + gen_txt_according_json(known_json_path, osp.join(tmp_path, 'known.txt')) + gen_txt_according_json(aug_unknown_json, osp.join(tmp_path, 'aug_unknown.txt')) + get_new_train_json( + new_json, + aug_img_folder, + osp.join(tmp_path, 'known.txt'), + osp.join(tmp_path, 'aug_unknown.txt'), + out_dir=osp.join(tmp_path, 'new_training_weight.json')) + + return osp.join(tmp_path, 'new_training_weight.json'), aug_img_folder diff --git a/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_tta.py b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_tta.py new file mode 100644 index 00000000..e7b6f53a --- /dev/null +++ b/core/testcasecontroller/algorithm/paradigm/singletask_learning/singletask_learning_tta.py @@ -0,0 +1,98 @@ +import json +import os +import torch +from mmdet.apis import init_detector +from core.common.constant import ParadigmType +from examples.yaoba.singletask_learning_yolox_tta.resource.utils.TTA_strategy import TTA_Strategy +from .singletask_learning import SingleTaskLearning + + +class SingleTaskLearningTTA(SingleTaskLearning): + + def __init__(self, workspace, **kwargs): + super(SingleTaskLearningTTA, self).__init__(workspace, **kwargs) + + def run(self): + # Build an experimental task + job = self.build_paradigm_job(str(ParadigmType.SINGLE_TASK_LEARNING.value)) + + # If there are no initialized model weights, then train a new model from scratch + if self.initial_model != "": + trained_model = self.initial_model + else: + trained_model = self._train(job, None) + + # Search for the optimal test-time augmentation policies + searched_strategy = self._search_tta_strategy(job, trained_model) + + # Merging the optimal policies with original default policy + merged_strategy = self._prepare_infer_strategy(job, searched_strategy) + + # infer the test set with searched policies + inference_result = self._inference_w_tta(job, trained_model, merged_strategy) + self.system_metric_info['use_raw']=True + return inference_result, self.system_metric_info + + def _inference_w_tta(self, job, trained_model, strategy): + # Load test set data + img_prefix = self.dataset.image_folder_url + ann_file_path = self.dataset.test_url + ann_file = json.load(open(ann_file_path, mode="r", encoding="utf-8")) + test_set = [] + for i in ann_file['images']: + test_set.append(os.path.join(img_prefix, i['file_name'])) + + # Perform inference with data augmentation policy. + job.load(trained_model) + print(f"Total infer strategy is :{strategy}") + infer_res = job.tta_predict(test_set, strategy) + + return infer_res + + def _prepare_infer_strategy(self, job, searched_strategy): + default_img_size = None + # The default inference policy + for p in job.cfg.data.test.pipeline: + if p['type'] == 'MultiScaleFlipAug': + default_img_size = p['img_scale'] + if default_img_size: + combined_strategy = [[("TTA_Resize", default_img_size), ]] + else: + raise ValueError("can not find img_scale model cfg") + combined_strategy.append(searched_strategy[0]) + + return combined_strategy + + def _search_tta_strategy(self, job, model_url): + # Load validation dataset + img_prefix = self.dataset.image_folder_url + ann_file = self.dataset.val_url + + # Create a search agent to search for the best data augmentation strategy. + model_cfg = job.cfg + model = init_detector(model_cfg, model_url) + torch.multiprocessing.set_start_method("spawn", force=True) + search_agent = TTA_Strategy( + model=model, + val_image_path=img_prefix, + val_anno_path=ann_file, + log_dir=os.path.join(model_cfg.work_dir, "log"), + worker=6, + nms_thr=0.5 + ) + # Search for single policies for TTA + single_strategies = search_agent.search_single_strategy(top_num=3) + + # Search for Cascade policies for TTA, which based on single policies + cascade_strategies = search_agent.search_cascade_strategy( + single_strategies, + cascade_num=3, + top_num=5 + ) + return cascade_strategies + + def _train(self, job, initial_model): + img_prefix = self.dataset.image_folder_url + ann_file = self.dataset.train_url + checkpoint_path = job.train((img_prefix, ann_file)) + return checkpoint_path diff --git a/examples/yaoba/__init__.py b/examples/yaoba/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/yaoba/singletask_learning_boost/benchmarkingjob.yaml b/examples/yaoba/singletask_learning_boost/benchmarkingjob.yaml new file mode 100644 index 00000000..25637c5e --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/benchmarkingjob.yaml @@ -0,0 +1,70 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "examples/yaoba/singletask_learning_boost/workspace" + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "examples/yaoba/singletask_learning_boost/testenv/testenv.yaml" + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "mmlab-model" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml; + url: "examples/yaoba/singletask_learning_boost/testalgorithms/algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "map": "descend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "f1_score" + metrics: [ "map" ] + + # model of save selected and all dataitems in workspace; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/yaoba/singletask_learning_boost/readme/README.md b/examples/yaoba/singletask_learning_boost/readme/README.md new file mode 100644 index 00000000..3ce7311a --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/readme/README.md @@ -0,0 +1,153 @@ +## About Industrial Defect Detection + +Defects are an unwanted thing in manufacturing industry. There are many types of defect in manufacturing like blow +holes, pinholes, burr, shrinkage defects, mould material defects, pouring metal defects, metallurgical defects, etc. For +removing this defective product all industry have their defect detection department. But the main problem is this +inspection process is carried out manually. It is a very time-consuming process and due to human accuracy, this is not +100% accurate. This can because of the rejection of the whole order. So it creates a big loss in the company. + +## About Method + +Unknown tasks refer to samples outside the knowledge distribution of the task model, which can lead to severe performance degradation of a model. The instance-based method aims to identify the known training samples that contribute to fitting the unknown data distribution, and then boosting their weight in training, as shown in the below figures. It is a model-agnostic method since it manipulates the training data rather than changing the model architecture, holding considerable generality. + +diagram1 + +In this project, we present an instance-weighting method for defect detection based on active learning, significantly improving a detector's performance on unknown data. The workflow of the method is shown in the flowchart below + +diagram2 + +## About dataset + +The industrial crank defect detection dataset consists of 627 images, which hold three types of defects: 'yanse', ' +huahen', and 'mosun', and the image resolution is 4024 × 3036. Please follow [this link](https://baidu.com) to download +the dataset and learn more about it. Additionally, below is an example figure from the dataset. + +diagram3 + +### Dataset splitting for unknown task processing + +After unzipping dataset.zip, your folder format should look like below: + +``` +dataset + ├─images + └─jsons + ├─known_train.json + ├─NG_labeled.json + └─NG_test.json +``` + +All images of this dataset are placed in the ```images``` folder, and the dataset splitting is based on JSON files +in ```jsons``` folder. In this project, we define an image defect detection Precision or Recall less than 0.9 as unknown +data. Unknown training set means unknown data that can be used for training. + +## Install +First, you need to install the dependencies of Ianvs and Sedna, please follow the instructions +in [How to install Ianvs](https://github.com/kubeedge/ianvs/blob/main/docs/guides/how-to-install-ianvs.md). We are using +the MMDetection object detection framework in this project, which requires some essential dependencies to be installed. You +can install them manually one by one or use the ```pip install -r requirements.txt``` command directly in the terminal. +To avoid unpredictable errors, we strongly recommend creating a new Conda virtual environment before installing these +dependencies. + +``` +python==3.9.0 +mmcv-full==1.7.1 +mmdet==2.28.2 +torch==1.13.0 +torchvision==0.14.0 +numpy==1.24.2 +opencv_python==4.5.5.64 +Pillow==9.4.0 +``` + +## Config Setting + +Key settings to run instance-weighting method for unknown task processing of defect detection. + +### Testenv Setting + +Configure the ```known_dataset_url```, ```unknown_dataset_url```, ```image_folder_url```, and ```metrics``` properties +in ```testenv.yaml``` based on the location of your dataset folder like the below code block. + +``` +testenv: + # dataset configuration + dataset: + train_url: "dataset/jsons/known_train.json" + val_url: "dataset/jsons/known_train.json" + test_url: "dataset/jsons/NG_test.json" + known_dataset_url: "dataset/jsons/known_train.json" + unknown_dataset_url: "dataset/jsons/NG_labeled.json" + image_folder_url: "dataset/images" + type; + metrics: + # metric name; string type; + - name: "map" + url: "examples/yaoba/singletask_learning_boost/testenv/map.py" +``` + +### Algorithm Setting + +Configure the `basemodel url`, `config url`, `resource_dir` and `work_dir url` properties in `algorithm.yaml` based on the location of your project path like the below code block. + +``` +algorithm: + initial_model_url: "" + modules: + - type: "basemodel" + name: "FPN_ac_boost" + url: "examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py" + hyperparameters: + - config: + values: + - "examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py" + - work_dir: + values: + - "examples/yaoba/singletask_learning_boost/work_dir" + - resource_dir: + values: + - "examples/yaoba/singletask_learning_boost/resource" +``` +### benchmarking Setting + +Configure the `basemodel url`, `config url`, `resource_dir` and `work_dir url` properties in `benchmarkingjob.yaml` based on the location of your project path like the below code block. +``` + name: "benchmarkingjob" + workspace: "examples/yaoba/singletask_learning_boost/workspace" + testenv: "examples/yaoba/singletask_learning_boost/testenv/testenv.yaml" + test_object: + type: "algorithms" + algorithms: + - name: "mmlab-model" + url: "examples/yaoba/singletask_learning_boost/testalgorithms/algorithm.yaml" +``` + +## Usage + +We are now ready to run the instance-weighting method for unknown task processing on the industrial crank defect +detection dataset. execute the below command in your terminal: + +``` +python benchmarking.py -f examples/yaoba/singletask_learning_boost/benchmarkingjob.yaml +``` + +## Result + +We present the reference results of mean average precision on unknown task test sets with and without the +instance-weighting method. It is worth noting that, even with a fixed random seed, the results may fluctuate each time +the program runs + +| Model | Method | bs | lr | AP50 | AP75 | +|:-------------:|:--------:|:---:|:----:|:-----:|-------| +| ResNet-50-FPN | baseline | 8 | 0.01 | 69.79 | 41.02 | +| ResNet-50-FPN | ac_boost | 8 | 0.01 | 71.79 | 41.99 | + +## Endnote + +In this project, we set the training batch size to 8 by default, which can result in GPU memory overflow. Users can +change this by adjusting the 'samples_per_gpu' parameter in both the 'base_config.py' and the "FPN_model_config.py" +files. + +## License + +MIT © Richard McRichface \ No newline at end of file diff --git a/examples/yaoba/singletask_learning_boost/readme/diagram1.jpg b/examples/yaoba/singletask_learning_boost/readme/diagram1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..448fdd0859c7afe8f6839c9fd875b7c430403ece GIT binary patch literal 194888 zcmd42XIN8d*e)6sL_mym1tBv^m8Jrsh-55)fPjD$X&FSMM2ILzz>p}tGZtV3B`Qst zkrGf^gir<;HI}jh?AH?<);=;eL{~NUeiHDC~?KyJ3x=r3`5KuF`ekWF3LP7DsK_zt!O)c%CM#d*i zPMtQraM8lj>XNmM^HmpDH}`8EemDGY1_TBL-wltr7m1IGj*Cx7OiE5kef;EUc24e} z&+=Xu78RG2z9}oOsjZ{dH@yGQ*iP^0?CSoj=hM*e$mrPk#3X~w;r{dG>)f~b1@On} z+B$Co`nmb9T{{rF{`->sr(yqtUDB{!J4HlxiHQAc*N&Z$@Yp3SBKqs`-7@DK#eBnL z51)9rNACQiSJiFeM+}_+`RjKEB^1<**y`ZFru~Oy|8s`L{x4bf-wpde?P4JI?%Dxw z-Y#hb8X**Fy-r2^|87wFm>FcijRa|$EY0)X@Fu)pp+3Gn{%#wlZX?S;u%j4HVpq=Y 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zB(H9{^g9!h4zxqV=Z`}0w;VECRB%8yl3N_L=vtk=t+YrT_*RD(cvTvVWNaU8pj}%& z_*hh0&X^W9>looS4!rXXhd)854FiA(JLspys}qE)30{+C61Z+}`!*UmLEeGq89Q9k JAoFkezW^qHg2Vs- literal 0 HcmV?d00001 diff --git a/examples/yaoba/singletask_learning_boost/readme/requirements.txt b/examples/yaoba/singletask_learning_boost/readme/requirements.txt new file mode 100644 index 00000000..3a86935a --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/readme/requirements.txt @@ -0,0 +1,7 @@ +mmcv-full==1.7.1 +mmdet==2.28.2 +torch==1.13.0 +torchvision==0.14.0 +numpy==1.24.2 +opencv_python==4.5.5.64 +Pillow==9.4.0 diff --git a/examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py b/examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py new file mode 100644 index 00000000..5e164ed2 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py @@ -0,0 +1,222 @@ +custom_imports = dict( + imports=[ + 'examples.yaoba.singletask_learning_boost.resource.fpn_component.weighted_standard_roi_head', + 'examples.yaoba.singletask_learning_boost.resource.fpn_component.convfc_bbox_head_weightv4', + 'examples.yaoba.singletask_learning_boost.resource.fpn_component.tradboost_coco', + 'examples.yaoba.singletask_learning_boost.resource.fpn_component.tradboost_pipe' + ], + allow_failed_imports=False) +model = dict( + type='FasterRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='WeightRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHeadWeightV4', + reg_decoded_bbox=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=3, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=10.0))), + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) +dataset_type = 'CocoDataset' +data_root = '' +classes = ('yanse', 'huahen', 'mosun') +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +data = dict( + samples_per_gpu=8, + workers_per_gpu=8, + train=dict( + type='TRCOCO', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/custom_ianvs/singletask_learning_yolox_boost/exp/ac_bosst/tmp_folder/new_training_weight.json', + img_prefix= + '/home/wjj/wjj/Public/code/ianvs/custom_ianvs/singletask_learning_yolox_boost/work_dir/tmp_folder/aug_img_folder', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='TRLoadAnnotations', with_bbox=True, with_weight=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels'], + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg', 'weights')) + ]), + val=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/custom_ianvs/singletask_learning_boost/resource/json/NG_test.json', + img_prefix= + '/home/wjj/wjj/Public/code/ianvs/custom_ianvs/singletask_learning_yolox_boost/work_dir/tmp_folder/aug_img_folder', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/custom_ianvs/singletask_learning_boost/resource/json/NG_test.json', + img_prefix= + '/home/wjj/wjj/Public/code/ianvs/custom_ianvs/singletask_learning_yolox_boost/work_dir/tmp_folder/aug_img_folder', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ])) +evaluation = dict(interval=1, metric='bbox') +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=50, + warmup_ratio=0.001, + step=[22, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +checkpoint_config = dict(interval=24) +log_config = dict(interval=5, hooks=[dict(type='TextLoggerHook')]) +custom_hooks = [dict(type='NumClassCheckHook')] +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +opencv_num_threads = 0 +mp_start_method = 'fork' +auto_scale_lr = dict(enable=True, base_batch_size=16) +work_dir = '/home/wjj/wjj/Public/code/huawei/custom_code/instance_based/model/weight_fpn/cls' +auto_resume = False +gpu_ids = [0] diff --git a/examples/yaoba/singletask_learning_boost/resource/__init__.py b/examples/yaoba/singletask_learning_boost/resource/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/yaoba/singletask_learning_boost/resource/base_config.py b/examples/yaoba/singletask_learning_boost/resource/base_config.py new file mode 100644 index 00000000..18acb021 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/base_config.py @@ -0,0 +1,208 @@ +model = dict( + type='FasterRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + reg_decoded_bbox=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=3, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0.0, 0.0, 0.0, 0.0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=10.0))), + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) +dataset_type = 'CocoDataset' +data_root = '' +classes = ('yanse', 'huahen', 'mosun') +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +data = dict( + samples_per_gpu=8, + workers_per_gpu=8, + train=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/custom_ianvs/singletask_learning_yolox_boost/exp/ac_bosst/tmp_folder/aug_unknown.json', + img_prefix='/home/wjj/wjj/Public/code/ianvs/custom_ianvs/singletask_learning_yolox_boost/work_dir/tmp_folder/aug_img_folder', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) + ]), + val=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/home/wjj/wjj/Public/code/huawei/custom_code/instance_based/json/NG_test.json', + img_prefix='/media/huawei_YaoBa/Images', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/home/wjj/wjj/Public/code/huawei/custom_code/instance_based/json/NG_test.json', + img_prefix='/media/huawei_YaoBa/Images', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ])) +# evaluation = dict(interval=1, save_best='auto', metric='bbox') +evaluation = dict(interval=1, metric='bbox') +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=50, + warmup_ratio=0.001, + step=[22, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +checkpoint_config = dict(interval=24) +log_config = dict(interval=5, hooks=[dict(type='TextLoggerHook')]) +custom_hooks = [dict(type='NumClassCheckHook')] +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +opencv_num_threads = 0 +mp_start_method = 'fork' +auto_scale_lr = dict(enable=True, base_batch_size=16) +work_dir = '/home/wjj/wjj/Public/code/huawei/custom_code/instance_based/model/fpn/known' +auto_resume = False +gpu_ids = range(0, 4) diff --git a/examples/yaoba/singletask_learning_boost/resource/fpn_component/__init__.py b/examples/yaoba/singletask_learning_boost/resource/fpn_component/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/yaoba/singletask_learning_boost/resource/fpn_component/convfc_bbox_head_weightv4.py b/examples/yaoba/singletask_learning_boost/resource/fpn_component/convfc_bbox_head_weightv4.py new file mode 100644 index 00000000..9e52cc95 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/fpn_component/convfc_bbox_head_weightv4.py @@ -0,0 +1,188 @@ +import numpy as np +from mmdet.core.bbox.iou_calculators import build_iou_calculator +from mmdet.models.builder import HEADS +from mmdet.models.roi_heads.bbox_heads.convfc_bbox_head import Shared2FCBBoxHead +from mmcv.runner import force_fp32 +import torch +from mmdet.models.losses import accuracy +from mmdet.core import multi_apply + + +@HEADS.register_module() +class Shared2FCBBoxHeadWeightV4(Shared2FCBBoxHead): + + def __init__(self, **kwargs): + super(Shared2FCBBoxHeadWeightV4, self).__init__(**kwargs) + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def loss(self, + custom_weight, + gt_labels, + cls_score, + bbox_pred, + rois, + labels, + label_weights, + bbox_targets, + bbox_weights, + bbox_gt_inds, + reduction_override=None): + torch.set_printoptions(threshold=np.inf) + # 获取有效预测结果的mask,非有效预测的结果弄成零方便取权重,最后通过mask筛选取出的值 + bbox_gt_inds_mask = bbox_gt_inds != -1 + bbox_gt_inds[bbox_gt_inds == -1] = 0 + # 通过bbox的weight和每个bbox属于的类别计算出类别的权重， + # [1,0.5,0.8] 对应的类别为[0,0,1] 那么类别权重和背景权重为[0.75,0.8,0.76] + custom_label_weight = [] + for i in range(len(custom_weight)): + custom_label_weight.append([0 for _ in range(self.num_classes + 1)]) + for i in range(len(custom_label_weight)): + for j in range(self.num_classes + 1): + # num_classes代表背景 + if j == self.num_classes: + mask = np.asarray(custom_label_weight[i]) > 0 + background_weight = np.average(np.asarray(custom_label_weight[i])[mask]) + custom_label_weight[i][j] = background_weight + else: + img_i_gt_labels_wrt_class_j = (gt_labels[i] == j).cpu().numpy() + img_i_class_j_weight = custom_weight[i][img_i_gt_labels_wrt_class_j] + if len(img_i_class_j_weight) > 0: + custom_label_weight[i][j] = np.average(img_i_class_j_weight) + else: + custom_label_weight[i][j] = 0 + start_index = 0 + lengths = [] + bbox_weight_list = [] + label_weight_list=[] + predict_img_index = rois[:, 0] + num_imgs = len(custom_weight) + # 得出每个img有多少个预测结果,一个img一个img的处理 + for i in range(num_imgs): + lengths.append(torch.count_nonzero(predict_img_index == i).item()) + for index, length in enumerate(lengths): + cur_custom_bbox_weight = torch.from_numpy(custom_weight[index]).type_as(bbox_pred) + cur_custom_label_weight = torch.from_numpy(np.asarray(custom_label_weight[index])).type_as(labels) + cur_custom_bbox_weight = cur_custom_bbox_weight[bbox_gt_inds[start_index:length + start_index]] + cur_custom_label_weight = cur_custom_label_weight[labels[start_index:length + start_index]] + cur_custom_bbox_weight[~bbox_gt_inds_mask[start_index:length + start_index]] = 0 + bbox_weight_list.append(cur_custom_bbox_weight) + label_weight_list.append(cur_custom_label_weight) + start_index += length + final_custom_bbox_weight = torch.concatenate(bbox_weight_list, dim=0) + final_custom_label_weight = torch.concatenate(label_weight_list, dim=0) + bbox_weights = final_custom_bbox_weight.unsqueeze(-1) * bbox_weights + label_weights = final_custom_label_weight * label_weights + losses = dict() + if cls_score is not None: + avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) + if cls_score.numel() > 0: + loss_cls_ = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=avg_factor, + reduction_override=reduction_override) + if isinstance(loss_cls_, dict): + losses.update(loss_cls_) + else: + losses['loss_cls'] = loss_cls_ + if self.custom_activation: + acc_ = self.loss_cls.get_accuracy(cls_score, labels) + losses.update(acc_) + else: + losses['acc'] = accuracy(cls_score, labels) + if bbox_pred is not None: + bg_class_ind = self.num_classes + # 0~self.num_classes-1 are FG, self.num_classes is BG + pos_inds = (labels >= 0) & (labels < bg_class_ind) + # do not perform bounding box regression for BG anymore. + if pos_inds.any(): + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, + # `GIouLoss`, `DIouLoss`) is applied directly on + # the decoded bounding boxes, it decodes the + # already encoded coordinates to absolute format. + bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred) + if self.reg_class_agnostic: + pos_bbox_pred = bbox_pred.view( + bbox_pred.size(0), 4)[pos_inds.type(torch.bool)] + else: + pos_bbox_pred = bbox_pred.view( + bbox_pred.size(0), -1, + 4)[pos_inds.type(torch.bool), + labels[pos_inds.type(torch.bool)]] + losses['loss_bbox'] = self.loss_bbox( + pos_bbox_pred, + bbox_targets[pos_inds.type(torch.bool)], + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=bbox_targets.size(0), + reduction_override=reduction_override) + else: + losses['loss_bbox'] = bbox_pred[pos_inds].sum() + return losses + + def get_targets(self, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + concat=True): + # 重写这个方法是为了加入pos_assigned_gt_inds,方便判断pos的pred_box是预测的哪个gt_box，在tradboost中每个标签框的权重不一样 + pos_bboxes_list = [res.pos_bboxes for res in sampling_results] + neg_bboxes_list = [res.neg_bboxes for res in sampling_results] + pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] + pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] + pos_assigned_gt_inds_list = [res.pos_assigned_gt_inds for res in sampling_results] + labels, label_weights, bbox_targets, bbox_weights, bbox_gt_inds = multi_apply( + self._get_target_single, + pos_bboxes_list, + neg_bboxes_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + pos_assigned_gt_inds_list, + cfg=rcnn_train_cfg) + if concat: + labels = torch.cat(labels, 0) + label_weights = torch.cat(label_weights, 0) + bbox_targets = torch.cat(bbox_targets, 0) + bbox_weights = torch.cat(bbox_weights, 0) + bbox_gt_inds = torch.cat(bbox_gt_inds, 0) + return labels, label_weights, bbox_targets, bbox_weights, bbox_gt_inds + + def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, + pos_gt_labels, pos_assigned_gt_inds_list, cfg): + num_pos = pos_bboxes.size(0) + num_neg = neg_bboxes.size(0) + num_samples = num_pos + num_neg + # original implementation uses new_zeros since BG are set to be 0 + # now use empty & fill because BG cat_id = num_classes, + # FG cat_id = [0, num_classes-1] + labels = pos_bboxes.new_full((num_samples,), + self.num_classes, + dtype=torch.long) + label_weights = pos_bboxes.new_zeros(num_samples) + bbox_targets = pos_bboxes.new_zeros(num_samples, 4) + bbox_weights = pos_bboxes.new_zeros(num_samples, 4) + bbox_gt_inds = pos_bboxes.new_full((num_samples,), + -1, + dtype=torch.long) + if num_pos > 0: + labels[:num_pos] = pos_gt_labels + pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight + label_weights[:num_pos] = pos_weight + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + pos_bboxes, pos_gt_bboxes) + else: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, both + # the predicted boxes and regression targets should be with + # absolute coordinate format. + pos_bbox_targets = pos_gt_bboxes + bbox_targets[:num_pos, :] = pos_bbox_targets + bbox_gt_inds[:num_pos] = pos_assigned_gt_inds_list + bbox_weights[:num_pos, :] = 1 + if num_neg > 0: + label_weights[-num_neg:] = 1.0 + + return labels, label_weights, bbox_targets, bbox_weights, bbox_gt_inds diff --git a/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_coco.py b/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_coco.py new file mode 100644 index 00000000..829ea20c --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_coco.py @@ -0,0 +1,69 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np +from mmdet.datasets.builder import DATASETS +from mmdet.datasets.coco import CocoDataset + + +@DATASETS.register_module() +class TRCOCO(CocoDataset): + + def _parse_ann_info(self, img_info, ann_info): + """Parse bbox and mask annotation. + extra return anno weight + Args: + ann_info (list[dict]): Annotation info of an image. + with_mask (bool): Whether to parse mask annotations. + + Returns: + dict: A dict containing the following keys: bboxes, bboxes_ignore,\ + labels, masks, seg_map. "masks" are raw annotations and not \ + decoded into binary masks. + """ + gt_bboxes = [] + gt_labels = [] + gt_bboxes_ignore = [] + gt_masks_ann = [] + weights = [] + for i, ann in enumerate(ann_info): + if ann.get('ignore', False): + continue + x1, y1, w, h = ann['bbox'] + + inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0)) + inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + continue + if ann['area'] <= 0 or w < 1 or h < 1: + continue + if ann['category_id'] not in self.cat_ids: + continue + bbox = [x1, y1, x1 + w, y1 + h] + if ann.get('iscrowd', False): + gt_bboxes_ignore.append(bbox) + else: + gt_bboxes.append(bbox) + gt_labels.append(self.cat2label[ann['category_id']]) + gt_masks_ann.append(ann.get('segmentation', None)) + weights.append(ann['weight']) + if gt_bboxes: + gt_bboxes = np.array(gt_bboxes, dtype=np.float32) + gt_labels = np.array(gt_labels, dtype=np.int64) + weights = np.array(weights, dtype=np.float32) + else: + gt_bboxes = np.zeros((0, 4), dtype=np.float32) + gt_labels = np.array([], dtype=np.int64) + weights=np.array([], dtype=np.float32) + if gt_bboxes_ignore: + gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32) + else: + gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32) + + seg_map = img_info['filename'].rsplit('.', 1)[0] + self.seg_suffix + ann = dict( + bboxes=gt_bboxes, + labels=gt_labels, + bboxes_ignore=gt_bboxes_ignore, + masks=gt_masks_ann, + seg_map=seg_map, + weights=weights) + return ann diff --git a/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_pipe.py b/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_pipe.py new file mode 100644 index 00000000..17d601be --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/fpn_component/tradboost_pipe.py @@ -0,0 +1,46 @@ +from mmdet.datasets.builder import PIPELINES +from mmdet.datasets.pipelines.loading import LoadAnnotations + + +@PIPELINES.register_module() +class TRLoadAnnotations(LoadAnnotations): + def __init__(self, with_weight=False, **kwargs): + super().__init__(**kwargs) + self.with_weight = with_weight + + def _load_weights(self, results): + """Private function to load label annotations. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded label annotations. + """ + results['weights'] = results['ann_info']['weights'].copy() + return results + + def __call__(self, results): + """Call function to load multiple types annotations. + + Args: + results (dict): Result dict from :obj:`mmdet.CustomDataset`. + + Returns: + dict: The dict contains loaded bounding box, label, mask and + semantic segmentation annotations. + """ + + if self.with_bbox: + results = self._load_bboxes(results) + if results is None: + return None + if self.with_weight: + results = self._load_weights(results) + if self.with_label: + results = self._load_labels(results) + if self.with_mask: + results = self._load_masks(results) + if self.with_seg: + results = self._load_semantic_seg(results) + return results diff --git a/examples/yaoba/singletask_learning_boost/resource/fpn_component/weighted_standard_roi_head.py b/examples/yaoba/singletask_learning_boost/resource/fpn_component/weighted_standard_roi_head.py new file mode 100644 index 00000000..79a2f14a --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/fpn_component/weighted_standard_roi_head.py @@ -0,0 +1,74 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import json +import numpy as np +import torch +from mmdet.core import bbox2roi +from mmdet.core.bbox.iou_calculators import build_iou_calculator +from mmdet.models.builder import HEADS +from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class WeightRoIHead(StandardRoIHead): + def __init__(self, + **kwargs): + super(WeightRoIHead, self).__init__(**kwargs) + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + **kwargs): + if self.with_bbox or self.with_mask: + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + for i in range(num_imgs): + assign_result = self.bbox_assigner.assign( + proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], + gt_labels[i]) + sampling_result = self.bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=[lvl_feat[i][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + losses = dict() + # bbox head forward and loss + if self.with_bbox: + bbox_results = self._bbox_forward_train(x, sampling_results, + gt_bboxes, gt_labels, + img_metas) + losses.update(bbox_results['loss_bbox']) + + # mask head forward and loss + if self.with_mask: + mask_results = self._mask_forward_train(x, sampling_results, + bbox_results['bbox_feats'], + gt_masks, img_metas) + losses.update(mask_results['loss_mask']) + + return losses + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + """Run forward function and calculate loss for box head in training.""" + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward(x, rois) + bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, self.train_cfg) + + loss_bbox = self.bbox_head.loss([i['weights'] for i in img_metas], + gt_labels, + bbox_results['cls_score'], + bbox_results['bbox_pred'], + rois, + *bbox_targets) + bbox_results.update(loss_bbox=loss_bbox) + return bbox_results + diff --git a/examples/yaoba/singletask_learning_boost/resource/json/NG_labeled.json b/examples/yaoba/singletask_learning_boost/resource/json/NG_labeled.json new file mode 100644 index 00000000..52b305f1 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/json/NG_labeled.json @@ -0,0 +1 @@ +{"images": [{"file_name": "2021-05-16-14-07-48.jpg", "id": 10000, "height": 3036, "width": 4024}, {"file_name": "2021-05-17-15-17-02.jpg", "id": 10001, "height": 3036, "width": 4024}, {"file_name": "2021-05-16-18-46-22.jpg", "id": 10002, "height": 3036, "width": 4024}, {"file_name": "2021-05-16-18-34-30.jpg", "id": 10003, "height": 3036, "width": 4024}, {"file_name": "2021-05-17-11-54-16.jpg", "id": 10004, "height": 3036, "width": 4024}, {"file_name": "2021-05-17-08-43-52.jpg", "id": 10005, "height": 3036, "width": 4024}, 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"area": 245180}, {"image_id": 10198, "bbox": [2471, 1080, 580, 543], "category_id": 1, "id": 644, "iscrowd": 0, "weight": 0.7119866710434128, "segmentation": [], "area": 314940}, {"image_id": 10199, "bbox": [1434, 593, 610, 534], "category_id": 0, "id": 645, "iscrowd": 0, "weight": 0.8820627056597748, "segmentation": [], "area": 325740}, {"image_id": 10199, "bbox": [1438, 930, 623, 827], "category_id": 0, "id": 646, "iscrowd": 0, "weight": 0.5790959025962131, "segmentation": [], "area": 515221}, {"image_id": 10199, "bbox": [3528, 2187, 440, 676], "category_id": 2, "id": 647, "iscrowd": 0, "weight": 0.2290474464468868, "segmentation": [], "area": 297440}, {"image_id": 10199, "bbox": [1153, 600, 81, 84], "category_id": 0, "id": 648, "iscrowd": 0, "weight": 0.13713852381719682, "segmentation": [], "area": 6804}], "categories": [{"id": 0, "name": "yanse"}, {"id": 1, "name": "huahen"}, {"id": 2, "name": "mosun"}]} \ No newline at end of file diff --git a/examples/yaoba/singletask_learning_boost/resource/train.py b/examples/yaoba/singletask_learning_boost/resource/train.py new file mode 100644 index 00000000..8b5c7a29 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/train.py @@ -0,0 +1,242 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import copy +import os +import os.path as osp +import time +import warnings + +import mmcv +import torch +import torch.distributed as dist +from mmcv import Config, DictAction +from mmcv.runner import get_dist_info, init_dist +from mmcv.utils import get_git_hash + +from mmdet import __version__ +from mmdet.apis import init_random_seed, set_random_seed, train_detector +from mmdet.datasets import build_dataset +from mmdet.models import build_detector +from mmdet.utils import (collect_env, get_device, get_root_logger, + replace_cfg_vals, setup_multi_processes, + update_data_root) + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a detector') + parser.add_argument('config', help='train config file path') + parser.add_argument('--work-dir', help='the dir to save logs and models') + parser.add_argument( + '--resume-from', help='the checkpoint file to resume from') + parser.add_argument( + '--auto-resume', + action='store_true', + help='resume from the latest checkpoint automatically') + parser.add_argument( + '--no-validate', + action='store_true', + help='whether not to evaluate the checkpoint during training') + group_gpus = parser.add_mutually_exclusive_group() + group_gpus.add_argument( + '--gpus', + type=int, + help='(Deprecated, please use --gpu-id) number of gpus to use ' + '(only applicable to non-distributed training)') + group_gpus.add_argument( + '--gpu-ids', + type=int, + nargs='+', + help='(Deprecated, please use --gpu-id) ids of gpus to use ' + '(only applicable to non-distributed training)') + group_gpus.add_argument( + '--gpu-id', + type=int, + default=0, + help='id of gpu to use ' + '(only applicable to non-distributed training)') + parser.add_argument('--seed', type=int, default=None, help='random seed') + parser.add_argument( + '--diff-seed', + action='store_true', + help='Whether or not set different seeds for different ranks') + parser.add_argument( + '--deterministic', + action='store_true', + help='whether to set deterministic options for CUDNN backend.') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file (deprecate), ' + 'change to --cfg-options instead.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + parser.add_argument( + '--auto-scale-lr', + action='store_true', + help='enable automatically scaling LR.') + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + if args.options and args.cfg_options: + raise ValueError( + '--options and --cfg-options cannot be both ' + 'specified, --options is deprecated in favor of --cfg-options') + if args.options: + warnings.warn('--options is deprecated in favor of --cfg-options') + args.cfg_options = args.options + + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + + # replace the ${key} with the value of cfg.key + cfg = replace_cfg_vals(cfg) + + # update data root according to MMDET_DATASETS + update_data_root(cfg) + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + if args.auto_scale_lr: + if 'auto_scale_lr' in cfg and \ + 'enable' in cfg.auto_scale_lr and \ + 'base_batch_size' in cfg.auto_scale_lr: + cfg.auto_scale_lr.enable = True + else: + warnings.warn('Can not find "auto_scale_lr" or ' + '"auto_scale_lr.enable" or ' + '"auto_scale_lr.base_batch_size" in your' + ' configuration file. Please update all the ' + 'configuration files to mmdet >= 2.24.1.') + + # set multi-process settings + setup_multi_processes(cfg) + + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + + # work_dir is determined in this priority: CLI > segment in file > filename + if args.work_dir is not None: + # update configs according to CLI args if args.work_dir is not None + cfg.work_dir = args.work_dir + elif cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(args.config))[0]) + + if args.resume_from is not None: + cfg.resume_from = args.resume_from + cfg.auto_resume = args.auto_resume + if args.gpus is not None: + cfg.gpu_ids = range(1) + warnings.warn('`--gpus` is deprecated because we only support ' + 'single GPU mode in non-distributed training. ' + 'Use `gpus=1` now.') + if args.gpu_ids is not None: + cfg.gpu_ids = args.gpu_ids[0:1] + warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. ' + 'Because we only support single GPU mode in ' + 'non-distributed training. Use the first GPU ' + 'in `gpu_ids` now.') + if args.gpus is None and args.gpu_ids is None: + cfg.gpu_ids = [args.gpu_id] + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + # re-set gpu_ids with distributed training mode + _, world_size = get_dist_info() + cfg.gpu_ids = range(world_size) + + # create work_dir + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # dump config + cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) + # init the logger before other steps + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + log_file = osp.join(cfg.work_dir, f'{timestamp}.log') + logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) + + # init the meta dict to record some important information such as + # environment info and seed, which will be logged + meta = dict() + # log env info + env_info_dict = collect_env() + env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) + dash_line = '-' * 60 + '\n' + logger.info('Environment info:\n' + dash_line + env_info + '\n' + + dash_line) + meta['env_info'] = env_info + meta['config'] = cfg.pretty_text + # log some basic info + logger.info(f'Distributed training: {distributed}') + logger.info(f'Config:\n{cfg.pretty_text}') + + cfg.device = get_device() + # set random seeds + seed = init_random_seed(args.seed, device=cfg.device) + seed = seed + dist.get_rank() if args.diff_seed else seed + logger.info(f'Set random seed to {seed}, ' + f'deterministic: {args.deterministic}') + set_random_seed(seed, deterministic=args.deterministic) + cfg.seed = seed + meta['seed'] = seed + meta['exp_name'] = osp.basename(args.config) + + model = build_detector( + cfg.model, + train_cfg=cfg.get('train_cfg'), + test_cfg=cfg.get('test_cfg')) + model.init_weights() + + datasets = [build_dataset(cfg.data.train)] + if len(cfg.workflow) == 2: + val_dataset = copy.deepcopy(cfg.data.val) + val_dataset.pipeline = cfg.data.train.pipeline + datasets.append(build_dataset(val_dataset)) + if cfg.checkpoint_config is not None: + # save mmdet version, config file content and class names + # checkpoints as meta data + cfg.checkpoint_config.meta = dict( + mmdet_version=__version__ + get_git_hash()[:7], + CLASSES=datasets[0].CLASSES) + # add an attribute for visualization convenience + model.CLASSES = datasets[0].CLASSES + train_detector( + model, + datasets, + cfg, + distributed=distributed, + validate=(not args.no_validate), + timestamp=timestamp, + meta=meta) + + +if __name__ == '__main__': + main() diff --git a/examples/yaoba/singletask_learning_boost/resource/utils/F1-score.py b/examples/yaoba/singletask_learning_boost/resource/utils/F1-score.py new file mode 100644 index 00000000..adf4ebca --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/utils/F1-score.py @@ -0,0 +1,137 @@ +import json +import os +import cv2 +import numpy as np +from tqdm import tqdm +from mmdet.apis import init_detector, inference_detector +from utils.c1c.cal_json_file_MAP import topleftxywh_to_xyxy + +""" +接口参考map计算接口，评估一个模型在某json文件下的F1-score +""" +THR = 0.5 +SCORE_THR = 0.9 + + +def _single_F1score(predict_result, labels, thr=THR): + """ + 计算单张图片的F1-score + """ + mis_classify = 0 + mis_iou = 0 + all_mis = 0 + TP = 0 + predict_count = 0 + FN = np.zeros(len(labels)) + for index, cur_category_result in enumerate(predict_result): + if cur_category_result.shape[0] == 0: + continue + else: + for each_result in cur_category_result: + predict_box = each_result[:-1] + confidence = each_result[-1] + if confidence < 0.3: + continue + max_iou = 0 + label_category = -1 + flag = -1 + for j, label in enumerate(labels): + label_box = label[:-1] + iou = compute_iou(predict_box, label_box) + if iou > max_iou: + max_iou = iou + label_category = label[-1] + flag = j # 标记是哪个标签框被成功预测了 + if max_iou > thr: + if index == label_category: + TP += 1 + FN[flag] = 1 + else: + mis_classify += 1 + elif max_iou < thr and index == label_category: + mis_iou += 1 + elif max_iou < thr and index != label_category: + all_mis += 1 + predict_count += 1 + FP = predict_count - TP + FN = len(labels) - np.count_nonzero(FN) + precision = TP / (TP + FP + 1e-5) + recall = TP / (TP + FN + 1e-5) + f1score = 2 * precision * recall / (precision + recall + 1e-5) + return f1score, precision, recall + + +def F1score(config_file, checkpoint_file, img_path, anno_path, out_path): + fp = open(anno_path, 'r', encoding='utf8') + loaded_json = json.load(fp) + annos = loaded_json['annotations'] + images = loaded_json['images'] + fp.close() + model = init_detector(config_file, checkpoint_file, device='cuda:0') + image_name_id_list = [] + for i in images: + image_name_id_list.append((i['file_name'], i['id'])) + for item in tqdm(image_name_id_list): + image_name = item[0] + image_id = item[1] + image = os.path.join(img_path, image_name) + predict_result = inference_detector(model, image) + label = [] + for anno in annos: + anno_belong_img_id = anno['image_id'] + if anno_belong_img_id == image_id: + bbox = topleftxywh_to_xyxy(anno['bbox']) + category = anno['category_id'] + bbox.append(category) + label.append(bbox) + f1score, precision, recall = _single_F1score(predict_result, label, THR) + if precision>=SCORE_THR and recall>=SCORE_THR: + write_names(out_path, image_name, True) + else: + write_names(out_path, image_name, False) + write_txt(out_path, image_name, f1score) + + +def write_txt(path, name, score): + txt = open(path, mode="a+", encoding='utf-8') + txt.write(name) + txt.write(" ") + txt.write(str(score)) + txt.write('\n') + txt.close() + + +def write_names(path, name, qualified): + dir_name = os.path.dirname(path) + if qualified: + txt = open(os.path.join(dir_name, "pass.txt"), mode="a+", encoding='utf-8') + else: + txt = open(os.path.join(dir_name, "NG.txt"), mode="a+", encoding='utf-8') + txt.write(name) + txt.write('\n') + txt.close() + + +def compute_iou(rec_1, rec_2): + s_rec1 = (rec_1[2] - rec_1[0]) * (rec_1[3] - rec_1[1]) + s_rec2 = (rec_2[2] - rec_2[0]) * (rec_2[3] - rec_2[1]) + sum_s = s_rec1 + s_rec2 + left = max(rec_1[0], rec_2[0]) + right = min(rec_1[2], rec_2[2]) + bottom = max(rec_1[1], rec_2[1]) + top = min(rec_1[3], rec_2[3]) + if left >= right or top <= bottom: + return 0 + else: + inter = (right - left) * (top - bottom) + iou = (inter / (sum_s - inter)) * 1.0 + return iou + + +if __name__ == '__main__': + F1score( + config_file="/custom_code/instance_based/model/fpn/known/faster_rcnn_r50_fpn_1x_yaoba.py", + checkpoint_file="/custom_code/instance_based/model/fpn/known/epoch_48.pth", + img_path='/media/huawei_YaoBa/Images', + anno_path='/custom_code/instance_based/json/known_test.json', + out_path='/custom_code/instance_based/txt/known_part/f1score.txt') diff --git a/examples/yaoba/singletask_learning_boost/resource/utils/TTA_augs_xyxy_cv2.py b/examples/yaoba/singletask_learning_boost/resource/utils/TTA_augs_xyxy_cv2.py new file mode 100644 index 00000000..c20876b0 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/utils/TTA_augs_xyxy_cv2.py @@ -0,0 +1,513 @@ +import cv2 +import PIL +import mmcv +import numpy as np +from PIL import Image +import PIL.ImageOps, PIL.ImageDraw, PIL.ImageEnhance + +FILL_COLOR = (0, 0, 0) + + +def xyxy_to_xywh(boxes): + width = boxes[2] - boxes[0] + height = boxes[3] - boxes[1] + return [boxes[0], boxes[1], width, height] + + +def xywh_to_xyxy(boxes): + x = boxes[0] + boxes[2] + y = boxes[1] + boxes[3] + return [boxes[0], boxes[1], x, y] + + +def draw_bboxes(save_dir, img, bboxes): + img_copy = img.copy() + if bboxes: + for bbox in bboxes: + x1, y1 = int(bbox[0]), int(bbox[1]) + x2, y2 = int(bbox[2]), int(bbox[3]) + cv2.rectangle(img_copy, (x1, y1), (x2, y2), (0, 255, 0), 3) + print('show image shape: ', img_copy.shape) + cv2.imwrite(save_dir, img_copy) + + +def TTA_Resize(img, v=(800, 1333), bboxes=None): + img = img.copy() + h, w = img.shape[0], img.shape[1] + m = min(max(v) / max(h, w), min(v) / min(h, w)) + img_aug = cv2.resize(img, (int(w * m + 0.5), int(h * m + 0.5)), cv2.INTER_CUBIC) + # img_aug = padding_square(img_aug, v) + bboxes_aug = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i * m + 0.5) for i in bbox[:]] + bboxes_aug.append([x1, y1, x2, y2]) + return img_aug, bboxes_aug, m + + +def TTA_Resize_mmcv(img, v, bboxes=None): + aug_img, scale_factor = mmcv.imrescale(img, v, return_scale=True) + return aug_img, None, scale_factor + + +def TTA_Resize_re(bboxes, v): + bboxes_aug = [] + for bbox in bboxes: + bbox_aug = [int(i / v + 0.5) for i in bbox] + bboxes_aug.append(bbox_aug) + return bboxes_aug + + +def TTA_Flip(img, v, bboxes=None): + img = img.copy() + width = img.shape[1] + height = img.shape[0] + bboxes_aug = [] + if v == 0: + img_aug = cv2.flip(img, 0) + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + h = y2 - y1 + y1 = height - y2 + y2 = y1 + h + bboxes_aug.append([x1, y1, x2, y2]) + elif v == 1: + img_aug = cv2.flip(img, 1) + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + x1 = width - x2 + x2 = x1 + w + bboxes_aug.append([x1, y1, x2, y2]) + else: + img_aug = cv2.flip(img, -1) + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + h = y2 - y1 + x1 = width - x2 + y1 = height - y2 + x2 = x1 + w + y2 = y1 + h + bboxes_aug.append([x1, y1, x2, y2]) + return img_aug, bboxes_aug, [v, img_aug.shape[1], img_aug.shape[0]] + + +def TTA_Flip_re(bboxes, v): + m = v[0] + width = v[1] + height = v[2] + if m == 0: + bboxes_re = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + h = y2 - y1 + y1 = height - y2 + y2 = y1 + h + bboxes_re.append([x1, y1, x2, y2]) + elif m == 1: + bboxes_re = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + x1 = width - x2 + x2 = x1 + w + bboxes_re.append([x1, y1, x2, y2]) + else: + bboxes_re = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + h = y2 - y1 + x1 = width - x2 + y1 = height - y2 + x2 = x1 + w + y2 = y1 + h + bboxes_re.append([x1, y1, x2, y2]) + return bboxes_re + + +def TTA_Rotate_no_pad(img, v, bboxes=None): + img = img.copy() + h, w = img.shape[:2] + bboxes_aug = [] + if v == 90: + img_aug = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) + if bboxes: + for box in bboxes: + bboxes_aug.append([h - box[1], box[0], h - box[3], box[2]]) + elif v == 180: + img_aug = cv2.rotate(img, cv2.ROTATE_180) + if bboxes: + for box in bboxes: + bboxes_aug.append([w - box[0], h - box[1], w - box[2], h - box[3]]) + else: + img_aug = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) + if bboxes: + for box in bboxes: + bboxes_aug.append([box[1], w - box[0], box[3], w - box[2]]) + return img_aug, bboxes_aug, [v, img_aug.shape[0], img_aug.shape[1]] + + +def TTA_Rotate_no_pad_re(bboxes, v): + bboxes_re = [] + if bboxes: + if v[0] == 90: + for box in bboxes: + bboxes_re.append([box[1], v[2] - box[0], box[3], v[2] - box[2]]) + elif v[0] == 180: + for box in bboxes: + bboxes_re.append([v[2] - box[0], v[1] - box[1], v[2] - box[2], v[1] - box[3]]) + else: + for box in bboxes: + bboxes_re.append([v[1] - box[1], box[0], v[1] - box[3], box[2]]) + return bboxes_re + + +def TTA_Color(img, v, bboxes=None): # (0, 1) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Color(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Color_mmcv(img, v, bboxes=None): + gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + gray_img = np.tile(gray_img[..., None], [1, 1, 3]) + beta = 1 - v + colored_img = cv2.addWeighted(img, v, gray_img, beta, 0) + if not colored_img.dtype == np.uint8: + colored_img = np.clip(colored_img, 0, 255) + return colored_img.astype(img.dtype), bboxes, v + + +def TTA_Color_re(bboxes, v): + return bboxes + + +def TTA_Contrast(img, v, bboxes=None): # (0, 1) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Contrast(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Contrast_mmcv(img, v, bboxes=None): + gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + hist = np.histogram(gray_img, 256, (0, 255))[0] + mean = round(np.sum(gray_img) / np.sum(hist)) + degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype) + degenerated = cv2.cvtColor(degenerated, cv2.COLOR_GRAY2BGR) + contrasted_img = cv2.addWeighted( + img.astype(np.float32), v, degenerated.astype(np.float32), + 1 - v, 0) + contrasted_img = np.clip(contrasted_img, 0, 255) + return contrasted_img.astype(img.dtype), bboxes, v + + +def TTA_Contrast_re(bboxes, v): + return bboxes + + +def TTA_Brightness(img, v, bboxes=None): # (0, 1) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Brightness(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Brightness_mmcv(img, v, bboxes=None): + degenerated = np.zeros_like(img) + brightened_img = cv2.addWeighted( + img.astype(np.float32), v, degenerated.astype(np.float32), + 1 - v, 0) + brightened_img = np.clip(brightened_img, 0, 255) + return brightened_img.astype(img.dtype), bboxes, v + + +def TTA_Brightness_re(bboxes, v): + return bboxes + + +def TTA_Sharpness(img, v, bboxes=None): + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Sharpness(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Sharpness_re(bboxes, v): + return bboxes + + +def TTA_SHarpness_mmcv(img, v, bboxes=None): + kernel = np.array([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]]) / 13 + degenerated = cv2.filter2D(img, -1, kernel) + sharpened_img = cv2.addWeighted( + img.astype(np.float32), v, degenerated.astype(np.float32), + 1 - v, 0) + sharpened_img = np.clip(sharpened_img, 0, 255) + return sharpened_img.astype(img.dtype), bboxes, v + + +def TTA_AutoContrast(img, v, bboxes=None): + img = Image.fromarray(img.copy()) + cutoff = abs(v) + img_aug = PIL.ImageOps.autocontrast(img, cutoff) + return np.array(img_aug), bboxes, v + + +def TTA_AutoContrast_re(bboxes, v): + return bboxes + + +def TTA_AutoContrast_mmcv(img, v, bboxes=None): + def _auto_contrast_channel(im, c, cutoff): + im = im[:, :, c] + # Compute the histogram of the image channel. + histo = np.histogram(im, 256, (0, 255))[0] + # Remove cut-off percent pixels from histo + histo_sum = np.cumsum(histo) + cut_low = histo_sum[-1] * cutoff[0] // 100 + cut_high = histo_sum[-1] - histo_sum[-1] * cutoff[1] // 100 + histo_sum = np.clip(histo_sum, cut_low, cut_high) - cut_low + histo = np.concatenate([[histo_sum[0]], np.diff(histo_sum)], 0) + + # Compute mapping + low, high = np.nonzero(histo)[0][0], np.nonzero(histo)[0][-1] + # If all the values have been cut off, return the origin img + if low >= high: + return im + scale = 255.0 / (high - low) + offset = -low * scale + lut = np.array(range(256)) + lut = lut * scale + offset + lut = np.clip(lut, 0, 255) + return lut[im] + + if isinstance(v, (int, float)): + cutoff = (v, v) + else: + assert isinstance(v, tuple), 'cutoff must be of type int, ' \ + f'float or tuple, but got {type(v)} instead.' + # Auto adjusts contrast for each channel independently and then stacks + # the result. + s1 = _auto_contrast_channel(img, 0, cutoff) + s2 = _auto_contrast_channel(img, 1, cutoff) + s3 = _auto_contrast_channel(img, 2, cutoff) + contrasted_img = np.stack([s1, s2, s3], axis=-1) + return contrasted_img.astype(img.dtype), bboxes, v + + +def TTA_Equalize(img, v, bboxes=None): + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageOps.equalize(img) + return np.array(img_aug), bboxes, v + + +def TTA_Equalize_mmcv(img, v, bboxes=None): + def _scale_channel(im, c): + im = im[:, :, c] + histo = np.histogram(im, 256, (0, 255))[0] + nonzero_histo = histo[histo > 0] + step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255 + if not step: + lut = np.array(range(256)) + else: + lut = (np.cumsum(histo) + (step // 2)) // step + lut = np.concatenate([[0], lut[:-1]], 0) + lut[lut > 255] = 255 + return np.where(np.equal(step, 0), im, lut[im]) + + s1 = _scale_channel(img, 0) + s2 = _scale_channel(img, 1) + s3 = _scale_channel(img, 2) + equalized_img = np.stack([s1, s2, s3], axis=-1) + return equalized_img.astype(img.dtype), bboxes, v + + +def TTA_Equalize_re(bboxes, v): + return bboxes + + +def TTA_Grey(img, v, bboxes=None): + img = img.copy() + img_aug = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + img_aug = cv2.cvtColor(img_aug, cv2.COLOR_GRAY2BGR) + return img_aug, bboxes, v + + +def TTA_Grey_re(bboxes, v): + return bboxes + + +def TTA_Invert(img, v, bboxes=None): + img = img.copy() + img_aug = 255 - img + return img_aug, bboxes, v + + +def TTA_Invert_re(bboxes, v): + return bboxes + + +def TTA_Posterize(img, v, bboxes=None): + v = int(8 - abs(v) * 7) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageOps.posterize(img, v) + return np.array(img_aug), bboxes, v + + +def TTA_Posterize_mmcv(img, v, bboxes=None): + shift = 8 - int(abs(8 * v)) + img = np.left_shift(np.right_shift(img, shift), shift) + return img, bboxes, v + + +def TTA_Posterize_re(bboxes, v): + return bboxes + + +def TTA_Solarize(img, v, bboxes=None): + v = int((1 - abs(v)) * 255) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageOps.solarize(img, v) + return np.array(img_aug), bboxes, v + + +def TTA_Solarize_mmcv(img, v, bboxes=None): + v = int((1 - abs(v)) * 255) + img = np.where(img < v, img, 255 - img) + return img, bboxes, v + + +def TTA_Solarize_re(bboxes, v): + return bboxes + + +def TTA_HSV(img, v, bboxes=None): + img = img.copy() + img_aug = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + return img_aug, bboxes, v + + +def TTA_HSV_re(bboxes, v): + return bboxes + + +def TTA_PepperNoise(img, v, bboxes=None): + img = img.copy() + per = abs(v) / 2 + pix = img.shape[0] * img.shape[1] + num = int(pix * per * 0.5) + coords = [np.random.randint(0, i - 1, num) for i in img.shape[:2]] + img[coords[0], coords[1], :] = [255, 255, 255] + coords = [np.random.randint(0, i - 1, num) for i in img.shape[:2]] + img[coords[0], coords[1], :] = [0, 0, 0] + return img, bboxes, v + + +def TTA_PepperNoise_re(bboxes, v): + return bboxes + + +def TTA_GaussNoise(img, v, bboxes=None): + img = img.copy() + mean = 0 + sigma = abs(v) * 100 + gauss = np.random.normal(mean, sigma, (img.shape[0], img.shape[1], img.shape[2])) + noisy_img = img + gauss + noisy_img = np.clip(noisy_img, a_min=0, a_max=255) + return np.uint8(noisy_img), bboxes, v + + +def TTA_GaussNoise_re(bboxes, v): + return bboxes + + +def printf(x): + print(x) + exit(0) + + +def padding_square(img, out_size): + h, w = img.shape[:2] + ret = cv2.copyMakeBorder(img, 0, out_size[0] - h, 0, out_size[1] - w, cv2.BORDER_CONSTANT, value=FILL_COLOR) + return ret + + +def topleftxywh_to_xyxy(boxes): + """ + args: + boxes:list of topleft_x,topleft_y,width,height, + return: + boxes:list of x,y,x,y,cooresponding to top left and bottom right + """ + x_top_left = boxes[0] + y_top_left = boxes[1] + x_bottom_right = boxes[0] + boxes[2] + y_bottom_right = boxes[1] + boxes[3] + return [x_top_left, y_top_left, x_bottom_right, y_bottom_right] + + +def TTA_Aug_List(): + return [TTA_Resize, TTA_Flip, TTA_Rotate_no_pad, TTA_Color, TTA_Contrast, TTA_Brightness, TTA_Sharpness, + TTA_AutoContrast, TTA_Equalize, TTA_Grey, TTA_Invert, TTA_Posterize, TTA_Solarize, TTA_HSV, TTA_PepperNoise, + TTA_GaussNoise] + + +def TTA_Aug_Space(): + ''' + 0 TTA_Resize + 1 TTA_Flip + 2 TTA_Rotate_no_pad + 3 TTA_Color + 4 TTA_Contrast + 5 TTA_Brightness + 6 TTA_Sharpness + 7 TTA_AutoContrast + 8 TTA_Equalize + 9 TTA_Grey + 10 TTA_Invert + 11 TTA_Posterize + 12 TTA_Solarize + 13 TTA_HSV + 14 TTA_PepperNoise + 15 TTA_GaussNoise + ''' + return [(0, (640, 640)), + (0, (672, 672)), (0, (704, 704)), (0, (736, 736)), (0, (768, 768)), (0, (800, 800)), + (0, (832, 832)), (0, (864, 864)), + (0, (608, 608)), (0, (576, 576)), (0, (544, 544)), (0, (512, 512)), (0, (480, 480)), + (0, (448, 448)), (0, (416, 416)), + (1, 0), (1, 1), (1, -1), + (2, 90), (2, 180), (2, 270), + (3, 0.2), (3, 0.3), (3, 0.4), (3, 0.5), (3, 0.6), (3, 0.7), (3, 0.8), + (4, 0.2), (4, 0.3), (4, 0.4), (4, 0.5), (4, 0.6), (4, 0.7), (4, 0.8), + (5, 0.2), (5, 0.3), (5, 0.4), (5, 0.5), (5, 0.6), (5, 0.7), (5, 0.8), + (6, 0.2), (6, 0.3), (6, 0.4), (6, 0.5), (6, 0.6), (6, 0.7), (6, 0.8), + (7, 0.2), (7, 0.3), (7, 0.4), (7, 0.5), (7, 0.6), (7, 0.7), (7, 0.8), + (8, 1), + (9, 1), + (10, 1), + (11, 0.2), (11, 0.3), (11, 0.4), (11, 0.5), (11, 0.6), (11, 0.7), (11, 0.8), + (12, 0.2), (12, 0.3), (12, 0.4), (12, 0.5), (12, 0.6), (12, 0.7), (12, 0.8), + (13, 1), + (14, 0.2), (14, 0.3), (14, 0.4), (14, 0.5), (14, 0.6), (14, 0.7), (14, 0.8), + (15, 0.2), (15, 0.3), (15, 0.4), (15, 0.5), (15, 0.6), (15, 0.7), (15, 0.8)] + + +def Test_Aug_Space(): + return [(0, (672, 672)), + (1, -1), + (2, 90), + (3, 0.4), + (4, 0.2), + (5, 0.2), + (6, 0.2), + (7, 0.2), + (8, 1), + (9, 1), + ] diff --git a/examples/yaoba/singletask_learning_boost/resource/utils/infer_and_error.py b/examples/yaoba/singletask_learning_boost/resource/utils/infer_and_error.py new file mode 100644 index 00000000..4807e2b9 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/utils/infer_and_error.py @@ -0,0 +1,202 @@ +import json +import os +import cv2 +from mmdet.apis import inference_detector, init_detector +from custom_code.instance_based.utils.transform_unkonwn import xyxy_to_xywh +from tqdm import tqdm +from custom_code.test_time_aug.general_TTA_v4 import topleftxywh_to_xyxy + +CATEGORY = [{"id": 0, "name": "yanse"}, + {"id": 1, "name": "huahen"}, + {"id": 2, "name": "mosun"}, + ] + + +def compute_iou(rec_1, rec_2): + """ + 计算两个矩形框的交并比(IOU) + :param rec_1: 矩形框1，格式为 (x_min, y_min, x_max, y_max) + :param rec_2: 矩形框2，格式为 (x_min, y_min, x_max, y_max) + :return: 交并比(IOU) + """ + # 计算两个矩形框的面积 + s_rec1 = (rec_1[2] - rec_1[0]) * (rec_1[3] - rec_1[1]) + s_rec2 = (rec_2[2] - rec_2[0]) * (rec_2[3] - rec_2[1]) + sum_s = s_rec1 + s_rec2 + + # 计算两个矩形框的重叠部分面积 + left = max(rec_1[0], rec_2[0]) + right = min(rec_1[2], rec_2[2]) + bottom = max(rec_1[1], rec_2[1]) + top = min(rec_1[3], rec_2[3]) + if left >= right or top <= bottom: + return 0 + else: + inter = (right - left) * (top - bottom) + + # 计算交并比 + iou = (inter / (sum_s - inter)) * 1.0 + return iou + + +def compute_error(predict_result_json): + new_json = [] + iou_0_num = 0 # 标注框没有任何预测框的个数 + label_num = 0 # 标注框总个数 + with open(predict_result_json) as f: + info = json.load(f) + for key in info.keys(): + output = [] + output.append(key) + preds = info[key]['predict'] # 预测框信息 + labels = info[key]['label'] # 标注框信息(x_min, y_min, x_max, y_max, c) + for label in labels: + new_label = label.copy() + xyxy = label[:4] # 取出标注框的坐标信息(x_min, y_min, x_max, y_max) + c = label[4] # 取出标注框的类别信息 + max_iou = [0, 0, 0] # 最高的交并比，对应的预测框，对应预测框的类别概率 + + # 对于每个标注框，遍历所有的预测框，找到最高的交并比以及对应的预测框和类别概率 + for i, pred in enumerate(preds): + for pred_box in pred: + if pred_box: + iou = compute_iou(pred_box[:4], xyxy) # 计算交并比 + if iou > max_iou[0]: + max_iou[0] = iou + max_iou[1] = i + max_iou[2] = pred_box[4] + + if max_iou == [0, 0, 0]: # 如果找不到任何预测框和标注框重叠，认为交并比为0 + iou_0_num += 1 + error = 2 # 认为错误程度最大，用2表示 + new_label.append(error) + else: + error_iou = abs(1 - max_iou[0]) # 交并比的误差 + if c == max_iou[1]: # 如果预测的类别和标注的类别一致 + error_c = abs(1 - max_iou[2]) # 类别概率的误差 + else: + error_c = 1 # 类别不同，则认为是最大误差，用1表示 + error = error_iou + error_c # 误差综合 + new_label.append(error) + + output.append(new_label) # 把该标注框的信息加入到输出结果列表中 + label_num += 1 + new_json.append(output) # 把该图片的输出结果加入到整个输出结果列表中 + + # 输出统计信息 + print('标注框没有任何预测框的个数', iou_0_num) + print('标注框总个数', label_num) + + return new_json + + +def get_new_train_json(predict_results, img_path, known_name_list, unknown_name_list, out_dir): + fp_known = open(known_name_list, mode="r", encoding="utf-8") + fp_unknown = open(unknown_name_list, mode="r", encoding="utf-8") + known_result = fp_known.readlines() + unknown_result = fp_unknown.readlines() + for i in range(len(known_result)): + known_result[i] = known_result[i][:-1] + for i in range(len(unknown_result)): + unknown_result[i] = unknown_result[i][:-1] + images = [] + annotations = [] + id_num = 0 + image_id = 0 + for item in tqdm(predict_results): + img_name = item[0] + img_id = image_id + image = {} + image['file_name'] = img_name + image["id"] = img_id + height, width = cv2.imread(img_path + "/" + img_name).shape[:2] + image["height"] = height + image["width"] = width + images.append(image) + image_id+=1 + for i in range(1, len(item)): + bbox_item = item[i] + bbox = xyxy_to_xywh([bbox_item[0], bbox_item[1], bbox_item[2], bbox_item[3]]) + error = bbox_item[5] + annotation = {} + annotation["image_id"] = img_id + annotation["bbox"] = bbox + annotation["category_id"] = bbox_item[4] + if img_name in known_result: + annotation["weight"] = (2 - error) + 1 + elif img_name in unknown_result: + annotation["weight"] = error + 1 + annotation["id"] = id_num + annotation["iscrowd"] = 0 + annotation["segmentation"] = [] + annotation["area"] = bbox[2] * bbox[3] + id_num += 1 + annotations.append(annotation) + dataset_dict = {} + dataset_dict["images"] = images + dataset_dict["annotations"] = annotations + dataset_dict["categories"] = CATEGORY + json_str = json.dumps(dataset_dict) + with open(out_dir, 'w') as json_file: + json_file.write(json_str) + print("json file write done...") + + +def infer_anno(config_file, checkpoint_file, img_path, anno_path, out_path): + dir=os.path.dirname(out_path) + if not os.path.exists(dir): + os.mkdir(dir) + fp = open(anno_path, 'r', encoding='utf8') + label_json = json.load(fp) + annos = label_json['annotations'] + image_infos = label_json['images'] + model = init_detector(config_file, checkpoint_file, device='cuda:0') + predict_results = {} + for image_info in tqdm(image_infos): + image_name = image_info['file_name'] + image_id = image_info['id'] + predict_result = {} + image = os.path.join(img_path, image_name) + model_out = inference_detector(model, image) + for i in range(len(model_out)): + model_out[i] = model_out[i].tolist() + predict_result["predict"] = model_out + bboxes = [] + for anno in annos: + anno_belong_image = anno['image_id'] + if anno_belong_image == image_id: + bbox = topleftxywh_to_xyxy(anno['bbox']) + bbox.append(anno['category_id']) + bboxes.append(bbox) + predict_result["label"] = bboxes + predict_results[image_name] = predict_result + json_str = json.dumps(predict_results) + with open(out_path, 'w') as json_file: + json_file.write(json_str) + + +def merge_predict_results(result1, result2, out_dir): + new_predict_result = {} + fp1 = open(result1, 'r', encoding='utf8') + fp2 = open(result2, 'r', encoding='utf8') + json1 = json.load(fp1) + json2 = json.load(fp2) + for item in json1.keys(): + result = json1[item] + new_predict_result[item] = result + for item in json2.keys(): + result = json2[item] + new_predict_result[item] = result + json_str = json.dumps(new_predict_result) + with open(out_dir, 'w') as json_file: + json_file.write(json_str) + + +def gen_txt_according_json(label_json, out_dir): + fp = open(label_json, 'r', encoding='utf8') + anno = json.load(fp) + w = open(out_dir, 'a+', encoding='utf8') + images = anno['images'] + for image in images: + w.write(image['file_name']) + w.write('\n') diff --git a/examples/yaoba/singletask_learning_boost/resource/utils/random_select.py b/examples/yaoba/singletask_learning_boost/resource/utils/random_select.py new file mode 100644 index 00000000..fb40e815 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/utils/random_select.py @@ -0,0 +1,25 @@ +import os +import random +from random import sample + + +def random_select(src_txt, num, out_dir, select_name, rest_name): + fp = open(src_txt, mode="r", encoding='utf-8') + results = fp.readlines() + fp_select = open(os.path.join(out_dir, select_name), mode="a+", encoding='utf-8') + fp_rest = open(os.path.join(out_dir, rest_name), mode="a+", encoding='utf-8') + selected_items = sample(results, num) + for result in results: + if result in selected_items: + fp_select.write(result) + else: + fp_rest.write(result) + + +if __name__ == '__main__': + random.seed(5) + random_select("/custom_code/instance_based/txt/merged_part/NG.txt", + 139, + "/home/wjj/wjj/Public/code/huawei/custom_code/instance_based/txt/merged_part", + select_name="NG_test.txt", + rest_name="NG_labeled.txt") diff --git a/examples/yaoba/singletask_learning_boost/resource/utils/transform_unkonwn.py b/examples/yaoba/singletask_learning_boost/resource/utils/transform_unkonwn.py new file mode 100644 index 00000000..793b691c --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/utils/transform_unkonwn.py @@ -0,0 +1,167 @@ +import json +import shutil +import os.path +import copy +from tqdm import tqdm + +from .TTA_augs_xyxy_cv2 import * + + +def topleftxywh_to_xyxy(boxes): + """ + args: + boxes:list of topleft_x,topleft_y,width,height, + return: + boxes:list of x,y,x,y,cooresponding to top left and bottom right + """ + x_top_left = boxes[0] + y_top_left = boxes[1] + x_bottom_right = boxes[0] + boxes[2] + y_bottom_right = boxes[1] + boxes[3] + return [x_top_left, y_top_left, x_bottom_right, y_bottom_right] + + +def xyxy_to_xywh(boxes): + width = abs(boxes[2] - boxes[0]) + height = abs(boxes[3] - boxes[1]) + if boxes[0] < boxes[2]: + top_left_x = boxes[0] + else: + top_left_x = boxes[2] + if boxes[1] < boxes[3]: + top_left_y = boxes[1] + else: + top_left_y = boxes[3] + return [top_left_x, top_left_y, width, height] + + +def aug_image_bboxes_single(img_path, bboxes, labels, aug, image_id, anno_id, out_path): + new_bboxes = [] + bbox_category = [] + for i in range(len(bboxes)): + x, y, w, h = bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3] + xyxy_box = topleftxywh_to_xyxy([x, y, w, h]) + bbox_category.append(labels[i]) + new_bboxes.append(xyxy_box) + img = mmcv.imread(img_path) + method, v = aug[0], aug[1] + if method == "flip": + aug_img, aug_bboxes, _ = TTA_Flip(img, v=v, bboxes=new_bboxes) + elif method == "rotate": + aug_img, aug_bboxes, _ = TTA_Rotate_no_pad(img, v=v, bboxes=new_bboxes) + elif method == "brightness": + aug_img, aug_bboxes, _ = TTA_Brightness(img, v=v, bboxes=new_bboxes) + elif method == "resize": + aug_img, aug_bboxes, _ = TTA_Resize(img, v=v, bboxes=new_bboxes) + else: + raise ValueError("not implement") + aug_bboxes = [xyxy_to_xywh(aug_bbox) for aug_bbox in aug_bboxes] + image = {} + img_name = os.path.basename(img_path) + if img_name.endswith("jpg"): + img_name = img_name.replace(".jpg", "") + elif img_name.endswith("jpeg"): + img_name = img_name.replace(".jpeg", "") + else: + raise ValueError("current code only support .jpg or .jpeg") + aug_image_name = img_name + f"_{method}_{str(v)}.jpg" + height, width = img.shape[:2] + image["file_name"] = aug_image_name + image["height"] = height + image["width"] = width + image["id"] = image_id + annotations=[] + for i in range(len(aug_bboxes)): + annotation = {} + annotation["image_id"] = image_id + annotation["bbox"] = aug_bboxes[i] + annotation["category_id"] = bbox_category[i] + annotation["id"] = anno_id + annotation["iscrowd"] = 0 + annotation["segmentation"] = [] + annotation["area"] = aug_bboxes[i][2] * aug_bboxes[i][3] + annotations.append(annotation) + anno_id += 1 + mmcv.imwrite(aug_img, os.path.join(out_path, aug_image_name)) + return image, annotations, anno_id + + +def aug_image_bboxes(anno, augs, image_path, out_path): + if not os.path.exists(os.path.join(out_path,"aug_img_folder")): + os.mkdir(os.path.join(out_path,"aug_img_folder")) + aug_img_folder=os.path.join(out_path,"aug_img_folder") + fp = open(anno, mode="r", encoding="utf-8") + anno_json = json.load(fp) + fp.close() + images = anno_json['images'] + annotations = anno_json['annotations'] + categories = anno_json['categories'] + new_images = copy.deepcopy(images) + new_annotations = copy.deepcopy(annotations) + next_img_id = new_images[-1]['id'] + 1 + next_bbox_id = new_annotations[-1]['id'] + 1 + + for item in os.listdir(image_path): + shutil.copy(os.path.join(image_path,item),aug_img_folder) + for image in tqdm(images): + image_name, img_id = image['file_name'], image['id'] + bboxes = [] + labels = [] + for annotation in annotations: + if annotation['image_id'] == img_id: + bboxes.append(annotation['bbox']) + labels.append(annotation['category_id']) + for aug in augs: + image, annotation, next_bbox_id = aug_image_bboxes_single(os.path.join(image_path, image_name), + bboxes, + labels, + aug, + next_img_id, + next_bbox_id, + aug_img_folder, + ) + new_images.append(image) + for i in annotation: + new_annotations.append(i) + next_img_id += 1 + dataset_dict = {} + dataset_dict["images"] = new_images + dataset_dict["annotations"] = new_annotations + dataset_dict["categories"] = categories + json_str = json.dumps(dataset_dict) + with open(os.path.join(out_path, "aug_unknown.json"), 'w') as json_file: + json_file.write(json_str) + return aug_img_folder + +def merge_two_anno(anno1, anno2, out_dir): + fp1 = open(anno1, mode="r", encoding="utf-8") + anno_json1 = json.load(fp1) + fp1.close() + fp2 = open(anno2, mode="r", encoding="utf-8") + anno_json2 = json.load(fp2) + fp2.close() + categories = anno_json1['categories'] + anno1_images = anno_json1['images'] + anno1_bbox = anno_json1['annotations'] + anno2_images = anno_json2['images'] + anno2_bbox = anno_json2['annotations'] + next_image_id = anno1_images[-1]['id'] + 1 + next_bbox_id = anno1_bbox[-1]['id'] + 1 + for image in anno2_images: + old_image_id = image['id'] + for bbox in anno2_bbox: + if bbox['image_id'] == old_image_id: + bbox['id'] = next_bbox_id + bbox['image_id'] = next_image_id + anno1_bbox.append(bbox) + next_bbox_id += 1 + image['id'] = next_image_id + anno1_images.append(image) + next_image_id += 1 + dataset_dict = {} + dataset_dict["images"] = anno1_images + dataset_dict["annotations"] = anno1_bbox + dataset_dict["categories"] = categories + json_str = json.dumps(dataset_dict) + with open(os.path.join(out_dir, "merged.json"), 'w') as json_file: + json_file.write(json_str) diff --git a/examples/yaoba/singletask_learning_boost/resource/utils/txt2json_yaoba_change_id.py b/examples/yaoba/singletask_learning_boost/resource/utils/txt2json_yaoba_change_id.py new file mode 100644 index 00000000..9b59e988 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/resource/utils/txt2json_yaoba_change_id.py @@ -0,0 +1,81 @@ +import json +from tqdm import tqdm +import os +import random + +category_list = ['yanse', 'huahen', 'mosun'] + + +def xyxy_to_xywh(boxes): + width = abs(boxes[2] - boxes[0]) + height = abs(boxes[3] - boxes[1]) + if boxes[0] < boxes[2]: + top_left_x = boxes[0] + else: + top_left_x = boxes[2] + if boxes[1] < boxes[3]: + top_left_y = boxes[1] + else: + top_left_y = boxes[3] + return [top_left_x, top_left_y, width, height] + + +def Convert_ZhouCheng_to_COCO(anno_dir, name_txt, output_dir): + categories = [{"id": 0, "name": "yanse"}, + {"id": 1, "name": "huahen"}, + {"id": 2, "name": "mosun"}, + ] + with open(name_txt, mode="r", encoding='utf-8') as fp: + results = fp.readlines() + name_list = [] + for item in results: + item = item[:-1] + name_list.append(item) + fp.close() + images = [] + annotations = [] + id_num = 0 + image_id = 10000 + for item in tqdm(name_list): + image = {} + image["file_name"] = item + image["id"] = image_id + with open(os.path.join(anno_dir, item.replace(".jpg", ".json")), 'r', encoding='utf8') as fp: + json_data = json.load(fp) + height, width = json_data["imageHeight"], json_data["imageWidth"] + image["height"] = height + image["width"] = width + images.append(image) + for v in json_data['shapes']: + annotation = {} + label = v['label'] + if len(v['points']) == 1: + continue + else: + bbox = [int(v['points'][0][0]), int(v['points'][0][1]), int(v['points'][1][0]), + int(v['points'][1][1])] + bbox = xyxy_to_xywh(bbox) + annotation["image_id"] = image_id + annotation["bbox"] = bbox + annotation["category_id"] = category_list.index(label) + annotation["id"] = id_num + annotation["iscrowd"] = 0 + annotation["weight"] = random.random() + annotation["segmentation"] = [] + annotation["area"] = bbox[2] * bbox[3] + id_num += 1 + annotations.append(annotation) + image_id += 1 + dataset_dict = {} + dataset_dict["images"] = images + dataset_dict["annotations"] = annotations + dataset_dict["categories"] = categories + json_str = json.dumps(dataset_dict) + with open(output_dir, 'w') as json_file: + json_file.write(json_str) + + +if __name__ == '__main__': + Convert_ZhouCheng_to_COCO(anno_dir=r"/media/huawei_YaoBa/Annotations", + name_txt=r"/home/wjj/wjj/Public/code/huawei/custom_code/instance_based/txt/merged_part/NG_test.txt", + output_dir=r'/custom_code/instance_based/json/NG_test.json') diff --git a/examples/yaoba/singletask_learning_boost/testalgorithms/algorithm.yaml b/examples/yaoba/singletask_learning_boost/testalgorithms/algorithm.yaml new file mode 100644 index 00000000..da834c29 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/testalgorithms/algorithm.yaml @@ -0,0 +1,37 @@ +algorithm: + # paradigm name; string type; + # The paradigm determines how to execute the test program, + # In active_boost learning, the paradigm is singletasklearning_acboost + paradigm_type: "singletasklearning_acboost" + # The folder URL address for accessing some important base files + # e.g, the mmdet train script file train.py, + # the base config file base_config.py for active learning + initial_model_url: "" + # algorithm module configuration in the paradigm; list type; + modules: + # kind of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel" + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "FPN_ac_boost" + # the url address of python module; string type; + url: "examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py" + + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + # The config file URL address of the model to run the benchmark + # This case uses the FPN config file of mmdet, which is a python file + # you can refer to https://github.com/open-mmlab/mmdetection for more details of mmdet + - config: + values: + - "examples/yaoba/singletask_learning_boost/resource/FPN_model_config.py" + # The folder URL address to save the training log, checkpoints, training JSON file, etc. + - work_dir: + values: + - "examples/yaoba/singletask_learning_boost/work_dir" + - resource_dir: + values: + - "examples/yaoba/singletask_learning_boost/resource" diff --git a/examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py b/examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py new file mode 100644 index 00000000..160c2947 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/testalgorithms/basemodel.py @@ -0,0 +1,231 @@ +from __future__ import absolute_import, division, print_function +import json +from sedna.common.class_factory import ClassType, ClassFactory +from sedna.datasources import TxtDataParse, JSONDataParse +from pycocotools.coco import COCO +import copy +import os +import os.path as osp +import time +import mmcv +import torch +from mmcv import Config +from mmcv.utils import get_git_hash +from multiprocessing import Pool +from mmdet import __version__ +from mmdet.apis import train_detector +from mmdet.datasets import build_dataset +from mmdet.models import build_detector +from mmdet.utils import (collect_env, get_device, get_root_logger, + replace_cfg_vals, + setup_multi_processes, update_data_root) +from mmdet.apis import init_detector, inference_detector + +__all__ = ["BaseModel"] + +from tqdm import tqdm + + + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + + +@ClassFactory.register(ClassType.GENERAL, alias="FPN_ac_boost") +class BaseModel: + + def __init__(self, config, work_dir, resource_dir, **kwargs): + self.config = config + self.work_dir = work_dir + self.resource_dir=resource_dir + cfg = Config.fromfile(self.config) + cfg.work_dir = self.work_dir + cfg = replace_cfg_vals(cfg) + update_data_root(cfg) + setup_multi_processes(cfg) + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + if cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(self.config))[0]) + self.distributed = False + cfg.gpu_ids = [0] + # create work_dir + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # dump config + cfg.dump(osp.join(cfg.work_dir, osp.basename(self.config))) + # init the logger before other steps + self.timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + log_file = osp.join(cfg.work_dir, f'{self.timestamp}.log') + logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) + self.meta = dict() + env_info_dict = collect_env() + env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) + dash_line = '-' * 60 + '\n' + logger.info('Environment info:\n' + dash_line + env_info + '\n' + + dash_line) + self.meta['env_info'] = env_info + self.meta['config'] = cfg.pretty_text + # log some basic info + logger.info(f'Distributed training: {self.distributed}') + logger.info(f'Config:\n{cfg.pretty_text}') + cfg.device = get_device() + self.device = cfg.device + seed = 1 + logger.info(f'Set random seed to {seed}, ' + f'deterministic: False') + cfg.seed = seed + self.meta['seed'] = seed + self.meta['exp_name'] = osp.basename(self.config) + self.cfg = cfg + + def train(self, train_data, **kwargs): + img_prefix, ann_file = train_data[0], train_data[1] + # Get the number of categories and their names. + labels = json.load(open(ann_file, mode="r", encoding="utf-8")) + categories = labels['categories'] + classes = [c['name'] for c in categories] + num_classes = len(categories) + + # check dataset + if "dataset" in self.cfg.data.train.keys(): + self.cfg.data.train.dataset.classes = classes + self.cfg.data.train.dataset.ann_file = ann_file + self.cfg.data.train.dataset.img_prefix = img_prefix + else: + self.cfg.data.train.classes = classes + self.cfg.data.train.ann_file = ann_file + self.cfg.data.train.img_prefix = img_prefix + self.cfg.data.val.classes = classes + self.cfg.data.test.classes = classes + + # check detection head + if "roi_head" in self.cfg.model.keys(): + self.cfg.model.roi_head.bbox_head.num_classes = num_classes + else: + self.cfg.model.bbox_head.num_classes = num_classes + + + self.model = build_detector( + self.cfg.model, + train_cfg=self.cfg.get('train_cfg'), + test_cfg=self.cfg.get('test_cfg')) + self.model.init_weights() + + print("A total of", self.cfg.runner.max_epochs, "epoch") + + datasets = [build_dataset(self.cfg.data.train)] + if len(self.cfg.workflow) == 2: + val_dataset = copy.deepcopy(self.cfg.data.val) + val_dataset.pipeline = self.cfg.data.train.pipeline + datasets.append(build_dataset(val_dataset)) + if self.cfg.checkpoint_config is not None: + # save mmdet version, config file content and class names + # checkpoints as meta data + self.cfg.checkpoint_config.meta = dict( + mmdet_version=__version__ + get_git_hash()[:7], + CLASSES=datasets[0].CLASSES) + # add an attribute for visualization convenience + self.model.CLASSES = datasets[0].CLASSES + train_detector( + self.model, + datasets, + self.cfg, + distributed=self.distributed, + validate=False, + timestamp=self.timestamp, + meta=self.meta) + self.last_checkpoint_path = os.path.join(self.cfg.work_dir, "latest.pth") + return self.last_checkpoint_path + + def save(self, model_path): + return model_path + + def load(self, model_url=None): + self.load_model = init_detector(self.cfg, model_url) + return self.load_model + + def predict(self, data, **kwargs): + # ianvs格式 + predict_dict = {} + for imgPath in data: + print("inference predict->", imgPath) + imgName = osp.basename(imgPath) + predict_dict[str(imgName)] = [] + predict_result = inference_detector(self.load_model, imgPath) + for i, vs in enumerate(predict_result): + temp_dict = {} + temp_dict["category_id"] = i + temp_dict["bbox"] = vs + predict_dict[str(imgName)].append(temp_dict) + return predict_dict + + def _getAnnFromJSON(self, ann_file): + coco = COCO(ann_file) + imgIDs = coco.getImgIds() + catIDs = coco.getCatIds() + + y_true = {} + for id in imgIDs: + img = coco.loadImgs(id)[0] + name = img['file_name'] + y_true[name] = [] + for catID in catIDs: + temp_dict = {} + temp_dict["category_id"] = catID + + annIDs = coco.getAnnIds(imgIds=id, catIds=catID) + annotations = coco.loadAnns(annIDs) + temp_dict["annotations"] = annotations + y_true[name].append(temp_dict) + + return y_true + + def _JSONDataParse(self, data): + jsonfile = str(data.x["filepath"]) + img_prefix = str(data.data_dir) + + return img_prefix, jsonfile + + def _TXTDataParse(self, data): + # 生成COCO格式的JSON文件 + # 传入数据为图像名和COCO格式的JSON文件路径 + if data is None or data.x is None or data.y is None: + raise Exception("Train data is None.") + + ann_file = data.y[0] + coco = COCO(ann_file) + name2ID = {} + imgs = coco.loadImgs(coco.getImgIds()) + for img in imgs: + name = img["file_name"] + name2ID[name] = img["id"] + + images = [] + annotations = [] + for imgName in data.x: + imgName = osp.basename(imgName) + imgID = name2ID[imgName] + images.extend(coco.loadImgs(imgID)) + annID = coco.getAnnIds(imgIds=imgID) + annotations.extend(coco.loadAnns(annID)) + + categories = coco.loadCats(coco.getCatIds()) + coco_format_json = dict( + images=images, + annotations=annotations, + categories=categories) + + img_prefix = osp.split(data.x[0])[0] + out_file = osp.join(self.work_dir, osp.basename(ann_file)) + mmcv.dump(coco_format_json, out_file) # 保存标注文件(临时目录下) + return img_prefix, out_file + + def _data2coco(self, data): + if isinstance(data, TxtDataParse): + return self._TXTDataParse(data) + elif isinstance(data, JSONDataParse): + return self._JSONDataParse(data) + else: + raise Exception("Train data type is error.") diff --git a/examples/yaoba/singletask_learning_boost/testenv/map.py b/examples/yaoba/singletask_learning_boost/testenv/map.py new file mode 100644 index 00000000..a703a89e --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/testenv/map.py @@ -0,0 +1,35 @@ +import json + +from mmdet.datasets import build_dataset +from sedna.common.class_factory import ClassType, ClassFactory +__all__ = ["map"] + + +@ClassFactory.register(ClassType.GENERAL, alias="map") +def map(y_true, y_pred): + img_prefix = y_true[0] + ann_file = y_true[1] + fp = open(ann_file, mode="r", encoding="utf-8") + test_anno_json = json.load(fp) + categories = test_anno_json["categories"] + categories = [i['name'] for i in categories] + test_cfg = dict( + type='CocoDataset', + classes=categories, + ann_file=ann_file, + img_prefix=img_prefix, + pipeline=[], + test_mode=True + ) + dataset = build_dataset(test_cfg) + real_eval_items = list(y_pred.keys()) + predict_results = [] + for item in real_eval_items: + cur_predict = y_pred[item] + new_predict = [] + for result in cur_predict: + bbox = result['bbox'] + new_predict.append(bbox) + predict_results.append(new_predict) + metric = dataset.evaluate(predict_results) + return metric['bbox_mAP_50'] diff --git a/examples/yaoba/singletask_learning_boost/testenv/testenv.yaml b/examples/yaoba/singletask_learning_boost/testenv/testenv.yaml new file mode 100644 index 00000000..25ee1c70 --- /dev/null +++ b/examples/yaoba/singletask_learning_boost/testenv/testenv.yaml @@ -0,0 +1,24 @@ +testenv: + # dataset configuration + dataset: + # the url address of train dataset index; string type; + train_url: "examples/yaoba/singletask_learning_boost/resource/json/known_train.json" + # the url address of val dataset index; string type; + val_url: "examples/yaoba/singletask_learning_boost/resource/json/known_train.json" + # the url address of test dataset index; string type; + test_url: "examples/yaoba/singletask_learning_boost/resource/json/NG_test.json" + # the URL address of known dataset for training; + # string type; The data format type is MS COCO JSON format, which is commonly used in object detection; + # which you can refer https://zhuanlan.zhihu.com/p/29393415 for the format details + known_dataset_url: "examples/yaoba/singletask_learning_boost/resource/json/known_train.json" + # the URL address of unknown dataset for training; + # string type; the same as above known_data_url; + unknown_dataset_url: "examples/yaoba/singletask_learning_boost/resource/json/NG_labeled.json" + # the image folder URL address of known and unknown datasets above; + image_folder_url: "/home/wjj/wjj/Public/code/ianvs/dataset/yaoba/images" + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "map" + # the python file URL address for calculating the above metric + url: "examples/yaoba/singletask_learning_boost/testenv/map.py" \ No newline at end of file diff --git a/examples/yaoba/singletask_learning_yolox_tta/benchmarkingjob.yaml b/examples/yaoba/singletask_learning_yolox_tta/benchmarkingjob.yaml new file mode 100644 index 00000000..e400054d --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/benchmarkingjob.yaml @@ -0,0 +1,70 @@ +benchmarkingjob: + # job name of bechmarking; string type; + name: "benchmarkingjob" + # the url address of job workspace that will reserve the output of tests; string type; + workspace: "examples/yaoba/singletask_learning_yolox_tta/workspace" + # the url address of test environment configuration file; string type; + # the file format supports yaml/yml; + testenv: "examples/yaoba/singletask_learning_yolox_tta/testenv/testenv.yaml" + # the configuration of test object + test_object: + # test type; string type; + # currently the option of value is "algorithms",the others will be added in succession. + type: "algorithms" + # test algorithm configuration files; list type; + algorithms: + # algorithm name; string type; + - name: "mmlab-model" + # the url address of test algorithm configuration file; string type; + # the file format supports yaml/yml; + url: "examples/yaoba/singletask_learning_yolox_tta/testalgorithms/algorithm.yaml" + + # the configuration of ranking leaderboard + rank: + # rank leaderboard with metric of test case's evaluation and order ; list type; + # the sorting priority is based on the sequence of metrics in the list from front to back; + sort_by: [ { "map": "descend" } ] + + # visualization configuration + visualization: + # mode of visualization in the leaderboard; string type; + # There are quite a few possible dataitems in the leaderboard. Not all of them can be shown simultaneously on the screen. + # In the leaderboard, we provide the "selected_only" mode for the user to configure what is shown or is not shown. + mode: "selected_only" + # method of visualization for selected dataitems; string type; + # currently the options of value are as follows: + # 1> "print_table": print selected dataitems; + method: "print_table" + + # selected dataitem configuration + # The user can add his/her interested dataitems in terms of "paradigms", "modules", "hyperparameters" and "metrics", + # so that the selected columns will be shown. + selected_dataitem: + # currently the options of value are as follows: + # 1> "all": select all paradigms in the leaderboard; + # 2> paradigms in the leaderboard, e.g., "singletasklearning" + paradigms: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all modules in the leaderboard; + # 2> modules in the leaderboard, e.g., "basemodel" + modules: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all hyperparameters in the leaderboard; + # 2> hyperparameters in the leaderboard, e.g., "momentum" + hyperparameters: [ "all" ] + # currently the options of value are as follows: + # 1> "all": select all metrics in the leaderboard; + # 2> metrics in the leaderboard, e.g., "f1_score" + metrics: [ "map" ] + + # model of save selected and all dataitems in workspace; string type; + # currently the options of value are as follows: + # 1> "selected_and_all": save selected and all dataitems; + # 2> "selected_only": save selected dataitems; + save_mode: "selected_and_all" + + + + + + diff --git a/examples/yaoba/singletask_learning_yolox_tta/dataset/test.json b/examples/yaoba/singletask_learning_yolox_tta/dataset/test.json new file mode 100644 index 00000000..d2e706f7 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/dataset/test.json @@ -0,0 +1 @@ +{"images": [{"file_name": "2021-05-16-10-09-46.jpg", "id": 10000, 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/dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/readme/README.md @@ -0,0 +1,166 @@ +## About Industrial Defect Detection + +Defects are an unwanted thing in manufacturing industry. There are many types of defect in manufacturing like blow +holes, pinholes, burr, shrinkage defects, mould material defects, pouring metal defects, metallurgical defects, etc. For +removing this defective product all industry have their defect detection department. But the main problem is this +inspection process is carried out manually. It is a very time-consuming process and due to human accuracy, this is not +100% accurate. This can because of the rejection of the whole order. So it creates a big loss in the company. + +## About Method + +Unknown tasks refer to samples outside the knowledge distribution of the task model, which can lead to severe performance degradation of a model. The TTA (Test-time Augmentation) method applies various augmentations to an image during the inference stage, generates multiple augmented images, and infers them alongside the original image. It then combines the inference results of all images to obtain improved performance compared to only inferring on the original image, as shown in below figure. + +diagram1 + +In this project, we present a policy search-based test-time augmentation method for unknown defect detection tasks. It first searches for the optimal augmentation policy on the validation set, and then applies these policies to improve the performance of unknown defect detection tasks during inference of the test set, as shown in below figure. + +diagram2 + +Three test-time augmentation schemes: single policy, cascade policy, and combined policy are proposed in this method. Their differences among them are shown in the following figure. + +diagram3 + +## About dataset + +The industrial crank defect detection dataset consists of 627 images, which hold three types of defects: 'yanse', ' +huahen', and 'mosun', and the image resolution is 4024 × 3036. Please follow [this link](https://baidu.com) to download +the dataset and learn more about it. Additionally, below is an example figure from the dataset. + +diagram4 + +## Dataset splitting for unknown task processing +After unzipping dataset.zip, your folder format should look like below: + +``` +dataset + ├─images + └─jsons + ├─test.json + ├─train.json + └─val.json +``` + +All images of this dataset are placed in the ```images``` folder, and the dataset splitting is based on JSON files +in ```jsons``` folder. In this project, we define an image defect detection Precision or Recall less than 0.9 as unknown +data. Unknown training set means unknown data that can be used for training. + +## Install + +First, you need to install the dependencies of Ianvs and Sedna, please follow the instructions +in [How to install Ianvs](https://github.com/kubeedge/ianvs/blob/main/docs/guides/how-to-install-ianvs.md). We are using +the MMDet object detection framework in this project, which requires some essential dependencies to be installed. You +can install them manually one by one or use the ```pip install -r requirements.txt``` command directly in the terminal. +To avoid unpredictable errors, we strongly recommend creating a new Conda virtual environment before installing these +dependencies. + +``` +python==3.9.0 +mmcv-full==1.7.1 +mmdet==2.28.2 +torch==1.13.0 +torchvision==0.14.0 +numpy==1.24.2 +opencv_python==4.5.5.64 +Pillow==9.4.0 +``` + +## Config Setting + +Key settings to run policy search-based test-time augmentation method for unknown task processing of defect detection. + +### Testenv Setting + +Configure the `train_url`, `val_url`, `test_url`, `image_folder_url`, and `metrics` properties in `testenv.yaml` based on the location of +your dataset folder path like the below code block. + +``` +testenv: + dataset: + train_url: "datset/jsons/train.json" + val_url: "datset/jsons/val.json" + test_url: "datset/jsons/test.json" + image_folder_url: "/media/huawei_YaoBa/Images" + type; + metrics: + - name: "map" + url: "examples/yaoba/singletask_learning_yolox_tta/testenv/map.py" +``` + +### Algorithm Setting + +Configure the `paradigm_type`, `initial_model_url`, `basemodel`, `config`, and `work_dir` properties +in `algorithm.yaml` based on the location of your project path like the below code block. + +``` +algorithm: + paradigm_type: "singletasklearning_tta" + initial_model_url: "examples/yaoba/singletask_learning_yolox_tta/resource/epoch_300.pth" + modules: + - type: "basemodel" + name: "mmlab" + url: "examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py" + hyperparameters: + - config: + values: + - "examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py" + - work_dir: + values: + - "examples/yaoba/singletask_learning_yolox_tta/work_dir" +``` + +### Benchmarking Setting + +Configure the `workspace`, `testenv`, and `algorithms` properties in `benchmarkingjob.yaml` based on the location of +your project path like the below code block. + +``` +benchmarkingjob: + name: "benchmarkingjob" + workspace: "examples/yaoba/singletask_learning_yolox_tta/workspace" + testenv: "examples/yaoba/singletask_learning_yolox_tta/testenv/testenv.yaml" + test_object: + type: "algorithms" + algorithms: + - name: "mmlab-model" + url: "examples/yaoba/singletask_learning_yolox_tta/testalgorithms/algorithm.yaml" +``` + +## Usage + +We are now ready to run the policy search-based test-time augmentation method for unknown task processing on the +industrial crank defect detection dataset. execute the below command in your terminal: + +``` +python benchmarking.py -f examples/yaoba/singletask_learning_yolox_tta/benchmarkingjob.yaml +``` + +## Result + +We present reference results of mean Average Precision (mAP) on the test sets of unknown defect detection tasks for +policy search-based test-time augmentation method. Compared to the standard baseline, the maximum improvement achieved +by the top 5 single policies searched by this method is 2.2 mAP. + +| Model | Method | Single policy | Val set AP50 | Test set AP50 | +|:-----------:|:--------:|:-------------:|:------------:|:-------------:| +| YOLOXs-300e | Baseline | None | 69.3 | 69.7 | +| YOLOXs-300e | TTA | Resize | 73.29 | 71.9 | +| YOLOXs-300e | TTA | Flip | 72.14 | 71.4 | +| YOLOXs-300e | TTA | AutoContrast | 70.57 | 69.4 | +| YOLOXs-300e | TTA | Sharpness | 69.83 | 68.7 | +| YOLOXs-300e | TTA | Brightness | 69.51 | 69.7 | + +Compared to the standard baseline, the maximum improvement achieved by the top 5 Cascade policies searched by this +method is 2.5 mAP. + +| Model | Method | Cascade policy | Val set AP50 | Test set AP50 | +|:-----------:|:--------:|:------------------------:|:------------:|:-------------:| +| YOLOXs-300e | Baseline | None | 69.3 | 69.7 | +| YOLOXs-300e | TTA | Flip+AutoContrast+Resize | 73.68 | 70.7 | +| YOLOXs-300e | TTA | AutoContrast+Flip+Resize | 73.68 | 70.7 | +| YOLOXs-300e | TTA | AutoContrast+Resize+Flip | 73.64 | 70.1 | +| YOLOXs-300e | TTA | Flip+Resize | 73.6 | 70.8 | +| YOLOXs-300e | TTA | Sharpness+Resize | 73.58 | 72.2 | + +## License + +MIT © Richard McRichface_ \ No newline at end of file diff --git a/examples/yaoba/singletask_learning_yolox_tta/readme/diagram1.png b/examples/yaoba/singletask_learning_yolox_tta/readme/diagram1.png new file mode 100644 index 0000000000000000000000000000000000000000..375277732d5087ec70c45e7ea28dfc36d7a32277 GIT binary patch literal 148615 zcmZ6yWmKHYwl<2pd!Uiv4grE|g1fuBH|`Le0KwfIg1fsr1Uk4i?oNJ3dTY-4SVbx;N};0=qd-AHq02~%t3pA+GeW+C~8_CgFbR**@_jtg1|l^z@09!<4?7_Sfclx^!LWeqX#|yhYugHGkCtK zT#tYM@tAV?>3^@+#Nn%~$p5b6lV7J~0awkKJqH7%o;`asY~Mgx?|W>UPidHfkC`M& zIc?h`Npz09Pn!?jgdZc~00RC8rCBpuF3&lhIl`Wx^2Wo9?#mL-%|8RUbdFuOHH6)d z!w=nO`?YPCB@a2mz6aqXK;CbsED!6xvvP(nJ9>`o`@hT8va+%eF&)VGUG|pI>1H;6 zxX$+VyI#CZTg~KjUUL;@^LyXyMdH$7-Ttbl;>i~9Kbbnl9gW>^ySYPUTmMs`^*ZO! 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zB(H9{^g9!h4zxqV=Z`}0w;VECRB%8yl3N_L=vtk=t+YrT_*RD(cvTvVWNaU8pj}%& z_*hh0&X^W9>looS4!rXXhd)854FiA(JLspys}qE)30{+C61Z+}`!*UmLEeGq89Q9k JAoFkezW^qHg2Vs- literal 0 HcmV?d00001 diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_augs_xyxy_cv2.py b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_augs_xyxy_cv2.py new file mode 100644 index 00000000..c98a306a --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_augs_xyxy_cv2.py @@ -0,0 +1,517 @@ +import cv2 +import PIL +import mmcv +import numpy as np +from PIL import Image +import PIL.ImageOps, PIL.ImageDraw, PIL.ImageEnhance +from collections import OrderedDict + +FILL_COLOR = (0, 0, 0) + + +def xyxy_to_xywh(boxes): + width = boxes[2] - boxes[0] + height = boxes[3] - boxes[1] + return [boxes[0], boxes[1], width, height] + + +def xywh_to_xyxy(boxes): + x = boxes[0] + boxes[2] + y = boxes[1] + boxes[3] + return [boxes[0], boxes[1], x, y] + + +def draw_bboxes(save_dir, img, bboxes): + img_copy = img.copy() + if bboxes: + for bbox in bboxes: + x1, y1 = int(bbox[0]), int(bbox[1]) + x2, y2 = int(bbox[2]), int(bbox[3]) + cv2.rectangle(img_copy, (x1, y1), (x2, y2), (0, 255, 0), 3) + print('show image shape: ', img_copy.shape) + cv2.imwrite(save_dir, img_copy) + + +def TTA_Resize(img, v=(800, 1333), bboxes=None): + img = img.copy() + h, w = img.shape[0], img.shape[1] + m = min(max(v) / max(h, w), min(v) / min(h, w)) + img_aug = cv2.resize(img, (int(w * m + 0.5), int(h * m + 0.5)), cv2.INTER_CUBIC) + # img_aug = padding_square(img_aug, v) + bboxes_aug = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i * m + 0.5) for i in bbox[:]] + bboxes_aug.append([x1, y1, x2, y2]) + return img_aug, bboxes_aug, m + + +def TTA_Resize_mmcv(img, v, bboxes=None): + aug_img, scale_factor = mmcv.imrescale(img, v, return_scale=True) + return aug_img, None, scale_factor + + +def TTA_Resize_re(bboxes, v): + bboxes_aug = [] + for bbox in bboxes: + bbox_aug = [int(i / v + 0.5) for i in bbox] + bboxes_aug.append(bbox_aug) + return bboxes_aug + + +def TTA_Flip(img, v, bboxes=None): + img = img.copy() + width = img.shape[1] + height = img.shape[0] + bboxes_aug = [] + if v == 0: + img_aug = cv2.flip(img, 0) + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + h = y2 - y1 + y1 = height - y2 + y2 = y1 + h + bboxes_aug.append([x1, y1, x2, y2]) + elif v == 1: + img_aug = cv2.flip(img, 1) + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + x1 = width - x2 + x2 = x1 + w + bboxes_aug.append([x1, y1, x2, y2]) + else: + img_aug = cv2.flip(img, -1) + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + h = y2 - y1 + x1 = width - x2 + y1 = height - y2 + x2 = x1 + w + y2 = y1 + h + bboxes_aug.append([x1, y1, x2, y2]) + return img_aug, bboxes_aug, [v, img_aug.shape[1], img_aug.shape[0]] + + +def TTA_Flip_re(bboxes, v): + m = v[0] + width = v[1] + height = v[2] + if m == 0: + bboxes_re = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + h = y2 - y1 + y1 = height - y2 + y2 = y1 + h + bboxes_re.append([x1, y1, x2, y2]) + elif m == 1: + bboxes_re = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + x1 = width - x2 + x2 = x1 + w + bboxes_re.append([x1, y1, x2, y2]) + else: + bboxes_re = [] + if bboxes: + for bbox in bboxes: + x1, y1, x2, y2 = [int(i) for i in bbox[:]] + w = x2 - x1 + h = y2 - y1 + x1 = width - x2 + y1 = height - y2 + x2 = x1 + w + y2 = y1 + h + bboxes_re.append([x1, y1, x2, y2]) + return bboxes_re + + +def TTA_Rotate_no_pad(img, v, bboxes=None): + img = img.copy() + h, w = img.shape[:2] + bboxes_aug = [] + if v == 90: + img_aug = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) + if bboxes: + for box in bboxes: + bboxes_aug.append([h - box[1], box[0], h - box[3], box[2]]) + elif v == 180: + img_aug = cv2.rotate(img, cv2.ROTATE_180) + if bboxes: + for box in bboxes: + bboxes_aug.append([w - box[0], h - box[1], w - box[2], h - box[3]]) + else: + img_aug = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) + if bboxes: + for box in bboxes: + bboxes_aug.append([box[1], w - box[0], box[3], w - box[2]]) + return img_aug, bboxes_aug, [v, img_aug.shape[0], img_aug.shape[1]] + + +def TTA_Rotate_no_pad_re(bboxes, v): + bboxes_re = [] + if bboxes: + if v[0] == 90: + for box in bboxes: + bboxes_re.append([box[1], v[2] - box[0], box[3], v[2] - box[2]]) + elif v[0] == 180: + for box in bboxes: + bboxes_re.append([v[2] - box[0], v[1] - box[1], v[2] - box[2], v[1] - box[3]]) + else: + for box in bboxes: + bboxes_re.append([v[1] - box[1], box[0], v[1] - box[3], box[2]]) + return bboxes_re + + +def TTA_Color(img, v, bboxes=None): # (0, 1) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Color(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Color_mmcv(img, v, bboxes=None): + gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + gray_img = np.tile(gray_img[..., None], [1, 1, 3]) + beta = 1 - v + colored_img = cv2.addWeighted(img, v, gray_img, beta, 0) + if not colored_img.dtype == np.uint8: + colored_img = np.clip(colored_img, 0, 255) + return colored_img.astype(img.dtype), bboxes, v + + +def TTA_Color_re(bboxes, v): + return bboxes + + +def TTA_Contrast(img, v, bboxes=None): # (0, 1) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Contrast(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Contrast_mmcv(img, v, bboxes=None): + gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + hist = np.histogram(gray_img, 256, (0, 255))[0] + mean = round(np.sum(gray_img) / np.sum(hist)) + degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype) + degenerated = cv2.cvtColor(degenerated, cv2.COLOR_GRAY2BGR) + contrasted_img = cv2.addWeighted( + img.astype(np.float32), v, degenerated.astype(np.float32), + 1 - v, 0) + contrasted_img = np.clip(contrasted_img, 0, 255) + return contrasted_img.astype(img.dtype), bboxes, v + + +def TTA_Contrast_re(bboxes, v): + return bboxes + + +def TTA_Brightness(img, v, bboxes=None): # (0, 1) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Brightness(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Brightness_mmcv(img, v, bboxes=None): + degenerated = np.zeros_like(img) + brightened_img = cv2.addWeighted( + img.astype(np.float32), v, degenerated.astype(np.float32), + 1 - v, 0) + brightened_img = np.clip(brightened_img, 0, 255) + return brightened_img.astype(img.dtype), bboxes, v + + +def TTA_Brightness_re(bboxes, v): + return bboxes + + +def TTA_Sharpness(img, v, bboxes=None): + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageEnhance.Sharpness(img).enhance(v) + return np.array(img_aug), bboxes, v + + +def TTA_Sharpness_re(bboxes, v): + return bboxes + + +def TTA_SHarpness_mmcv(img, v, bboxes=None): + kernel = np.array([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]]) / 13 + degenerated = cv2.filter2D(img, -1, kernel) + sharpened_img = cv2.addWeighted( + img.astype(np.float32), v, degenerated.astype(np.float32), + 1 - v, 0) + sharpened_img = np.clip(sharpened_img, 0, 255) + return sharpened_img.astype(img.dtype), bboxes, v + + +def TTA_AutoContrast(img, v, bboxes=None): + img = Image.fromarray(img.copy()) + cutoff = abs(v) + img_aug = PIL.ImageOps.autocontrast(img, cutoff) + return np.array(img_aug), bboxes, v + + +def TTA_AutoContrast_re(bboxes, v): + return bboxes + + +def TTA_AutoContrast_mmcv(img, v, bboxes=None): + def _auto_contrast_channel(im, c, cutoff): + im = im[:, :, c] + # Compute the histogram of the image channel. + histo = np.histogram(im, 256, (0, 255))[0] + # Remove cut-off percent pixels from histo + histo_sum = np.cumsum(histo) + cut_low = histo_sum[-1] * cutoff[0] // 100 + cut_high = histo_sum[-1] - histo_sum[-1] * cutoff[1] // 100 + histo_sum = np.clip(histo_sum, cut_low, cut_high) - cut_low + histo = np.concatenate([[histo_sum[0]], np.diff(histo_sum)], 0) + + # Compute mapping + low, high = np.nonzero(histo)[0][0], np.nonzero(histo)[0][-1] + # If all the values have been cut off, return the origin img + if low >= high: + return im + scale = 255.0 / (high - low) + offset = -low * scale + lut = np.array(range(256)) + lut = lut * scale + offset + lut = np.clip(lut, 0, 255) + return lut[im] + + if isinstance(v, (int, float)): + cutoff = (v, v) + else: + assert isinstance(v, tuple), 'cutoff must be of type int, ' \ + f'float or tuple, but got {type(v)} instead.' + # Auto adjusts contrast for each channel independently and then stacks + # the result. + s1 = _auto_contrast_channel(img, 0, cutoff) + s2 = _auto_contrast_channel(img, 1, cutoff) + s3 = _auto_contrast_channel(img, 2, cutoff) + contrasted_img = np.stack([s1, s2, s3], axis=-1) + return contrasted_img.astype(img.dtype), bboxes, v + + +def TTA_Equalize(img, v, bboxes=None): + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageOps.equalize(img) + return np.array(img_aug), bboxes, v + + +def TTA_Equalize_mmcv(img, v, bboxes=None): + def _scale_channel(im, c): + im = im[:, :, c] + histo = np.histogram(im, 256, (0, 255))[0] + nonzero_histo = histo[histo > 0] + step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255 + if not step: + lut = np.array(range(256)) + else: + lut = (np.cumsum(histo) + (step // 2)) // step + lut = np.concatenate([[0], lut[:-1]], 0) + lut[lut > 255] = 255 + return np.where(np.equal(step, 0), im, lut[im]) + + s1 = _scale_channel(img, 0) + s2 = _scale_channel(img, 1) + s3 = _scale_channel(img, 2) + equalized_img = np.stack([s1, s2, s3], axis=-1) + return equalized_img.astype(img.dtype), bboxes, v + + +def TTA_Equalize_re(bboxes, v): + return bboxes + + +def TTA_Grey(img, v, bboxes=None): + img = img.copy() + img_aug = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + img_aug = cv2.cvtColor(img_aug, cv2.COLOR_GRAY2BGR) + return img_aug, bboxes, v + + +def TTA_Grey_re(bboxes, v): + return bboxes + + +def TTA_Invert(img, v, bboxes=None): + img = img.copy() + img_aug = 255 - img + return img_aug, bboxes, v + + +def TTA_Invert_re(bboxes, v): + return bboxes + + +def TTA_Posterize(img, v, bboxes=None): + v = int(8 - abs(v) * 7) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageOps.posterize(img, v) + return np.array(img_aug), bboxes, v + + +def TTA_Posterize_mmcv(img, v, bboxes=None): + shift = 8 - int(abs(8 * v)) + img = np.left_shift(np.right_shift(img, shift), shift) + return img, bboxes, v + + +def TTA_Posterize_re(bboxes, v): + return bboxes + + +def TTA_Solarize(img, v, bboxes=None): + v = int((1 - abs(v)) * 255) + img = Image.fromarray(img.copy()) + img_aug = PIL.ImageOps.solarize(img, v) + return np.array(img_aug), bboxes, v + + +def TTA_Solarize_mmcv(img, v, bboxes=None): + v = int((1 - abs(v)) * 255) + img = np.where(img < v, img, 255 - img) + return img, bboxes, v + + +def TTA_Solarize_re(bboxes, v): + return bboxes + + +def TTA_HSV(img, v, bboxes=None): + img = img.copy() + img_aug = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + return img_aug, bboxes, v + + +def TTA_HSV_re(bboxes, v): + return bboxes + + +def TTA_PepperNoise(img, v, bboxes=None): + img = img.copy() + per = abs(v) / 2 + pix = img.shape[0] * img.shape[1] + num = int(pix * per * 0.5) + coords = [np.random.randint(0, i - 1, num) for i in img.shape[:2]] + img[coords[0], coords[1], :] = [255, 255, 255] + coords = [np.random.randint(0, i - 1, num) for i in img.shape[:2]] + img[coords[0], coords[1], :] = [0, 0, 0] + return img, bboxes, v + + +def TTA_PepperNoise_re(bboxes, v): + return bboxes + + +def TTA_GaussNoise(img, v, bboxes=None): + img = img.copy() + mean = 0 + sigma = abs(v) * 100 + gauss = np.random.normal(mean, sigma, (img.shape[0], img.shape[1], img.shape[2])) + noisy_img = img + gauss + noisy_img = np.clip(noisy_img, a_min=0, a_max=255) + return np.uint8(noisy_img), bboxes, v + + +def TTA_GaussNoise_re(bboxes, v): + return bboxes + + +def printf(x): + print(x) + exit(0) + + +def padding_square(img, out_size): + h, w = img.shape[:2] + ret = cv2.copyMakeBorder(img, 0, out_size[0] - h, 0, out_size[1] - w, cv2.BORDER_CONSTANT, value=FILL_COLOR) + return ret + + +def topleftxywh_to_xyxy(boxes): + """ + args: + boxes:list of topleft_x,topleft_y,width,height, + return: + boxes:list of x,y,x,y,cooresponding to top left and bottom right + """ + x_top_left = boxes[0] + y_top_left = boxes[1] + x_bottom_right = boxes[0] + boxes[2] + y_bottom_right = boxes[1] + boxes[3] + return [x_top_left, y_top_left, x_bottom_right, y_bottom_right] + + +def TTA_Aug_List(): + return [TTA_Resize, TTA_Flip, TTA_Rotate_no_pad, TTA_Color, TTA_Contrast, TTA_Brightness, TTA_Sharpness, + TTA_AutoContrast, TTA_Equalize, TTA_Grey, TTA_Invert, TTA_Posterize, TTA_Solarize, TTA_HSV, TTA_PepperNoise, + TTA_GaussNoise] + + +def TTA_Aug_Space(resolution=(640, 640), size_divisor=32): + ''' + 0 TTA_Resize + 1 TTA_Flip + 2 TTA_Rotate_no_pad + 3 TTA_Color + 4 TTA_Contrast + 5 TTA_Brightness + 6 TTA_Sharpness + 7 TTA_AutoContrast + 8 TTA_Equalize + 9 TTA_Grey + 10 TTA_Invert + 11 TTA_Posterize + 12 TTA_Solarize + 13 TTA_HSV + 14 TTA_PepperNoise + 15 TTA_GaussNoise + ''' + default_aug_space = [ + (1, 0), (1, 1), (1, -1), + (2, 90), (2, 180), (2, 270), + (3, 0.2), (3, 0.3), (3, 0.4), (3, 0.5), (3, 0.6), (3, 0.7), (3, 0.8), + (4, 0.2), (4, 0.3), (4, 0.4), (4, 0.5), (4, 0.6), (4, 0.7), (4, 0.8), + (5, 0.2), (5, 0.3), (5, 0.4), (5, 0.5), (5, 0.6), (5, 0.7), (5, 0.8), + (6, 0.2), (6, 0.3), (6, 0.4), (6, 0.5), (6, 0.6), (6, 0.7), (6, 0.8), + (7, 0.2), (7, 0.3), (7, 0.4), (7, 0.5), (7, 0.6), (7, 0.7), (7, 0.8), + (8, 1), + (9, 1), + (10, 1), + (11, 0.2), (11, 0.3), (11, 0.4), (11, 0.5), (11, 0.6), (11, 0.7), (11, 0.8), + (12, 0.2), (12, 0.3), (12, 0.4), (12, 0.5), (12, 0.6), (12, 0.7), (12, 0.8), + (13, 1), + (14, 0.2), (14, 0.3), (14, 0.4), (14, 0.5), (14, 0.6), (14, 0.7), (14, 0.8), + (15, 0.2), (15, 0.3), (15, 0.4), (15, 0.5), (15, 0.6), (15, 0.7), (15, 0.8) + ] + forward_resolution_space = [(0, (resolution[0] + (i * size_divisor), resolution[1] + (i * size_divisor))) for i in range(8)] + backward_resolution_space = [(0, (resolution[0] - (i * size_divisor), resolution[1] - (i * size_divisor))) for i in range(8)] + forward_resolution_space.extend(backward_resolution_space) + unique_resolution_space = list(OrderedDict.fromkeys(forward_resolution_space)) + unique_resolution_space.extend(default_aug_space) + return unique_resolution_space + + +def Test_Aug_Space(): + return [(0, (672, 672)), + (1, -1), + (2, 90), + (3, 0.4), + (4, 0.2), + (5, 0.2), + (6, 0.2), + (7, 0.2), + (8, 1), + (9, 1), + ] diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_strategy.py b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_strategy.py new file mode 100644 index 00000000..2780af01 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/TTA_strategy.py @@ -0,0 +1,199 @@ +import os.path +import warnings + +warnings.filterwarnings("ignore") +from .general_TTA_v5 import * +from itertools import zip_longest, combinations, permutations +import sys + +sys.path.append("/home/wjj/wjj/Public/code/huawei") + +augment_list = TTA_Aug_List() + + +class TTA_Strategy(object): + def __init__(self, model, val_image_path, val_anno_path, log_dir, worker=4, nms_thr=0.5): + self.default_img_size = None # will override by _prepare_for_search() + self.model = self._prepare_for_search(model, val_anno_path, val_image_path) + self.val_image_path = val_image_path + self.val_anno_path = val_anno_path + self.augments = TTA_Aug_List() + self.augments_space = TTA_Aug_Space(resolution=self.default_img_size, size_divisor=32) + # self.augments_space =Test_Aug_Space() + if not os.path.exists(log_dir): + os.mkdir(log_dir) + self.log_dir = log_dir + self.nms_thr = nms_thr + self.worker = worker + + def search_single_strategy(self, top_num): + val_aug_results = [] + log_txt = open(os.path.join(self.log_dir, "single_log.txt"), 'w') + for idx, parameter in tqdm(self.augments_space): + method = self.augments[idx] + v = parameter + if method.__name__ != 'TTA_Resize': + augs = [[('TTA_Resize', self.default_img_size)], + [('TTA_Resize', self.default_img_size), (method.__name__, v)], ] + else: + augs = [[('TTA_Resize', self.default_img_size)], + [(method.__name__, v)], ] + print(augs) + ap, _ = model_TTA_infer(model=self.model, + img_path=self.val_image_path, + anno_path=self.val_anno_path, + augs=augs, + worker=self.worker, + nms_thr=self.nms_thr) + log_txt.write(str(augs) + ' ' + str(round(ap, 3)) + '\n') + val_aug_results.append(ap) + val_aug_results_np = np.array(val_aug_results) + sort_idx = np.flipud(val_aug_results_np.argsort()) + single_method_idx_top = [] + single_method_name_top = [] + single_factor_top = [] + single_ap_top = [] + for idx in sort_idx: + method_index = self.augments_space[idx][0] + method_name = self.augments[self.augments_space[idx][0]].__name__ + v = self.augments_space[idx][1] + ap = val_aug_results_np[idx] + if method_index not in single_method_idx_top: + single_method_idx_top.append(method_index) + single_method_name_top.append(method_name) + single_factor_top.append(v) + single_ap_top.append(ap) + else: + site = single_method_idx_top.index(method_index) + if ap > single_ap_top[site]: + single_factor_top[site] = v + single_ap_top[site] = ap + if len(single_method_idx_top) == top_num: + break + self._write_single_log(single_method_name_top, single_factor_top, single_ap_top, "search_single_strategy.txt") + return list(zip_longest(single_method_name_top, single_factor_top)) + + def search_cascade_strategy(self, single_top_strategies, cascade_num=3, top_num=5): + cascade_strategies = [] + for num in range(1, cascade_num + 1): + cascade_strategies.extend(list(permutations(single_top_strategies, num))) + cascade_ap = [] + strategy_log = [] + for strategy in tqdm(cascade_strategies): + strategy_list = list(strategy) + tmp = False + for s in strategy_list: + if s[0] == 'TTA_Resize': + tmp = True + augs = [[('TTA_Resize', self.default_img_size)]] + if tmp: + augs.append(strategy_list) + else: + strategy_list.insert(0, ('TTA_Resize', self.default_img_size)) + augs.append(strategy_list) + strategy_log.append(strategy_list) + print(augs) + ap, _ = model_TTA_infer(model=self.model, + img_path=self.val_image_path, + anno_path=self.val_anno_path, + augs=augs, + worker=self.worker, + nms_thr=self.nms_thr + ) + cascade_ap.append(ap) + cascade_ap_np = np.array(cascade_ap) + sort_idx = np.flipud(cascade_ap_np.argsort()) + cascade_method_top = [] + cascade_ap_top = [] + for idx in sort_idx[:top_num]: + cascade_method_top.append(strategy_log[idx]) + cascade_ap_top.append(cascade_ap[idx]) + self._write_cascade_log(cascade_method_top, cascade_ap_top, "search_cascade_strategy.txt") + return cascade_method_top + + def search_combine_single_strategy(self, single_top_strategies, top_num, ): + combine_strategies = [] + for num in range(2, len(single_top_strategies) + 1): + for combine_strategy in combinations(single_top_strategies, num): + combine_strategies.append(list(combine_strategy)) + log_txt = open(os.path.join(self.log_dir, "combine_single_log.txt"), 'w') + combine_single_ap = [] + combine_strategies_log = [] + for c_s in tqdm(combine_strategies): + augs = [[('TTA_Resize', self.default_img_size)]] + for s in c_s: + if s[0] == 'TTA_Resize': + augs.append([s]) + else: + new_s = [('TTA_Resize', self.default_img_size)] + new_s.append(s) + augs.append(new_s) + print(augs) + combine_strategies_log.append(augs) + ap, _ = model_TTA_infer(model=self.model, + img_path=self.val_image_path, + anno_path=self.val_anno_path, + augs=augs, + worker=self.worker, + nms_thr=self.nms_thr + ) + combine_single_ap.append(ap) + log_txt.write(str(augs) + ' ' + '\n') + combine_single_ap_np = np.array(combine_single_ap) + sort_idx = np.flipud(combine_single_ap_np.argsort()) + combine_single_method_top = [] + combine_single_ap_top = [] + for idx in sort_idx[:top_num]: + combine_single_method_top.append(combine_strategies_log[idx]) + combine_single_ap_top.append(combine_single_ap[idx]) + self._write_combine_log(combine_single_method_top, "search_combine_single_strategy.txt") + return combine_single_method_top + + def _prepare_for_search(self, model, val_anno_path, val_img_prefix): + model_cfg = copy.deepcopy(model.cfg) + + # Get the default image resolution of the model + for p in model_cfg.data.test.pipeline: + if p['type'] == 'MultiScaleFlipAug': + self.default_img_size = p['img_scale'] + if not hasattr(self, "default_img_size"): + raise ValueError("can not find img_scale model cfg") + + # Override the data.test part in cfg with the validation set info. + model_cfg.data.test.ann_file = val_anno_path + model_cfg.data.test.img_prefix = val_img_prefix + test_transforms = model_cfg.data.test.pipeline[1].transforms + + # Remove the Resize and flip operations from the original test_pipeline. + new_test_transforms = [i for i in test_transforms if i['type'] not in ['Resize', 'RandomFlip']] + model_cfg.data.test.pipeline[1].transforms = new_test_transforms + model.cfg = model_cfg + return model + + def _write_single_log(self, method_name_list, method_factor_list, method_ap_list, out_name): + fp = open(os.path.join(self.log_dir, out_name), 'w') + for i in range(len(method_name_list)): + fp.write(f"{method_name_list[i]} {method_factor_list[i]} {method_ap_list[i]}\n") + fp.close() + + def _write_cascade_log(self, method_name_list, method_ap_list, out_name): + fp = open(os.path.join(self.log_dir, out_name), 'w') + for i in range(len(method_name_list)): + fp.write(f"{method_name_list[i]} {method_ap_list[i]}\n") + fp.close() + + def _write_combine_log(self, method_idx_list, out_name): + fp = open(os.path.join(self.log_dir, out_name), 'w') + for method_sequence in method_idx_list: + for method in method_sequence: + name, factor = method[0], method[1] + method_idx = -1 + for i in range(len(augment_list)): + if augment_list[i].__name__ == name: + method_idx = i + if isinstance(factor, tuple): + factor = f"{factor[0]}.{factor[1]}" + else: + factor = str(factor) + fp.write(f"{str(method_idx)},{factor};") + fp.write("\n") diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/utils/__init__.py b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/utils/demo.py b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/demo.py new file mode 100644 index 00000000..96cb2792 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/demo.py @@ -0,0 +1,41 @@ +import torch +import sys + +sys.path.append('/home/wjj/wjj/Public/code/ianvs') +from mmdet.apis import init_detector + +from custom_ianvs.test_time_aug.TTA_augs_xyxy_cv2 import TTA_Aug_List, TTA_Aug_Space +from custom_ianvs.test_time_aug.TTA_strategy import TTA_Strategy +from custom_ianvs.test_time_aug.general_TTA_v5 import model_TTA_infer + +if __name__ == '__main__': + torch.multiprocessing.set_start_method('spawn') + yolo_model = init_detector(r"/home/wjj/wjj/Public/code/ianvs/custom_ianvs/mmdet_related/yolox_s_8x8_300e_yaoba.py", + r"/home/wjj/wjj/Public/code/ianvs/custom_ianvs/mmdet_related/epoch_300.pth", + 'cuda:0') + search_agent = TTA_Strategy(model=yolo_model, + val_image_path="/media/huawei_YaoBa/Images", + val_anno_path="/home/wjj/wjj/Public/code/ianvs/dataset/yaoba/yaoba_tta/val.json", + augments=TTA_Aug_List(), + augments_space=TTA_Aug_Space(), + log_dir="/home/wjj/wjj/Public/code/ianvs/custom_ianvs/test_time_aug/log", + worker=6 + ) + search_agent.search_single_strategy(top_num=5) + single_strategies = [('TTA_Resize', (800, 800)), + ('TTA_Flip', -1), + ('TTA_AutoContrast', 0.3), + ('TTA_Sharpness', 0.5), + ('TTA_Brightness', 0.7)] + search_agent.search_cascade_strategy(single_strategies, cascade_num=3, top_num=5) + search_agent.search_combine_single_strategy(single_strategies, top_num=3) + # 多进程推理 + model_TTA_infer(model=yolo_model, + img_path="/media/huawei_YaoBa/Images", + anno_path="/home/wjj/wjj/Public/code/ianvs/dataset/yaoba/yaoba_tta/test.json", + augs=[ + [('TTA_Resize', (640, 640))], + [('TTA_Brightness', (640, 640)), ] + ], + worker=4, + nms_thr=0.5) diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v4.py b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v4.py new file mode 100644 index 00000000..b79c62e1 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v4.py @@ -0,0 +1,205 @@ +import time +from multiprocessing import Pool +import warnings + +warnings.filterwarnings("ignore") +import copy +from mmdet.datasets import build_dataset +import json +import os +from mmdet.core.post_processing.bbox_nms import batched_nms +import torch +from mmdet.core import bbox2result +from tqdm import tqdm +from mmdet.apis import init_detector, inference_detector +from .TTA_augs_xyxy_cv2 import * + +torch.set_printoptions(sci_mode=False) +IOU_THR = 0.5 + + +# 与V3版本相比，改变了eval_map部分，主要为了解决内网数据集存在正样本的情况 +# 这个版本的eval_map跟mmdet的test对齐，而不是调用eval_map的API +def topleftxywh_to_xyxy(boxes): + """ + args: + boxes:list of topleft_x,topleft_y,width,height, + return: + boxes:list of x,y,x,y,cooresponding to top left and bottom right + """ + x_top_left = boxes[0] + y_top_left = boxes[1] + x_bottom_right = boxes[0] + boxes[2] + y_bottom_right = boxes[1] + boxes[3] + return [x_top_left, y_top_left, x_bottom_right, y_bottom_right] + + +def model_TTA_infer(model, img_path, anno_path, augs=None, worker=4, nms_thr=0.5): + # 读取标注信息 + with open(anno_path, 'r', encoding='utf-8') as fp: + labels = json.load(fp) + + pending_infer_images = labels['images'] # 待预测的图片列表 + + # 进度条显示 + pbar = tqdm(total=len(pending_infer_images)) + pbar.set_description('progress bar') + update = lambda *args: pbar.update() + + # 多进程执行 TTA 预测 + with Pool(processes=worker) as pool: + tasks = [] + for img_file in pending_infer_images: + single_img_path = os.path.join(img_path, img_file['file_name']) + task = pool.apply_async(TTA_single_img, (model, single_img_path, augs, nms_thr), callback=update) + tasks.append(task) + merged_results = [task.get() for task in tasks] + + # 计算平均精度值 + dataset = model.cfg.data.test + ap = eval_ap_coco(dataset, merged_results) + + return ap, None + + +def eval_ap_coco(dataset_config, predict_results): + dataset_config.test_mode = True + dataset = build_dataset(dataset_config) + predict_results = [i['result'] for i in predict_results] + metric = dataset.evaluate(predict_results) + return metric['bbox_mAP_50'] + + +def TTA_single_img(model, img_path, augs, nms_thr=0.5): + """对单张图片进行 TTA 预测 + + Args: + model (nn.Module): 模型 + img_path (str): 图片路径 + augs (list[list[tuple]]): 图像增广列表，每个元素为一个变换方法及其参数列表 + nms_thr (float, optional): NMS 阈值. 默认是 to 0.5. + Returns: + dict: 预测结果字典，包含图片名和预测框等信息 + """ + # 读取图片 + orig_img = mmcv.imread(img_path) + + # 存储所有变换的预测结果 + total_bboxes = [] # 所有变换后的预测框 + total_scores = [] # 所有变换后的预测框得分 + total_idxs = [] # 所有变换后的预测框所属类别索引 + + # 对每种图像增广方式进行 TTA 预测 + for aug in augs: + # 对原图像进行深拷贝 + img = copy.deepcopy(orig_img) + + # 存储当前 augment 中所有的变换方法和对应参数 + factors = [] # 所有变换因子（例如缩放倍数、旋转角度等） + methods = [] # 所有变换方法名称 + + # 对当前 augment 中所有的变换方法进行遍历 + for transform in aug: + method, v = transform[0], transform[1] + img, factor = TTA_aug_single_img(img, method, v) + factors.append(factor) + methods.append(method) + + # 将 factors 和 methods 列表中的元素反转，以便逆向执行各个变换 + factors.reverse() + methods.reverse() + + # 进行预测并还原预测框坐标 + predict_result = inference_detector(model, img) + for idx, results in enumerate(predict_result): + if len(results) > 0: + for result in results: + # 还原当前预测框的坐标至原始图像中 + recover_box = [result[:-1]] + for method, factor in zip(methods, factors): + recover_box = mapping_bboxes_back(recover_box, method, factor) + + # 组合所有变换后的预测结果 + total_bboxes.append(recover_box[0]) + total_scores.append(result[-1]) + total_idxs.append(idx) + + # 对所有变换后的预测框进行 NMS 并将结果保存到 merged_result 中 + total_bboxes = np.asarray(total_bboxes) + total_scores = np.asarray(total_scores) + total_idxs = np.asarray(total_idxs) + total_bboxes = torch.from_numpy(total_bboxes).float().to("cuda:0") + total_scores = torch.from_numpy(total_scores).float().to("cuda:0") + total_idxs = torch.from_numpy(total_idxs).int().to("cuda:0") + if len(total_bboxes) == 0: + merged_dict = dict(name=os.path.basename(img_path), result=predict_result) + return merged_dict + else: + kept_bboxes, keep = batched_nms(total_bboxes, total_scores, total_idxs, + nms_cfg=dict(type='nms', iou_threshold=nms_thr)) + merged_result = bbox2result(kept_bboxes, total_idxs[keep], len(predict_result)) + merged_dict = dict(name=os.path.basename(img_path), result=merged_result) + + return merged_dict + + + +def TTA_aug_single_img(img, method, factor): + """对单张图片应用指定的数据增强方法. + + Args: + img (ndarray): 待增强的图像数组. + method (str): 数据增强方法名称. + factor (float): 方法强度. + + Returns: + tuple: 增强后的图像数组和方法强度. + """ + methods = { + 'TTA_Rotate_no_pad': TTA_Rotate_no_pad, + 'TTA_Brightness': TTA_Brightness, + 'TTA_Flip': TTA_Flip, + 'TTA_Resize': TTA_Resize_mmcv, + 'TTA_Color': TTA_Color, + 'TTA_Contrast': TTA_Contrast, + 'TTA_Sharpness': TTA_Sharpness, + 'TTA_AutoContrast': TTA_AutoContrast, + 'TTA_Equalize': TTA_Equalize, + 'TTA_Invert': TTA_Invert, + 'TTA_Posterize': TTA_Posterize, + 'TTA_Solarize': TTA_Solarize, + 'TTA_HSV': TTA_HSV, + 'TTA_PepperNoise': TTA_PepperNoise, + 'TTA_GaussNoise': TTA_GaussNoise, + 'TTA_Grey': TTA_Grey + } + + try: + aug_func = methods[method] + except KeyError: + raise ValueError('未实现的数据增强方法') + + aug_img, _, factors = aug_func(img, factor) + return aug_img, factors + + + +def mapping_bboxes_back(bboxes, method, factor): + """根据指定的方法和强度反向映射边界框. + Args: + bboxes (list): 边界框列表. + method (str): 反向映射方法. + factor (float): 方法对应强度. + Returns: + list: 映射回原始尺寸的边界框列表. + """ + methods = { + 'TTA_Rotate_no_pad': TTA_Rotate_no_pad_re, + 'TTA_Flip': TTA_Flip_re, + 'TTA_Resize': TTA_Resize_re + } + + if method in methods: + return methods[method](bboxes, factor) + else: + return bboxes diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v5.py b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v5.py new file mode 100644 index 00000000..7a183f77 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/resource/utils/general_TTA_v5.py @@ -0,0 +1,183 @@ +import time +from multiprocessing import Pool +import warnings + +warnings.filterwarnings("ignore") +import copy +from mmdet.datasets import build_dataset +import json +import os +from mmdet.core.post_processing.bbox_nms import batched_nms +import torch +from mmdet.core import bbox2result +from tqdm import tqdm +from mmdet.apis import inference_detector +from .TTA_augs_xyxy_cv2 import * + +torch.set_printoptions(sci_mode=False) + + +# 与V4版本相比，获取传入模型的cuda信息来决定用哪个cuda算，同时去掉model_TTA_infer内部的进度条， +# 进度条让TTA_strategy展示，这样方便看整体进度 + + +def model_TTA_infer(model, img_path, anno_path, augs=None, worker=4, nms_thr=0.5): + # 读取标注信息 + with open(anno_path, 'r', encoding='utf-8') as fp: + labels = json.load(fp) + pending_infer_images = labels['images'] # 待预测的图片列表 + # 多进程执行 TTA 预测 + with Pool(processes=worker) as pool: + tasks = [] + for img_file in pending_infer_images: + single_img_path = os.path.join(img_path, img_file['file_name']) + task = pool.apply_async(TTA_single_img, (model, single_img_path, augs, nms_thr)) + tasks.append(task) + merged_results = [task.get() for task in tasks] + pool.close() + pool.join() + # 计算平均精度值 + dataset = model.cfg.data.test + ap = eval_ap_coco(dataset, merged_results) + + return ap, None + + +def TTA_single_img(model, img_path, augs, nms_thr=0.5): + """对单张图片进行 TTA 预测 + + Args: + model (nn.Module): 模型 + img_path (str): 图片路径 + augs (list[list[tuple]]): 图像增广列表，每个元素为一个变换方法及其参数列表 + nms_thr (float, optional): NMS 阈值. 默认是 to 0.5. + Returns: + dict: 预测结果字典，包含图片名和预测框等信息 + """ + # 读取图片 + orig_img = mmcv.imread(img_path) + # 存储所有变换的预测结果 + total_bboxes = [] # 所有变换后的预测框 + total_scores = [] # 所有变换后的预测框得分 + total_idxs = [] # 所有变换后的预测框所属类别索引 + + # 对每种图像增广方式进行 TTA 预测 + for aug in augs: + # 对原图像进行深拷贝 + img = copy.deepcopy(orig_img) + + # 存储当前 augment 中所有的变换方法和对应参数 + factors = [] # 所有变换因子（例如缩放倍数、旋转角度等） + methods = [] # 所有变换方法名称 + + # 对当前 augment 中所有的变换方法进行遍历 + for transform in aug: + method, v = transform[0], transform[1] + img, factor = TTA_aug_single_img(img, method, v) + factors.append(factor) + methods.append(method) + + # 将 factors 和 methods 列表中的元素反转，以便逆向执行各个变换 + factors.reverse() + methods.reverse() + + # 进行预测并还原预测框坐标 + predict_result = inference_detector(model, img) + for idx, results in enumerate(predict_result): + if len(results) > 0: + for result in results: + # 还原当前预测框的坐标至原始图像中 + recover_box = [result[:-1]] + for method, factor in zip(methods, factors): + recover_box = mapping_bboxes_back(recover_box, method, factor) + + # 组合所有变换后的预测结果 + total_bboxes.append(recover_box[0]) + total_scores.append(result[-1]) + total_idxs.append(idx) + + # 对所有变换后的预测框进行 NMS 并将结果保存到 merged_result 中 + total_bboxes = np.asarray(total_bboxes) + total_scores = np.asarray(total_scores) + total_idxs = np.asarray(total_idxs) + total_bboxes = torch.from_numpy(total_bboxes).float().to(f"cuda:0") + total_scores = torch.from_numpy(total_scores).float().to(f"cuda:0") + total_idxs = torch.from_numpy(total_idxs).int().to(f"cuda:0") + if len(total_bboxes) == 0: + merged_dict = dict(name=os.path.basename(img_path), result=predict_result) + return merged_dict + else: + kept_bboxes, keep = batched_nms(total_bboxes, total_scores, total_idxs, + nms_cfg=dict(type='nms', iou_threshold=nms_thr)) + merged_result = bbox2result(kept_bboxes, total_idxs[keep], len(predict_result)) + merged_dict = dict(name=os.path.basename(img_path), result=merged_result) + + return merged_dict + + +def TTA_aug_single_img(img, method, factor): + """对单张图片应用指定的数据增强方法. + + Args: + img (ndarray): 待增强的图像数组. + method (str): 数据增强方法名称. + factor (float): 方法强度. + + Returns: + tuple: 增强后的图像数组和方法强度. + """ + methods = { + 'TTA_Rotate_no_pad': TTA_Rotate_no_pad, + 'TTA_Brightness': TTA_Brightness, + 'TTA_Flip': TTA_Flip, + 'TTA_Resize': TTA_Resize_mmcv, + 'TTA_Color': TTA_Color, + 'TTA_Contrast': TTA_Contrast, + 'TTA_Sharpness': TTA_Sharpness, + 'TTA_AutoContrast': TTA_AutoContrast, + 'TTA_Equalize': TTA_Equalize, + 'TTA_Invert': TTA_Invert, + 'TTA_Posterize': TTA_Posterize, + 'TTA_Solarize': TTA_Solarize, + 'TTA_HSV': TTA_HSV, + 'TTA_PepperNoise': TTA_PepperNoise, + 'TTA_GaussNoise': TTA_GaussNoise, + 'TTA_Grey': TTA_Grey + } + + try: + aug_func = methods[method] + except KeyError: + raise ValueError('未实现的数据增强方法') + + aug_img, _, factors = aug_func(img, factor) + return aug_img, factors + + +def eval_ap_coco(dataset_config, predict_results): + dataset_config.test_mode = True + dataset = build_dataset(dataset_config) + predict_results = [i['result'] for i in predict_results] + metric = dataset.evaluate(predict_results) + return metric['bbox_mAP_50'] + + +def mapping_bboxes_back(bboxes, method, factor): + """根据指定的方法和强度反向映射边界框. + Args: + bboxes (list): 边界框列表. + method (str): 反向映射方法. + factor (float): 方法对应强度. + Returns: + list: 映射回原始尺寸的边界框列表. + """ + methods = { + 'TTA_Rotate_no_pad': TTA_Rotate_no_pad_re, + 'TTA_Flip': TTA_Flip_re, + 'TTA_Resize': TTA_Resize_re + } + + if method in methods: + return methods[method](bboxes, factor) + else: + return bboxes diff --git a/examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py b/examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py new file mode 100644 index 00000000..6d7cf12a --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py @@ -0,0 +1,215 @@ +optimizer = dict( + type='SGD', + lr=0.01, + momentum=0.9, + weight_decay=0.0005, + nesterov=True, + paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0)) +optimizer_config = dict(grad_clip=None) +lr_config = dict( + policy='YOLOX', + warmup='exp', + by_epoch=False, + warmup_by_epoch=True, + warmup_ratio=1, + warmup_iters=5, + num_last_epochs=15, + min_lr_ratio=0.05) +runner = dict(type='EpochBasedRunner', max_epochs=300) +checkpoint_config = dict(interval=50) +log_config = dict(interval=5, hooks=[dict(type='TextLoggerHook')]) +custom_hooks = [ + dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48), + dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48), + dict( + type='ExpMomentumEMAHook', + resume_from=None, + momentum=0.0001, + priority=49) +] +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +opencv_num_threads = 0 +mp_start_method = 'fork' +auto_scale_lr = dict(enable=False, base_batch_size=64) +classes = ('yanse', 'huahen', 'mosun') +img_scale = (640, 640) +model = dict( + type='YOLOX', + input_size=(640, 640), + random_size_range=(15, 25), + random_size_interval=10, + backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5), + neck=dict( + type='YOLOXPAFPN', + in_channels=[128, 256, 512], + out_channels=128, + num_csp_blocks=1), + bbox_head=dict( + type='YOLOXHead', num_classes=3, in_channels=128, feat_channels=128), + train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)), + test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65))) +data_root = 'data/coco/' +dataset_type = 'CocoDataset' +train_dataset = dict( + type='MultiImageMixDataset', + dataset=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/home/wjj/wjj/Public/code/huawei/experiments/23-02-18_yaoba/yolox/train.json', + img_prefix='/media/huawei_YaoBa/Images', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True) + ], + filter_empty_gt=False), + pipeline=[ + dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), + dict( + type='RandomAffine', + scaling_ratio_range=(0.1, 2), + border=(-320, -320)), + dict( + type='MixUp', + img_scale=(640, 640), + ratio_range=(0.8, 1.6), + pad_val=114.0), + dict(type='YOLOXHSVRandomAug'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Resize', img_scale=(640, 640), keep_ratio=True), + dict( + type='Pad', + pad_to_square=True, + pad_val=dict(img=(114.0, 114.0, 114.0))), + dict( + type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels'], + meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape', + 'pad_shape', 'scale_factor', 'flip', 'flip_direction', + 'img_norm_cfg')) + ]) +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Pad', + pad_to_square=True, + pad_val=dict(img=(114.0, 114.0, 114.0))), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=12, + workers_per_gpu=12, + persistent_workers=True, + train=dict( + type='MultiImageMixDataset', + dataset=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/home/wjj/wjj/Public/code/huawei/experiments/23-02-18_yaoba/yolox/train.json', + img_prefix='/media/huawei_YaoBa/Images', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True) + ], + filter_empty_gt=False), + pipeline=[ + dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0), + dict( + type='RandomAffine', + scaling_ratio_range=(0.1, 2), + border=(-320, -320)), + dict( + type='MixUp', + img_scale=(640, 640), + ratio_range=(0.8, 1.6), + pad_val=114.0), + dict(type='YOLOXHSVRandomAug'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Resize', img_scale=(640, 640), keep_ratio=True), + dict( + type='Pad', + pad_to_square=True, + pad_val=dict(img=(114.0, 114.0, 114.0))), + dict( + type='FilterAnnotations', + min_gt_bbox_wh=(1, 1), + keep_empty=False), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels'], + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg')) + ]), + val=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/home/wjj/wjj/Public/code/huawei/experiments/23-02-18_yaoba/yolox/val.json', + img_prefix='/media/huawei_YaoBa/Images', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Pad', + pad_to_square=True, + pad_val=dict(img=(114.0, 114.0, 114.0))), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( + type='CocoDataset', + classes=('yanse', 'huahen', 'mosun'), + ann_file= + '/dataset/yaoba/yaoba_tta/test.json', + img_prefix='/media/huawei_YaoBa/Images', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Pad', + pad_to_square=True, + pad_val=dict(img=(114.0, 114.0, 114.0))), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']) + ]) + ])) +max_epochs = 300 +num_last_epochs = 15 +interval = 10 +evaluation = dict( + save_best='auto', interval=50, dynamic_intervals=[(285, 1)], metric='bbox') +work_dir = '/home/wjj/wjj/Public/code/huawei/experiments/23-02-18_yaoba/yolox' +auto_resume = False +gpu_ids = range(0, 4) diff --git a/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/algorithm.yaml b/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/algorithm.yaml new file mode 100644 index 00000000..355c2a9a --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/algorithm.yaml @@ -0,0 +1,30 @@ +algorithm: + # paradigm name; string type; + # currently the options of value are as follows: + # 1> "singletasklearning" + # 2> "incrementallearning" + paradigm_type: "singletasklearning_tta" + # the url address of initial model; string type; optional; + initial_model_url: "examples/yaoba/singletask_learning_yolox_tta/resource/epoch_300.pth" + + # algorithm module configuration in the paradigm; list type; + modules: + # kind of algorithm module; string type; + # currently the options of value are as follows: + # 1> "basemodel" + - type: "basemodel" + # name of python module; string type; + # example: basemodel.py has BaseModel module that the alias is "FPN" for this benchmarking; + name: "mmlab" + # the url address of python module; string type; + url: "examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py" + + # hyperparameters configuration for the python module; list type; + hyperparameters: + # name of the hyperparameter; string type; + - config: + values: + - "examples/yaoba/singletask_learning_yolox_tta/resource/yolox_s_8x8_300e_yaoba.py" + - work_dir: + values: + - "examples/yaoba/singletask_learning_yolox_tta/work_dir" \ No newline at end of file diff --git a/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py b/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py new file mode 100644 index 00000000..ac36fda8 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/testalgorithms/basemodel.py @@ -0,0 +1,184 @@ +from __future__ import absolute_import, division, print_function +import json +from sedna.common.class_factory import ClassType, ClassFactory +import copy +import os +import os.path as osp +import time +import mmcv +import torch +from mmcv import Config +from mmcv.utils import get_git_hash +from multiprocessing import Pool +from mmdet import __version__ +from mmdet.apis import train_detector +from mmdet.datasets import build_dataset +from mmdet.models import build_detector +from mmdet.utils import (collect_env, get_device, get_root_logger, + replace_cfg_vals, + setup_multi_processes, update_data_root) +from mmdet.apis import init_detector, inference_detector + +__all__ = ["BaseModel"] + +from tqdm import tqdm + +from examples.yaoba.singletask_learning_yolox_tta.resource.utils.general_TTA_v5 import TTA_single_img + +# set backend +os.environ['BACKEND_TYPE'] = 'PYTORCH' + + +@ClassFactory.register(ClassType.GENERAL, alias="mmlab") +class BaseModel: + + def __init__(self, config, work_dir, **kwargs): + self.config = config + self.work_dir = work_dir + cfg = Config.fromfile(self.config) + cfg.work_dir = self.work_dir + cfg = replace_cfg_vals(cfg) + update_data_root(cfg) + setup_multi_processes(cfg) + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + if cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(self.config))[0]) + self.distributed = False + cfg.gpu_ids = [0] + # create work_dir + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # dump config + cfg.dump(osp.join(cfg.work_dir, osp.basename(self.config))) + # init the logger before other steps + self.timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + log_file = osp.join(cfg.work_dir, f'{self.timestamp}.log') + logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) + self.meta = dict() + env_info_dict = collect_env() + env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) + dash_line = '-' * 60 + '\n' + logger.info('Environment info:\n' + dash_line + env_info + '\n' + + dash_line) + self.meta['env_info'] = env_info + self.meta['config'] = cfg.pretty_text + # log some basic info + logger.info(f'Distributed training: {self.distributed}') + logger.info(f'Config:\n{cfg.pretty_text}') + cfg.device = get_device() + self.device = cfg.device + seed = 1 + logger.info(f'Set random seed to {seed}, ' + f'deterministic: False') + cfg.seed = seed + self.meta['seed'] = seed + self.meta['exp_name'] = osp.basename(self.config) + self.cfg = cfg + + def train(self, train_data, valid_data=None, **kwargs): + img_prefix, ann_file = train_data[0], train_data[1] + # Get the number of categories and their names. + labels = json.load(open(ann_file, mode="r", encoding="utf-8")) + categories = labels['categories'] + classes = [c['name'] for c in categories] + num_classes = len(categories) + + if "dataset" in self.cfg.data.train.keys(): + self.cfg.data.train.dataset.classes = classes + self.cfg.data.train.dataset.ann_file = ann_file + self.cfg.data.train.dataset.img_prefix = img_prefix + else: + self.cfg.data.train.classes = classes + self.cfg.data.train.ann_file = ann_file + self.cfg.data.train.img_prefix = img_prefix + self.cfg.model.bbox_head.num_classes = num_classes + self.cfg.data.val.classes = classes + self.cfg.data.test.classes = classes + + self.model = build_detector( + self.cfg.model, + train_cfg=self.cfg.get('train_cfg'), + test_cfg=self.cfg.get('test_cfg')) + self.model.init_weights() + + print("A total of", self.cfg.runner.max_epochs, "epoch") + + datasets = [build_dataset(self.cfg.data.train)] + if len(self.cfg.workflow) == 2: + val_dataset = copy.deepcopy(self.cfg.data.val) + val_dataset.pipeline = self.cfg.data.train.pipeline + datasets.append(build_dataset(val_dataset)) + if self.cfg.checkpoint_config is not None: + # save mmdet version, config file content and class names + # checkpoints as meta data + self.cfg.checkpoint_config.meta = dict( + mmdet_version=__version__ + get_git_hash()[:7], + CLASSES=datasets[0].CLASSES) + # add an attribute for visualization convenience + self.model.CLASSES = datasets[0].CLASSES + train_detector( + self.model, + datasets, + self.cfg, + distributed=self.distributed, + validate=False, + timestamp=self.timestamp, + meta=self.meta) + self.last_checkpoint_path = os.path.join(self.cfg.work_dir, "latest.pth") + return self.last_checkpoint_path + + def save(self, model_path): + return self.last_checkpoint_path + + def load(self, model_url=None): + self.load_model = init_detector(self.cfg, model_url) + return self.load_model + + def predict(self, data, **kwargs): + # ianvs格式 + predict_dict = {} + for imgPath in data: + print("inference predict->", imgPath) + imgName = osp.basename(imgPath) + predict_dict[str(imgName)] = [] + predict_result = inference_detector(self.load_model, imgPath) + for i, vs in enumerate(predict_result): + temp_dict = {} + temp_dict["category_id"] = i + temp_dict["bbox"] = vs + predict_dict[str(imgName)].append(temp_dict) + return predict_dict + + def tta_predict(self, data, strategy): + torch.multiprocessing.set_start_method('spawn', force=True) + predict_dict = {} + pbar = tqdm(total=len(data)) + pbar.set_description('progress bar') + update = lambda *args: pbar.update() + start = time.time() + with Pool(processes=4) as pool: # Specify the number of processes + merged_results = [ + pool.apply_async(TTA_single_img, (self.load_model, img_file, strategy), + callback=update) + for img_file in data] + end = time.time() + merged_results = [res.get() for res in merged_results] + pool.close() + pool.join() + total_time = ((end - start) * 1000) + print( + f"The total inference time is:{total_time}mm, " + f"and the average inference time for a single sample is{total_time / len(data)}") + # Convert to the Ianvs format + for merged_result in merged_results: + name = merged_result['name'] + predict_dict[name] = [] + result = merged_result['result'] + for i, vs in enumerate(result): + temp_dict = {} + temp_dict["category_id"] = i + temp_dict["bbox"] = vs + predict_dict[name].append(temp_dict) + return predict_dict diff --git a/examples/yaoba/singletask_learning_yolox_tta/testenv/map.py b/examples/yaoba/singletask_learning_yolox_tta/testenv/map.py new file mode 100644 index 00000000..a9933969 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/testenv/map.py @@ -0,0 +1,35 @@ +import json + +from mmdet.datasets import build_dataset +from sedna.common.class_factory import ClassType, ClassFactory +__all__ = ["map"] + + +@ClassFactory.register(ClassType.GENERAL, alias="map") +def map(y_true, y_pred): + img_prefix = y_true[0] + ann_file = y_true[1] + fp = open(ann_file, mode="r", encoding="utf-8") + test_anno_json = json.load(fp) + categories=test_anno_json["categories"] + categories=[i['name'] for i in categories] + test_cfg = dict( + type='CocoDataset', + classes=categories, + ann_file=ann_file, + img_prefix=img_prefix, + pipeline=[], + test_mode=True + ) + dataset = build_dataset(test_cfg) + real_eval_items = list(y_pred.keys()) + predict_results = [] + for item in real_eval_items: + cur_predict = y_pred[item] + new_predict = [] + for result in cur_predict: + bbox = result['bbox'] + new_predict.append(bbox) + predict_results.append(new_predict) + metric = dataset.evaluate(predict_results) + return metric['bbox_mAP_50'] diff --git a/examples/yaoba/singletask_learning_yolox_tta/testenv/testenv.yaml b/examples/yaoba/singletask_learning_yolox_tta/testenv/testenv.yaml new file mode 100644 index 00000000..37ff9e03 --- /dev/null +++ b/examples/yaoba/singletask_learning_yolox_tta/testenv/testenv.yaml @@ -0,0 +1,21 @@ +testenv: + # dataset configuration + dataset: + # the url address of training set index; string type; + train_url: "examples/yaoba/singletask_learning_yolox_tta/dataset/train.json" + + # the url address of validation set index; string type; + val_url: "examples/yaoba/singletask_learning_yolox_tta/dataset/val.json" + + # the url address of test set index; string type; + test_url: "examples/yaoba/singletask_learning_yolox_tta/dataset/test.json" + + # the image folder url address of train, val, and test sets; string type; + image_folder_url: "/media/huawei_YaoBa/Images" + # classes: "" + # metrics configuration for test case's evaluation; list type; + metrics: + # metric name; string type; + - name: "map" + # the url address of python file + url: "examples/yaoba/singletask_learning_yolox_tta/testenv/map.py" \ No newline at end of file