├── .gitignore ├── .gitmodules ├── LICENSE ├── README.md ├── binary_model.py ├── binary_test.py ├── binary_train.py ├── data ├── activity_net.v1-2.min.json ├── activity_net.v1-3.min.json ├── activitynet1.2_tag_train_normalized_proposal_list.txt ├── activitynet1.2_tag_val_normalized_proposal_list.txt ├── dataset_actionness_cfg.yaml ├── dataset_cfg.yaml ├── reference_models.yaml ├── thumos14_tag_test_normalized_proposal_list.txt ├── thumos14_tag_val_normalized_proposal_list.txt └── thumos_14 │ ├── temporal_annotations_test │ ├── Ambiguous_test.txt │ ├── BaseballPitch_test.txt │ ├── BasketballDunk_test.txt │ ├── Billiards_test.txt │ ├── CleanAndJerk_test.txt │ ├── CliffDiving_test.txt │ ├── CricketBowling_test.txt │ ├── CricketShot_test.txt │ ├── Diving_test.txt │ ├── FrisbeeCatch_test.txt │ ├── GolfSwing_test.txt │ ├── HammerThrow_test.txt │ ├── HighJump_test.txt │ ├── JavelinThrow_test.txt │ ├── LongJump_test.txt │ ├── PoleVault_test.txt │ ├── Shotput_test.txt │ ├── SoccerPenalty_test.txt │ ├── TennisSwing_test.txt │ ├── ThrowDiscus_test.txt │ └── VolleyballSpiking_test.txt │ ├── temporal_annotations_validation │ ├── Ambiguous_val.txt │ ├── BaseballPitch_val.txt │ ├── BasketballDunk_val.txt │ ├── Billiards_val.txt │ ├── CleanAndJerk_val.txt │ ├── CliffDiving_val.txt │ ├── CricketBowling_val.txt │ ├── CricketShot_val.txt │ ├── Diving_val.txt │ ├── FrisbeeCatch_val.txt │ ├── GolfSwing_val.txt │ ├── HammerThrow_val.txt │ ├── HighJump_val.txt │ ├── JavelinThrow_val.txt │ ├── LongJump_val.txt │ ├── PoleVault_val.txt │ ├── Shotput_val.txt │ ├── SoccerPenalty_val.txt │ ├── TennisSwing_val.txt │ ├── ThrowDiscus_val.txt │ └── VolleyballSpiking_val.txt │ ├── test_avoid_videos.txt │ ├── test_durations.txt │ ├── th14_test_durations.txt │ ├── th14_val_durations.txt │ ├── validation_avoid_videos.txt │ └── validation_durations.txt ├── eval_detection_results.py ├── gen_bottom_up_proposals.py ├── gen_proposal_list.py ├── gen_sliding_window_proposals.py ├── load_binary_score.py ├── ops ├── __init__.py ├── anet_db.py ├── detection_metrics.py ├── io.py ├── metrics.py ├── sequence_funcs.py ├── ssn_ops.py ├── thumos_db.py ├── utils.py └── video_funcs.py ├── requirements.txt ├── ssn_dataset.py ├── ssn_models.py ├── ssn_opts.py ├── ssn_test.py ├── ssn_train.py └── transforms.py /.gitignore: -------------------------------------------------------------------------------- 1 | # local files 2 | local/ 3 | 4 | # data files 5 | # data/ 6 | 7 | # model folder 8 | models/ 9 | 10 | # IDEA project configs 11 | .idea/ 12 | 13 | # Byte-compiled / optimized / DLL files 14 | __pycache__/ 15 | *.py[cod] 16 | *$py.class 17 | 18 | # C extensions 19 | *.so 20 | 21 | # Distribution / packaging 22 | .Python 23 | env/ 24 | build/ 25 | develop-eggs/ 26 | dist/ 27 | downloads/ 28 | eggs/ 29 | .eggs/ 30 | lib/ 31 | lib64/ 32 | parts/ 33 | sdist/ 34 | var/ 35 | *.egg-info/ 36 | .installed.cfg 37 | *.egg 38 | 39 | # PyInstaller 40 | # Usually these files are written by a python script from a template 41 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 42 | *.manifest 43 | *.spec 44 | 45 | # Installer logs 46 | pip-log.txt 47 | pip-delete-this-directory.txt 48 | 49 | # Unit test / coverage reports 50 | htmlcov/ 51 | .tox/ 52 | .coverage 53 | .coverage.* 54 | .cache 55 | nosetests.xml 56 | coverage.xml 57 | *,cover 58 | .hypothesis/ 59 | 60 | # Translations 61 | *.mo 62 | *.pot 63 | 64 | # Django stuff: 65 | *.log 66 | local_settings.py 67 | 68 | # Flask stuff: 69 | instance/ 70 | .webassets-cache 71 | 72 | # Scrapy stuff: 73 | .scrapy 74 | 75 | # Sphinx documentation 76 | docs/_build/ 77 | 78 | # PyBuilder 79 | target/ 80 | 81 | # IPython Notebook 82 | .ipynb_checkpoints 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # celery beat schedule file 88 | celerybeat-schedule 89 | 90 | # dotenv 91 | .env 92 | 93 | # virtualenv 94 | venv/ 95 | ENV/ 96 | 97 | # Spyder project settings 98 | .spyderproject 99 | 100 | # Rope project settings 101 | .ropeproject 102 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "model_zoo"] 2 | path = model_zoo 3 | branch=master 4 | url = https://github.com/yjxiong/tensorflow-model-zoo.torch 5 | [submodule "anet_toolkit"] 6 | path = anet_toolkit 7 | url = https://github.com/yjxiong/ActivityNet-Toolkit 8 | branch=master 9 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2017, Multimedia Laboratory, The Chinese University of Hong Kong. 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | * Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /binary_test.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import time 3 | import pdb 4 | import numpy as np 5 | 6 | from load_binary_score import BinaryDataSet 7 | from binary_model import BinaryClassifier 8 | from transforms import * 9 | 10 | from torch import multiprocessing 11 | from torch.utils import model_zoo 12 | from ops.utils import get_actionness_configs, get_reference_model_url 13 | 14 | global args 15 | parser = argparse.ArgumentParser(description = 'extract actionnes score') 16 | parser.add_argument('dataset', type=str, choices=['activitynet1.2', 'thumos14']) 17 | parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff']) 18 | parser.add_argument('subset', type=str, choices=['training','validation','testing']) 19 | parser.add_argument('weights', type=str) 20 | parser.add_argument('save_scores', type=str) 21 | parser.add_argument('--arch', type=str, default='BNInception') 22 | parser.add_argument('--save_raw_scores', type=str, default=None) 23 | parser.add_argument('--frame_interval', type=int, default=5) 24 | parser.add_argument('--test_batchsize', type=int, default=512) 25 | parser.add_argument('--max_num', type=int, default=-1) 26 | parser.add_argument('--test_crops', type=int, default=10) 27 | parser.add_argument('--input_size', type=int, default=224) 28 | parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', 29 | help='number of data loading workers (default: 4)') 30 | parser.add_argument('--gpus', nargs='+', type=int, default=None) 31 | parser.add_argument('--flow_pref', type=str, default='') 32 | parser.add_argument('--use_reference', default=False, action='store_true') 33 | parser.add_argument('--use_kinetics_reference', default=False, action='store_true') 34 | 35 | args = parser.parse_args() 36 | 37 | dataset_configs = get_actionness_configs(args.dataset) 38 | num_class = dataset_configs['num_class'] 39 | 40 | if args.dataset == 'thumos14': 41 | if args.subset == 'validation': 42 | test_prop_file = 'data/{}_proposal_list.txt'.format(dataset_configs['train_list']) 43 | elif args.subset == 'testing': 44 | test_prop_file = 'data/{}_proposal_list.txt'.format(dataset_configs['test_list']) 45 | elif args.dataset == 'activitynet1.2': 46 | if args.subset == 'training': 47 | test_prop_file = 'data/{}_proposal_list.txt'.format(dataset_configs['train_list']) 48 | elif args.subset == 'validation': 49 | test_prop_file = 'data/{}_proposal_list.txt'.format(dataset_configs['test_list']) 50 | 51 | 52 | if args.modality == 'RGB': 53 | data_length = 1 54 | elif args.modality in ['Flow', 'RGBDiff']: 55 | data_length = 5 56 | else: 57 | raise ValueError('unknown modality {}'.format(args.modality)) 58 | 59 | gpu_list = args.gpus if args.gpus is not None else range(8) 60 | 61 | 62 | 63 | def runner_func(dataset, state_dict, gpu_id, index_queue, result_queue): 64 | torch.cuda.set_device(gpu_id) 65 | net = BinaryClassifier(num_class, 5, 66 | args.modality, test_mode=True, new_length=data_length, 67 | base_model=args.arch) 68 | 69 | net.load_state_dict(state_dict) 70 | net.prepare_test_fc() 71 | net.eval() 72 | net.cuda() 73 | output_dim = net.test_fc.out_features 74 | while True: 75 | index = index_queue.get() 76 | frames_gen, frame_cnt = dataset[index] 77 | num_crop = args.test_crops 78 | length = 3 79 | if args.modality == 'Flow': 80 | length = 10 81 | elif args.modality == 'RGBDiff': 82 | length = 18 83 | 84 | output = torch.zeros((frame_cnt, num_crop, output_dim)).cuda() 85 | cnt = 0 86 | for frames in frames_gen: 87 | input_var = torch.autograd.Variable(frames.view(-1, length, frames.size(-2), frames.size(-1)).cuda(), 88 | volatile=True) 89 | rst, _ = net(input_var, None) 90 | sc = rst.data.view(-1, num_crop, output_dim) 91 | output[cnt:cnt + sc.size(0), :, :] = sc 92 | cnt += sc.size(0) 93 | 94 | result_queue.put((dataset.video_list[index].id.split('/')[-1], output.cpu().numpy())) 95 | 96 | 97 | 98 | if __name__ == '__main__': 99 | 100 | ctx = multiprocessing.get_context('spawn') 101 | net = BinaryClassifier(num_class, 5, 102 | args.modality, 103 | base_model=args.arch) 104 | 105 | if args.test_crops == 1: 106 | cropping = torchvision.transforms.Compose([ 107 | GroupScale(net.scale_size), 108 | GroupScale(net.input_size), 109 | ]) 110 | elif args.test_crops == 10: 111 | cropping = torchvision.transforms.Compose([ 112 | GroupOverSample(net.input_size, net.scale_size) 113 | ]) 114 | else: 115 | raise ValueError("only 1 and 10 crops are supported while we got {}".format(args.test_crop)) 116 | 117 | if not args.use_reference and not args.use_kinetics_reference: 118 | checkpoint = torch.load(args.weights) 119 | else: 120 | model_url = get_reference_model_url(args.dataset, args.modality, 121 | 'ImageNet' if args.use_reference else 'Kinetics', args.arch) 122 | checkpoint = model_zoo.load_url(model_url) 123 | print("use reference model: {}".format(model_url)) 124 | 125 | print("model epoch {} loss: {}".format(checkpoint['epoch'], checkpoint['best_loss'])) 126 | base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items())} 127 | dataset = BinaryDataSet("", test_prop_file, 128 | new_length=data_length, 129 | modality=args.modality, 130 | image_tmpl="img_{:05d}.jpg" if args.modality in ["RGB", 131 | "RGBDiff"] else args.flow_pref + "{}_{:05d}.jpg", 132 | test_mode=True, test_interval=args.frame_interval, 133 | transform=torchvision.transforms.Compose([ 134 | cropping, 135 | Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])), 136 | ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])), 137 | GroupNormalize(net.input_mean, net.input_std), 138 | ]), verbose=False) 139 | 140 | index_queue = ctx.Queue() 141 | result_queue = ctx.Queue() 142 | workers = [ctx.Process(target=runner_func, args=(dataset,base_dict, gpu_list[i % len(gpu_list)], index_queue, result_queue)) 143 | for i in range(args.workers)] 144 | 145 | del net 146 | 147 | max_num = args.max_num if args.max_num > 0 else len(dataset) 148 | 149 | 150 | for i in range(max_num): 151 | index_queue.put(i) 152 | 153 | 154 | for w in workers: 155 | w.daemon = True 156 | w.start() 157 | 158 | 159 | proc_start_time = time.time() 160 | out_dict = {} 161 | for i in range(max_num): 162 | rst = result_queue.get() 163 | out_dict[rst[0]] = rst[1] 164 | cnt_time = time.time() - proc_start_time 165 | print('video {} done, total {}/{}, average {:.04f} sec/video'.format(i, i + 1, 166 | max_num, 167 | float(cnt_time) / (i+1))) 168 | if args.save_scores is not None: 169 | save_dict = {k: v for k,v in out_dict.items()} 170 | import pickle 171 | 172 | pickle.dump(save_dict, open(args.save_scores, 'wb'), 2) 173 | -------------------------------------------------------------------------------- /data/dataset_actionness_cfg.yaml: -------------------------------------------------------------------------------- 1 | thumos14: 2 | train_list: thumos14_sw_val 3 | test_list: thumos14_sw_test 4 | num_class: 2 5 | sampling: 6 | fg_iou_thresh: 0.7 7 | bg_iou_thresh: 0.01 8 | incomplete_iou_thresh: 0.3 9 | bg_coverage_thresh: 0.02 10 | incomplete_overlap_thresh: 0.01 # on THUMOS14 we include more incomplete samples 11 | prop_per_video: 8 12 | fg_ratio: 1 13 | bg_ratio: 1 14 | incomplete_ratio: 6 15 | 16 | evaluation: 17 | top_k: 2000 18 | nms_threshold: 0.2 19 | softmax_before_filter: true 20 | 21 | stpp: [1, 1, 1] 22 | 23 | flow_init: 24 | BNInception: https://yjxiong.blob.core.windows.net/ssn-models/bninception_thumos_flow_init-89dfeaf803e.pth 25 | InceptionV3: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_thumos_flow_init-0527856bcec6.pth 26 | kinetics_pretrain: 27 | BNInception: 28 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/bninception_rgb_kinetics_init-d4ee618d3399.pth 29 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/bninception_flow_kinetics_init-1410c1ccb470.pth 30 | InceptionV3: 31 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_rgb_kinetics_init-c42e70a05e22.pth 32 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_flow_kinetics_init-374d56ea4e66.pth 33 | 34 | activitynet1.2: 35 | train_list: activitynet1.2_sw_train 36 | test_list: activitynet1.2_sw_val 37 | num_class: 100 38 | sampling: 39 | fg_iou_thresh: 0.7 40 | bg_iou_thresh: 0.01 41 | incomplete_iou_thresh: 0.3 42 | bg_coverage_thresh: 0.02 43 | incomplete_overlap_thresh: 0.7 44 | prop_per_video: 8 45 | fg_ratio: 1 46 | bg_ratio: 1 47 | incomplete_ratio: 6 48 | 49 | stpp: [1, 1, 1] 50 | 51 | evaluation: 52 | top_k: 60 53 | nms_threshold: 0.6 54 | softmax_before_filter: false 55 | 56 | flow_init: 57 | BNInception: https://yjxiong.blob.core.windows.net/ssn-models/bninception_activitynet1.2_flow_init-0090e716bd1563.pth 58 | InceptionV3: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_activitynet1.2_flow_init-cd9437aaedfb.pth 59 | kinetics_pretrain: 60 | BNInception: 61 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/bninception_rgb_kinetics_init-d4ee618d3399.pth 62 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/bninception_flow_kinetics_init-1410c1ccb470.pth 63 | InceptionV3: 64 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_rgb_kinetics_init-c42e70a05e22.pth 65 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_flow_kinetics_init-374d56ea4e66.pth 66 | 67 | 68 | -------------------------------------------------------------------------------- /data/dataset_cfg.yaml: -------------------------------------------------------------------------------- 1 | thumos14: 2 | train_list: thumos14_tag_val 3 | test_list: thumos14_tag_test 4 | num_class: 20 5 | sampling: 6 | fg_iou_thresh: 0.7 7 | bg_iou_thresh: 0.01 8 | incomplete_iou_thresh: 0.3 9 | bg_coverage_thresh: 0.02 10 | incomplete_overlap_thresh: 0.01 # on THUMOS14 we include more incomplete samples 11 | prop_per_video: 8 12 | fg_ratio: 1 13 | bg_ratio: 1 14 | incomplete_ratio: 6 15 | 16 | evaluation: 17 | top_k: 2000 18 | nms_threshold: 0.2 19 | softmax_before_filter: true 20 | 21 | stpp: [1, 1, 1] 22 | 23 | flow_init: 24 | BNInception: https://yjxiong.blob.core.windows.net/ssn-models/bninception_thumos_flow_init-89dfeaf803e.pth 25 | InceptionV3: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_thumos_flow_init-0527856bcec6.pth 26 | kinetics_pretrain: 27 | BNInception: 28 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/bninception_rgb_kinetics_init-d4ee618d3399.pth 29 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/bninception_flow_kinetics_init-1410c1ccb470.pth 30 | InceptionV3: 31 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_rgb_kinetics_init-c42e70a05e22.pth 32 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_flow_kinetics_init-374d56ea4e66.pth 33 | 34 | activitynet1.2: 35 | train_list: activitynet1.2_tag_train 36 | test_list: activitynet1.2_tag_val 37 | num_class: 100 38 | sampling: 39 | fg_iou_thresh: 0.7 40 | bg_iou_thresh: 0.01 41 | incomplete_iou_thresh: 0.3 42 | bg_coverage_thresh: 0.02 43 | incomplete_overlap_thresh: 0.7 44 | prop_per_video: 8 45 | fg_ratio: 1 46 | bg_ratio: 1 47 | incomplete_ratio: 6 48 | 49 | stpp: [1, 1, 1] 50 | 51 | evaluation: 52 | top_k: 60 53 | nms_threshold: 0.6 54 | softmax_before_filter: false 55 | 56 | flow_init: 57 | BNInception: https://yjxiong.blob.core.windows.net/ssn-models/bninception_activitynet1.2_flow_init-0090e716bd1563.pth 58 | InceptionV3: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_activitynet1.2_flow_init-cd9437aaedfb.pth 59 | kinetics_pretrain: 60 | BNInception: 61 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/bninception_rgb_kinetics_init-d4ee618d3399.pth 62 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/bninception_flow_kinetics_init-1410c1ccb470.pth 63 | InceptionV3: 64 | RGB: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_rgb_kinetics_init-c42e70a05e22.pth 65 | Flow: https://yjxiong.blob.core.windows.net/ssn-models/inceptionv3_flow_kinetics_init-374d56ea4e66.pth 66 | 67 | 68 | -------------------------------------------------------------------------------- /data/reference_models.yaml: -------------------------------------------------------------------------------- 1 | thumos14: 2 | ImageNet: 3 | BNInception: 4 | RGB: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_reference_thumos14_bninception_rgb-74e71b25d64a.pth.tar 5 | Flow: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_reference_thumos14_bninception_flow-dfe7aba61375.pth.tar 6 | InceptionV3: 7 | RGB: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_reference_thumos14_inceptionv3_rgb-20e223da6fb7.pth.tar 8 | Flow: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_reference_thumos14_inceptionv3_flow-918f932dd160.pth.tar 9 | Kinetics: 10 | BNInception: 11 | RGB: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_kinetics_reference_thumos14_bninception_rgb-9864666d118b.pth.tar 12 | Flow: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_kinetics_reference_thumos14_bninception_flow-d4974e0142ea.pth.tar 13 | InceptionV3: 14 | RGB: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_kinetics_reference_thumos14_inceptionv3_rgb-22568ca50690.pth.tar 15 | Flow: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_kinetics_reference_thumos14_inceptionv3_flow-e09c5c9cd1ee.pth.tar 16 | 17 | activitynet1.2: 18 | ImageNet: 19 | BNInception: 20 | RGB: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_reference_activitynet1.2_bninception_rgb-e2fd10a6c6b0.pth.tar 21 | Flow: https://yjxiong.blob.core.windows.net/ssn-reference-models/ssn_reference_activitynet1.2_bninception_flow-dfcdda9fe1f5.pth.tar 22 | # InceptionV3: 23 | # RGB: 24 | # Flow: 25 | # Kinetics: 26 | # BNInception: 27 | # RGB: 28 | # Flow: 29 | # InceptionV3: 30 | # RGB: 31 | # Flow: 32 | -------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_test/Ambiguous_test.txt: -------------------------------------------------------------------------------- 1 | video_test_0000278 0.0 1.4 2 | video_test_0000278 95.7 97.2 3 | video_test_0000293 50.6 54.6 4 | video_test_0000293 67.4 71.7 5 | video_test_0000293 99.7 106.4 6 | video_test_0000293 118.1 126.4 7 | video_test_0000293 145.8 149.8 8 | video_test_0000293 162.9 168.2 9 | video_test_0000293 181.3 184.0 10 | video_test_0000367 56.4 63.8 11 | video_test_0000367 167.8 170.7 12 | video_test_0000405 15.7 18.4 13 | video_test_0000426 17.8 18.6 14 | video_test_0000426 24.0 24.8 15 | video_test_0000426 40.1 41.8 16 | video_test_0000426 113.9 115.0 17 | video_test_0000426 118.8 119.7 18 | video_test_0000426 124.0 125.2 19 | video_test_0000426 135.2 136.9 20 | video_test_0000437 1.6 12.1 21 | video_test_0000437 47.2 48.4 22 | video_test_0000437 53.2 54.0 23 | video_test_0000437 65.9 67.8 24 | video_test_0000448 42.2 53.8 25 | video_test_0000461 14.0 16.7 26 | video_test_0000549 28.2 33.6 27 | video_test_0000549 14.4 17.4 28 | video_test_0000549 55.0 57.1 29 | video_test_0000593 23.6 29.0 30 | video_test_0000593 36.9 44.3 31 | video_test_0000611 43.0 45.9 32 | video_test_0000611 55.6 58.8 33 | video_test_0000611 59.9 71.0 34 | video_test_0000615 136.9 142.5 35 | video_test_0000615 152.7 159.8 36 | video_test_0000615 164.7 168.0 37 | video_test_0000624 2.5 6.8 38 | video_test_0000664 4.2 5.6 39 | video_test_0000691 36.3 80.8 40 | video_test_0000691 123.9 151.0 41 | video_test_0000714 136.7 137.5 42 | video_test_0000718 13.3 15.5 43 | video_test_0000847 33.7 35.4 44 | video_test_0000847 46.0 52.0 45 | video_test_0000847 58.7 67.0 46 | video_test_0000847 82.8 98.1 47 | video_test_0000847 136.2 171.2 48 | video_test_0000847 175.0 178.5 49 | video_test_0000847 204.5 212.8 50 | video_test_0000940 90.3 92.0 51 | video_test_0000989 170.6 188.4 52 | video_test_0001075 12.3 13.3 53 | video_test_0001075 142.6 143.9 54 | video_test_0001076 17.0 18.4 55 | video_test_0001076 23.8 25.9 56 | video_test_0001076 47.5 57.8 57 | video_test_0001079 335.1 342.8 58 | video_test_0001079 416.0 420.7 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-------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_validation/Ambiguous_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000160 155.2 160.6 2 | video_validation_0000160 178.3 186.1 3 | video_validation_0000160 187.0 196.7 4 | video_validation_0000162 3.6 10.6 5 | video_validation_0000162 19.0 20.3 6 | video_validation_0000162 22.9 23.6 7 | video_validation_0000162 35.6 39.1 8 | video_validation_0000162 140.0 143.5 9 | video_validation_0000162 163.0 164.0 10 | video_validation_0000162 180.3 183.5 11 | video_validation_0000166 20.3 24.4 12 | video_validation_0000169 18.8 19.7 13 | video_validation_0000169 102.4 104.6 14 | video_validation_0000169 100.1 100.9 15 | video_validation_0000169 144.7 149.1 16 | video_validation_0000169 24.6 26.5 17 | video_validation_0000169 31.4 32.0 18 | video_validation_0000169 41.7 43.3 19 | video_validation_0000169 46.3 46.9 20 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/data/thumos_14/temporal_annotations_validation/BaseballPitch_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000266 72.8 76.4 2 | video_validation_0000681 44.0 50.9 3 | video_validation_0000682 1.5 5.4 4 | video_validation_0000682 79.3 83.9 5 | video_validation_0000683 0.3 5.5 6 | video_validation_0000683 7.5 13.5 7 | video_validation_0000683 15.9 18.1 8 | video_validation_0000683 23.8 25.7 9 | video_validation_0000684 21.8 23.3 10 | video_validation_0000684 40.0 43.1 11 | video_validation_0000684 63.7 66.6 12 | video_validation_0000685 18.7 23.4 13 | video_validation_0000685 23.4 26.7 14 | video_validation_0000685 43.8 48.1 15 | video_validation_0000685 55.1 58.2 16 | video_validation_0000685 63.2 66.8 17 | video_validation_0000685 67.2 71.3 18 | video_validation_0000685 78.4 81.5 19 | video_validation_0000686 2.4 5.0 20 | video_validation_0000687 5.1 7.1 21 | video_validation_0000687 17.7 20.5 22 | 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-------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_validation/CleanAndJerk_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000151 10.2 22.9 2 | video_validation_0000152 12.8 28.4 3 | video_validation_0000152 56.0 62.6 4 | video_validation_0000152 98.1 124.8 5 | video_validation_0000153 4.3 10.9 6 | video_validation_0000153 16.3 21.1 7 | video_validation_0000153 28.5 36.8 8 | video_validation_0000153 56.2 73.0 9 | video_validation_0000153 85.7 101.0 10 | video_validation_0000153 121.4 141.6 11 | video_validation_0000154 5.4 11.6 12 | video_validation_0000154 13.0 19.7 13 | video_validation_0000154 21.4 29.3 14 | video_validation_0000155 35.6 45.2 15 | video_validation_0000156 3.5 10.7 16 | video_validation_0000156 19.5 27.2 17 | video_validation_0000156 116.8 121.5 18 | video_validation_0000156 127.2 133.4 19 | video_validation_0000156 144.9 151.4 20 | 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-------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_validation/CliffDiving_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000161 11.3 14.1 2 | video_validation_0000161 21.0 23.9 3 | video_validation_0000161 26.4 29.4 4 | video_validation_0000161 50.9 55.4 5 | video_validation_0000162 0.0 2.8 6 | video_validation_0000162 10.7 11.9 7 | video_validation_0000162 39.8 43.1 8 | video_validation_0000162 67.2 70.0 9 | video_validation_0000162 79.1 82.3 10 | video_validation_0000162 84.2 87.6 11 | video_validation_0000162 87.9 90.7 12 | video_validation_0000162 91.7 94.6 13 | video_validation_0000162 108.8 112.8 14 | video_validation_0000162 115.4 118.2 15 | video_validation_0000162 122.6 125.4 16 | video_validation_0000162 126.1 132.3 17 | video_validation_0000162 143.5 146.7 18 | video_validation_0000162 148.2 151.3 19 | video_validation_0000162 152.5 155.1 20 | 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video_validation_0000856 85.9 91.5 49 | video_validation_0000856 8.5 10.0 50 | video_validation_0000856 35.2 38.9 51 | video_validation_0000856 70.4 71.3 52 | video_validation_0000856 72.2 76.0 53 | video_validation_0000857 3.8 11.4 54 | video_validation_0000857 15.2 17.7 55 | video_validation_0000857 20.4 22.5 56 | video_validation_0000857 25.8 28.2 57 | video_validation_0000857 31.1 32.7 58 | video_validation_0000857 35.6 38.2 59 | video_validation_0000857 39.9 48.0 60 | video_validation_0000858 39.6 44.5 61 | video_validation_0000858 55.8 58.8 62 | video_validation_0000859 5.2 7.4 63 | video_validation_0000859 26.4 30.9 64 | video_validation_0000859 38.5 46.1 65 | video_validation_0000860 6.4 10.0 66 | video_validation_0000860 19.6 26.1 67 | -------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_validation/TennisSwing_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000931 31.4 34.0 2 | video_validation_0000931 34.5 37.1 3 | video_validation_0000931 38.8 40.5 4 | video_validation_0000931 57.5 59.2 5 | video_validation_0000931 61.6 64.4 6 | video_validation_0000931 65.4 67.1 7 | video_validation_0000932 51.4 51.9 8 | video_validation_0000932 52.0 52.6 9 | video_validation_0000932 62.8 64.0 10 | video_validation_0000932 3.3 3.8 11 | video_validation_0000932 4.6 4.8 12 | video_validation_0000932 49.6 51.2 13 | video_validation_0000932 52.7 53.1 14 | video_validation_0000932 53.5 53.9 15 | video_validation_0000932 54.1 54.4 16 | video_validation_0000933 6.7 7.4 17 | video_validation_0000933 9.4 10.1 18 | video_validation_0000933 10.9 11.9 19 | video_validation_0000933 12.9 13.7 20 | video_validation_0000933 14.4 15.2 21 | video_validation_0000933 16.4 18.0 22 | video_validation_0000933 20.3 22.1 23 | video_validation_0000933 29.1 33.0 24 | video_validation_0000933 88.5 89.5 25 | video_validation_0000933 113.4 115.0 26 | video_validation_0000933 115.6 117.2 27 | video_validation_0000934 9.4 10.6 28 | video_validation_0000934 28.5 33.4 29 | video_validation_0000934 54.5 58.2 30 | video_validation_0000935 34.2 35.7 31 | video_validation_0000935 37.8 39.0 32 | video_validation_0000935 41.3 42.5 33 | video_validation_0000935 44.4 45.7 34 | video_validation_0000935 47.6 48.8 35 | video_validation_0000935 51.5 53.1 36 | video_validation_0000935 54.4 55.6 37 | video_validation_0000935 57.9 59.0 38 | video_validation_0000935 61.0 62.7 39 | video_validation_0000935 64.3 65.7 40 | video_validation_0000935 67.8 70.1 41 | video_validation_0000936 0.5 6.5 42 | video_validation_0000936 19.4 27.9 43 | video_validation_0000936 28.7 32.4 44 | video_validation_0000936 37.8 42.0 45 | video_validation_0000937 4.5 4.8 46 | video_validation_0000937 8.3 9.3 47 | video_validation_0000937 11.4 12.6 48 | video_validation_0000937 13.6 15.3 49 | video_validation_0000937 15.9 17.2 50 | video_validation_0000937 22.6 24.1 51 | video_validation_0000937 39.7 40.8 52 | video_validation_0000937 41.5 42.3 53 | video_validation_0000937 44.0 45.0 54 | video_validation_0000938 7.9 19.6 55 | video_validation_0000938 25.1 34.0 56 | video_validation_0000939 71.0 73.2 57 | video_validation_0000939 74.7 77.0 58 | video_validation_0000939 78.9 80.7 59 | video_validation_0000939 86.2 91.5 60 | video_validation_0000939 16.8 19.4 61 | video_validation_0000939 19.4 22.2 62 | video_validation_0000939 33.0 34.1 63 | video_validation_0000939 34.9 37.3 64 | video_validation_0000939 38.8 40.8 65 | video_validation_0000939 42.4 44.8 66 | video_validation_0000939 51.6 54.2 67 | video_validation_0000939 54.8 56.8 68 | video_validation_0000940 9.3 13.3 69 | video_validation_0000940 13.7 18.1 70 | -------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_validation/ThrowDiscus_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000786 7.9 12.9 2 | video_validation_0000941 21.4 27.3 3 | video_validation_0000941 65.8 71.2 4 | video_validation_0000941 79.5 87.1 5 | video_validation_0000941 120.0 128.5 6 | video_validation_0000941 145.7 148.2 7 | video_validation_0000941 156.8 163.4 8 | video_validation_0000941 188.3 193.8 9 | video_validation_0000941 204.3 211.9 10 | video_validation_0000942 1.3 9.8 11 | video_validation_0000942 18.8 26.8 12 | video_validation_0000942 40.0 44.2 13 | video_validation_0000942 53.0 60.4 14 | video_validation_0000943 5.0 12.0 15 | video_validation_0000943 14.3 20.8 16 | video_validation_0000943 26.2 29.7 17 | video_validation_0000943 63.3 69.2 18 | video_validation_0000943 70.0 75.3 19 | video_validation_0000944 6.0 12.5 20 | video_validation_0000944 15.8 18.2 21 | video_validation_0000944 18.8 21.8 22 | video_validation_0000944 22.6 27.1 23 | video_validation_0000944 27.4 30.7 24 | video_validation_0000944 35.8 37.2 25 | video_validation_0000944 37.4 38.1 26 | video_validation_0000944 38.2 39.0 27 | video_validation_0000944 39.2 39.9 28 | video_validation_0000944 40.0 40.6 29 | video_validation_0000944 40.8 41.5 30 | video_validation_0000944 41.7 42.2 31 | video_validation_0000944 42.5 43.1 32 | video_validation_0000944 43.4 44.0 33 | video_validation_0000944 44.2 44.8 34 | video_validation_0000944 45.1 45.8 35 | video_validation_0000944 45.9 46.7 36 | video_validation_0000944 46.8 47.5 37 | video_validation_0000944 47.8 48.4 38 | video_validation_0000944 48.6 49.2 39 | video_validation_0000944 49.6 50.2 40 | video_validation_0000944 50.4 51.2 41 | video_validation_0000944 51.4 52.1 42 | video_validation_0000944 52.2 53.0 43 | video_validation_0000944 53.2 53.9 44 | video_validation_0000944 54.0 54.9 45 | video_validation_0000944 55.0 55.8 46 | video_validation_0000944 56.0 58.6 47 | video_validation_0000944 59.3 61.4 48 | video_validation_0000944 62.5 64.9 49 | video_validation_0000944 64.9 66.9 50 | video_validation_0000944 67.0 68.0 51 | 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144.3 144.8 76 | video_validation_0000944 144.9 145.7 77 | video_validation_0000944 145.8 146.3 78 | video_validation_0000944 146.4 146.8 79 | video_validation_0000944 146.8 147.7 80 | video_validation_0000944 147.7 148.2 81 | video_validation_0000944 148.3 148.8 82 | video_validation_0000944 148.8 149.6 83 | video_validation_0000944 149.6 153.1 84 | video_validation_0000944 153.3 155.8 85 | video_validation_0000944 156.8 160.0 86 | video_validation_0000944 160.1 164.3 87 | video_validation_0000944 164.6 167.0 88 | video_validation_0000944 167.7 170.2 89 | video_validation_0000944 170.8 174.4 90 | video_validation_0000944 174.8 177.9 91 | video_validation_0000944 178.1 180.3 92 | video_validation_0000944 181.4 184.1 93 | video_validation_0000944 184.6 187.1 94 | video_validation_0000945 2.4 7.0 95 | video_validation_0000945 28.0 29.3 96 | video_validation_0000945 29.5 31.9 97 | video_validation_0000945 32.2 35.5 98 | video_validation_0000945 36.0 42.0 99 | video_validation_0000945 42.3 45.8 100 | video_validation_0000945 46.2 52.3 101 | video_validation_0000945 52.6 56.4 102 | video_validation_0000945 56.6 61.8 103 | video_validation_0000945 62.2 66.4 104 | video_validation_0000945 66.7 72.2 105 | video_validation_0000945 72.5 76.1 106 | video_validation_0000945 76.3 82.4 107 | video_validation_0000945 82.6 86.5 108 | video_validation_0000945 86.8 91.7 109 | video_validation_0000945 91.9 97.8 110 | video_validation_0000945 98.0 101.5 111 | video_validation_0000945 101.8 109.4 112 | video_validation_0000945 109.5 115.2 113 | video_validation_0000945 115.4 121.0 114 | video_validation_0000945 121.4 123.7 115 | video_validation_0000946 5.5 12.2 116 | video_validation_0000946 16.6 82.2 117 | video_validation_0000947 0.0 98.2 118 | video_validation_0000948 0.0 24.7 119 | video_validation_0000949 4.9 9.2 120 | video_validation_0000950 4.9 9.2 121 | -------------------------------------------------------------------------------- /data/thumos_14/temporal_annotations_validation/VolleyballSpiking_val.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000981 4.3 7.0 2 | video_validation_0000981 7.5 9.1 3 | video_validation_0000981 12.5 14.2 4 | video_validation_0000981 22.2 23.2 5 | video_validation_0000981 37.6 38.9 6 | video_validation_0000981 45.1 46.5 7 | video_validation_0000981 54.9 57.0 8 | video_validation_0000981 62.3 63.8 9 | video_validation_0000981 69.7 73.6 10 | video_validation_0000981 75.1 76.4 11 | video_validation_0000981 77.7 80.6 12 | video_validation_0000981 84.5 85.6 13 | video_validation_0000981 88.6 90.1 14 | video_validation_0000981 91.7 92.3 15 | video_validation_0000981 92.4 92.8 16 | video_validation_0000981 94.4 95.4 17 | video_validation_0000981 103.6 104.9 18 | video_validation_0000981 110.7 111.1 19 | video_validation_0000981 115.2 116.4 20 | video_validation_0000981 90.8 91.3 21 | video_validation_0000982 5.8 6.2 22 | video_validation_0000982 16.3 18.2 23 | video_validation_0000983 36.2 36.8 24 | video_validation_0000983 37.3 38.2 25 | video_validation_0000983 41.8 43.3 26 | video_validation_0000983 46.0 47.2 27 | video_validation_0000983 47.2 48.3 28 | video_validation_0000983 48.8 49.9 29 | video_validation_0000983 50.7 52.0 30 | video_validation_0000983 52.3 53.0 31 | video_validation_0000983 53.2 53.8 32 | video_validation_0000983 54.0 54.7 33 | video_validation_0000983 58.4 59.0 34 | video_validation_0000983 63.0 63.7 35 | video_validation_0000983 63.9 64.5 36 | video_validation_0000983 64.8 65.4 37 | video_validation_0000983 65.7 66.3 38 | video_validation_0000983 66.5 67.3 39 | video_validation_0000983 72.4 73.2 40 | video_validation_0000983 74.5 75.6 41 | video_validation_0000983 76.7 77.8 42 | video_validation_0000983 78.7 79.8 43 | video_validation_0000983 80.9 81.7 44 | video_validation_0000983 81.9 82.6 45 | video_validation_0000983 83.5 84.1 46 | video_validation_0000983 84.1 84.7 47 | video_validation_0000983 84.8 85.4 48 | video_validation_0000983 85.6 86.4 49 | video_validation_0000983 87.5 88.9 50 | video_validation_0000983 93.0 94.3 51 | video_validation_0000983 93.9 94.8 52 | video_validation_0000983 96.1 97.4 53 | video_validation_0000983 96.9 97.7 54 | video_validation_0000983 98.5 99.5 55 | video_validation_0000983 100.0 100.6 56 | video_validation_0000983 101.0 101.5 57 | video_validation_0000983 101.8 102.2 58 | video_validation_0000983 102.7 103.0 59 | video_validation_0000983 103.3 103.9 60 | video_validation_0000983 104.2 104.6 61 | video_validation_0000983 105.1 105.5 62 | video_validation_0000983 105.8 106.3 63 | video_validation_0000983 106.5 107.0 64 | video_validation_0000983 107.9 108.8 65 | video_validation_0000983 109.7 110.9 66 | video_validation_0000983 111.7 113.0 67 | video_validation_0000983 82.8 83.1 68 | video_validation_0000984 16.3 17.5 69 | video_validation_0000984 27.3 30.8 70 | video_validation_0000984 39.1 43.4 71 | video_validation_0000984 34.0 35.8 72 | video_validation_0000985 8.7 10.6 73 | video_validation_0000985 44.3 46.4 74 | video_validation_0000985 54.8 67.3 75 | video_validation_0000985 80.4 86.9 76 | video_validation_0000985 112.3 120.0 77 | video_validation_0000985 128.8 130.5 78 | video_validation_0000985 147.7 154.1 79 | video_validation_0000985 155.9 163.7 80 | video_validation_0000985 164.1 167.5 81 | video_validation_0000985 180.4 186.3 82 | video_validation_0000985 20.5 27.8 83 | video_validation_0000985 29.9 34.8 84 | video_validation_0000986 2.7 4.0 85 | video_validation_0000986 7.5 8.8 86 | video_validation_0000986 12.3 14.0 87 | video_validation_0000986 16.7 18.4 88 | video_validation_0000987 1.0 2.7 89 | video_validation_0000987 4.8 6.7 90 | video_validation_0000987 9.6 11.0 91 | video_validation_0000987 12.6 13.9 92 | video_validation_0000987 15.3 16.5 93 | video_validation_0000987 17.6 18.9 94 | video_validation_0000987 20.5 21.7 95 | video_validation_0000987 23.2 24.6 96 | video_validation_0000987 26.1 26.9 97 | video_validation_0000987 27.9 29.1 98 | video_validation_0000987 30.6 31.5 99 | video_validation_0000987 32.5 33.7 100 | video_validation_0000987 35.4 36.2 101 | video_validation_0000987 37.1 38.0 102 | video_validation_0000987 39.2 40.0 103 | video_validation_0000987 40.6 42.1 104 | video_validation_0000987 43.7 44.8 105 | video_validation_0000987 46.1 47.0 106 | video_validation_0000987 49.8 51.0 107 | video_validation_0000987 52.4 53.8 108 | video_validation_0000987 55.5 57.0 109 | video_validation_0000987 57.6 58.5 110 | video_validation_0000987 60.6 61.6 111 | video_validation_0000987 62.3 63.1 112 | video_validation_0000987 64.4 65.8 113 | video_validation_0000987 66.6 67.9 114 | video_validation_0000987 69.8 70.9 115 | video_validation_0000987 71.7 73.9 116 | video_validation_0000987 75.9 76.7 117 | video_validation_0000987 78.8 80.3 118 | video_validation_0000987 81.1 82.1 119 | video_validation_0000987 83.3 84.4 120 | video_validation_0000987 86.1 86.9 121 | video_validation_0000987 87.7 88.9 122 | video_validation_0000987 90.2 91.5 123 | video_validation_0000987 93.3 94.5 124 | video_validation_0000987 96.8 98.6 125 | video_validation_0000987 99.6 102.2 126 | video_validation_0000988 7.2 10.2 127 | video_validation_0000988 11.3 29.9 128 | video_validation_0000988 53.9 70.2 129 | video_validation_0000988 80.9 97.6 130 | video_validation_0000988 104.3 106.4 131 | video_validation_0000989 15.1 17.3 132 | video_validation_0000989 17.9 19.2 133 | video_validation_0000989 19.6 22.2 134 | video_validation_0000989 22.6 24.4 135 | video_validation_0000989 24.7 25.5 136 | video_validation_0000989 26.8 28.4 137 | video_validation_0000989 28.9 30.4 138 | video_validation_0000989 31.3 33.3 139 | video_validation_0000990 53.4 55.4 140 | video_validation_0000990 61.2 62.8 141 | video_validation_0000990 67.5 69.6 142 | video_validation_0000990 78.7 80.5 143 | video_validation_0000990 83.3 85.3 144 | video_validation_0000990 92.1 93.9 145 | video_validation_0000990 99.7 101.6 146 | video_validation_0000990 103.6 110.4 147 | -------------------------------------------------------------------------------- /data/thumos_14/test_avoid_videos.txt: -------------------------------------------------------------------------------- 1 | video_test_0001496 CricketShot 2 | video_test_0000798 CricketBowling 3 | video_test_0000270 HammerThrow 4 | video_test_0001194 CricketBowling 5 | video_test_0000622 CricketBowling 6 | video_test_0000392 CricketShot 7 | video_test_0000569 CricketShot 8 | video_test_0001313 CricketShot 9 | video_test_0001164 CleanAndJerk 10 | video_test_0000786 CricketShot 11 | video_test_0001468 CleanAndJerk 12 | video_test_0000549 CricketShot 13 | video_test_0001358 CricketBowling 14 | video_test_0000367 HighJump 15 | -------------------------------------------------------------------------------- /data/thumos_14/validation_avoid_videos.txt: -------------------------------------------------------------------------------- 1 | video_validation_0000178 CricketShot 2 | video_validation_0000932 GolfSwing 3 | video_validation_0000173 CricketShot 4 | video_validation_0000665 LongJump 5 | video_validation_0000183 CricketBowling 6 | video_validation_0000786 ThrowDiscus 7 | video_validation_0000171 CricketShot 8 | -------------------------------------------------------------------------------- /gen_bottom_up_proposals.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import sys 4 | import math 5 | import numpy as np 6 | import multiprocessing 7 | from sklearn.metrics import confusion_matrix 8 | import time 9 | import pickle 10 | import multiprocessing as mp 11 | from ops.sequence_funcs import * 12 | from ops.anet_db import ANetDB 13 | from ops.thumos_db import THUMOSDB 14 | from ops.detection_metrics import get_temporal_proposal_recall, name_proposal 15 | from ops.sequence_funcs import temporal_nms 16 | from ops.io import dump_window_list 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('score_files', type=str, nargs='+') 19 | parser.add_argument("--anet_version", type=str, default='1.2', help='') 20 | parser.add_argument("--dataset", type=str, default='activitynet', choices=['activitynet', 'thumos14']) 21 | parser.add_argument("--cls_scores", type=str, default=None, 22 | help='classification scores, if set to None, will use groundtruth labels') 23 | parser.add_argument("--subset", type=str, default='validation', choices=['training', 'validation', 'testing']) 24 | parser.add_argument("--iou_thresh", type=float, nargs='+', default=[0.5, 0.75, 0.95]) 25 | parser.add_argument("--score_weights", type=float, nargs='+', default=None, help='') 26 | parser.add_argument("--write_proposals", type=str, default=None, help='') 27 | parser.add_argument("--minimum_len", type=float, default=0, help='minimum length of a proposal, in second') 28 | parser.add_argument("--reg_score_files", type=str, nargs='+', default=None) 29 | parser.add_argument("--frame_path", type=str, default='/mnt/SSD/ActivityNet/anet_v1.2_extracted_340/') 30 | 31 | args = parser.parse_args() 32 | 33 | 34 | if args.dataset == 'activitynet': 35 | db = ANetDB.get_db(args.anet_version) 36 | db.try_load_file_path('/mnt/SSD/ActivityNet/anet_v1.2_extracted_340/') 37 | elif args.dataset == 'thumos14': 38 | db = THUMOSDB.get_db() 39 | db.try_load_file_path('/mnt/SSD/THUMOS14/') 40 | 41 | # rename subset test 42 | if args.subset == 'testing': 43 | args.subset = 'test' 44 | else: 45 | raise ValueError("unknown dataset {}".format(args.dataset)) 46 | 47 | video_list = db.get_subset_videos(args.subset) 48 | video_list = [v for v in video_list if v.instances != []] 49 | print("video list size: {}".format(len(video_list))) 50 | # load scores 51 | print('loading scores...') 52 | score_list = [] 53 | for fname in args.score_files: 54 | score_list.append(pickle.load(open(fname, 'rb'))) 55 | print('load {} piles of scores'.format(len(score_list))) 56 | 57 | 58 | # load classification scores if specified 59 | if args.cls_scores: 60 | cls_scores = cPickle.load(open(args.cls_scores, 'rb')) 61 | else: 62 | cls_scores = None 63 | print('done') 64 | 65 | # load regression scores 66 | if args.reg_score_files is not None: 67 | print('loading regression scores') 68 | reg_score_list = [] 69 | for fname in args.reg_score_files: 70 | reg_score_list.append(cPickle.load(open(fname, 'rb'))) 71 | print('load {} piles of regression scores'.format(len(reg_score_list))) 72 | else: 73 | reg_score_list = None 74 | 75 | 76 | # merge scores 77 | print('merging scores') 78 | score_dict = {} 79 | for key in score_list[0].keys(): 80 | out_score = score_list[0][key].mean(axis=1) * (1.0 if args.score_weights is None else args.score_weights[0]) 81 | for i in range(1, len(score_list)): 82 | add_score = score_list[i][key].mean(axis=1) 83 | if add_score.shape[0] < out_score.shape[0]: 84 | out_score = out_score[:add_score.shape[0], :] 85 | elif add_score.shape[0] > out_score.shape[0]: 86 | tick = add_score.shape[0] / float(out_score.shape[0]) 87 | indices = [int(x * tick) for x in range(out_score.shape[0])] 88 | add_score = add_score[indices, :] 89 | out_score += add_score * (1.0 if args.score_weights is None else args.score_weights[i]) 90 | score_dict[key] = out_score 91 | print('done') 92 | 93 | # merge regression scores 94 | if reg_score_list is not None: 95 | print('merging regression scores') 96 | reg_score_dict = {} 97 | for key in reg_score_list[0].keys(): 98 | out_score = reg_score_list[0][key].mean(axis=1) 99 | for i in range(1, len(reg_score_list)): 100 | add_score = reg_score_list[i][key].mean(axis=1) 101 | if add_score.shape[0] < out_score.shape[0]: 102 | out_score = out_score[:add_score.shape[0], :] 103 | out_score += add_score 104 | reg_score_dict[key] = out_score / len(reg_score_list) 105 | print('done') 106 | else: 107 | reg_score_dict = None 108 | 109 | # bottom-up generate proposals 110 | print('generating proposals') 111 | pr_dict = {} 112 | pr_score_dict = {} 113 | topk = 1 114 | 115 | 116 | def gen_prop(v): 117 | if (args.dataset == 'activitynet') or (args.dataset == 'thumos14'): 118 | vid = v.id 119 | else: 120 | vid = v.path.split('/')[-1].split('.')[0] 121 | scores = score_dict[vid] 122 | frm_duration = len(scores) 123 | topk_cls = [0] 124 | topk_labels = label_frame_by_threshold(scores, topk_cls, bw=3, thresh=[0.01, 0.05, 0.1, .15, 0.25, .4, .5, .6, .7, .8, .9, .95, ], multicrop=False) 125 | 126 | bboxes = [] 127 | tol_lst = [0.05, .1, .2, .3, .4, .5, .6, 0.8, 1.0] 128 | 129 | bboxes.extend(build_box_by_search(topk_labels, np.array(tol_lst))) 130 | if reg_score_dict: 131 | reg_scores = reg_score_dict[score_id] 132 | bboxes = regress_box(bboxes, reg_scores, len(scores)) 133 | 134 | 135 | # print len(bboxes) 136 | bboxes = temporal_nms(bboxes, 0.9) 137 | 138 | pr_box = [(x[0] / float(frm_duration) * v.duration, x[1] / float(frm_duration) * v.duration) for x in bboxes] 139 | 140 | # filter out too short proposals 141 | pr_box = list(filter(lambda b: b[1] - b[0] > args.minimum_len, pr_box)) 142 | return v.id, pr_box, [x[3] for x in bboxes] 143 | 144 | 145 | def call_back(rst): 146 | pr_dict[rst[0]] = rst[1] 147 | pr_score_dict[rst[0]] = rst[2] 148 | import sys 149 | print(rst[0], len(pr_dict), len(rst[1])) 150 | sys.stdout.flush() 151 | 152 | pool = mp.Pool(processes = 32) 153 | lst = [] 154 | handle = [pool.apply_async(gen_prop, args=(x, ), callback=call_back) for x in video_list] 155 | pool.close() 156 | pool.join() 157 | 158 | # evaluate proposal info 159 | proposal_list = [pr_dict[v.id] for v in video_list if v.id in pr_dict] 160 | gt_spans_full = [[(x.num_label, x.time_span) for x in v.instances] for v in video_list if v.id in pr_dict] 161 | gt_spans = [[item[1] for item in x] for x in gt_spans_full] 162 | score_list = [score_dict[v.id] for v in video_list if v.id in pr_dict] 163 | duration_list = [v.duration for v in video_list if v.id in pr_dict] 164 | proposal_score_list = [pr_score_dict[v.id] for v in video_list if v.id in pr_dict] 165 | print('{} groundtruth boxes from'.format(sum(map(len, gt_spans)))) 166 | 167 | 168 | 169 | print('average # of proposals: {}'.format(np.mean(list(map(len, proposal_list))))) 170 | IOU_thresh = np.arange(0.5, 1, 0.2) 171 | p_list = [] 172 | for th in IOU_thresh: 173 | pv, pi = get_temporal_proposal_recall(proposal_list, gt_spans, th) 174 | print('IOU threshold {}. per video recall: {:02f}, per instance recall: {:02f}'.format(th, pv * 100, pi * 100)) 175 | p_list.append((pv, pi)) 176 | print('Average Recall: {:.04f} {:.04f}'.format(*(np.mean(p_list, axis=0)*100))) 177 | 178 | if args.write_proposals: 179 | 180 | name_pattern = 'img_*.jpg' 181 | frame_path = args.frame_path 182 | 183 | named_proposal_list = [name_proposal(x, y) for x, y in zip(gt_spans_full, proposal_list)] 184 | allow_empty = args.dataset == 'activitynet' and args.subset == 'testing' 185 | dumped_list = [dump_window_list(v, prs, frame_path, name_pattern, score=score, allow_empty=allow_empty) for v, prs, score in 186 | zip(filter(lambda x: x.id in pr_dict, video_list), named_proposal_list, score_list)] 187 | 188 | with open(args.write_proposals, 'w') as of: 189 | for i, e in enumerate(dumped_list): 190 | of.write('# {}\n'.format(i + 1)) 191 | of.write(e) 192 | 193 | print('list written. got {} videos'.format(len(dumped_list))) 194 | -------------------------------------------------------------------------------- /gen_proposal_list.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | from ops.io import process_proposal_list, parse_directory 4 | from ops.utils import get_configs 5 | 6 | 7 | parser = argparse.ArgumentParser( 8 | description="Generate proposal list to be used for training") 9 | parser.add_argument('dataset', type=str, choices=['activitynet1.2', 'thumos14']) 10 | parser.add_argument('frame_path', type=str) 11 | 12 | args = parser.parse_args() 13 | 14 | configs = get_configs(args.dataset) 15 | 16 | norm_list_tmpl = 'data/{}_normalized_proposal_list.txt' 17 | out_list_tmpl = 'data/{}_proposal_list.txt' 18 | 19 | 20 | if args.dataset == 'activitynet1.2': 21 | key_func = lambda x: x[-11:] 22 | elif args.dataset == 'thumos14': 23 | key_func = lambda x: x.split('/')[-1] 24 | else: 25 | raise ValueError("unknown dataset {}".format(args.dataset)) 26 | 27 | 28 | # parse the folders holding the extracted frames 29 | frame_dict = parse_directory(args.frame_path, key_func=key_func) 30 | 31 | process_proposal_list(norm_list_tmpl.format(configs['train_list']), 32 | out_list_tmpl.format(configs['train_list']), frame_dict) 33 | process_proposal_list(norm_list_tmpl.format(configs['test_list']), 34 | out_list_tmpl.format(configs['test_list']), frame_dict) 35 | 36 | print("proposal lists for dataset {} are ready for training.".format(args.dataset)) 37 | -------------------------------------------------------------------------------- /gen_sliding_window_proposals.py: -------------------------------------------------------------------------------- 1 | from ops.anet_db import ANetDB 2 | from ops.thumos_db import THUMOSDB 3 | import numpy as np 4 | import multiprocessing 5 | import argparse 6 | from ops.detection_metrics import get_temporal_proposal_recall, name_proposal 7 | from ops.sequence_funcs import gen_exponential_sw_proposal 8 | from ops.io import dump_window_list 9 | 10 | 11 | parser = argparse.ArgumentParser(description="Make window file used for detection") 12 | parser.add_argument("subset") 13 | parser.add_argument("modality", choices=['rgb', 'flow']) 14 | parser.add_argument("frame_path") 15 | parser.add_argument("output_file") 16 | parser.add_argument("--overlap", type=float, default=0.7) 17 | parser.add_argument("--max_level", type=int, default=8) 18 | parser.add_argument("--time_step", type=float, default=1) 19 | parser.add_argument("--version", default="1.2") 20 | parser.add_argument("--avoid", default=None, type=str) 21 | parser.add_argument("--dataset", default="activitynet", choices=['thumos14', 'activitynet']) 22 | args = parser.parse_args() 23 | 24 | name_pattern = 'img_*.jpg' if args.modality == 'rgb' else 'flow_x_*.jpg' 25 | 26 | if args.dataset == 'activitynet': 27 | db = ANetDB.get_db(args.version) 28 | db.try_load_file_path(args.frame_path) 29 | elif args.dataset == 'thumos14': 30 | db = THUMOSDB.get_db() 31 | db.try_load_file_path(args.frame_path) 32 | 33 | if args.subset == 'testing': 34 | args.subset = 'test' 35 | 36 | else: 37 | raise ValueError("Unknown dataset {}".format(args.dataset)) 38 | 39 | avoid_list = [x.strip() for x in open(args.avoid)] if args.avoid else [] 40 | 41 | 42 | videos = db.get_subset_videos(args.subset) 43 | 44 | # generate proposals and name them 45 | gt_spans = [[(x.num_label, x.time_span) for x in v.instances] for v in videos] 46 | proposal_list = list(map(lambda x: gen_exponential_sw_proposal(x, 47 | overlap=args.overlap, 48 | time_step=args.time_step, 49 | max_level=args.max_level), videos)) 50 | print('average # of proposals: {} at overlap param {}'.format(np.mean(list(map(len, proposal_list))), args.overlap)) 51 | 52 | named_proposal_list = [name_proposal(x, y) for x,y in zip(gt_spans, proposal_list)] 53 | recall_list = [] 54 | IOU_thresh = [0.5, 0.7, 0.9] 55 | for th in IOU_thresh: 56 | pv, pi = get_temporal_proposal_recall(proposal_list, [[y[1] for y in x] for x in gt_spans], th) 57 | print('IOU threshold {}. per video recall: {:02f}, per instance recall: {:02f}'.format(th, pv * 100, pi * 100)) 58 | recall_list.append([args.overlap, th, np.mean(list(map(len, proposal_list))), pv, pi]) 59 | print("average per video recall: {:.2f}, average per instance recall: {:.2f}".format( 60 | np.mean([x[3] for x in recall_list]), np.mean([x[4] for x in recall_list]))) 61 | 62 | dumped_list = [dump_window_list(v, prs, args.frame_path, name_pattern) for v, prs in zip(videos, named_proposal_list) if v.id not in avoid_list] 63 | 64 | with open(args.output_file, 'w') as of: 65 | for i, e in enumerate(dumped_list): 66 | of.write('# {}\n'.format(i + 1)) 67 | of.write(e) 68 | 69 | print('list written. got {} videos'.format(len(dumped_list))) 70 | -------------------------------------------------------------------------------- /ops/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import get_actionness_configs 2 | -------------------------------------------------------------------------------- /ops/anet_db.py: -------------------------------------------------------------------------------- 1 | #from .utils import * 2 | from collections import OrderedDict 3 | 4 | 5 | class Instance(object): 6 | """ 7 | Representing an instance of activity in the videos 8 | """ 9 | 10 | def __init__(self, idx, anno, vid_id, vid_info, name_num_mapping): 11 | self._starting, self._ending = anno['segment'][0], anno['segment'][1] 12 | self._str_label = anno['label'] 13 | self._total_duration = vid_info['duration'] 14 | self._idx = idx 15 | self._vid_id = vid_id 16 | self._file_path = None 17 | 18 | if name_num_mapping: 19 | self._num_label = name_num_mapping[self._str_label] 20 | 21 | @property 22 | def time_span(self): 23 | return self._starting, self._ending 24 | 25 | @property 26 | def covering_ratio(self): 27 | return self._starting / float(self._total_duration), self._ending / float(self._total_duration) 28 | 29 | @property 30 | def num_label(self): 31 | return self._num_label 32 | 33 | @property 34 | def label(self): 35 | return self._str_label 36 | 37 | @property 38 | def name(self): 39 | return '{}_{}'.format(self._vid_id, self._idx) 40 | 41 | @property 42 | def path(self): 43 | if self._file_path is None: 44 | raise ValueError("This instance is not associated to a file on disk. Maybe the file is missing?") 45 | return self._file_path 46 | 47 | @path.setter 48 | def path(self, path): 49 | self._file_path = path 50 | 51 | 52 | class Video(object): 53 | """ 54 | This class represents one video in the activity-net db 55 | """ 56 | def __init__(self, key, info, name_idx_mapping=None): 57 | self._id = key 58 | self._info_dict = info 59 | self._instances = [Instance(i, x, self._id, self._info_dict, name_idx_mapping) 60 | for i, x in enumerate(self._info_dict['annotations'])] 61 | self._file_path = None 62 | 63 | @property 64 | def id(self): 65 | return self._id 66 | 67 | @property 68 | def url(self): 69 | return self._info_dict['url'] 70 | 71 | @property 72 | def instances(self): 73 | return self._instances 74 | 75 | @property 76 | def duration(self): 77 | return self._info_dict['duration'] 78 | 79 | @property 80 | def subset(self): 81 | return self._info_dict['subset'] 82 | 83 | @property 84 | def instance(self): 85 | return self._instances 86 | 87 | @property 88 | def path(self): 89 | if self._file_path is None: 90 | raise ValueError("This video is not associated to a file on disk. Maybe the file is missing?") 91 | return self._file_path 92 | 93 | @path.setter 94 | def path(self, path): 95 | self._file_path = path 96 | 97 | 98 | class ANetDB(object): 99 | """ 100 | This class is the abstraction of the activity-net db 101 | """ 102 | 103 | _CONSTRUCTOR_LOCK = object() 104 | 105 | def __init__(self, token): 106 | """ 107 | Disabled constructor 108 | :param token: 109 | :return: 110 | """ 111 | if token is not self._CONSTRUCTOR_LOCK: 112 | raise ValueError("Use get_db to construct an instance, do not directly use the constructor") 113 | 114 | @classmethod 115 | def get_db(cls, version="1.2"): 116 | """ 117 | Build the internal representation of Activity Net databases 118 | We use the alphabetic order to transfer the label string to its numerical index in learning 119 | :param version: 120 | :return: 121 | """ 122 | if version not in ['1.2', '1.3']: 123 | raise ValueError("Unsupported database version {}".format(version)) 124 | 125 | import os 126 | raw_db_file = 'data/activity_net.v{}.min.json'.format('-'.join(version.split('.'))) 127 | 128 | import json 129 | db_data = json.load(open(raw_db_file)) 130 | 131 | me = cls(cls._CONSTRUCTOR_LOCK) 132 | me.version = version 133 | me.prepare_data(db_data) 134 | 135 | return me 136 | 137 | def prepare_data(self, raw_db): 138 | self._version = raw_db['version'] 139 | 140 | # deal with taxonomy 141 | self._taxonomy = raw_db['taxonomy'] 142 | self._parse_taxonomy() 143 | 144 | self._database = raw_db['database'] 145 | self._video_dict = {k: Video(k, v, self._name_idx_table) for k,v in self._database.items()} 146 | 147 | 148 | 149 | # split testing/training/validation set 150 | self._testing_dict = OrderedDict(sorted([(k, v) for k, v in self._video_dict.items() if v.subset == 'testing'], key=lambda x: x[0])) 151 | self._training_dict = OrderedDict(sorted([(k, v) for k, v in self._video_dict.items() if v.subset == 'training'], key=lambda x: x[0])) 152 | self._validation_dict = OrderedDict(sorted([(k, v) for k, v in self._video_dict.items() if v.subset == 'validation'], key=lambda x: x[0])) 153 | 154 | self._training_inst_dict = {i.name: i for v in self._training_dict.values() for i in v.instances} 155 | self._validation_inst_dict = {i.name: i for v in self._validation_dict.values() for i in v.instances} 156 | 157 | print("There are {} videos for training, {} for validation, {} for testing".format( 158 | len(self._training_dict), len(self._validation_dict), len(self._testing_dict) 159 | )) 160 | print("There are {} instances for training, {} for validataion".format( 161 | len(self._training_inst_dict), len(self._validation_inst_dict) 162 | )) 163 | 164 | def get_subset_videos(self, subset_name): 165 | if subset_name == 'training': 166 | return self._training_dict.values() 167 | elif subset_name == 'validation': 168 | return self._validation_dict.values() 169 | elif subset_name == 'testing': 170 | return self._testing_dict.values() 171 | else: 172 | raise ValueError("Unknown subset {}".format(subset_name)) 173 | 174 | def get_subset_instance(self, subset_name): 175 | if subset_name == 'training': 176 | return self._training_inst_dict.values() 177 | elif subset_name == 'validation': 178 | return self._validation_inst_dict.values() 179 | else: 180 | raise ValueError("Unknown subset {}".format(subset_name)) 181 | 182 | def get_ordered_label_list(self): 183 | return [self._idx_name_table[x] for x in sorted(self._idx_name_table.keys())] 184 | 185 | def _parse_taxonomy(self): 186 | """ 187 | This function just parse the taxonomy file 188 | It gives alphabetical ordered indices to the classes in competition 189 | :return: 190 | """ 191 | name_dict = {x['nodeName']: x for x in self._taxonomy} 192 | parents = set() 193 | for x in self._taxonomy: 194 | parents.add(x['parentName']) 195 | 196 | # leaf nodes are those without any child 197 | leaf_nodes = [name_dict[x] for x 198 | in list(set(name_dict.keys()).difference(parents))] 199 | sorted_lead_nodes = sorted(leaf_nodes, key=lambda l: l['nodeName']) 200 | self._idx_name_table = {i: e['nodeName'] for i, e in enumerate(sorted_lead_nodes)} 201 | self._name_idx_table = {e['nodeName']: i for i, e in enumerate(sorted_lead_nodes)} 202 | self._name_table = {x['nodeName']: x for x in sorted_lead_nodes} 203 | print("Got {} leaf classes out of {}".format(len(self._name_table), len(name_dict))) 204 | 205 | def try_load_file_path(self, frame_path): 206 | """ 207 | Simple version of path finding 208 | :return: 209 | """ 210 | import glob 211 | import os 212 | folders = glob.glob(os.path.join(frame_path, '*')) 213 | ids = [os.path.splitext(name)[0][-11:] for name in folders] 214 | 215 | folder_dict = dict(zip(ids, folders)) 216 | 217 | cnt = 0 218 | for k in self._video_dict.keys(): 219 | if k in folder_dict: 220 | self._video_dict[k].path = folder_dict[k] 221 | cnt += 1 222 | print("loaded {} video folders".format(cnt)) 223 | -------------------------------------------------------------------------------- /ops/detection_metrics.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module provides some utils for calculating metrics in temporal action detection 3 | """ 4 | import numpy as np 5 | 6 | 7 | def temporal_iou(span_A, span_B): 8 | """ 9 | Calculates the intersection over union of two temporal "bounding boxes" 10 | 11 | span_A: (start, end) 12 | span_B: (start, end) 13 | """ 14 | union = min(span_A[0], span_B[0]), max(span_A[1], span_B[1]) 15 | inter = max(span_A[0], span_B[0]), min(span_A[1], span_B[1]) 16 | 17 | if inter[0] >= inter[1]: 18 | return 0 19 | else: 20 | return float(inter[1] - inter[0]) / float(union[1] - union[0]) 21 | 22 | 23 | def overlap_over_b(span_A, span_B): 24 | inter = max(span_A[0], span_B[0]), min(span_A[1], span_B[1]) 25 | if inter[0] >= inter[1]: 26 | return 0 27 | else: 28 | return float(inter[1] - inter[0]) / float(span_B[1] - span_B[0]) 29 | 30 | 31 | def temporal_recall(gt_spans, est_spans, thresh=0.5): 32 | """ 33 | Calculate temporal recall of boxes and estimated boxes 34 | Parameters 35 | ---------- 36 | gt_spans: [(start, end), ...] 37 | est_spans: [(start, end), ...] 38 | 39 | Returns 40 | recall_info: (hit, total) 41 | ------- 42 | 43 | """ 44 | hit_slot = [False] * len(gt_spans) 45 | for i, gs in enumerate(gt_spans): 46 | for es in est_spans: 47 | if temporal_iou(gs, es) > thresh: 48 | hit_slot[i] = True 49 | break 50 | recall_info = (np.sum(hit_slot), len(hit_slot)) 51 | return recall_info 52 | 53 | 54 | def name_proposal(gt_spans, est_spans, thresh=0.0): 55 | """ 56 | Assigng label to positive proposals 57 | :param gt_spans: [(label, (start, end)), ...] 58 | :param est_spans: [(start, end), ...] 59 | :param thresh: 60 | :return: [(label, overlap, start, end), ...] same number of est_spans 61 | """ 62 | ret = [] 63 | for es in est_spans: 64 | max_overlap = 0 65 | max_overlap_over_self = 0 66 | label = 0 67 | for gs in gt_spans: 68 | ov = temporal_iou(gs[1], es) 69 | ov_pr = overlap_over_b(gs[1], es) 70 | if ov > thresh and ov > max_overlap: 71 | label = gs[0] + 1 72 | max_overlap = ov 73 | max_overlap_over_self = ov_pr 74 | ret.append((label, max_overlap, max_overlap_over_self, es[0], es[1])) 75 | 76 | return ret 77 | 78 | 79 | def get_temporal_proposal_recall(pr_list, gt_list, thresh): 80 | recall_info_list = [temporal_recall(x, y, thresh=thresh) for x, y in zip(gt_list, pr_list)] 81 | per_video_recall = np.sum([x[0] == x[1] for x in recall_info_list]) / float(len(recall_info_list)) 82 | per_inst_recall = np.sum([x[0] for x in recall_info_list]) / float(np.sum([x[1] for x in recall_info_list])) 83 | return per_video_recall, per_inst_recall 84 | 85 | -------------------------------------------------------------------------------- /ops/io.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import glob 3 | import os 4 | import fnmatch 5 | 6 | 7 | def load_proposal_file(filename): 8 | lines = list(open(filename)) 9 | from itertools import groupby 10 | groups = groupby(lines, lambda x: x.startswith('#')) 11 | 12 | info_list = [[x.strip() for x in list(g)] for k, g in groups if not k] 13 | 14 | def parse_group(info): 15 | offset = 0 16 | vid = info[offset] 17 | offset += 1 18 | 19 | n_frame = int(float(info[1]) * float(info[2])) 20 | n_gt = int(info[3]) 21 | offset = 4 22 | 23 | gt_boxes = [x.split() for x in info[offset:offset+n_gt]] 24 | offset += n_gt 25 | n_pr = int(info[offset]) 26 | offset += 1 27 | pr_boxes = [x.split() for x in info[offset:offset+n_pr]] 28 | 29 | return vid, n_frame, gt_boxes, pr_boxes 30 | 31 | return [parse_group(l) for l in info_list] 32 | 33 | 34 | def process_proposal_list(norm_proposal_list, out_list_name, frame_dict): 35 | norm_proposals = load_proposal_file(norm_proposal_list) 36 | 37 | processed_proposal_list = [] 38 | for idx, prop in enumerate(norm_proposals): 39 | vid = prop[0] 40 | frame_info = frame_dict[vid] 41 | frame_cnt = frame_info[1] 42 | frame_path = frame_info[0] 43 | 44 | gt = [[int(x[0]), int(float(x[1]) * frame_cnt), int(float(x[2]) * frame_cnt)] for x in prop[2]] 45 | 46 | prop = [[int(x[0]), float(x[1]), float(x[2]), int(float(x[3]) * frame_cnt), int(float(x[4]) * frame_cnt)] for x 47 | in prop[3]] 48 | 49 | out_tmpl = "# {idx}\n{path}\n{fc}\n1\n{num_gt}\n{gt}{num_prop}\n{prop}" 50 | 51 | gt_dump = '\n'.join(['{} {:d} {:d}'.format(*x) for x in gt]) + ('\n' if len(gt) else '') 52 | prop_dump = '\n'.join(['{} {:.04f} {:.04f} {:d} {:d}'.format(*x) for x in prop]) + ( 53 | '\n' if len(prop) else '') 54 | 55 | processed_proposal_list.append(out_tmpl.format( 56 | idx=idx, path=frame_path, fc=frame_cnt, 57 | num_gt=len(gt), gt=gt_dump, 58 | num_prop=len(prop), prop=prop_dump 59 | )) 60 | 61 | open(out_list_name, 'w').writelines(processed_proposal_list) 62 | 63 | 64 | def parse_directory(path, key_func=lambda x: x[-11:], 65 | rgb_prefix='img_', flow_x_prefix='flow_x_', flow_y_prefix='flow_y_'): 66 | """ 67 | Parse directories holding extracted frames from standard benchmarks 68 | """ 69 | print('parse frames under folder {}'.format(path)) 70 | frame_folders = glob.glob(os.path.join(path, '*')) 71 | 72 | def count_files(directory, prefix_list): 73 | lst = os.listdir(directory) 74 | cnt_list = [len(fnmatch.filter(lst, x+'*')) for x in prefix_list] 75 | return cnt_list 76 | 77 | # check RGB 78 | frame_dict = {} 79 | for i, f in enumerate(frame_folders): 80 | all_cnt = count_files(f, (rgb_prefix, flow_x_prefix, flow_y_prefix)) 81 | k = key_func(f) 82 | 83 | x_cnt = all_cnt[1] 84 | y_cnt = all_cnt[2] 85 | if x_cnt != y_cnt: 86 | raise ValueError('x and y direction have different number of flow images. video: '+f) 87 | if i % 200 == 0: 88 | print('{} videos parsed'.format(i)) 89 | 90 | frame_dict[k] = (f, all_cnt[0], x_cnt) 91 | 92 | print('frame folder analysis done') 93 | return frame_dict 94 | 95 | def dump_window_list(video_info, named_proposals, frame_path, name_pattern, allow_empty=False, score=None): 96 | 97 | # first count frame numbers 98 | try: 99 | video_name = video_info.path.split('/')[-1].split('.')[0] 100 | files = glob.glob(os.path.join(frame_path, video_name, name_pattern)) 101 | frame_cnt = len(files) 102 | except: 103 | if allow_empty: 104 | frame_cnt = score.shape[0] * 6 105 | video_name = video_info.id 106 | else: 107 | raise 108 | 109 | # convert time to frame number 110 | real_fps = float(frame_cnt) / float(video_info.duration) 111 | 112 | # get groundtruth windows 113 | gt_w = [(x.num_label, x.time_span) for x in video_info.instance] 114 | gt_windows = [(x[0]+1, int(x[1][0] * real_fps), int(x[1][1] * real_fps)) for x in gt_w] 115 | 116 | dump_gt = [] 117 | for gt in gt_windows: 118 | dump_gt.append('{} {} {}'.format(*gt)) 119 | 120 | dump_proposals = [] 121 | for pr in named_proposals: 122 | real_start = int(pr[3] * real_fps) 123 | real_end = int(pr[4] * real_fps) 124 | label = pr[0] 125 | overlap = pr[1] 126 | overlap_self = pr[2] 127 | dump_proposals.append('{} {:.04f} {:.04f} {} {}'.format(label, overlap, overlap_self, real_start, real_end)) 128 | 129 | ret_str = '{path}\n{duration}\n{fps}\n{num_gt}\n{gts}{num_window}\n{prs}\n'.format( 130 | path=os.path.join(frame_path, video_name), duration=frame_cnt, fps=1, 131 | num_gt=len(dump_gt), gts='\n'.join(dump_gt) + ('\n' if len(dump_gt) else ''), 132 | num_window=len(dump_proposals), prs='\n'.join(dump_proposals)) 133 | 134 | return ret_str 135 | -------------------------------------------------------------------------------- /ops/metrics.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module provides some utils for calculating metrics 3 | """ 4 | import numpy as np 5 | from sklearn.metrics import average_precision_score, confusion_matrix 6 | 7 | 8 | def softmax(raw_score, T=1): 9 | exp_s = np.exp((raw_score - raw_score.max(axis=-1)[..., None])*T) 10 | sum_s = exp_s.sum(axis=-1) 11 | return exp_s / sum_s[..., None] 12 | 13 | 14 | def top_k_acc(lb_set, scores, k=3): 15 | idx = np.argsort(scores)[-k:] 16 | return len(lb_set.intersection(idx)), len(lb_set) 17 | 18 | 19 | def top_k_hit(lb_set, scores, k=3): 20 | idx = np.argsort(scores)[-k:] 21 | return len(lb_set.intersection(idx)) > 0, 1 22 | 23 | 24 | def top_3_accuracy(score_dict, video_list): 25 | return top_k_accuracy(score_dict, video_list, 3) 26 | 27 | 28 | def top_k_accuracy(score_dict, video_list, k): 29 | video_labels = [set([i.num_label for i in v.instances]) for v in video_list] 30 | 31 | video_top_k_acc = np.array( 32 | [top_k_hit(lb, score_dict[v.id], k=k) for v, lb in zip(video_list, video_labels) 33 | if v.id in score_dict]) 34 | 35 | tmp = video_top_k_acc.sum(axis=0).astype(float) 36 | top_k_acc = tmp[0] / tmp[1] 37 | 38 | return top_k_acc 39 | 40 | 41 | def video_mean_ap(score_dict, video_list): 42 | avail_video_labels = [set([i.num_label for i in v.instances]) for v in video_list if 43 | v.id in score_dict] 44 | pred_array = np.array([score_dict[v.id] for v in video_list if v.id in score_dict]) 45 | gt_array = np.zeros(pred_array.shape) 46 | 47 | for i in xrange(pred_array.shape[0]): 48 | gt_array[i, list(avail_video_labels[i])] = 1 49 | mean_ap = average_precision_score(gt_array, pred_array, average='macro') 50 | return mean_ap 51 | 52 | 53 | def mean_class_accuracy(scores, labels): 54 | pred = np.argmax(scores, axis=1) 55 | cf = confusion_matrix(labels, pred).astype(float) 56 | 57 | cls_cnt = cf.sum(axis=1) 58 | cls_hit = np.diag(cf) 59 | 60 | return np.mean(cls_hit/cls_cnt) 61 | -------------------------------------------------------------------------------- /ops/sequence_funcs.py: -------------------------------------------------------------------------------- 1 | from .metrics import softmax 2 | 3 | import sys 4 | import numpy as np 5 | from scipy.ndimage import gaussian_filter 6 | try: 7 | from nms.nms_wrapper import nms 8 | except ImportError: 9 | nms = None 10 | 11 | def label_frame_by_threshold(score_mat, cls_lst, bw=None, thresh=list([0.05]), multicrop=True): 12 | """ 13 | Build frame labels by thresholding the foreground class responses 14 | :param score_mat: 15 | :param cls_lst: 16 | :param bw: 17 | :param thresh: 18 | :param multicrop: 19 | :return: 20 | """ 21 | if multicrop: 22 | f_score = score_mat.mean(axis=1) 23 | else: 24 | f_score = score_mat 25 | 26 | ss = softmax(f_score) 27 | 28 | rst = [] 29 | for cls in cls_lst: 30 | cls_score = ss[:, cls+1] if bw is None else gaussian_filter(ss[:, cls+1], bw) 31 | for th in thresh: 32 | rst.append((cls, cls_score > th, f_score[:, cls+1])) 33 | 34 | return rst 35 | 36 | 37 | def gen_exponential_sw_proposal(video_info, time_step=1, max_level=8, overlap=0.4): 38 | spans = [2 ** x for x in range(max_level)] 39 | duration = video_info.duration 40 | pr = [] 41 | for t_span in spans: 42 | span = t_span * time_step 43 | step = int(np.ceil(span * (1 - overlap))) 44 | local_boxes = [(i, i + t_span) for i in np.arange(0, duration, step)] 45 | pr.extend(local_boxes) 46 | 47 | # fileter proposals 48 | # a valid proposal should have at least one second in the video 49 | def valid_proposal(duration, span): 50 | real_span = min(duration, span[1]) - span[0] 51 | return real_span >= 1 52 | 53 | pr = list(filter(lambda x: valid_proposal(duration, x), pr)) 54 | return pr 55 | 56 | 57 | def temporal_nms(bboxes, thresh, score_ind=3): 58 | """ 59 | One-dimensional non-maximal suppression 60 | :param bboxes: [[st, ed, cls, score], ...] 61 | :param thresh: 62 | :return: 63 | """ 64 | if not nms: 65 | return temporal_nms_fallback(bboxes, thresh, score_ind=score_ind) 66 | else: 67 | keep = nms(np.array([[x[0], x[1], x[3]] for x in bboxes]), thresh, device_id=0) 68 | return [bboxes[i] for i in keep] 69 | 70 | 71 | def temporal_nms_fallback(bboxes, thresh, score_ind=3): 72 | """ 73 | One-dimensional non-maximal suppression 74 | :param bboxes: [[st, ed, cls, score], ...] 75 | :param thresh: 76 | :return: 77 | """ 78 | t1 = np.array([x[0] for x in bboxes]) 79 | t2 = np.array([x[1] for x in bboxes]) 80 | scores = np.array([x[score_ind] for x in bboxes]) 81 | 82 | durations = t2 - t1 + 1 83 | order = scores.argsort()[::-1] 84 | 85 | keep = [] 86 | while order.size > 0: 87 | i = order[0] 88 | keep.append(i) 89 | tt1 = np.maximum(t1[i], t1[order[1:]]) 90 | tt2 = np.minimum(t2[i], t2[order[1:]]) 91 | intersection = tt2 - tt1 + 1 92 | IoU = intersection / (durations[i] + durations[order[1:]] - intersection).astype(float) 93 | 94 | inds = np.where(IoU <= thresh)[0] 95 | order = order[inds + 1] 96 | 97 | return [bboxes[i] for i in keep] 98 | 99 | 100 | 101 | def build_box_by_search(frm_label_lst, tol, min=1): 102 | boxes = [] 103 | for cls, frm_labels, frm_scores in frm_label_lst: 104 | length = len(frm_labels) 105 | diff = np.empty(length+1) 106 | diff[1:-1] = frm_labels[1:].astype(int) - frm_labels[:-1].astype(int) 107 | diff[0] = float(frm_labels[0]) 108 | diff[length] = 0 - float(frm_labels[-1]) 109 | cs = np.cumsum(1 - frm_labels) 110 | offset = np.arange(0, length, 1) 111 | 112 | up = np.nonzero(diff == 1)[0] 113 | down = np.nonzero(diff == -1)[0] 114 | 115 | assert len(up) == len(down), "{} != {}".format(len(up), len(down)) 116 | for i, t in enumerate(tol): 117 | signal = cs - t * offset 118 | for x in range(len(up)): 119 | s = signal[up[x]] 120 | for y in range(x + 1, len(up)): 121 | if y < len(down) and signal[up[y]] > s: 122 | boxes.append((up[x], down[y-1]+1, cls, sum(frm_scores[up[x]:down[y-1]+1]))) 123 | break 124 | else: 125 | boxes.append((up[x], down[-1] + 1, cls, sum(frm_scores[up[x]:down[-1] + 1]))) 126 | 127 | for x in range(len(down) - 1, -1, -1): 128 | s = signal[down[x]] if down[x] < length else signal[-1] - t 129 | for y in range(x - 1, -1, -1): 130 | if y >= 0 and signal[down[y]] < s: 131 | boxes.append((up[y+1], down[x] + 1, cls, sum(frm_scores[up[y+1]:down[x] + 1]))) 132 | break 133 | else: 134 | boxes.append((up[0], down[x] + 1, cls, sum(frm_scores[0:down[x]+1 + 1]))) 135 | 136 | return boxes 137 | -------------------------------------------------------------------------------- /ops/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | import yaml 4 | 5 | 6 | def get_configs(dataset): 7 | data = yaml.load(open('data/dataset_cfg.yaml')) 8 | return data[dataset] 9 | 10 | def get_actionness_configs(dataset): 11 | data = yaml.load(open('data/dataset_actionness_cfg.yaml')) 12 | return data[dataset] 13 | 14 | 15 | def get_reference_model_url(dataset, modality, init, arch): 16 | data = yaml.load(open('data/reference_models.yaml')) 17 | return data[dataset][init][arch][modality] 18 | 19 | 20 | def get_grad_hook(name): 21 | def hook(m, grad_in, grad_out): 22 | print(len(grad_in), len(grad_out)) 23 | print((name, grad_out[0].data.abs().mean(), grad_in[0].data.abs().mean())) 24 | print((grad_out[0].size())) 25 | print((grad_in[0].size())) 26 | print((grad_in[1].size())) 27 | print((grad_in[2].size())) 28 | 29 | # print((grad_out[0])) 30 | # print((grad_in[0])) 31 | 32 | return hook 33 | 34 | 35 | def softmax(scores): 36 | es = np.exp(scores - scores.max(axis=-1)[..., None]) 37 | return es / es.sum(axis=-1)[..., None] 38 | 39 | 40 | def temporal_iou(span_A, span_B): 41 | """ 42 | Calculates the intersection over union of two temporal "bounding boxes" 43 | 44 | span_A: (start, end) 45 | span_B: (start, end) 46 | """ 47 | union = min(span_A[0], span_B[0]), max(span_A[1], span_B[1]) 48 | inter = max(span_A[0], span_B[0]), min(span_A[1], span_B[1]) 49 | 50 | if inter[0] >= inter[1]: 51 | return 0 52 | else: 53 | return float(inter[1] - inter[0]) / float(union[1] - union[0]) 54 | 55 | 56 | def temporal_nms(bboxes, thresh): 57 | """ 58 | One-dimensional non-maximal suppression 59 | :param bboxes: [[st, ed, score, ...], ...] 60 | :param thresh: 61 | :return: 62 | """ 63 | t1 = bboxes[:, 0] 64 | t2 = bboxes[:, 1] 65 | scores = bboxes[:, 2] 66 | 67 | durations = t2 - t1 68 | order = scores.argsort()[::-1] 69 | 70 | keep = [] 71 | while order.size > 0: 72 | i = order[0] 73 | keep.append(i) 74 | tt1 = np.maximum(t1[i], t1[order[1:]]) 75 | tt2 = np.minimum(t2[i], t2[order[1:]]) 76 | intersection = tt2 - tt1 77 | IoU = intersection / (durations[i] + durations[order[1:]] - intersection).astype(float) 78 | 79 | inds = np.where(IoU <= thresh)[0] 80 | order = order[inds + 1] 81 | 82 | return bboxes[keep, :] 83 | -------------------------------------------------------------------------------- /ops/video_funcs.py: -------------------------------------------------------------------------------- 1 | """ 2 | This module provides our implementation of different functions to do video-level classification and stream fusion 3 | """ 4 | import numpy as np 5 | from .metrics import softmax 6 | 7 | 8 | def default_aggregation_func(score_arr, normalization=True, crop_agg=None): 9 | """ 10 | This is the default function for make video-level prediction 11 | :param score_arr: a 3-dim array with (frame, crop, class) layout 12 | :return: 13 | """ 14 | crop_agg = np.mean if crop_agg is None else crop_agg 15 | if normalization: 16 | return softmax(crop_agg(score_arr, axis=1).mean(axis=0)) 17 | else: 18 | return crop_agg(score_arr, axis=1).mean(axis=0) 19 | 20 | 21 | def top_k_aggregation_func(score_arr, k, normalization=True, crop_agg=None): 22 | crop_agg = np.mean if crop_agg is None else crop_agg 23 | if normalization: 24 | return softmax(np.sort(crop_agg(score_arr, axis=1), axis=0)[-k:, :].mean(axis=0)) 25 | else: 26 | return np.sort(crop_agg(score_arr, axis=1), axis=0)[-k:, :].mean(axis=0) 27 | 28 | 29 | def sliding_window_aggregation_func(score, spans=[1, 2, 4, 8, 16], overlap=0.2, norm=True, fps=1): 30 | """ 31 | This is the aggregation function used for ActivityNet Challenge 2016 32 | :param score: 33 | :param spans: 34 | :param overlap: 35 | :param norm: 36 | :param fps: 37 | :return: 38 | """ 39 | frm_max = score.mean(axis=1) 40 | slide_score = [] 41 | 42 | def top_k_pool(scores, k): 43 | return np.sort(scores, axis=0)[-k:, :].mean(axis=0) 44 | 45 | for t_span in spans: 46 | span = t_span * fps 47 | step = int(np.ceil(span * (1-overlap))) 48 | local_agg = [frm_max[i: i+span].max(axis=0) for i in xrange(0, frm_max.shape[0], step)] 49 | k = max(15, len(local_agg)/4) 50 | slide_score.append(top_k_pool(np.array(local_agg), k)) 51 | 52 | out_score = np.mean(slide_score, axis=0) 53 | 54 | if norm: 55 | return softmax(out_score) 56 | else: 57 | return out_score 58 | 59 | 60 | def tpp_aggregation_func(score, num_class): 61 | crop_avg = score.mean(axis=1) 62 | stage = crop_avg.shape[1]/ num_class 63 | length = score.shape[0] 64 | step = float(stage) / length 65 | out = np.zeros(num_class) 66 | for t in xrange(length): 67 | k = int(t * step) 68 | out += crop_avg[t, k * num_class: (k+1)*num_class] 69 | 70 | return out / length 71 | 72 | 73 | def default_fusion_func(major_score, other_scores, fusion_weights, norm=True): 74 | assert len(other_scores) == len(fusion_weights) 75 | out_score = major_score 76 | for s, w in zip(other_scores, fusion_weights): 77 | out_score += s * w 78 | 79 | if norm: 80 | return softmax(out_score) 81 | else: 82 | return out_score 83 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torchvision 2 | numpy 3 | scikit-learn 4 | terminaltables 5 | pandas 6 | scipy 7 | -------------------------------------------------------------------------------- /ssn_opts.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | parser = argparse.ArgumentParser(description="PyTorch code to train Structured Segment Networks (SSN)") 3 | parser.add_argument('dataset', type=str, choices=['activitynet1.2', 'thumos14']) 4 | parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff']) 5 | 6 | # ========================= Model Configs ========================== 7 | parser.add_argument('--arch', type=str, default="BNInception") 8 | parser.add_argument('--num_aug_segments', type=int, default=2) 9 | parser.add_argument('--num_body_segments', type=int, default=5) 10 | 11 | parser.add_argument('--dropout', '--do', default=0.8, type=float, 12 | metavar='DO', help='dropout ratio (default: 0.8)') 13 | 14 | # ========================= Learning Configs ========================== 15 | parser.add_argument('--epochs', default=7, type=int, metavar='N', 16 | help='number of total epochs to run') 17 | parser.add_argument('--training_epoch_multiplier', '--tem', default=10, type=int, 18 | help='replicate the training set by N times in one epoch') 19 | parser.add_argument('-b', '--batch-size', default=16, type=int, 20 | metavar='N', help='mini-batch size (default: 256)') 21 | parser.add_argument('-i', '--iter-size', default=1, type=int, 22 | metavar='N', help='number of iterations before on update') 23 | parser.add_argument('--lr', '--learning-rate', default=0.001, type=float, 24 | metavar='LR', help='initial learning rate') 25 | parser.add_argument('--lr_steps', default=[3, 6], type=float, nargs="+", 26 | metavar='LRSteps', help='epochs to decay learning rate by 10') 27 | parser.add_argument('--momentum', default=0.9, type=float, metavar='M', 28 | help='momentum') 29 | parser.add_argument('--weight-decay', '--wd', default=5e-4, type=float, 30 | metavar='W', help='weight decay (default: 5e-4)') 31 | parser.add_argument('--clip-gradient', '--gd', default=None, type=float, 32 | metavar='W', help='gradient norm clipping (default: disabled)') 33 | parser.add_argument('--bn_mode', '--bn', default='frozen', type=str, 34 | help="the mode of bn layers") 35 | parser.add_argument('--comp_loss_weight', '--lw', default=0.1, type=float, 36 | metavar='LW', help='the weight for the completeness loss') 37 | parser.add_argument('--reg_loss_weight', '--rw', default=0.1, type=float, 38 | metavar='LW', help='the weight for the location regression loss') 39 | 40 | # ========================= Monitor Configs ========================== 41 | parser.add_argument('--print-freq', '-p', default=20, type=int, 42 | metavar='N', help='print frequency (default: 10)') 43 | parser.add_argument('--eval-freq', '-ef', default=1, type=int, 44 | metavar='N', help='evaluation frequency (default: 5)') 45 | 46 | # ========================= Runtime Configs ========================== 47 | parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', 48 | help='number of data loading workers (default: 4)') 49 | parser.add_argument('--resume', default='', type=str, metavar='PATH', 50 | help='path to latest checkpoint (default: none)') 51 | parser.add_argument('--kinetics_pretrain', '--kin', default=False, action='store_true', 52 | help='whether to use kinetics pretrained models') 53 | parser.add_argument('--init_weights', default='', type=str, metavar='PATH', 54 | help='path to pretrained weights') 55 | parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', 56 | help='evaluate model on validation set') 57 | parser.add_argument('--snapshot_pref', type=str, default="") 58 | parser.add_argument('--start-epoch', default=0, type=int, metavar='N', 59 | help='manual epoch number (useful on restarts)') 60 | parser.add_argument('--gpus', nargs='+', type=int, default=None) 61 | parser.add_argument('--flow_prefix', default="", type=str) 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | -------------------------------------------------------------------------------- /ssn_test.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import time 3 | 4 | import numpy as np 5 | 6 | from ssn_dataset import SSNDataSet 7 | from ssn_models import SSN 8 | from transforms import * 9 | from ops.ssn_ops import STPPReorgainzed 10 | from torch import multiprocessing 11 | from torch.utils import model_zoo 12 | from ops.utils import get_configs, get_reference_model_url 13 | 14 | 15 | parser = argparse.ArgumentParser( 16 | description="SSN Testing Tool") 17 | parser.add_argument('dataset', type=str, choices=['activitynet1.2', 'thumos14']) 18 | parser.add_argument('modality', type=str, choices=['RGB', 'Flow', 'RGBDiff']) 19 | parser.add_argument('weights', type=str) 20 | parser.add_argument('save_scores', type=str) 21 | parser.add_argument('--arch', type=str, default="BNInception") 22 | parser.add_argument('--save_raw_scores', type=str, default=None) 23 | parser.add_argument('--aug_ratio', type=float, default=0.5) 24 | parser.add_argument('--frame_interval', type=int, default=6) 25 | parser.add_argument('--test_batchsize', type=int, default=512) 26 | parser.add_argument('--no_regression', action="store_true", default=False) 27 | parser.add_argument('--max_num', type=int, default=-1) 28 | parser.add_argument('--test_crops', type=int, default=10) 29 | parser.add_argument('--input_size', type=int, default=224) 30 | parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', 31 | help='number of data loading workers (default: 4)') 32 | parser.add_argument('--gpus', nargs='+', type=int, default=None) 33 | parser.add_argument('--flow_pref', type=str, default='') 34 | parser.add_argument('--use_reference', default=False, action='store_true') 35 | parser.add_argument('--use_kinetics_reference', default=False, action='store_true') 36 | 37 | args = parser.parse_args() 38 | 39 | dataset_configs = get_configs(args.dataset) 40 | 41 | num_class = dataset_configs['num_class'] 42 | stpp_configs = tuple(dataset_configs['stpp']) 43 | test_prop_file = 'data/{}_proposal_list.txt'.format(dataset_configs['test_list']) 44 | 45 | if args.modality == 'RGB': 46 | data_length = 1 47 | elif args.modality in ['Flow', 'RGBDiff']: 48 | data_length = 5 49 | else: 50 | raise ValueError("unknown modality {}".format(args.modality)) 51 | 52 | gpu_list = args.gpus if args.gpus is not None else range(8) 53 | 54 | 55 | def runner_func(dataset, state_dict, stats, gpu_id, index_queue, result_queue): 56 | torch.cuda.set_device(gpu_id) 57 | net = SSN(num_class, 2, 5, 2, 58 | args.modality, test_mode=True, 59 | base_model=args.arch, no_regression=args.no_regression, stpp_cfg=stpp_configs) 60 | net.load_state_dict(state_dict) 61 | net.prepare_test_fc() 62 | net.eval() 63 | net.cuda() 64 | output_dim = net.test_fc.out_features 65 | reorg_stpp = STPPReorgainzed(output_dim, num_class + 1, num_class, 66 | num_class * 2, True, stpp_cfg=stpp_configs) 67 | 68 | while True: 69 | index = index_queue.get() 70 | frames_gen, frame_cnt, rel_props, prop_ticks, prop_scaling = dataset[index] 71 | num_crop = args.test_crops 72 | length = 3 73 | if args.modality == 'Flow': 74 | length = 10 75 | elif args.modality == 'RGBDiff': 76 | length = 18 77 | 78 | output = torch.zeros((frame_cnt, output_dim)).cuda() 79 | cnt = 0 80 | for frames in frames_gen: 81 | input_var = torch.autograd.Variable(frames.view(-1, length, frames.size(-2), frames.size(-1)).cuda(), 82 | volatile=True) 83 | rst, _ = net(input_var, None, None, None, None) 84 | sc = rst.data.view(num_crop, -1, output_dim).mean(dim=0) 85 | output[cnt: cnt + sc.size(0), :] = sc 86 | cnt += sc.size(0) 87 | act_scores, comp_scores, reg_scores = reorg_stpp.forward(output, prop_ticks, prop_scaling) 88 | 89 | if reg_scores is not None: 90 | reg_scores = reg_scores.view(-1, num_class, 2) 91 | reg_scores[:, :, 0] = reg_scores[:, :, 0] * stats[1, 0] + stats[0, 0] 92 | reg_scores[:, :, 1] = reg_scores[:, :, 1] * stats[1, 1] + stats[0, 1] 93 | 94 | # perform stpp on scores 95 | result_queue.put((dataset.video_list[index].id, rel_props.numpy(), act_scores.cpu().numpy(), \ 96 | comp_scores.cpu().numpy(), reg_scores.cpu().numpy(), output.cpu().numpy())) 97 | 98 | 99 | if __name__ == '__main__': 100 | ctx = multiprocessing.get_context('spawn') # this is crucial to using multiprocessing processes with PyTorch 101 | 102 | # This net is used to provides setup settings. It is not used for testing. 103 | net = SSN(num_class, 2, 5, 2, 104 | args.modality, test_mode=True, 105 | base_model=args.arch, no_regression=args.no_regression, stpp_cfg=stpp_configs) 106 | 107 | if args.test_crops == 1: 108 | cropping = torchvision.transforms.Compose([ 109 | GroupScale(net.scale_size), 110 | GroupCenterCrop(net.input_size), 111 | ]) 112 | elif args.test_crops == 10: 113 | cropping = torchvision.transforms.Compose([ 114 | GroupOverSample(net.input_size, net.scale_size) 115 | ]) 116 | else: 117 | raise ValueError("Only 1 and 10 crops are supported while we got {}".format(args.test_crops)) 118 | 119 | if not args.use_reference and not args.use_kinetics_reference: 120 | checkpoint = torch.load(args.weights) 121 | else: 122 | model_url = get_reference_model_url(args.dataset, args.modality, 123 | 'ImageNet' if args.use_reference else 'Kinetics', args.arch) 124 | checkpoint = model_zoo.load_url(model_url) 125 | print("using reference model: {}".format(model_url)) 126 | 127 | print("model epoch {} loss: {}".format(checkpoint['epoch'], checkpoint['best_loss'])) 128 | base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items())} 129 | stats = checkpoint['reg_stats'].numpy() 130 | 131 | dataset = SSNDataSet("", test_prop_file, 132 | new_length=data_length, 133 | modality=args.modality, 134 | aug_seg=2, body_seg=5, 135 | image_tmpl="img_{:05d}.jpg" if args.modality in ["RGB", 136 | "RGBDiff"] else args.flow_pref + "{}_{:05d}.jpg", 137 | test_mode=True, test_interval=args.frame_interval, 138 | transform=torchvision.transforms.Compose([ 139 | cropping, 140 | Stack(roll=(args.arch in ['BNInception', 'InceptionV3'])), 141 | ToTorchFormatTensor(div=(args.arch not in ['BNInception', 'InceptionV3'])), 142 | GroupNormalize(net.input_mean, net.input_std), 143 | ]), reg_stats=stats, verbose=False) 144 | 145 | index_queue = ctx.Queue() 146 | result_queue = ctx.Queue() 147 | workers = [ctx.Process(target=runner_func, args=(dataset, base_dict, stats, gpu_list[i % len(gpu_list)], index_queue, result_queue)) 148 | for i in range(args.workers)] 149 | 150 | del net 151 | 152 | for w in workers: 153 | w.daemon = True 154 | w.start() 155 | 156 | max_num = args.max_num if args.max_num > 0 else len(dataset) 157 | 158 | for i in range(max_num): 159 | index_queue.put(i) 160 | 161 | proc_start_time = time.time() 162 | out_dict = {} 163 | for i in range(max_num): 164 | rst = result_queue.get() 165 | out_dict[rst[0]] = rst[1:] 166 | cnt_time = time.time() - proc_start_time 167 | print('video {} done, total {}/{}, average {:.04f} sec/video'.format(i, i + 1, 168 | max_num, 169 | float(cnt_time) / (i + 1))) 170 | 171 | if args.save_scores is not None: 172 | save_dict = {k: v[:-1] for k,v in out_dict.items()} 173 | import pickle 174 | 175 | pickle.dump(save_dict, open(args.save_scores, 'wb'), pickle.HIGHEST_PROTOCOL) 176 | 177 | if args.save_raw_scores is not None: 178 | save_dict = {k: v[-1] for k,v in out_dict.items()} 179 | import pickle 180 | 181 | pickle.dump(save_dict, open(args.save_raw_scores, 'wb'), pickle.HIGHEST_PROTOCOL) 182 | --------------------------------------------------------------------------------