├── __init__.py ├── imgs └── pull7f-web_e2.png ├── checkpoints ├── checkpoint └── checkpoint.ckpt-1.index ├── test_example ├── target │ ├── 000000.jpg │ ├── 000001.jpg │ ├── 000002.jpg │ ├── 000003.jpg │ ├── 000004.jpg │ ├── 000005.jpg │ ├── 000006.jpg │ ├── 000007.jpg │ ├── 000008.jpg │ ├── 000009.jpg │ ├── 000010.jpg │ ├── 000011.jpg │ ├── 000012.jpg │ ├── 000013.jpg │ ├── 000014.jpg │ ├── 000015.jpg │ ├── 000016.jpg │ ├── 000017.jpg │ ├── 000018.jpg │ ├── 000019.jpg │ ├── 000020.jpg │ ├── 000021.jpg │ ├── 000022.jpg │ ├── 000023.jpg │ ├── 000024.jpg │ ├── 000025.jpg │ ├── 000026.jpg │ ├── 000027.jpg │ ├── 000028.jpg │ ├── 000029.jpg │ ├── 000030.jpg │ ├── 000031.jpg │ ├── 000032.jpg │ ├── 000033.jpg │ ├── 000034.jpg │ ├── 000035.jpg │ ├── 000036.jpg │ ├── 000037.jpg │ ├── 000038.jpg │ ├── 000039.jpg │ ├── 000040.jpg │ ├── 000041.jpg │ ├── 000042.jpg │ ├── 000043.jpg │ ├── 000044.jpg │ ├── 000045.jpg │ ├── 000046.jpg │ ├── 000047.jpg │ ├── 000048.jpg │ ├── 000049.jpg │ ├── 000050.jpg │ ├── 000051.jpg │ ├── 000052.jpg │ ├── 000053.jpg │ ├── 000054.jpg │ ├── 000055.jpg │ ├── 000056.jpg │ ├── 000057.jpg │ ├── 000058.jpg │ ├── 000059.jpg │ ├── 000060.jpg │ ├── 000061.jpg │ ├── 000062.jpg │ ├── 000063.jpg │ ├── 000064.jpg │ ├── 000065.jpg │ ├── 000066.jpg │ ├── 000067.jpg │ ├── 000068.jpg │ ├── 000069.jpg │ ├── 000070.jpg │ ├── 000071.jpg │ ├── 000072.jpg │ ├── 000073.jpg │ ├── 000074.jpg │ ├── 000075.jpg │ ├── 000076.jpg │ ├── 000077.jpg │ ├── 000078.jpg │ ├── 000079.jpg │ ├── 000080.jpg │ ├── 000081.jpg │ ├── 000082.jpg │ ├── 000083.jpg │ ├── 000084.jpg │ ├── 000085.jpg │ ├── 000086.jpg │ ├── 000087.jpg │ ├── 000088.jpg │ ├── 000089.jpg │ ├── 000090.jpg │ ├── 000091.jpg │ ├── 000092.jpg │ ├── 000093.jpg │ ├── 000094.jpg │ ├── 000095.jpg │ ├── 000096.jpg │ ├── 000097.jpg │ ├── 000098.jpg │ ├── 000099.jpg │ └── 000100.jpg └── searching │ ├── 000000.jpg │ ├── 000001.jpg │ ├── 000002.jpg │ ├── 000003.jpg │ ├── 000004.jpg │ ├── 000005.jpg │ ├── 000006.jpg │ ├── 000007.jpg │ ├── 000008.jpg │ ├── 000009.jpg │ ├── 000010.jpg │ ├── 000011.jpg │ ├── 000012.jpg │ ├── 000013.jpg │ ├── 000014.jpg │ ├── 000015.jpg │ ├── 000016.jpg │ ├── 000017.jpg │ ├── 000018.jpg │ ├── 000019.jpg │ ├── 000020.jpg │ ├── 000021.jpg │ ├── 000022.jpg │ ├── 000023.jpg │ ├── 000024.jpg │ ├── 000025.jpg │ ├── 000026.jpg │ ├── 000027.jpg │ ├── 000028.jpg │ ├── 000029.jpg │ ├── 000030.jpg │ ├── 000031.jpg │ ├── 000032.jpg │ ├── 000033.jpg │ ├── 000034.jpg │ ├── 000035.jpg │ ├── 000036.jpg │ ├── 000037.jpg │ ├── 000038.jpg │ ├── 000039.jpg │ ├── 000040.jpg │ ├── 000041.jpg │ ├── 000042.jpg │ ├── 000043.jpg │ ├── 000044.jpg │ ├── 000045.jpg │ ├── 000046.jpg │ ├── 000047.jpg │ ├── 000048.jpg │ ├── 000049.jpg │ ├── 000050.jpg │ ├── 000051.jpg │ ├── 000052.jpg │ ├── 000053.jpg │ ├── 000054.jpg │ ├── 000055.jpg │ ├── 000056.jpg │ ├── 000057.jpg │ ├── 000058.jpg │ ├── 000059.jpg │ ├── 000060.jpg │ ├── 000061.jpg │ ├── 000062.jpg │ ├── 000063.jpg │ ├── 000064.jpg │ ├── 000065.jpg │ ├── 000066.jpg │ ├── 000067.jpg │ ├── 000068.jpg │ ├── 000069.jpg │ ├── 000070.jpg │ ├── 000071.jpg │ ├── 000072.jpg │ ├── 000073.jpg │ ├── 000074.jpg │ ├── 000075.jpg │ ├── 000076.jpg │ ├── 000077.jpg │ ├── 000078.jpg │ ├── 000079.jpg │ ├── 000080.jpg │ ├── 000081.jpg │ ├── 000082.jpg │ ├── 000083.jpg │ ├── 000084.jpg │ ├── 000085.jpg │ ├── 000086.jpg │ ├── 000087.jpg │ ├── 000088.jpg │ ├── 000089.jpg │ ├── 000090.jpg │ ├── 000091.jpg │ ├── 000092.jpg │ ├── 000093.jpg │ ├── 000094.jpg │ ├── 000095.jpg │ ├── 000096.jpg │ ├── 000097.jpg │ ├── 000098.jpg │ ├── 000099.jpg │ └── 000100.jpg ├── .gitignore ├── LICENCE ├── README.md ├── load_and_test.py ├── train.py ├── test_set.txt └── goturn_net.py /__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /imgs/pull7f-web_e2.png: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/tangyuhao/GOTURN-Tensorflow/HEAD/test_example/searching/000100.jpg -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .idea 2 | get_train_set* 3 | ignore_files 4 | __MACOSX 5 | *.log 6 | *.pdf 7 | test/ 8 | pkl/ 9 | train.zip 10 | .tags 11 | checkpoints/ 12 | train_old/ 13 | train/ 14 | 2DMOT2015/ 15 | MOT17Det/ 16 | __pycache__ 17 | *.pyc 18 | .DS_Store 19 | train_summary/ 20 | checkpoints_save/ 21 | summary_save/ 22 | train_old/ 23 | -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Yuhao Tang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GOTURN-Tensorflow 2 | 3 | This is a tensorflow implementation of GOTURN. 4 | 5 | Thanks to author **David Held** for his help of this implementation. 6 | 7 | The original paper is: 8 | 9 | **[Learning to Track at 100 FPS with Deep Regression Networks](http://davheld.github.io/GOTURN/GOTURN.html)**, 10 |
11 | [David Held](http://davheld.github.io/), 12 | [Sebastian Thrun](http://robots.stanford.edu/), 13 | [Silvio Savarese](http://cvgl.stanford.edu/silvio/), 14 |
15 | 16 | The github repo for caffe implementation is given by the authors: 17 | **[davheld/GOTURN](https://github.com/davheld/GOTURN)** 18 | 19 | Brief illustration of how this network works: 20 | 21 | 22 | 23 | You can refer to the paper or github repo above for more details. 24 | 25 | ## Environment 26 | - python3 27 | - tensorflow 1.0+, both cpu and gpu work fine 28 | 29 | ## How to use it 30 | ### Finetune for your own dataset 31 | 1. Create a folder, fill in all training images 32 | 2. Create a .txt file 33 | - It should contains target image, searching image and ground-truth bounding box 34 | - Bounding box is in the form of ``, usually from 0 to 1, but exceeding this range is also fine. 35 | - Example of one line: 36 | `train/target/000024.jpg,train/searching/000024.jpg,0.29269230769230764,0.22233115468409587,0.7991794871794871,0.7608061002178649` 37 | 3. Change related places in `train.py` 38 | 4. Train it and wait! 39 | ```python 40 | python train.py 41 | ``` 42 | 5. The log file is `train.log` by default 43 | 44 | ### Test 45 | 1. Download pretrained model from: [GOTURN_MODEL](https://drive.google.com/open?id=0BwToyaMzz69QZ3Zlc0h4NzhBNDg) 46 | 2. Uncompress the `checkpoints` folder, and put it in the root directory of this repo 47 | 3. Test on examples just by running `load_and_test.py` 48 | ```python 49 | python load_and_test.py 50 | ``` 51 | 4. The log file is `test.log` by default 52 | 53 | ### TIPS 54 | Be careful, the output of this network actually always from 0 to 10 thus I multiplied the ground-truth bounding boxes( always ranging from 0 to 1) by 10. 55 | 56 | -------------------------------------------------------------------------------- /load_and_test.py: -------------------------------------------------------------------------------- 1 | # train file 2 | 3 | import logging 4 | import time 5 | import tensorflow as tf 6 | import os 7 | import goturn_net 8 | 9 | NUM_EPOCHS = 500 10 | BATCH_SIZE = 10 11 | WIDTH = 227 12 | HEIGHT = 227 13 | 14 | logfile = "test.log" 15 | test_txt = "test_set.txt" 16 | def load_train_test_set(train_file): 17 | ''' 18 | return train_set or test_set 19 | example line in the file: 20 | ,,,,, 21 | (,,, all relative to search image) 22 | ''' 23 | ftrain = open(train_file, "r") 24 | trainlines = ftrain.read().splitlines() 25 | train_target = [] 26 | train_search = [] 27 | train_box = [] 28 | for line in trainlines: 29 | #print(line) 30 | line = line.split(",") 31 | # remove too extreme cases 32 | # if (float(line[2]) < -0.3 or float(line[3]) < -0.3 or float(line[4]) > 1.2 or float(line[5]) > 1.2): 33 | # continue 34 | train_target.append(line[0]) 35 | train_search.append(line[1]) 36 | box = [10*float(line[2]), 10*float(line[3]), 10*float(line[4]), 10*float(line[5])] 37 | train_box.append(box) 38 | ftrain.close() 39 | print("len:%d"%(len(train_target))) 40 | 41 | return [train_target, train_search, train_box] 42 | 43 | def data_reader(input_queue): 44 | ''' 45 | this function only reads the image from the queue 46 | ''' 47 | search_img = tf.read_file(input_queue[0]) 48 | target_img = tf.read_file(input_queue[1]) 49 | 50 | search_tensor = tf.to_float(tf.image.decode_jpeg(search_img, channels = 3)) 51 | search_tensor = tf.image.resize_images(search_tensor,[HEIGHT,WIDTH], 52 | method=tf.image.ResizeMethod.BILINEAR) 53 | target_tensor = tf.to_float(tf.image.decode_jpeg(target_img, channels = 3)) 54 | target_tensor = tf.image.resize_images(target_tensor,[HEIGHT,WIDTH], 55 | method=tf.image.ResizeMethod.BILINEAR) 56 | box_tensor = input_queue[2] 57 | return [search_tensor, target_tensor, box_tensor] 58 | 59 | 60 | def next_batch(input_queue): 61 | min_queue_examples = 128 62 | num_threads = 8 63 | [search_tensor, target_tensor, box_tensor] = data_reader(input_queue) 64 | [search_batch, target_batch, box_batch] = tf.train.batch( 65 | [search_tensor, target_tensor, box_tensor], 66 | batch_size=BATCH_SIZE, 67 | num_threads=num_threads, 68 | capacity=min_queue_examples + (num_threads+2)*BATCH_SIZE) 69 | return [search_batch, target_batch, box_batch] 70 | 71 | 72 | if __name__ == "__main__": 73 | if (os.path.isfile(logfile)): 74 | os.remove(logfile) 75 | logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s', 76 | level=logging.DEBUG,filename=logfile) 77 | 78 | [train_target, train_search, train_box] = load_train_test_set(test_txt) 79 | target_tensors = tf.convert_to_tensor(train_target, dtype=tf.string) 80 | search_tensors = tf.convert_to_tensor(train_search, dtype=tf.string) 81 | box_tensors = tf.convert_to_tensor(train_box, dtype=tf.float64) 82 | input_queue = tf.train.slice_input_producer([search_tensors, target_tensors, box_tensors],shuffle=False) 83 | batch_queue = next_batch(input_queue) 84 | tracknet = goturn_net.TRACKNET(BATCH_SIZE, train = False) 85 | tracknet.build() 86 | 87 | 88 | sess = tf.Session() 89 | init = tf.global_variables_initializer() 90 | init_local = tf.local_variables_initializer() 91 | sess.run(init) 92 | sess.run(init_local) 93 | 94 | coord = tf.train.Coordinator() 95 | # start the threads 96 | tf.train.start_queue_runners(sess=sess, coord=coord) 97 | 98 | ckpt_dir = "./checkpoints" 99 | if not os.path.exists(ckpt_dir): 100 | os.makedirs(ckpt_dir) 101 | ckpt = tf.train.get_checkpoint_state(ckpt_dir) 102 | if ckpt and ckpt.model_checkpoint_path: 103 | saver = tf.train.Saver() 104 | saver.restore(sess, ckpt.model_checkpoint_path) 105 | try: 106 | for i in range(0, int(len(train_box)/BATCH_SIZE)): 107 | cur_batch = sess.run(batch_queue) 108 | start_time = time.time() 109 | [batch_loss, fc4] = sess.run([tracknet.loss, tracknet.fc4],feed_dict={tracknet.image:cur_batch[0], 110 | tracknet.target:cur_batch[1], tracknet.bbox:cur_batch[2]}) 111 | logging.info('batch box: %s' %(fc4)) 112 | logging.info('gt batch box: %s' %(cur_batch[2])) 113 | logging.info('batch loss = %f'%(batch_loss)) 114 | logging.debug('test: time elapsed: %.3fs.'%(time.time()-start_time)) 115 | except KeyboardInterrupt: 116 | print("get keyboard interrupt") 117 | 118 | 119 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # train file 2 | 3 | import logging 4 | import time 5 | import tensorflow as tf 6 | import os 7 | import goturn_net 8 | 9 | NUM_EPOCHS = 500 10 | BATCH_SIZE = 50 11 | WIDTH = 227 12 | HEIGHT = 227 13 | train_txt = "train_set.txt" 14 | logfile = "train.log" 15 | 16 | 17 | def load_training_set(train_file): 18 | ''' 19 | return train_set 20 | ''' 21 | ftrain = open(train_file, "r") 22 | trainlines = ftrain.read().splitlines() 23 | train_target = [] 24 | train_search = [] 25 | train_box = [] 26 | for line in trainlines: 27 | line = line.split(",") 28 | train_target.append(line[0]) 29 | train_search.append(line[1]) 30 | box = [10 * float(line[2]), 10 * float(line[3]), 10 * float(line[4]), 10 * float(line[5])] 31 | train_box.append(box) 32 | ftrain.close() 33 | 34 | return [train_target, train_search, train_box] 35 | 36 | 37 | def data_reader(input_queue): 38 | ''' 39 | this function only read the one pair of images and from the queue 40 | ''' 41 | search_img = tf.read_file(input_queue[0]) 42 | target_img = tf.read_file(input_queue[1]) 43 | search_tensor = tf.to_float(tf.image.decode_jpeg(search_img, channels=3)) 44 | search_tensor = tf.image.resize_images(search_tensor, [HEIGHT, WIDTH], 45 | method=tf.image.ResizeMethod.BILINEAR) 46 | target_tensor = tf.to_float(tf.image.decode_jpeg(target_img, channels=3)) 47 | target_tensor = tf.image.resize_images(target_tensor, [HEIGHT, WIDTH], 48 | method=tf.image.ResizeMethod.BILINEAR) 49 | box_tensor = input_queue[2] 50 | return [search_tensor, target_tensor, box_tensor] 51 | 52 | 53 | def next_batch(input_queue): 54 | min_queue_examples = 128 55 | num_threads = 8 56 | [search_tensor, target_tensor, box_tensor] = data_reader(input_queue) 57 | [search_batch, target_batch, box_batch] = tf.train.shuffle_batch( 58 | [search_tensor, target_tensor, box_tensor], 59 | batch_size=BATCH_SIZE, 60 | num_threads=num_threads, 61 | capacity=min_queue_examples + (num_threads + 2) * BATCH_SIZE, 62 | seed=88, 63 | min_after_dequeue=min_queue_examples) 64 | return [search_batch, target_batch, box_batch] 65 | 66 | 67 | if __name__ == "__main__": 68 | if (os.path.isfile(logfile)): 69 | os.remove(logfile) 70 | logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s', 71 | level=logging.DEBUG, filename=logfile) 72 | 73 | [train_target, train_search, train_box] = load_training_set(train_txt) 74 | target_tensors = tf.convert_to_tensor(train_target, dtype=tf.string) 75 | search_tensors = tf.convert_to_tensor(train_search, dtype=tf.string) 76 | box_tensors = tf.convert_to_tensor(train_box, dtype=tf.float64) 77 | input_queue = tf.train.slice_input_producer([search_tensors, target_tensors, box_tensors], shuffle=True) 78 | batch_queue = next_batch(input_queue) 79 | tracknet = goturn_net.TRACKNET(BATCH_SIZE) 80 | tracknet.build() 81 | 82 | global_step = tf.Variable(0, trainable=False, name="global_step") 83 | 84 | train_step = tf.train.AdamOptimizer(0.00001, 0.9).minimize( \ 85 | tracknet.loss_wdecay, global_step=global_step) 86 | merged_summary = tf.summary.merge_all() 87 | sess = tf.Session() 88 | train_writer = tf.summary.FileWriter('./train_summary', sess.graph) 89 | init = tf.global_variables_initializer() 90 | init_local = tf.local_variables_initializer() 91 | sess.run(init) 92 | sess.run(init_local) 93 | 94 | coord = tf.train.Coordinator() 95 | # start the threads 96 | tf.train.start_queue_runners(sess=sess, coord=coord) 97 | 98 | ckpt_dir = "./checkpoints" 99 | if not os.path.exists(ckpt_dir): 100 | os.makedirs(ckpt_dir) 101 | ckpt = tf.train.get_checkpoint_state(ckpt_dir) 102 | start = 0 103 | if ckpt and ckpt.model_checkpoint_path: 104 | start = int(ckpt.model_checkpoint_path.split("-")[1]) 105 | logging.info("start by iteration: %d" % (start)) 106 | saver = tf.train.Saver() 107 | saver.restore(sess, ckpt.model_checkpoint_path) 108 | assign_op = global_step.assign(start) 109 | sess.run(assign_op) 110 | model_saver = tf.train.Saver(max_to_keep=3) 111 | try: 112 | for i in range(start, int(len(train_box) / BATCH_SIZE * NUM_EPOCHS)): 113 | if i % int(len(train_box) / BATCH_SIZE) == 0: 114 | logging.info("start epoch[%d]" % (int(i / len(train_box) * BATCH_SIZE))) 115 | if i > start: 116 | save_ckpt = "checkpoint.ckpt" 117 | last_save_itr = i 118 | model_saver.save(sess, "checkpoints/" + save_ckpt, global_step=i + 1) 119 | print(global_step.eval(session=sess)) 120 | 121 | cur_batch = sess.run(batch_queue) 122 | 123 | start_time = time.time() 124 | [_, loss] = sess.run([train_step, tracknet.loss], feed_dict={tracknet.image: cur_batch[0], 125 | tracknet.target: cur_batch[1], 126 | tracknet.bbox: cur_batch[2]}) 127 | logging.debug( 128 | 'Train: time elapsed: %.3fs, average_loss: %f' % (time.time() - start_time, loss / BATCH_SIZE)) 129 | 130 | if i % 10 == 0 and i > start: 131 | summary = sess.run(merged_summary, feed_dict={tracknet.image: cur_batch[0], 132 | tracknet.target: cur_batch[1], 133 | tracknet.bbox: cur_batch[2]}) 134 | train_writer.add_summary(summary, i) 135 | except KeyboardInterrupt: 136 | print("get keyboard interrupt") 137 | if (i - start > 1000): 138 | model_saver = tf.train.Saver() 139 | save_ckpt = "checkpoint.ckpt" 140 | model_saver.save(sess, "checkpoints/" + save_ckpt, global_step=i + 1) 141 | -------------------------------------------------------------------------------- /test_set.txt: -------------------------------------------------------------------------------- 1 | test_example/target/000000.jpg,test_example/searching/000000.jpg,0.5259842519685036,0.02973154362416112,1.055905511811023,0.5781879194630871 2 | test_example/target/000001.jpg,test_example/searching/000001.jpg,0.1585714285714289,0.021351351351351328,0.6392857142857142,0.5735135135135134 3 | test_example/target/000002.jpg,test_example/searching/000002.jpg,-0.13374100719424484,0.3125,0.35035971223021617,0.7989285714285715 4 | test_example/target/000003.jpg,test_example/searching/000003.jpg,-0.16269230769230753,0.5395205479452055,0.3549999999999998,1.0992465753424658 5 | test_example/target/000004.jpg,test_example/searching/000004.jpg,0.40503649635036504,0.10271028037383173,0.8962773722627735,0.4845794392523364 6 | test_example/target/000005.jpg,test_example/searching/000005.jpg,0.15741007194244602,-0.004090909090909062,0.6564748201438849,0.566818181818182 7 | test_example/target/000006.jpg,test_example/searching/000006.jpg,0.1201886792452831,0.008314606741572977,0.774622641509434,0.5023033707865169 8 | test_example/target/000007.jpg,test_example/searching/000007.jpg,0.21992537313432856,0.08187898089171977,0.8147761194029847,0.7340764331210191 9 | test_example/target/000008.jpg,test_example/searching/000008.jpg,0.23798611111111098,0.1691190476190476,0.7915277777777779,0.6567142857142857 10 | test_example/target/000009.jpg,test_example/searching/000009.jpg,-0.2109322033898304,0.4776682926829268,0.4645762711864411,1.0175121951219512 11 | test_example/target/000010.jpg,test_example/searching/000010.jpg,0.007485380116958905,0.38268722466960353,0.5703508771929823,0.8884581497797357 12 | test_example/target/000011.jpg,test_example/searching/000011.jpg,0.020095693779904524,0.3684162895927601,0.48062200956937823,0.887873303167421 13 | test_example/target/000012.jpg,test_example/searching/000012.jpg,0.17528089887640474,0.19647058823529406,0.7160112359550564,0.7159276018099548 14 | test_example/target/000013.jpg,test_example/searching/000013.jpg,0.3151578947368421,0.2336842105263158,0.8217368421052631,0.791622807017544 15 | test_example/target/000014.jpg,test_example/searching/000014.jpg,0.23533707865168532,0.1845263157894737,0.776067415730337,0.8540526315789474 16 | test_example/target/000015.jpg,test_example/searching/000015.jpg,0.25141509433962245,0.13083214285714287,0.7541981132075471,0.5999285714285714 17 | test_example/target/000016.jpg,test_example/searching/000016.jpg,0.044857142857142665,0.15531365313653137,0.5918095238095237,0.6628782287822877 18 | test_example/target/000017.jpg,test_example/searching/000017.jpg,0.17881118881118876,0.2195805243445693,0.6236363636363637,0.7502621722846442 19 | test_example/target/000018.jpg,test_example/searching/000018.jpg,0.236578947368421,0.1705055187637969,0.7563947368421051,0.7160927152317881 20 | test_example/target/000019.jpg,test_example/searching/000019.jpg,0.20057268722466948,0.265671388101983,0.6356607929515419,0.9658356940509915 21 | test_example/target/000020.jpg,test_example/searching/000020.jpg,-0.13129449838187718,0.29418666666666665,0.5079611650485438,0.95328 22 | test_example/target/000021.jpg,test_example/searching/000021.jpg,0.1254394299287411,0.2078243512974052,0.5946318289786224,0.7011377245508982 23 | test_example/target/000022.jpg,test_example/searching/000022.jpg,0.10779904306220096,0.22724999999999998,0.5803588516746412,0.7889545454545455 24 | test_example/target/000023.jpg,test_example/searching/000023.jpg,0.3138823529411764,0.2148421052631579,0.7786588235294116,0.735178947368421 25 | test_example/target/000024.jpg,test_example/searching/000024.jpg,0.29269230769230764,0.22233115468409587,0.7991794871794871,0.7608061002178649 26 | test_example/target/000025.jpg,test_example/searching/000025.jpg,0.22591029023746703,0.07951807228915661,0.7470976253298153,0.5758032128514057 27 | test_example/target/000026.jpg,test_example/searching/000026.jpg,-0.01071794871794859,0.26054279749478076,0.49576923076923085,0.7765135699373695 28 | test_example/target/000027.jpg,test_example/searching/000027.jpg,0.13523690773067337,0.43711815561959655,0.6278304239401497,1.1493659942363113 29 | test_example/target/000028.jpg,test_example/searching/000028.jpg,0.22883233532934116,0.5130977130977131,0.820239520958084,1.0484407484407485 30 | test_example/target/000029.jpg,test_example/searching/000029.jpg,0.40412060301507546,0.3327126805778491,0.9004271356783919,0.7460192616372391 31 | test_example/target/000030.jpg,test_example/searching/000030.jpg,0.33628140703517595,0.22064638783269963,0.8325879396984924,0.710171102661597 32 | test_example/target/000031.jpg,test_example/searching/000031.jpg,0.2510096153846154,-0.08862422997946612,0.7258413461538462,0.4401026694045175 33 | test_example/target/000032.jpg,test_example/searching/000032.jpg,0.44914351851851847,0.029763779527559063,0.9063888888888888,0.5366338582677166 34 | test_example/target/000033.jpg,test_example/searching/000033.jpg,0.5226448362720403,0.17657729468599032,1.0202015113350125,0.7985507246376812 35 | test_example/target/000034.jpg,test_example/searching/000034.jpg,0.6721393034825872,0.2888631578947369,1.1635074626865671,0.8309473684210528 36 | test_example/target/000035.jpg,test_example/searching/000035.jpg,0.18795918367346925,0.2763198458574181,0.6918622448979592,0.7724470134874759 37 | test_example/target/000036.jpg,test_example/searching/000036.jpg,0.22726562499999994,0.23356677524429967,0.7416666666666666,0.6529315960912052 38 | test_example/target/000037.jpg,test_example/searching/000037.jpg,0.08846547314578013,0.4832056451612903,0.5936572890025575,1.0023387096774192 39 | test_example/target/000038.jpg,test_example/searching/000038.jpg,-0.05032258064516136,0.1366135458167331,0.48067204301075256,0.6495418326693229 40 | test_example/target/000039.jpg,test_example/searching/000039.jpg,0.02236276849642006,0.27702702702702703,0.4937947494033412,0.7633333333333333 41 | test_example/target/000040.jpg,test_example/searching/000040.jpg,0.140709219858156,0.1550968858131488,0.632127659574468,0.6614186851211072 42 | test_example/target/000041.jpg,test_example/searching/000041.jpg,0.07857461024498881,-0.015362244897959185,0.5415367483296213,0.4823469387755102 43 | test_example/target/000042.jpg,test_example/searching/000042.jpg,0.263357314148681,0.34955911823647295,0.7618465227817747,0.9360320641282565 44 | test_example/target/000043.jpg,test_example/searching/000043.jpg,0.4401154734411085,0.15037606837606837,0.9201847575057738,0.6506324786324786 45 | test_example/target/000044.jpg,test_example/searching/000044.jpg,0.014857881136950904,0.34674698795180725,0.5519896640826873,0.850447504302926 46 | test_example/target/000045.jpg,test_example/searching/000045.jpg,0.25696517412935327,0.12456834532374099,0.7740547263681591,0.6509172661870503 47 | test_example/target/000046.jpg,test_example/searching/000046.jpg,0.43584245076586436,0.04019607843137257,0.8907002188183807,0.5618538324420678 48 | test_example/target/000047.jpg,test_example/searching/000047.jpg,0.30165929203539826,0.1275049115913556,0.7615486725663717,0.7024557956777995 49 | test_example/target/000048.jpg,test_example/searching/000048.jpg,0.09415512465373964,0.07190781563126251,0.6355955678670361,0.6335070140280561 50 | test_example/target/000049.jpg,test_example/searching/000049.jpg,0.35602500000000004,0.22477973568281936,0.8447,0.8420704845814978 51 | test_example/target/000050.jpg,test_example/searching/000050.jpg,0.22149333333333335,0.17550701402805613,0.7427199999999999,0.7371142284569137 52 | test_example/target/000051.jpg,test_example/searching/000051.jpg,0.09753117206982546,0.14453641207815274,0.5849625935162096,0.6423090586145649 53 | test_example/target/000052.jpg,test_example/searching/000052.jpg,0.21263768115942033,0.14743103448275863,0.7791884057971015,0.6306034482758621 54 | test_example/target/000054.jpg,test_example/searching/000054.jpg,0.2299737532808399,0.18321757322175733,0.7429921259842519,0.7694979079497907 55 | test_example/target/000055.jpg,test_example/searching/000055.jpg,0.3096216216216216,0.11663135593220339,0.8379189189189189,0.7103813559322034 56 | test_example/target/000056.jpg,test_example/searching/000056.jpg,0.544108910891089,0.14670377019748654,1.0330445544554456,0.6089946140035907 57 | test_example/target/000057.jpg,test_example/searching/000057.jpg,0.7602666666666664,0.18303867403314913,1.263866666666667,0.6916574585635359 58 | test_example/target/000058.jpg,test_example/searching/000058.jpg,0.22796178343949022,0.1327877094972067,0.7090445859872615,0.6470949720670391 59 | test_example/target/000059.jpg,test_example/searching/000059.jpg,0.5087898089171974,-0.009080924855491317,0.989872611464968,0.5230635838150289 60 | test_example/target/000060.jpg,test_example/searching/000060.jpg,0.3146012269938649,0.28711801242236024,0.7526380368098157,0.8332298136645964 61 | test_example/target/000061.jpg,test_example/searching/000061.jpg,0.20882758620689637,0.23398591549295775,0.7012413793103445,0.8531690140845071 62 | test_example/target/000062.jpg,test_example/searching/000062.jpg,0.23518796992481183,0.5580763358778627,0.7720300751879695,1.229236641221374 63 | test_example/target/000063.jpg,test_example/searching/000063.jpg,0.21423841059602663,0.6044567901234568,0.6322516556291392,1.083395061728395 64 | test_example/target/000064.jpg,test_example/searching/000064.jpg,0.32365217391304374,0.22503184713375796,0.8725217391304351,0.719171974522293 65 | test_example/target/000065.jpg,test_example/searching/000065.jpg,0.16076190476190472,0.44857142857142857,0.7224761904761906,0.9623129251700681 66 | test_example/target/000066.jpg,test_example/searching/000066.jpg,0.2277966101694915,0.27541095890410966,0.7276271186440679,0.7926712328767123 67 | test_example/target/000067.jpg,test_example/searching/000067.jpg,0.40552845528455284,0.23097560975609754,0.8850406504065041,0.7945528455284552 68 | test_example/target/000068.jpg,test_example/searching/000068.jpg,0.4274285714285714,0.2529285714285714,0.9300952380952378,0.7480714285714285 69 | test_example/target/000069.jpg,test_example/searching/000069.jpg,0.295593220338983,0.08428571428571427,0.7428813559322032,0.5794285714285715 70 | test_example/target/000070.jpg,test_example/searching/000070.jpg,0.38526785714285694,-0.21598591549295776,0.875,0.2866901408450705 71 | test_example/target/000071.jpg,test_example/searching/000071.jpg,0.4927027027027032,0.15071724137931033,1.0241441441441437,0.714344827586207 72 | test_example/target/000072.jpg,test_example/searching/000072.jpg,0.5068269230769235,0.21873563218390807,1.074038461538462,0.6883908045977012 73 | test_example/target/000073.jpg,test_example/searching/000073.jpg,0.4362937062937062,1.047218045112782,0.9066433566433565,1.786015037593985 74 | test_example/target/000074.jpg,test_example/searching/000074.jpg,0.24220689655172417,0.1919387755102041,0.7060000000000001,0.693265306122449 75 | test_example/target/000075.jpg,test_example/searching/000075.jpg,0.1796212121212124,0.18386740331491713,0.7204545454545457,0.7267403314917127 76 | test_example/target/000076.jpg,test_example/searching/000076.jpg,0.06912751677852319,0.17273127753303968,0.5483221476510071,0.6055947136563876 77 | test_example/target/000077.jpg,test_example/searching/000077.jpg,0.22053435114503805,0.18593495934959353,0.7655725190839692,1.0184552845528456 78 | test_example/target/000078.jpg,test_example/searching/000078.jpg,0.101655629139073,0.07219512195121949,0.5745033112582781,0.5717073170731707 79 | test_example/target/000079.jpg,test_example/searching/000079.jpg,-0.11401408450704263,0.27536458333333336,0.3887323943661975,0.8086979166666667 80 | test_example/target/000080.jpg,test_example/searching/000080.jpg,0.2550349650349652,0.36567010309278347,0.6964335664335667,0.7975773195876288 81 | test_example/target/000081.jpg,test_example/searching/000081.jpg,0.4175486381322955,0.24604361370716513,0.9249027237354087,0.7873208722741434 82 | test_example/target/000082.jpg,test_example/searching/000082.jpg,0.450464135021097,0.1573456790123457,1.0004219409282697,0.6936111111111112 83 | test_example/target/000083.jpg,test_example/searching/000083.jpg,0.4223880597014927,0.20550684931506852,0.9089552238805967,0.6815342465753425 84 | test_example/target/000084.jpg,test_example/searching/000084.jpg,0.137313432835821,0.2539498432601881,0.7860696517412935,0.7986206896551724 85 | test_example/target/000085.jpg,test_example/searching/000085.jpg,0.27602739726027364,0.3130817610062893,0.7222602739726031,0.8594654088050314 86 | test_example/target/000086.jpg,test_example/searching/000086.jpg,0.31910569105691056,0.25915068493150684,0.8487804878048779,0.7351780821917807 87 | test_example/target/000087.jpg,test_example/searching/000087.jpg,0.24818840579710144,0.2575728155339806,0.7202898550724636,0.8198705501618122 88 | test_example/target/000088.jpg,test_example/searching/000088.jpg,0.27581227436823136,0.2875075987841945,0.7465703971119132,0.8156231003039512 89 | test_example/target/000089.jpg,test_example/searching/000089.jpg,0.6161016949152546,0.10058641975308644,1.16864406779661,0.6368518518518518 90 | test_example/target/000090.jpg,test_example/searching/000090.jpg,0.1662551440329222,0.29015337423312887,0.7028806584362138,0.8231288343558282 91 | test_example/target/000091.jpg,test_example/searching/000091.jpg,-0.06518518518518485,0.28405405405405404,0.41777777777777764,0.8058258258258257 92 | test_example/target/000092.jpg,test_example/searching/000092.jpg,0.3984615384615381,0.17252252252252254,1.0126923076923076,0.7704204204204205 93 | test_example/target/000093.jpg,test_example/searching/000093.jpg,0.23626666666666643,0.24916434540389973,0.6621333333333332,0.8037604456824513 94 | test_example/target/000094.jpg,test_example/searching/000094.jpg,0.36480446927374277,0.2563037249283668,0.8108938547486032,0.8267908309455587 95 | test_example/target/000095.jpg,test_example/searching/000095.jpg,0.23533333333333303,0.22463592233009708,0.7676666666666665,0.707888349514563 96 | test_example/target/000096.jpg,test_example/searching/000096.jpg,0.38969696969696943,0.14957494407158836,0.8736363636363635,0.5949664429530201 97 | test_example/target/000097.jpg,test_example/searching/000097.jpg,0.2804597701149423,0.16855263157894737,0.7393678160919539,0.6051535087719297 98 | test_example/target/000098.jpg,test_example/searching/000098.jpg,0.6309815950920247,0.30201570680628276,1.1208588957055217,0.8231937172774868 99 | test_example/target/000099.jpg,test_example/searching/000099.jpg,0.09237804878048766,0.3017223650385604,0.6170731707317076,0.8135218508997428 100 | test_example/target/000100.jpg,test_example/searching/000100.jpg,0.26532951289398254,0.20740641711229946,0.7584527220630374,0.73975935828877 -------------------------------------------------------------------------------- /goturn_net.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | class TRACKNET: 4 | def __init__(self, batch_size, train = True): 5 | self.parameters = {} 6 | self.batch_size = batch_size 7 | self.target = tf.placeholder(tf.float32, [batch_size, 227, 227, 3]) 8 | self.image = tf.placeholder(tf.float32, [batch_size, 227, 227, 3]) 9 | self.bbox = tf.placeholder(tf.float32, [batch_size, 4]) 10 | self.train = train 11 | self.wd = 0.0005 12 | def build(self): 13 | ########### for target ########### 14 | # [filter_height, filter_width, in_channels, out_channels] 15 | self.target_conv1 = self._conv_relu_layer(bottom = self.target, filter_size = [11, 11, 3, 96], 16 | strides = [1,4,4,1], name = "target_conv_1") 17 | 18 | # now 55 x 55 x 96 19 | self.target_pool1 = tf.nn.max_pool(self.target_conv1, ksize = [1, 3, 3, 1], strides=[1, 2, 2, 1], 20 | padding='VALID', name='target_pool1') 21 | # now 27 x 27 x 96 22 | self.target_lrn1 = tf.nn.local_response_normalization(self.target_pool1, depth_radius = 2, alpha=0.0001, 23 | beta=0.75, name="target_lrn1") 24 | # now 27 x 27 x 96 25 | 26 | self.target_conv2 = self._conv_relu_layer(bottom = self.target_lrn1,filter_size = [5, 5, 48, 256], 27 | strides = [1,1,1,1], pad = 2, bias_init = 1.0, group = 2, name="target_conv_2") 28 | # now 27 x 27 x 256 29 | 30 | self.target_pool2 = tf.nn.max_pool(self.target_conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], 31 | padding='VALID', name='target_pool2') 32 | # now 13 x 13 x 256 33 | self.target_lrn2 = tf.nn.local_response_normalization(self.target_pool2, depth_radius = 2, alpha=0.0001, 34 | beta=0.75, name="target_lrn2") 35 | # now 13 x 13 x 256 36 | self.target_conv3 = self._conv_relu_layer(bottom = self.target_lrn2,filter_size = [3, 3, 256, 384], 37 | strides = [1,1,1,1], pad = 1, name="target_conv_3") 38 | # now 13 x 13 x 384 39 | self.target_conv4 = self._conv_relu_layer(bottom = self.target_conv3,filter_size = [3, 3, 192, 384], bias_init = 1.0, 40 | strides = [1,1,1,1], pad = 1, group = 2, name="target_conv_4") 41 | # now 13 x 13 x 384 42 | self.target_conv5 = self._conv_relu_layer(bottom = self.target_conv4,filter_size = [3, 3, 192, 256], bias_init = 1.0, 43 | strides = [1,1,1,1], pad = 1, group = 2, name="target_conv_5") 44 | # now 13 x 13 x 256 45 | self.target_pool5 = tf.nn.max_pool(self.target_conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], 46 | padding='VALID', name='target_pool5') 47 | # now 6 x 6 x 256 48 | 49 | 50 | ########### for image ########### 51 | # [filter_height, filter_width, in_channels, out_channels] 52 | self.image_conv1 = self._conv_relu_layer(bottom = self.image, filter_size = [11, 11, 3, 96], 53 | strides = [1,4,4,1], name = "image_conv_1") 54 | 55 | # now 55 x 55 x 96 56 | self.image_pool1 = tf.nn.max_pool(self.image_conv1, ksize = [1, 3, 3, 1], strides=[1, 2, 2, 1], 57 | padding='VALID', name='image_pool1') 58 | 59 | # now 27 x 27 x 96 60 | self.image_lrn1 = tf.nn.local_response_normalization(self.image_pool1, depth_radius = 2, alpha=0.0001, 61 | beta=0.75, name="image_lrn1") 62 | 63 | # now 27 x 27 x 96 64 | 65 | self.image_conv2 = self._conv_relu_layer(bottom = self.image_lrn1,filter_size = [5, 5, 48, 256], 66 | strides = [1,1,1,1], pad = 2, bias_init = 1.0, group = 2, name="image_conv_2") 67 | 68 | # now 27 x 27 x 256 69 | 70 | self.image_pool2 = tf.nn.max_pool(self.image_conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], 71 | padding='VALID', name='image_pool2') 72 | 73 | # now 13 x 13 x 256 74 | self.image_lrn2 = tf.nn.local_response_normalization(self.image_pool2, depth_radius = 2, alpha=0.0001, 75 | beta=0.75, name="image_lrn2") 76 | 77 | # now 13 x 13 x 256 78 | self.image_conv3 = self._conv_relu_layer(bottom = self.image_lrn2,filter_size = [3, 3, 256, 384], 79 | strides = [1,1,1,1], pad = 1, name="image_conv_3") 80 | 81 | # now 13 x 13 x 384 82 | self.image_conv4 = self._conv_relu_layer(bottom = self.image_conv3,filter_size = [3, 3, 192, 384], 83 | strides = [1,1,1,1], pad = 1, group = 2, name="image_conv_4") 84 | 85 | # now 13 x 13 x 384 86 | self.image_conv5 = self._conv_relu_layer(bottom = self.image_conv4,filter_size = [3, 3, 192, 256], bias_init = 1.0, 87 | strides = [1,1,1,1], pad = 1, group = 2, name="image_conv_5") 88 | 89 | # now 13 x 13 x 256 90 | self.image_pool5 = tf.nn.max_pool(self.image_conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], 91 | padding='VALID', name='image_pool5') 92 | 93 | # now 6 x 6 x 256 94 | # tensorflow layer: n * w * h * c 95 | # but caffe layer is: n * c * h * w 96 | 97 | # tensorflow kernel: h * w * in_c * out_c 98 | # caffe kernel: out_c * in_c * h * w 99 | 100 | ########### Concatnate two layers ########### 101 | self.concat = tf.concat([self.target_pool5, self.image_pool5], axis = 3) # 0, 1, 2, 3 - > 2, 3, 1, 0 102 | 103 | # important, since caffe has different layer dimension order 104 | self.concat = tf.transpose(self.concat, perm=[0,3,1,2]) 105 | 106 | ########### fully connencted layers ########### 107 | # 6 * 6 * 256 * 2 == 18432 108 | # assert self.fc1.get_shape().as_list()[1:] == [6, 6, 512] 109 | self.fc1 = self._fc_relu_layers(self.concat, dim = 4096, name = "fc1") 110 | if (self.train): 111 | self.fc1 = tf.nn.dropout(self.fc1, 0.5) 112 | 113 | 114 | self.fc2 = self._fc_relu_layers(self.fc1, dim = 4096, name = "fc2") 115 | if (self.train): 116 | self.fc2 = tf.nn.dropout(self.fc2, 0.5) 117 | 118 | self.fc3 = self._fc_relu_layers(self.fc2, dim = 4096, name = "fc3") 119 | if (self.train): 120 | self.fc3 = tf.nn.dropout(self.fc3, 0.5) 121 | 122 | self.fc4 = self._fc_layers(self.fc3, dim = 4, name = "fc4") 123 | 124 | self.print_shapes() 125 | self.loss = self._loss_layer(self.fc4, self.bbox ,name = "loss") 126 | l2_loss = tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES), name='l2_weight_loss') 127 | self.loss_wdecay = self.loss + l2_loss 128 | 129 | def _loss_layer(self, bottom, label, name = None): 130 | diff = tf.subtract(self.fc4, self.bbox) 131 | diff_flat = tf.abs(tf.reshape(diff,[-1])) 132 | loss = tf.reduce_sum(diff_flat, name = name) 133 | return loss 134 | 135 | def _conv_relu_layer(self,bottom,filter_size, strides, pad = 0,bias_init = 0.0, group = 1, trainable = False, name = None): 136 | with tf.name_scope(name) as scope: 137 | 138 | if (pad > 0): 139 | paddings = [[0,0],[pad,pad],[pad,pad],[0,0]] 140 | bottom = tf.pad(bottom, paddings, "CONSTANT") 141 | kernel = tf.Variable(tf.truncated_normal(filter_size, dtype=tf.float32, 142 | stddev=1e-2), trainable=trainable, name='weights') 143 | biases = tf.Variable(tf.constant(bias_init, shape=[filter_size[3]], dtype=tf.float32), trainable=trainable, name='biases') 144 | self.parameters[name] = [kernel, biases] 145 | if (group == 1): 146 | conv = tf.nn.conv2d(bottom, kernel, strides, padding='VALID') 147 | out = tf.nn.bias_add(conv, biases) 148 | elif (group == 2): 149 | kernel1, kernel2 = tf.split(kernel, num_or_size_splits=group, axis=3) 150 | bottom1, bottom2 = tf.split(bottom, num_or_size_splits=group, axis=3) 151 | conv1 = tf.nn.conv2d(bottom1, kernel1, strides, padding='VALID') 152 | conv2 = tf.nn.conv2d(bottom2, kernel2, strides, padding='VALID') 153 | conv = tf.concat([conv1, conv2], axis=3) 154 | out = tf.nn.bias_add(conv, biases) 155 | else: 156 | raise TypeError("number of groups not supported") 157 | 158 | # if not tf.get_variable_scope().reuse: 159 | # weight_decay = tf.multiply(tf.nn.l2_loss(kernel), self.wd, 160 | # name='kernel_loss') 161 | # tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, 162 | # weight_decay) 163 | 164 | 165 | out2 = tf.nn.relu(out, name=scope) 166 | _activation_summary(out2) 167 | out2 = tf.Print(out2, [tf.shape(out2)], message='Shape of %s' % name, first_n = 1, summarize=4) 168 | return out2 169 | 170 | def _fc_relu_layers(self, bottom, dim, name = None): 171 | with tf.name_scope(name) as scope: 172 | shape = int(np.prod(bottom.get_shape()[1:])) 173 | weights = tf.Variable(tf.truncated_normal([shape, dim], 174 | dtype=tf.float32, stddev=0.005), name='weights') 175 | bias = tf.Variable(tf.constant(1.0, shape=[dim], dtype=tf.float32), name='biases') 176 | bottom_flat = tf.reshape(bottom, [-1, shape]) 177 | fc_weights = tf.nn.bias_add(tf.matmul(bottom_flat, weights), bias) 178 | self.parameters[name] = [weights, bias] 179 | 180 | 181 | if not tf.get_variable_scope().reuse: 182 | weight_decay = tf.multiply(tf.nn.l2_loss(weights), self.wd, 183 | name='fc_relu_weight_loss') 184 | tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, 185 | weight_decay) 186 | 187 | 188 | 189 | top = tf.nn.relu(fc_weights, name=scope) 190 | _activation_summary(top) 191 | top = tf.Print(top, [tf.shape(top)], message='Shape of %s' % name, first_n = 1, summarize=4) 192 | return top 193 | 194 | def _fc_layers(self, bottom, dim, name = None): 195 | with tf.name_scope(name) as scope: 196 | shape = int(np.prod(bottom.get_shape()[1:])) 197 | weights = tf.Variable(tf.truncated_normal([shape, dim], 198 | dtype=tf.float32, stddev=0.005), name='weights') 199 | bias = tf.Variable(tf.constant(1.0, shape=[dim], dtype=tf.float32), name='biases') 200 | bottom_flat = tf.reshape(bottom, [-1, shape]) 201 | top = tf.nn.bias_add(tf.matmul(bottom_flat, weights), bias, name=scope) 202 | self.parameters[name] = [weights, bias] 203 | 204 | if not tf.get_variable_scope().reuse: 205 | weight_decay = tf.multiply(tf.nn.l2_loss(weights), self.wd, 206 | name='fc_weight_loss') 207 | tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, 208 | weight_decay) 209 | 210 | _activation_summary(top) 211 | top = tf.Print(top, [tf.shape(top)], message='Shape of %s' % name, first_n = 1, summarize=4) 212 | return top 213 | def _add_wd_and_summary(self, var, wd, collection_name=None): 214 | if collection_name is None: 215 | collection_name = tf.GraphKeys.REGULARIZATION_LOSSES 216 | if wd and (not tf.get_variable_scope().reuse): 217 | weight_decay = tf.multiply( 218 | tf.nn.l2_loss(var), wd, name='weight_loss') 219 | tf.add_to_collection(collection_name, weight_decay) 220 | _variable_summaries(var) 221 | return var 222 | def print_shapes(self): 223 | print("%s:"%(self.image_conv1),self.image_conv1.get_shape().as_list()) 224 | print("%s:"%(self.image_pool1),self.image_pool1.get_shape().as_list()) 225 | print("%s:"%(self.image_lrn1),self.image_lrn1.get_shape().as_list()) 226 | print("%s:"%(self.image_conv2),self.image_conv2.get_shape().as_list()) 227 | print("%s:"%(self.image_pool2),self.image_pool2.get_shape().as_list()) 228 | print("%s:"%(self.image_lrn2),self.image_lrn2.get_shape().as_list()) 229 | print("%s:"%(self.image_conv3),self.image_conv3.get_shape().as_list()) 230 | print("%s:"%(self.image_conv4),self.image_conv4.get_shape().as_list()) 231 | print("%s:"%(self.image_conv5),self.image_conv5.get_shape().as_list()) 232 | print("%s:"%(self.image_pool5),self.image_pool5.get_shape().as_list()) 233 | print("%s:"%(self.concat),self.concat.get_shape().as_list()) 234 | print("%s:"%(self.fc1),self.fc1.get_shape().as_list()) 235 | print("%s:"%(self.fc2),self.fc2.get_shape().as_list()) 236 | print("%s:"%(self.fc3),self.fc3.get_shape().as_list()) 237 | print("%s:"%(self.fc4),self.fc4.get_shape().as_list()) 238 | print("kernel_sizes:") 239 | for key in self.parameters: 240 | print("%s:"%(key),self.parameters[key][0].get_shape().as_list()) 241 | 242 | def load_weight_from_dict(self,weights_dict,sess): 243 | # for convolutional layers 244 | sess.run(self.parameters['target_conv_1'][0].assign(weights_dict['conv1']['weights'])) 245 | sess.run(self.parameters['target_conv_2'][0].assign(weights_dict['conv2']['weights'])) 246 | sess.run(self.parameters['target_conv_3'][0].assign(weights_dict['conv3']['weights'])) 247 | sess.run(self.parameters['target_conv_4'][0].assign(weights_dict['conv4']['weights'])) 248 | sess.run(self.parameters['target_conv_5'][0].assign(weights_dict['conv5']['weights'])) 249 | sess.run(self.parameters['image_conv_1'][0].assign(weights_dict['conv1_p']['weights'])) 250 | sess.run(self.parameters['image_conv_2'][0].assign(weights_dict['conv2_p']['weights'])) 251 | sess.run(self.parameters['image_conv_3'][0].assign(weights_dict['conv3_p']['weights'])) 252 | sess.run(self.parameters['image_conv_4'][0].assign(weights_dict['conv4_p']['weights'])) 253 | sess.run(self.parameters['image_conv_5'][0].assign(weights_dict['conv5_p']['weights'])) 254 | 255 | sess.run(self.parameters['target_conv_1'][1].assign(weights_dict['conv1']['bias'])) 256 | sess.run(self.parameters['target_conv_2'][1].assign(weights_dict['conv2']['bias'])) 257 | sess.run(self.parameters['target_conv_3'][1].assign(weights_dict['conv3']['bias'])) 258 | sess.run(self.parameters['target_conv_4'][1].assign(weights_dict['conv4']['bias'])) 259 | sess.run(self.parameters['target_conv_5'][1].assign(weights_dict['conv5']['bias'])) 260 | sess.run(self.parameters['image_conv_1'][1].assign(weights_dict['conv1_p']['bias'])) 261 | sess.run(self.parameters['image_conv_2'][1].assign(weights_dict['conv2_p']['bias'])) 262 | sess.run(self.parameters['image_conv_3'][1].assign(weights_dict['conv3_p']['bias'])) 263 | sess.run(self.parameters['image_conv_4'][1].assign(weights_dict['conv4_p']['bias'])) 264 | sess.run(self.parameters['image_conv_5'][1].assign(weights_dict['conv5_p']['bias'])) 265 | 266 | # for fully connected layers 267 | sess.run(self.parameters['fc1'][0].assign(weights_dict['fc6-new']['weights'])) 268 | sess.run(self.parameters['fc2'][0].assign(weights_dict['fc7-new']['weights'])) 269 | sess.run(self.parameters['fc3'][0].assign(weights_dict['fc7-newb']['weights'])) 270 | sess.run(self.parameters['fc4'][0].assign(weights_dict['fc8-shapes']['weights'])) 271 | 272 | sess.run(self.parameters['fc1'][1].assign(weights_dict['fc6-new']['bias'])) 273 | sess.run(self.parameters['fc2'][1].assign(weights_dict['fc7-new']['bias'])) 274 | sess.run(self.parameters['fc3'][1].assign(weights_dict['fc7-newb']['bias'])) 275 | sess.run(self.parameters['fc4'][1].assign(weights_dict['fc8-shapes']['bias'])) 276 | 277 | 278 | 279 | def test(self): 280 | sess = tf.Session() 281 | a = np.full((self.batch_size,227,227,3), 1) # numpy.full(shape, fill_value, dtype=None, order='C') 282 | b = np.full((self.batch_size,227,227,3), 2) 283 | sess.run(tf.global_variables_initializer()) 284 | 285 | sess.run([self.fc4],feed_dict={self.image:a, self.target:b}) 286 | 287 | 288 | 289 | 290 | 291 | def _activation_summary(x): 292 | """Helper to create summaries for activations. 293 | 294 | Creates a summary that provides a histogram of activations. 295 | Creates a summary that measure the sparsity of activations. 296 | 297 | Args: 298 | x: Tensor 299 | Returns: 300 | nothing 301 | """ 302 | # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training 303 | # session. This helps the clarity of presentation on tensorboard. 304 | tensor_name = x.op.name 305 | # tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) 306 | tf.summary.histogram(tensor_name + '/activations', x) 307 | tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) 308 | 309 | def _variable_summaries(var): 310 | """Attach a lot of summaries to a Tensor.""" 311 | if not tf.get_variable_scope().reuse: 312 | name = var.op.name 313 | logging.debug("Creating Summary for: %s" % name) 314 | with tf.name_scope('summaries'): 315 | mean = tf.reduce_mean(var) 316 | tf.summary.scalar(name + '/mean', mean) 317 | with tf.name_scope('stddev'): 318 | stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean))) 319 | tf.summary.scalar(name + '/sttdev', stddev) 320 | tf.summary.scalar(name + '/max', tf.reduce_max(var)) 321 | tf.summary.scalar(name + '/min', tf.reduce_min(var)) 322 | tf.summary.histogram(name, var) 323 | 324 | if __name__ == "__main__": 325 | tracknet = TRACKNET(10) 326 | tracknet.build() 327 | sess = tf.Session() 328 | a = np.full((tracknet.batch_size,227,227,3), 1) 329 | b = np.full((tracknet.batch_size,227,227,3), 2) 330 | sess.run(tf.global_variables_initializer()) 331 | sess.run([tracknet.image_pool5],feed_dict={tracknet.image:a, tracknet.target:b}) 332 | 333 | 334 | 335 | --------------------------------------------------------------------------------