├── .gitignore ├── BlazeFace.py ├── LICENSE ├── README.md ├── train.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /BlazeFace.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | 4 | 5 | def build_backbone(img_size: tuple, channels=3): 6 | inputs = tf.placeholder(dtype=tf.float32, shape=(None, *img_size, channels), name='inputs') 7 | phrase_train = tf.placeholder(dtype=tf.bool, name='phase_train') 8 | with tf.variable_scope('blaze_fafce'): 9 | with tf.variable_scope('first_conv'): 10 | # pad_inputs = tf.pad(inputs, [[0,0],[1,1],[1,1],[0,0]], mode='CONSTANT') 11 | conv1 = tf.layers.conv2d(inputs, 24, kernel_size=3, strides=2, padding='SAME') 12 | conv1 = tf.layers.batch_normalization(conv1, training=phrase_train, momentum=24.) 13 | conv1 = tf.nn.relu(conv1) 14 | with tf.variable_scope('blaze_block'): 15 | bb1 = blaze_block(conv1, filters=24, phase_train=phrase_train) 16 | bb1 = blaze_block(bb1, filters=24, phase_train=phrase_train) 17 | bb1 = blaze_block(bb1, filters=48, stride=2, phase_train=phrase_train) 18 | bb1 = blaze_block(bb1, filters=48, phase_train=phrase_train) 19 | bb1 = blaze_block(bb1, filters=48, phase_train=phrase_train) 20 | 21 | with tf.variable_scope('double_blaze'): 22 | db1 = double_blaze_block(bb1, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 23 | db1 = double_blaze_block(db1, filters=96, mid_channels=24, phase_train=phrase_train) 24 | feature32by32 = double_blaze_block(db1, filters=96, mid_channels=24, phase_train=phrase_train) 25 | db2 = double_blaze_block(feature32by32, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 26 | db2 = double_blaze_block(db2, filters=96, mid_channels=24, phase_train=phrase_train) 27 | db2 = double_blaze_block(db2, filters=96, mid_channels=24, phase_train=phrase_train) 28 | feature16by16 = double_blaze_block(db2, filters=96, mid_channels=24, phase_train=phrase_train) 29 | db3 = double_blaze_block(feature16by16, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 30 | db3 = double_blaze_block(db3, filters=96, mid_channels=24, phase_train=phrase_train) 31 | db3 = double_blaze_block(db3, filters=96, mid_channels=24, phase_train=phrase_train) 32 | feature8by8 = double_blaze_block(db3, filters=96, mid_channels=24, phase_train=phrase_train) 33 | db4 = double_blaze_block(feature8by8, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 34 | db4 = double_blaze_block(db4, filters=96, mid_channels=24, phase_train=phrase_train) 35 | db4 = double_blaze_block(db4, filters=96, mid_channels=24, phase_train=phrase_train) 36 | feature4by4 = double_blaze_block(db4, filters=96, mid_channels=24, phase_train=phrase_train) 37 | db5 = double_blaze_block(feature4by4, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 38 | db5 = double_blaze_block(db5, filters=96, mid_channels=24, phase_train=phrase_train) 39 | db5 = double_blaze_block(db5, filters=96, mid_channels=24, phase_train=phrase_train) 40 | feature2by2 = double_blaze_block(db5, filters=96, mid_channels=24, phase_train=phrase_train) 41 | # db6 = double_blaze_block(feature2by2, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 42 | # db6 = double_blaze_block(db6, filters=96, mid_channels=24, phase_train=phrase_train) 43 | # db6 = double_blaze_block(db6, filters=96, mid_channels=24, phase_train=phrase_train) 44 | # feature1by1 = double_blaze_block(db6, filters=96, mid_channels=24, stride=2, phase_train=phrase_train) 45 | 46 | feature1 = tf.identity(feature32by32, 'feature_map1') 47 | feature2 = tf.identity(feature16by16, 'feature_map2') 48 | feature3 = tf.identity(feature8by8, 'feature_map3') 49 | feature4 = tf.identity(feature4by4, 'feature_map4') 50 | feature5 = tf.identity(feature2by2, 'feature_map5') 51 | # feature6 = tf.identity(feature1by1, 'feature_map6') 52 | return inputs, phrase_train, feature1, feature2, feature3, feature4, feature5, #feature6 53 | 54 | 55 | def blaze_block(x: tf.Tensor, filters, mid_channels=None, stride=1, phase_train=True): 56 | # input is n,w,h,c 57 | mid_channels = mid_channels or x.get_shape()[3] 58 | assert stride in [1, 2] 59 | use_pool = stride > 1 60 | # tensorflow way to implement pad size = 2 61 | pad_x = tf.pad(x, [[0, 0], [2, 2], [2, 2], [0, 0]], mode='CONSTANT') 62 | conv1 = tf.layers.separable_conv2d(pad_x, filters=mid_channels, kernel_size=(5, 5), strides=stride, padding='VALID') 63 | bn1 = tf.layers.batch_normalization(conv1, training=phase_train) 64 | conv2 = tf.layers.conv2d(bn1, filters=filters, kernel_size=1, strides=1, padding='SAME') 65 | bn2 = tf.layers.batch_normalization(conv2, training=phase_train) 66 | 67 | if use_pool: 68 | shortcut = tf.layers.max_pooling2d(x, pool_size=stride, strides=stride, padding='SAME') 69 | shortcut = tf.layers.conv2d(shortcut, filters=filters, kernel_size=1, strides=1, padding='SAME') 70 | shortcut = tf.layers.batch_normalization(shortcut, training=phase_train) 71 | shortcut = tf.nn.relu(shortcut) 72 | return tf.nn.relu(bn2 + shortcut) 73 | return tf.nn.relu(bn2 + x) 74 | 75 | 76 | def double_blaze_block(x: tf.Tensor, filters, mid_channels=None, stride=1, phase_train=True): 77 | assert stride in [1, 2] 78 | mid_channels = mid_channels or x.get_shape()[3] 79 | usepool = stride > 1 80 | 81 | # padding = 2 82 | pad_x = tf.pad(x, [[0, 0], [2, 2], [2, 2], [0, 0]], mode='CONSTANT') 83 | conv1 = tf.layers.separable_conv2d(pad_x, filters=filters, kernel_size=5, strides=stride, padding='VALID') 84 | bn1 = tf.layers.batch_normalization(conv1, training=phase_train) 85 | conv1 = tf.layers.conv2d(bn1, filters=mid_channels, kernel_size=1, strides=1, padding='SAME') 86 | bn2 = tf.layers.batch_normalization(conv1, training=phase_train) 87 | relu1 = tf.nn.relu(bn2) 88 | 89 | # padding = 2 90 | pad_relu1 = tf.pad(relu1, [[0, 0], [2, 2], [2, 2], [0, 0]], mode='CONSTANT') 91 | conv2 = tf.layers.separable_conv2d(pad_relu1, filters=mid_channels, kernel_size=5, strides=1, padding='VALID') 92 | bn2 = tf.layers.batch_normalization(conv2, training=phase_train) 93 | conv2 = tf.layers.conv2d(bn2, filters=filters, kernel_size=1, strides=1, padding='SAME') 94 | bn2 = tf.layers.batch_normalization(conv2, training=phase_train) 95 | 96 | # if use pool: 97 | if usepool: 98 | max_pool1 = tf.layers.max_pooling2d(x, pool_size=stride, strides=stride, padding='SAME') 99 | conv3 = tf.layers.conv2d(max_pool1, filters=filters, kernel_size=1, strides=1, padding='SAME') 100 | bn3 = tf.layers.batch_normalization(conv3, training=phase_train) 101 | return tf.nn.relu(bn2 + bn3) 102 | 103 | return tf.nn.relu(bn2 + x) 104 | 105 | 106 | def build_prediction_convs(input_shape=(128, 128)): 107 | inputs, phrase_train, feature1, feature2, feature3, feature4, feature5 = build_backbone(input_shape) 108 | # attach feature map1 to a conv to output coordinate prediction 109 | # anchor ratio is always 1*1 110 | predict1 = tf.layers.conv2d(feature1, filters=5, strides=2, kernel_size=2, padding='SAME') 111 | predict2 = tf.layers.conv2d(feature2, filters=5, strides=2, kernel_size=2, padding='SAME') 112 | predict3 = tf.layers.conv2d(feature3, filters=5, strides=2, kernel_size=2, padding='SAME') 113 | predict4 = tf.layers.conv2d(feature4, filters=5, strides=2, kernel_size=2, padding='SAME') 114 | predict5 = tf.layers.conv2d(feature5, filters=5, strides=2, kernel_size=2, padding='SAME') 115 | # predict6 = tf.layers.conv2d(feature6, filters=5, strides=1, kernel_size=1, padding='SAME') 116 | return inputs, phrase_train, tf.identity(predict1, 'predict1'), tf.identity(predict2, 'predict2'), predict3, \ 117 | predict4, predict5 118 | 119 | 120 | if __name__ == '__main__': 121 | # test output feature's shape 122 | shape = (512, 512) 123 | inputs, phrase_train, predict1, predict2, predict3, predict4, predict5 = build_prediction_convs(shape) 124 | # run_meta = tf.RunMetadata() 125 | with tf.Session() as sess: 126 | sess.run(tf.global_variables_initializer()) 127 | out1, out2, out3, out4, out5 = sess.run((predict1, predict2, predict3, predict4, predict5), 128 | feed_dict={inputs: np.random.random((16, *shape, 3)), 129 | phrase_train: True}) 130 | print(out1.shape) 131 | print(out2.shape) 132 | print(out3.shape) 133 | print(out4.shape) 134 | print(out5.shape) 135 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # blazeface-tensorflow 2 | tensorflow implementation of BlazeFace 3 | 4 | Dependencies: 5 | Tensorflow 1.14 6 | 7 | TODO list: 8 |
    9 |
  1. build model architecture. √
    2. 10 |
  2. make it adapt to multiple scale (currently only 128*128 image)
    3. 11 |
  3. build loss and training pipeline
    4. 12 |
  4. build dataset pipeline.
    5. 13 |
14 | 15 | paper: https://arxiv.org/abs/1907.05047 16 | Please help me to check if model architecture is right 17 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import BlazeFace 2 | 3 | shape = (512, 512) 4 | 5 | 6 | def main(): 7 | # build training pipeline 8 | inputs, phrase_train, predict2, predict3, predict4, predict5 = BlazeFace.build_prediction_convs(shape) 9 | 10 | 11 | if __name__ == '__main__': 12 | main() 13 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dl-maxwang/blazeface-tensorflow/7ea28914598893974e74348c067540881809cbce/utils.py --------------------------------------------------------------------------------