文件列表:

keras-fcn\.travis.yml (2286, 2018-07-28)
keras-fcn\data\VOC2011 (40, 2018-07-28)
keras-fcn\data\VOC2012 (40, 2018-07-28)
keras-fcn\fcn_vgg16.png (127772, 2018-07-28)
keras-fcn\keras_fcn\backend\common.py (1306, 2018-07-28)
keras-fcn\keras_fcn\backend\tensorflow_backend.py (254, 2018-07-28)
keras-fcn\keras_fcn\backend\__init__.py (548, 2018-07-28)
keras-fcn\keras_fcn\blocks.py (7561, 2018-07-28)
keras-fcn\keras_fcn\callbacks.py (1265, 2018-07-28)
keras-fcn\keras_fcn\decoders.py (4183, 2018-07-28)
keras-fcn\keras_fcn\encoders.py (6171, 2018-07-28)
keras-fcn\keras_fcn\layers.py (5953, 2018-07-28)
keras-fcn\keras_fcn\losses.py (665, 2018-07-28)
keras-fcn\keras_fcn\metrics.py (815, 2018-07-28)
keras-fcn\keras_fcn\models.py (3321, 2018-07-28)
keras-fcn\keras_fcn\__init__.py (70, 2018-07-28)
keras-fcn\LICENSE (1086, 2018-07-28)
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# keras-fcn [](https://travis-ci.org/JihongJu/keras-fcn) [](https://codecov.io/gh/jihongju/keras-fcn) [](https://opensource.org/licenses/MIT) A re-implementation of Fully Convolutional Networks with Keras ## Installation ### Dependencies 1. [keras](https://keras.io/#installation) 2. [tensorflow](https://www.tensorflow.org/install/) ### Install with `pip` ```bash $ pip install git+https://github.com/JihongJu/keras-fcn.git ``` ### Build from source ```bash $ git clone https://github.com/JihongJu/keras-fcn.git $ cd keras-fcn $ pip install --editable . ``` ## Usage ### FCN with VGG16 ```python from keras_fcn import FCN fcn_vgg16 = FCN(input_shape=(500, 500, 3), classes=21, weights='imagenet', trainable_encoder=True) fcn_vgg16.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) fcn_vgg16.fit(X_train, y_train, batch_size=1) ``` ### FCN with VGG19 ```python from keras_fcn import FCN fcn_vgg19 = FCN_VGG19(input_shape=(500, 500, 3), classes=21, weights='imagenet', trainable_encoder=True) fcn_vgg19.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) fcn_vgg19.fit(X_train, y_train, batch_size=1) ``` ### Custom FCN (VGG16 as an example) ```python from keras.layers import Input from keras.models import Model from keras_fcn.encoders import Encoder from keras_fcn.decoders import VGGUpsampler from keras_fcn.blocks import (vgg_conv, vgg_fc) inputs = Input(shape=(224, 224, 3)) blocks = [vgg_conv(***, 2, 'block1'), vgg_conv(128, 2, 'block2'), vgg_conv(256, 3, 'block3'), vgg_conv(512, 3, 'block4'), vgg_conv(512, 3, 'block5'), vgg_fc(4096)] encoder = Encoder(inputs, blocks, weights='imagenet', trainable=True) feat_pyramid = encoder.outputs # A feature pyramid with 5 scales feat_pyramid = feat_pyramid[:3] # Select only the top three scale of the pyramid feat_pyramid.append(inputs) # Add image to the bottom of the pyramid outputs = VGGUpsampler(feat_pyramid, scales=[1, 1e-2, 1e-4], classes=21) outputs = Activation('softmax')(outputs) fcn_custom = Model(inputs=inputs, outputs=outputs) ``` And implement a custom Fully Convolutional Network becomes simply define a series of convolutional blocks that one stacks on top of another. ### Custom decoders ```python from keras_fcn.blocks import vgg_upsampling from keras_fcn.decoders import Decoder decode_blocks = [ vgg_upsampling(classes=21, target_shape=(None, 14, 14, None), scale=1), vgg_upsampling(classes=21, target_shape=(None, 28, 28, None), scale=0.01), vgg_upsampling(classes=21, target_shape=(None, 224, 224, None), scale=0.0001) ] outputs = Decoder(feat_pyramid[-1], decode_blocks) ``` The `decode_blocks` can be customized as well. ```python from keras_fcn.layers import BilinearUpSampling2D def vgg_upsampling(classes, target_shape=None, scale=1, block_name='featx'): """A VGG convolutional block with bilinear upsampling for decoding. :param classes: Integer, number of classes :param scale: Float, scale factor to the input feature, varing from 0 to 1 :param target_shape: 4D Tuples with targe_height, target_width as the 2nd, 3rd elements if `channels_last` or as the 3rd, 4th elements if `channels_first`. >>> from keras_fcn.blocks import vgg_upsampling >>> feat1, feat2, feat3 = feat_pyramid[:3] >>> y = vgg_upsampling(classes=21, target_shape=(None, 14, 14, None), >>> scale=1, block_name='feat1')(feat1, None) >>> y = vgg_upsampling(classes=21, target_shape=(None, 28, 28, None), >>> scale=1e-2, block_name='feat2')(feat2, y) >>> y = vgg_upsampling(classes=21, target_shape=(None, 224, 224, None), >>> scale=1e-4, block_name='feat3')(feat3, y) """ def f(x, y): score = Conv2D(filters=classes, kernel_size=(1, 1), activation='linear', padding='valid', kernel_initializer='he_normal', name='score_{}'.format(block_name))(x) if y is not None: def scaling(xx, ss=1): return xx * ss scaled = Lambda(scaling, arguments={'ss': scale}, name='scale_{}'.format(block_name))(score) score = add([y, scaled]) upscore = BilinearUpSampling2D( target_shape=target_shape, name='upscore_{}'.format(block_name))(score) return upscore return f ``` ## Try Examples 1. Download [VOC2011](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/) dataset ```bash $ wget "http://host.robots.ox.ac.uk/pascal/VOC/voc2011/VOCtrainval_25-May-2011.tar" $ tar -xvzf VOCtrainval_25-May-2011.tar $ mkdir ~/Datasets $ mv TrainVal/VOCdevkit/VOC2011 ~/Datasets ``` 2. Mount dataset from host to container and start bash in container image From repository `keras-fcn` ```bash $ nvidia-docker run -it --rm -v `pwd`:/root/workspace -v ${Home}/Datasets/:/root/workspace/data jihong/keras-gpu bash ``` or equivalently, ```bash $ make bash ``` 3. Within the container, run the following codes. ```bash $ cd ~/workspace $ pip setup.py -e . $ cd voc2011 $ python train.py ``` More details see source code of the example in [Training Pascal VOC2011 Segmention](https://github.com/JihongJu/keras-fcn/blob/master/voc2011/train.py) ### Model Architecture FCN8s with VGG16 as base net:  ### TODO - Add ResNet

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