Пример #1
0
def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):
    '''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout
        # Arguments
            x: input tensor 
            stage: index for dense block
            branch: layer index within each dense block
            nb_filter: number of filters
            dropout_rate: dropout rate
            weight_decay: weight decay factor
    '''
    eps = 1.1e-5
    conv_name_base = 'conv' + str(stage) + '_' + str(branch)
    relu_name_base = 'relu' + str(stage) + '_' + str(branch)

    # 1x1 Convolution (Bottleneck layer)
    inter_channel = nb_filter * 4  
    x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x)
    x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x)
    x = Activation('relu', name=relu_name_base+'_x1')(x)
    x = Convolution2D(inter_channel, 1, 1, name=conv_name_base+'_x1', bias=False)(x)

    if dropout_rate:
        x = Dropout(dropout_rate)(x)

    # 3x3 Convolution
    x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x)
    x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x)
    x = Activation('relu', name=relu_name_base+'_x2')(x)
    x = ZeroPadding2D((1, 1), name=conv_name_base+'_x2_zeropadding')(x)
    x = Convolution2D(nb_filter, 3, 3, name=conv_name_base+'_x2', bias=False)(x)

    if dropout_rate:
        x = Dropout(dropout_rate)(x)

    return x
Пример #2
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def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):
    ''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout 
        # Arguments
            x: input tensor
            stage: index for dense block
            nb_filter: number of filters
            compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.
            dropout_rate: dropout rate
            weight_decay: weight decay factor
    '''

    eps = 1.1e-5
    conv_name_base = 'conv' + str(stage) + '_blk'
    relu_name_base = 'relu' + str(stage) + '_blk'
    pool_name_base = 'pool' + str(stage) 

    x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x)
    x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x)
    x = Activation('relu', name=relu_name_base)(x)
    x = Convolution2D(int(nb_filter * compression), 1, 1, name=conv_name_base, bias=False)(x)

    if dropout_rate:
        x = Dropout(dropout_rate)(x)

    x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)

    return x
Пример #3
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def DenseNet121(nb_dense_block=4,
                growth_rate=32,
                nb_filter=64,
                reduction=0.0,
                dropout_rate=0.0,
                weight_decay=1e-4,
                classes=1000,
                weights_path=None):
    '''Instantiate the DenseNet 121 architecture,
        # Arguments
            nb_dense_block: number of dense blocks to add to end
            growth_rate: number of filters to add per dense block
            nb_filter: initial number of filters
            reduction: reduction factor of transition blocks.
            dropout_rate: dropout rate
            weight_decay: weight decay factor
            classes: optional number of classes to classify images
            weights_path: path to pre-trained weights
        # Returns
            A Keras model instance.
    '''
    eps = 1.1e-5

    # compute compression factor
    compression = 1.0 - reduction

    # Handle Dimension Ordering for different backends
    global concat_axis
    if K.image_dim_ordering() == 'tf':
        concat_axis = 3
        img_input = Input(shape=(224, 224, 3), name='data')
    else:
        concat_axis = 1
        img_input = Input(shape=(3, 224, 224), name='data')

    # From architecture for ImageNet (Table 1 in the paper)
    nb_filter = 64
    nb_layers = [6, 12, 24, 16]  # For DenseNet-121

    # Initial convolution
    x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
    x = Convolution2D(nb_filter,
                      7,
                      7,
                      subsample=(2, 2),
                      name='conv1',
                      bias=False)(x)
    x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)
    x = Scale(axis=concat_axis, name='conv1_scale')(x)
    x = Activation('relu', name='relu1')(x)
    x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x)
    x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)

    # Add dense blocks
    for block_idx in range(nb_dense_block - 1):
        stage = block_idx + 2
        x, nb_filter = dense_block(x,
                                   stage,
                                   nb_layers[block_idx],
                                   nb_filter,
                                   growth_rate,
                                   dropout_rate=dropout_rate,
                                   weight_decay=weight_decay)

        # Add transition_block
        x = transition_block(x,
                             stage,
                             nb_filter,
                             compression=compression,
                             dropout_rate=dropout_rate,
                             weight_decay=weight_decay)
        nb_filter = int(nb_filter * compression)

    final_stage = stage + 1
    x, nb_filter = dense_block(x,
                               final_stage,
                               nb_layers[-1],
                               nb_filter,
                               growth_rate,
                               dropout_rate=dropout_rate,
                               weight_decay=weight_decay)

    x = BatchNormalization(epsilon=eps,
                           axis=concat_axis,
                           name='conv' + str(final_stage) + '_blk_bn')(x)
    x = Scale(axis=concat_axis,
              name='conv' + str(final_stage) + '_blk_scale')(x)
    x = Activation('relu', name='relu' + str(final_stage) + '_blk')(x)
    x = GlobalAveragePooling2D(name='pool' + str(final_stage))(x)
    print(x.shape)
    out = Dense(classes, name='fc6')(x)
    out = Activation('softmax', name='prob')(out)

    model = Model(img_input, out, name='densenet')

    if weights_path is not None:
        model.load_weights(weights_path)

    model2 = Model(img_input, x)
    return model2