resnet.py 文件源码

def nn_base(input_tensor=None, trainable=False):

    # Determine proper input shape
    if K.image_dim_ordering() == 'th':
        input_shape = (3, None, None)
    else:
        input_shape = (None, None, 3)

    if input_tensor is None:
        img_input = Input(shape=input_shape)
    else:
        if not K.is_keras_tensor(input_tensor):
            img_input = Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor

    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    x = ZeroPadding2D((3, 3))(img_input)

    x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name='bn_conv1')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)

    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), trainable = trainable)
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable)
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable)

    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable)

    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable)
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable)
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable)
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable)
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable)
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable)

    return x