keras中的segnet：新数组的总大小必须保持不变

Question

我正在用Python实现segnet。下面是代码。在

img_w = 480
img_h = 360
pool_size = 2

def build_model(img_w, img_h, pool_size):
    n_labels = 12

    kernel = 3

    encoding_layers = [
        Conv2D(64, (kernel, kernel), input_shape=(img_h, img_w, 3), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(64, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        MaxPooling2D(pool_size = (pool_size,pool_size)),

        Convolution2D(128, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(128, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        MaxPooling2D(pool_size = (pool_size,pool_size)),

        Convolution2D(256, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(256, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(256, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        MaxPooling2D(pool_size = (pool_size,pool_size)),

        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        MaxPooling2D(pool_size = (pool_size,pool_size)),

        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        MaxPooling2D(pool_size = (pool_size,pool_size)),
    ]

    autoencoder = models.Sequential()
    autoencoder.encoding_layers = encoding_layers

    for l in autoencoder.encoding_layers:
        autoencoder.add(l)

    decoding_layers = [
        UpSampling2D(),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),

        UpSampling2D(),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(512, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(256, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),

        UpSampling2D(),
        Convolution2D(256, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(256, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(128, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),

        UpSampling2D(),
        Convolution2D(128, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(64, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),

        UpSampling2D(),
        Convolution2D(64, (kernel, kernel), padding='same'),
        BatchNormalization(),
        Activation('relu'),
        Convolution2D(n_labels, (1, 1), padding='valid', activation="sigmoid"),
        BatchNormalization(),
    ]
    autoencoder.decoding_layers = decoding_layers
    for l in autoencoder.decoding_layers:
        autoencoder.add(l)

    autoencoder.add(Reshape((n_labels, img_h * img_w)))
    autoencoder.add(Permute((2, 1)))
    autoencoder.add(Activation('softmax'))



    return autoencoder

model = build_model(img_w, img_h, pool_size)

但它返回错误。在

^{pr2}$

我看不出这个错误的任何原因。当我将img_w和img_h更改为256时，这个错误就解决了，但问题是这不是图像大小或原始数据集，所以我不能使用它。如何解决这个问题？一点帮助和洞察力将不胜感激。在

Answer 1

问题是您正在执行(2, 2)向下采样5次，因此，让我们跟踪形状：

(360, 480) -> (180, 240) -> (90, 120) -> (45, 60) -> (22, 30) -> (11, 15)

现在向上采样：

^{pr2}$

因此，当您尝试使用原始形状reshape输出时，问题是由于模型不匹配而引起的。在

可能的解决方案：

1. 调整图像大小，使两个输入维度都可以被32（例如(352, 480)或{}）整除。

2. 在第三次上采样后添加ZeroPadding2D(((1, 0), (0, 0)))以将形状从(44, 60)更改为{}，这将使您的网络以一个好的输出形状结束。

其他问题：

请注意最后一个MaxPooling2D后面是第一个Upsampling2D。这可能是一个问题，因为这是一个无用的瓶颈您的网络。在