Question

def get_unet(input_img, n_filters=16, dropout=0.5, batchnorm=True):

    # contracting path
    c1 = conv2d_block(input_img, n_filters=n_filters * 1, kernel_size=3, batchnorm=batchnorm)
    p1 = MaxPooling2D((2, 2))(c1)
    p1 = Dropout(dropout * 0.5)(p1)

    c2 = conv2d_block(p1, n_filters=n_filters * 2, kernel_size=3, batchnorm=batchnorm)
    p2 = MaxPooling2D((2, 2))(c2)
    p2 = Dropout(dropout)(p2)

    c3 = conv2d_block(p2, n_filters=n_filters * 4, kernel_size=3, batchnorm=batchnorm)
    p3 = MaxPooling2D((2, 2))(c3)
    p3 = Dropout(dropout)(p3)

    c4 = conv2d_block(p3, n_filters=n_filters * 8, kernel_size=3, batchnorm=batchnorm)
    p4 = MaxPooling2D(pool_size=(2, 2))(c4)
    p4 = Dropout(dropout)(p4)

    c5 = conv2d_block(p4, n_filters=n_filters * 16, kernel_size=3, batchnorm=batchnorm)

    # expansive path
    u6 = Conv2DTranspose(n_filters * 8, (3, 3), strides=(2, 2), padding='same')(c5)
    u6 = concatenate([u6, c4])
    u6 = Dropout(dropout)(u6)
    c6 = conv2d_block(u6, n_filters=n_filters * 8, kernel_size=3, batchnorm=batchnorm)

    u7 = Conv2DTranspose(n_filters * 4, (3, 3), strides=(2, 2), padding='same')(c6)
    u7 = concatenate([u7, c3])
    u7 = Dropout(dropout)(u7)
    c7 = conv2d_block(u7, n_filters=n_filters * 4, kernel_size=3, batchnorm=batchnorm)

    u8 = Conv2DTranspose(n_filters * 2, (3, 3), strides=(2, 2), padding='same')(c7)
    u8 = concatenate([u8, c2])
    u8 = Dropout(dropout)(u8)
    c8 = conv2d_block(u8, n_filters=n_filters * 2, kernel_size=3, batchnorm=batchnorm)

    u9 = Conv2DTranspose(n_filters * 1, (3, 3), strides=(2, 2), padding='same')(c8)
    u9 = concatenate([u9, c1], axis=3)
    u9 = Dropout(dropout)(u9)
    c9 = conv2d_block(u9, n_filters=n_filters * 1, kernel_size=3, batchnorm=batchnorm)

    outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)
    model = Model(inputs=[input_img], outputs=[outputs])
    return model

我从这里得到了Keras的模型。我似乎收到了错误：

  File "train.py", line 87, in get_unet
  u8 = concatenate([u8, c2])
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 256, 184, 32), (None, 256, 185, 32)]

因此我打印了每个张量的值，然后得到：

c1: Tensor("activation_2/Relu:0", shape=(?, 512, 370, 16), dtype=float32)
c2: Tensor("activation_4/Relu:0", shape=(?, 256, 185, 32), dtype=float32)
c3: Tensor("activation_6/Relu:0", shape=(?, 128, 92, 64), dtype=float32)
c4: Tensor("activation_8/Relu:0", shape=(?, 64, 46, 128), dtype=float32)
c5: Tensor("activation_10/Relu:0", shape=(?, 32, 23, 256), dtype=float32)
u6: Tensor("dropout_5/cond/Merge:0", shape=(?, 64, 46, 256), dtype=float32)
u7: Tensor("dropout_6/cond/Merge:0", shape=(?, 128, 92, 128), dtype=float32)
u8: Tensor("conv2d_transpose_3/BiasAdd:0", shape=(?, ?, ?, 32), dtype=float32)

C2发生了什么？为什么u8的第二维度是184，而C2的第二维度似乎是185。而且，C3的第二维度似乎是从184的最大2倍被最大化处理的。（可能是由于floor函数所致）

我将如何应对？我是否必须更改正在输入的图像的大小，或者在进行转置卷积时是否必须进行某些设计？我是否需要对一个额外的像素执行插值？

Answer 1

之所以发生这种情况是因为，即使在C2层中将其除以2时，第二维也没有。您将185的最大池数乘以2，这使您的92.5->下限降至92
但是，当您以另一种方式进行操作时，您会将92上采样2倍，得到184。

要避免这种情况，您只需将U8零垫板与C2兼容即可，如下所示：

u8 = Conv2DTranspose(n_filters * 2, (3, 3), strides=(2, 2), padding='same')(c7)
u8 = ZeroPadding2D(padding=((0, 0), (0, 1)))(u8)
u8 = concatenate([u8, c2])

如果您不想对键盘进行过零填充，则可以对输入图像进行整形，以使尺寸对应于2的幂，或者可以将其除以2的倍数而无需给出奇数，例如224（可以在得到7）之前除以5的两倍。

希望对您有帮助！

尝试执行转置卷积但缺少像素

1 个答案: