[BUG Report]: Deconvolution issue where input shape has 'None' dimensions - Conv2DTranspose()

Description

I am attempting to build a U-Net with the Keras api. For the expansive part of the network I am using Conv2DTranspose() to deconvolve/upscale. When the model is built with a fixed input size like Shape(256, 256, 3) it builds and compiles without issue. When I try to make the U-Net work for arbitrary input shapes (for difference sized/shaped images) I use an input shape of (None, None, 3) which is (-1, -1, 3) in C#. When the model builds, the output shapes of the deconvolution layers appear to be calculated incorrectly.

In the debugger I see each deconvolution layer with output shapes of: (None, -2, -2, 256), (None, -4, -4, 64), (None, -8, -8, 32), and finally (None, None6, None6, 16).

As you can see in the last shape, there is an obvious bug where there is a string concatenation of "None" + "6" = "None6". Maybe it should be an integer addition instead?

From making the same model in Python, I think all of these negative numbers should actually be "None". I had a look in the source code and found that -2 is used to represent "Unkown" dimension, so I think that might be where the -2 in the first shape comes from, and then it gets doubled in subsequent deconvolution layers.

I finally get an exception at the output layer: Tensorflow.InvalidArgumentError: 'Negative dimension size caused by subtracting 1 from -16 for '{{node conv2d_18/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](conv2d_17/Relu, conv2d_18/ReadVariableOp)' with input shapes: [?,-16,-16,16], [1,1,16,1].'

Reproduction Steps

The network building function:

public static Model GetModel(int imageWidth, int imageHeight, int imageChannels, float learningRate)
{
    var inputShape = new Shape(imageWidth, imageHeight, imageChannels);
    var inputs = keras.layers.Input(inputShape, name: "image");

    // contraction path
    var c1Input = keras.layers.Conv2D(16, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(inputs);
    var c1Dropout = keras.layers.Dropout(0.1f).Apply(c1Input);
    var c1PostDropout = keras.layers.Conv2D(16, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c1Dropout);
    var c1Pooling = keras.layers.MaxPooling2D((2, 2)).Apply(c1PostDropout);

    var c2Input = keras.layers.Conv2D(32, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c1Pooling);
    var c2Dropout = keras.layers.Dropout(0.1f).Apply(c2Input);
    var c2PostDropout = keras.layers.Conv2D(32, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c2Dropout);
    var c2Pooling = keras.layers.MaxPooling2D((2, 2)).Apply(c2PostDropout);

    var c3Input = keras.layers.Conv2D(64, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c2Pooling);
    var c3Dropout = keras.layers.Dropout(0.2f).Apply(c3Input);
    var c3PostDropout = keras.layers.Conv2D(64, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c3Dropout);
    var c3Pooling = keras.layers.MaxPooling2D((2, 2)).Apply(c3PostDropout);

    var c4Input = keras.layers.Conv2D(128, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c3Pooling);
    var c4Dropout = keras.layers.Dropout(0.2f).Apply(c4Input);
    var c4PostDropout = keras.layers.Conv2D(128, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c4Dropout);
    var c4Pooling = keras.layers.MaxPooling2D((2, 2)).Apply(c4PostDropout);

    // bottleneck
    var c5Input = keras.layers.Conv2D(256, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c4Pooling);
    var c5Dropout = keras.layers.Dropout(0.3f).Apply(c5Input);
    var c5PostDropout = keras.layers.Conv2D(256, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c5Dropout);

    // expansive path
    var u6Transpose = keras.layers.Conv2DTranspose(128, kernel_size: (2, 2), strides: (2, 2), output_padding: "same", activation: "relu", kernel_initializer: "he_normal");
    var u6Tensor = u6Transpose.Apply(c5PostDropout);
    var u6Concat = keras.layers.Concatenate().Apply(new Tensors(u6Tensor, c4PostDropout));
    var c6Input = keras.layers.Conv2D(128, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(u6Concat);
    var c6Dropout = keras.layers.Dropout(0.2f).Apply(c6Input);
    var c6PostDropout = keras.layers.Conv2D(128, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c6Dropout);

    var u7Transpose = keras.layers.Conv2DTranspose(64, kernel_size: (2, 2), strides: (2, 2), output_padding: "same", kernel_initializer: "he_normal");
    var u7Tensor = u7Transpose.Apply(c6PostDropout);
    var u7Concat = keras.layers.Concatenate().Apply(new Tensors(u7Tensor, c3PostDropout));
    var c7Input = keras.layers.Conv2D(64, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(u7Concat);
    var c7Dropout = keras.layers.Dropout(0.2f).Apply(c7Input);
    var c7PostDropout = keras.layers.Conv2D(64, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c7Dropout);

    var u8Transpose = keras.layers.Conv2DTranspose(32, kernel_size: (2, 2), strides: (2, 2), output_padding: "same");
    var u8Tensor = u8Transpose.Apply(c7PostDropout);
    var u8Concat = keras.layers.Concatenate().Apply(new Tensors(u8Tensor, c2PostDropout));
    var c8Input = keras.layers.Conv2D(32, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(u8Concat);
    var c8Dropout = keras.layers.Dropout(0.1f).Apply(c8Input);
    var c8PostDropout = keras.layers.Conv2D(32, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c8Dropout);

    var u9Transpose = keras.layers.Conv2DTranspose(16, kernel_size: (2, 2), strides: (2, 2), output_padding: "same");
    var u9Tensor = u9Transpose.Apply(c8PostDropout);
    var u9Concat = keras.layers.Concatenate().Apply(new Tensors(u9Tensor, c1PostDropout));
    var c9Input = keras.layers.Conv2D(16, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(u9Concat);
    var c9Dropout = keras.layers.Dropout(0.1f).Apply(c9Input);
    var c9PostDropout = keras.layers.Conv2D(16, kernel_size: (3, 3), activation: "relu", kernel_initializer: "he_normal", padding: "same").Apply(c9Dropout);

    // output layer
    var outputs = keras.layers.Conv2D(1, (1, 1), activation: "sigmoid").Apply(c9PostDropout);

    // build the model
    Model model = (Model)keras.Model(inputs, outputs, name: "U-Net");
    model.summary();

    model.compile(
        optimizer: keras.optimizers.Adam(learningRate),
        loss: keras.losses.BinaryCrossentropy(from_logits: true),
        metrics: new[] { "accuracy" }
        );

    return model;
}

When I call this function with args GetModel(256, 256, 3, 0.001f) it builds, prints the summary to console and compiles without error.

When I call this function with GetModel(-1, -1, 3, 0.001f) the output shapes after calling .Apply() after .Conv2DTranspose():

And the exception

The tensor passed into this method has the "None6" dimensions:

Known Workarounds

No response

Configuration and Other Information

OS: Windows 11 .Net: 6.0

Using latest nuget packages SciSharp.TensorFlow.Redist 2.16.0 TensorFlow.Keras 0.15.0 TensorFlow.Net 0.150.0

SciSharp / TensorFlow.NET