PyTorch convert function for op 'conv_transpose2d' not implemented.

[katelyn-chen]

• Name of layer type: 'Conv_transpose2d'
• Is this a PyTorch or a TensorFlow layer type: PyTorch
• Your version of coremltools: 7.0
• Your version of PyTorch/TensorFlow: PyTorch 2.0.1
• Impact of supporting this layer type. Why is adding support for this layer type important? Is it necessary to support a popular model or use case?

The conv_transpose2d layer is used in several neural network architectures. Its primary purpose is to upsample its input, making it spatially larger. Examples of models that it is used in include:

1. Generator in Generative Adversarial Networks (GANs): In GANs, the generator is often required to upsample a random noise vector to produce an image. Transposed convolutions are frequently used in the generator's architecture to gradually increase the spatial dimensions of intermediate feature maps.
2. Autoencoders: In the decoding phase of autoencoders, the goal is to upscale the low-dimensional encoded representation back into the original input dimension. Transposed convolutions are useful here.
3. U-Net and Similar Architectures: U-Net is a popular model for semantic segmentation tasks. The "U" shape consists of a contracting path (encoder) that captures context and a symmetric expanding path (decoder) that enables precise localization. The expanding path uses transposed convolutions to upsample feature maps.
4. Image Super-Resolution: Models like SRCNN and its variants sometimes utilize transposed convolutions to upscale lower resolution images to higher resolution.
5. PixelRNN/PixelCNN: These are generative models that produce images pixel by pixel. Some versions use transposed convolutions to upsample their intermediate representations.
6. Flow-Based Generative Models: Models like RealNVP and Glow, which are part of the normalizing flow family, may use transposed convolutions in certain layers.

Specifically, conv_transpose2d is important because:

1. Unlike fixed upsampling methods, transposed convolutions have learnable parameters, allowing the model to learn the most suitable upsampling pattern for a given task.
2. In tasks like image generation or semantic segmentation, it's crucial to recover spatial details lost during downsampling. Transposed convolutions help achieve higher resolution feature maps:
3. In deep networks, having layers that can propagate information (and gradients) from one end of the network to another aids in training. Transposed convolutions help connect deep layers with shallow ones, especially in architectures like U-Net, which leads to better gradient flow.

[alealv]

This #2011 should solve your problem