Forums - Unsupported ConvTranspose 2d output padding when converting from onnx to dlc format

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Unsupported ConvTranspose 2d output padding when converting from onnx to dlc format
Join Date: 6 Apr 21
Posts: 1
Posted: Tue, 2021-04-06 16:32

Hello everyone,

I'm currently trying to convert my onnx model (after quantized and exported with AIMET) to .dlc format through the usage of SNPE tool using snpe-onnx-to-dlc command.

My model contains mainly convolution 2D, relu and convolution transpose operations ( with the usage of output padding. During the conversion, I'm having a shape mismatch error:


Traceback (most recent call last):
  File "/snpe/snpe-", line 125, in get_broadcasted_shape
    output_shape = list(np.broadcast(*inputs).shape)
ValueError: shape mismatch: objects cannot be broadcast to a single shape

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/snpe/snpe-", line 157, in convert
  File "/snpe/snpe-", line 51, in apply_method_to_op
    return translation.apply_method(method_name, *args, **kwargs)
  File "/snpe/snpe-", line 17, in apply_method
    return self.indexed_methods[method_name](*args, **kwargs)
  File "/snpe/snpe-", line 200, in add_op
    broadcast_shape = translation_utils.get_broadcasted_shape(input_shapes)
  File "/snpe/snpe-", line 127, in get_broadcasted_shape
    raise ValueError("Shape mismatch, {} cannot be broadcast to a single shape".format(input_shapes))



After some investigation, it seems like the shape mismatch is caused by the function which calculate the output dim of the convolution transpose 2d. The function itself does not take into consideration the output padding hence the output dimension was calculated incorrectly. I'm wondering if anyone has come across this issue and if there's a work around for it (my solution is to switch to using bilinear upsampling completely, but I just want to make sure that I've tried everything).

Note: this is the output dim calculation for deconv operation using snpe toolkit


def calc_deconv_output_dim(input_size, filter_size, pad_before, pad_after, stride, padding_size_strategy):
    if padding_size_strategy == IRPaddingStrategies.PADDING_SIZE_IMPLICIT_VALID:
        output_dim = input_size * stride + max(filter_size - stride, 0)
    elif padding_size_strategy == IRPaddingStrategies.PADDING_SIZE_IMPLICIT_SAME_BEGIN \
            or padding_size_strategy == IRPaddingStrategies.PADDING_SIZE_IMPLICIT_SAME_END:
        output_dim = input_size * stride
        output_dim = stride * (input_size - 1) - (pad_before + pad_after) + filter_size

    return int(output_dim)


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