TensorRT8.5.2 conversion failed when converting groups convolutions

Description

It will report an error when converting a model with group convolution using TensorRT8.5.2.

Environment

TensorRT Version:
8.5.2.2

Relevant Files

The error message is as follows：

[08/29/2023-21:40:17] [TRT] [E] Conv_221:kernel weights has count 294912 but 147456 was expected
[08/29/2023-21:40:17] [TRT] [E] Conv_221: count of 294912 weights in kernel, but kernel dimensions (3,3) with 128 input channels, 256 output channels and 2 groups were specified. Expected Weights count is 128 * 3*3 * 256 / 2 = 147456
[08/29/2023-21:40:17] [TRT] [E] [convolutionNode.cpp::computeOutputExtents::57] Error Code 4: Internal Error (Conv_221: number of kernel weights does not match tensor dimensions)
[08/29/2023-21:40:17] [TRT] [E] ModelImporter.cpp:726: While parsing node number 221 [Conv -> "input.487"]:
[08/29/2023-21:40:17] [TRT] [E] ModelImporter.cpp:727: --- Begin node ---
[08/29/2023-21:40:17] [TRT] [E] ModelImporter.cpp:728: input: "input.483"
input: "bbox_head.reg_convs.1.conv.weight"
input: "bbox_head.reg_convs.1.conv.bias"
output: "input.487"
name: "Conv_221"
op_type: "Conv"
attribute {
  name: "dilations"
  ints: 1
  ints: 1
  type: INTS
}
attribute {
  name: "group"
  i: 2
  type: INT
}
attribute {
  name: "kernel_shape"
  ints: 3
  ints: 3
  type: INTS
}
attribute {
  name: "pads"
  ints: 1
  ints: 1
  ints: 1
  ints: 1
  type: INTS
}
attribute {
  name: "strides"
  ints: 1
  ints: 1
  type: INTS
}

[08/29/2023-21:40:17] [TRT] [E] ModelImporter.cpp:729: --- End node ---
[08/29/2023-21:40:17] [TRT] [E] ModelImporter.cpp:731: ERROR: ModelImporter.cpp:172 In function parseGraph:
[6] Invalid Node - Conv_221
Conv_221:kernel weights has count 294912 but 147456 was expected
Conv_221: count of 294912 weights in kernel, but kernel dimensions (3,3) with 128 input channels, 256 output channels and 2 groups were specified. Expected Weights count is 128 * 3*3 * 256 / 2 = 147456
[convolutionNode.cpp::computeOutputExtents::57] Error Code 4: Internal Error (Conv_221: number of kernel weights does not match tensor dimensions)

The error message display was caused by a mismatch between the weight and the actual convolution, but I was able to run normally on Pytorch using the same model and configuration file. The error message displays "but kernel dimensions (3,3) with 128 input channels, 256 output channels, and 2 groups were specified." However, in reality, the number of input channels for this convolution should be 256. The number of input channels I used when building the model is 256, which can be determined. Therefore, I suspect that grouping convolution caused an error in TensorRT calculation, as the model is actually continuous.

In addition, another issue was encountered while converting shuffleNet.

[08/29/2023-17:23:23] [TRT] [E] [convolutionNode.cpp::computeOutputExtents::54] Error Code 4: Internal Error (Conv_136: group count must divide input channel count)
[08/29/2023-17:23:23] [TRT] [E] ModelImporter.cpp:726: While parsing node number 136 [Conv -> "input.131"]:
[08/29/2023-17:23:23] [TRT] [E] ModelImporter.cpp:727: --- Begin node ---
[08/29/2023-17:23:23] [TRT] [E] ModelImporter.cpp:728: input: "input.123"
input: "onnx::Conv_2562"
input: "onnx::Conv_2563"
output: "input.131"
name: "Conv_136"
op_type: "Conv"
attribute {
  name: "dilations"
  ints: 1
  ints: 1
  type: INTS
}
attribute {
  name: "group"
  i: 116
  type: INT
}
attribute {
  name: "kernel_shape"
  ints: 3
  ints: 3
  type: INTS
}
attribute {
  name: "pads"
  ints: 1
  ints: 1
  ints: 1
  ints: 1
  type: INTS
}
attribute {
  name: "strides"
  ints: 2
  ints: 2
  type: INTS
}

[08/29/2023-17:23:23] [TRT] [E] ModelImporter.cpp:729: --- End node ---
[08/29/2023-17:23:23] [TRT] [E] ModelImporter.cpp:731: ERROR: ModelImporter.cpp:172 In function parseGraph:
[6] Invalid Node - Conv_136
[convolutionNode.cpp::computeOutputExtents::54] Error Code 4: Internal Error (Conv_136: group count must divide input channel count)

It is worth mentioning that both of these use the shuffle operation, which is the following code:

def channel_shuffle(x, groups):
   Channel Shuffle operation.

    This function enables cross-group information flow for multiple groups
    convolution layers.

    Args:
        x (Tensor): The input tensor.
        groups (int): The number of groups to divide the input tensor
            in the channel dimension.

    Returns:
        Tensor: The output tensor after channel shuffle operation.


    batch_size, num_channels, height, width = x.size()
    assert (num_channels % groups == 0), ('num_channels should be '
                                          'divisible by groups')
    channels_per_group = num_channels // groups

    x = x.view(batch_size, groups, channels_per_group, height, width)
    x = torch.transpose(x, 1, 2).contiguous()
    x = x.view(batch_size, -1, height, width)

    return x

Will this operation also affect the correct transformation of the TensorRT model?