mbrookhart commented on a change in pull request #9126: URL: https://github.com/apache/tvm/pull/9126#discussion_r716888182
########## File path: python/tvm/relay/frontend/paddlepaddle.py ########## @@ -138,12 +273,22 @@ def convert_conv2d(g, op, block): kernel = g.get_node(op.input("Filter")[0]) input_x = g.get_node(op.input("Input")[0]) out_channels, _, k_h, k_w = infer_shape(kernel) - in_h, in_w = infer_shape(input_x)[2:] if padding_algorithm == "VALID": paddings = [0, 0] elif padding_algorithm == "SAME": - pad_h = _get_pad_size(in_h, (k_h - 1) * dilations[0] + 1, strides[0]) - pad_w = _get_pad_size(in_w, (k_w - 1) * dilations[1] + 1, strides[1]) + if strides[0] == 1 and strides[1] == 1: + pad_h = _get_pad_size(0, (k_h - 1) * dilations[0] + 1, strides[0]) + pad_w = _get_pad_size(0, (k_w - 1) * dilations[1] + 1, strides[1]) + else: + input_shape = shape_of(input_x) + h_w = _op.strided_slice(input_shape, [2], [4]) + try: + in_h, in_w = infer_value(h_w, g.get_params()).numpy().tolist() + except Exception as e: + msg = "Dynamic shape is not supported in SAME padding algorithm while stride!=1" + raise tvm.error.OpAttributeInvalid(msg) from e Review comment: Just as a heads up, I supported `SAME` padding in the ONNX frontend with dynamic shapes here: https://github.com/apache/tvm/blob/d0c6ca5cacae8dcae26e26287d6d2a270ab6127c/python/tvm/relay/frontend/onnx.py#L412-L472 It's fairly complicate, I'm totally cool if you want to punt on that until you need it. ########## File path: python/tvm/relay/frontend/paddlepaddle.py ########## @@ -138,12 +273,22 @@ def convert_conv2d(g, op, block): kernel = g.get_node(op.input("Filter")[0]) input_x = g.get_node(op.input("Input")[0]) out_channels, _, k_h, k_w = infer_shape(kernel) - in_h, in_w = infer_shape(input_x)[2:] if padding_algorithm == "VALID": paddings = [0, 0] elif padding_algorithm == "SAME": - pad_h = _get_pad_size(in_h, (k_h - 1) * dilations[0] + 1, strides[0]) - pad_w = _get_pad_size(in_w, (k_w - 1) * dilations[1] + 1, strides[1]) + if strides[0] == 1 and strides[1] == 1: + pad_h = _get_pad_size(0, (k_h - 1) * dilations[0] + 1, strides[0]) + pad_w = _get_pad_size(0, (k_w - 1) * dilations[1] + 1, strides[1]) + else: + input_shape = shape_of(input_x) + h_w = _op.strided_slice(input_shape, [2], [4]) + try: + in_h, in_w = infer_value(h_w, g.get_params()).numpy().tolist() + except Exception as e: + msg = "Dynamic shape is not supported in SAME padding algorithm while stride!=1" + raise tvm.error.OpAttributeInvalid(msg) from e Review comment: Just as a heads up, I supported `SAME` padding in the ONNX frontend with dynamic shapes here: https://github.com/apache/tvm/blob/d0c6ca5cacae8dcae26e26287d6d2a270ab6127c/python/tvm/relay/frontend/onnx.py#L412-L472 It's fairly complicated, I'm totally cool if you want to punt on that until you need it. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org