mbrookhart commented on a change in pull request #9126:
URL: https://github.com/apache/tvm/pull/9126#discussion_r716888182
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File path: python/tvm/relay/frontend/paddlepaddle.py
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@@ -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.
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