jiangjiajun commented on a change in pull request #9126:
URL: https://github.com/apache/tvm/pull/9126#discussion_r717355007
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File path: python/tvm/relay/frontend/paddlepaddle.py
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@@ -40,28 +41,108 @@
__all__ = ["from_paddle"]
+def _get_pad_size(in_size, dilated_kernel_size, stride_size):
+ """calculate the paddings size"""
+
+ if stride_size == 1 or in_size % stride_size == 0:
+ pad = max(dilated_kernel_size - stride_size, 0)
+ else:
+ pad = max(dilated_kernel_size - (in_size % stride_size), 0)
+
+ pad_before = pad // 2
+ pad_after = pad - pad_before
+
+ return [pad_before, pad_after]
+
+
+def _dtype_shape_promotion(inputs):
+ """promote data type and shape for list of tensors."""
+
+ dtype_order = ["bool", "int8", "int16", "int32", "int64", "float32",
"float64"]
+
+ ranks = [len(infer_shape(x)) for x in inputs]
+ if set(ranks) == set([1, 0]):
+ for i, r in enumerate(ranks):
+ if r == 0:
+ inputs[i] = _op.expand_dims(inputs[i], axis=0)
+
+ dtypes = set(dtype_order.index(infer_type(x).checked_type.dtype) for x in
inputs)
+ if len(dtypes) == 1:
+ return inputs
+ max_dtype = dtype_order[max(dtypes)]
+ for i, input_op in enumerate(inputs):
+ if infer_type(input_op).checked_type.dtype != max_dtype:
+ inputs[i] = input_op.astype(max_dtype)
+ return inputs
+
+
def shape_of(x, dtype="int32"):
Review comment:
We have referred ONNX frontend, this function also comes from there
https://github.com/apache/tvm/blob/main/python/tvm/relay/frontend/onnx.py#L1411
It's a little different from `common::infer_shape`
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