wehub-resource-sync
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name
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"""Dilation operators"""
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import tvm
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from tvm import te
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from .. import tag, utils
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@te.tag_scope(tag=tag.INJECTIVE + ",dilate")
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def dilate(data, strides, dilation_value=0.0, name="DilatedInput"):
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"""Dilate data with given dilation value (0 by default).
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Parameters
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----------
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data : tvm.te.Tensor
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n-D, can be any layout.
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strides : list / tuple of n ints
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Dilation stride on each dimension, 1 means no dilation.
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dilation_value : int/float, optional
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Value used to dilate the input.
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name : str, optional
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The name prefix operators generated
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Returns
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-------
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Output : tvm.te.Tensor
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n-D, the same layout as data.
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"""
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n = len(data.shape)
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if len(strides) != n:
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raise ValueError(f"data dimension and strides size dismatch : {n} vs {len(strides)}")
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ana = tvm.arith.Analyzer()
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out_shape = tuple(ana.simplify((data.shape[i] - 1) * strides[i] + 1) for i in range(n))
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def _dilate(*indices):
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not_zero = []
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index_tuple = []
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idxdiv = tvm.tirx.indexdiv
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idxmod = tvm.tirx.indexmod
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for i in range(n):
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if not utils.equal_const_int(strides[i], 1):
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index_tuple.append(idxdiv(indices[i], strides[i]))
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not_zero.append(idxmod(indices[i], strides[i]).equal(0))
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else:
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index_tuple.append(indices[i])
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if not_zero:
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not_zero = tvm.tirx.all(*not_zero)
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return tvm.tirx.if_then_else(
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not_zero, data(*index_tuple), tvm.tirx.const(dilation_value, data.dtype)
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)
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return data(*index_tuple)
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return te.compute(out_shape, _dilate, name=name)
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