wehub-resource-sync
65 lines
2.1 KiB
Python
65 lines
2.1 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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"""Dilate operation in python"""
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import numpy as np
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def dilate_python(input_np, strides, dilation_value=0.0, out_dtype=None):
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"""Dilate operation.
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Parameters
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----------
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input_np : numpy.ndarray
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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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out_dtype : Option[str]
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The datatype of the dilated array. If unspecified, will use
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the same dtype as the input array.
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Returns
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-------
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output_np : numpy.ndarray
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n-D, the same layout as Input.
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"""
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assert len(input_np.shape) == len(strides), (
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f"Input dimension and strides size dismatch : {len(input_np.shape)} vs {len(strides)}"
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)
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if out_dtype is None:
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out_dtype = input_np.dtype
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output_size = [
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(input_dim - 1) * stride + 1 for input_dim, stride in zip(input_np.shape, strides)
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]
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non_zero_elements = np.ix_(
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*[range(0, output_dim, stride) for output_dim, stride in zip(output_size, strides)]
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)
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output_np = np.full(shape=output_size, fill_value=dilation_value, dtype=out_dtype)
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output_np[non_zero_elements] = input_np
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return output_np
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