wehub-resource-sync
71 lines
2.3 KiB
Python
71 lines
2.3 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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"""Common utility for topi test"""
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import numpy as np
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import scipy.signal
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def _convolve2d(data, weights):
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"""2d convolution operator in HW layout.
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This is intended to be used as a replacement for
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scipy.signals.convolve2d, with wider support for different dtypes.
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scipy.signal.convolve2d does not support all TVM-supported
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dtypes (e.g. float16). Where possible, this function uses
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scipy.signal.convolve2d to take advantage of compiled scipy
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routines, falling back to an explicit loop only where needed.
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Parameters
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----------
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data : numpy.ndarray
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2-D with shape [in_height, in_width]
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weights : numpy.ndarray
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2-D with shape [filter_height, filter_width].
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Returns
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-------
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b_np : np.ndarray
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2-D with shape [out_height, out_width]
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Return value and layout conventions are matched to
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``scipy.signal.convolve2d(data, weights, mode="valid")``
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"""
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try:
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return scipy.signal.convolve2d(data, weights, mode="valid")
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except ValueError:
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pass
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weights = np.rot90(weights, k=2)
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assert len(data.shape) == len(weights.shape) == 2
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dtype = data.dtype
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kernel_h, kernel_w = weights.shape
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output_shape = [a_dim - w_dim + 1 for a_dim, w_dim in zip(data.shape, weights.shape)]
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output = np.zeros(output_shape, dtype=dtype)
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for y in range(output_shape[0]):
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for x in range(output_shape[1]):
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output[y][x] = np.sum(data[y : y + kernel_h, x : x + kernel_w] * weights)
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return output
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