wehub-resource-sync
63 lines
2.2 KiB
Python
63 lines
2.2 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, line-too-long, unused-variable, too-many-locals
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"""Reorg in python"""
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import numpy as np
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def reorg_python(a_np, stride):
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"""Reorg operator
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Parameters
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----------
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a_np : numpy.ndarray
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4-D with shape [batch, in_channel, in_height, in_width]
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stride : int
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Stride size
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Returns
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-------
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b_np : np.ndarray
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4-D with shape [batch, out_channel, out_height, out_width]
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"""
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batch, in_channel, in_height, in_width = a_np.shape
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a_np = np.reshape(a_np, batch * in_channel * in_height * in_width)
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out_c = int(in_channel / (stride * stride))
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out_channel = in_channel * stride * stride
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out_height = int(in_height / stride)
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out_width = int(in_width / stride)
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b_np = np.zeros(batch * out_channel * out_height * out_width)
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cnt = 0
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for b in range(batch):
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for k in range(in_channel):
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for j in range(in_height):
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for i in range(in_width):
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c2 = k % out_c
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offset = int(k / out_c)
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w2 = int(i * stride + offset % stride)
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h2 = int(j * stride + offset / stride)
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out_index = int(
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w2 + in_width * stride * (h2 + in_height * stride * (c2 + out_c * b))
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)
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b_np[cnt] = a_np[int(out_index)]
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cnt = cnt + 1
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b_np = np.reshape(b_np, (batch, out_channel, out_height, out_width))
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return b_np
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