wehub-resource-sync
101 lines
2.9 KiB
Python
101 lines
2.9 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
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"""Operators of one-to-one-mapping on the first input"""
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import tvm
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from tvm import te
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from .. import tag
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@tvm.te.tag_scope(tag=tag.BROADCAST)
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def scale_shift_nchw(Input, Scale, Shift):
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"""Batch normalization operator in inference.
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Parameters
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----------
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Input : tvm.te.Tensor
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4-D input tensor, NCHW layout [batch, channel, height, width]
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Scale : tvm.te.Tensor
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Scale tensor, 1-D of size channel number
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Shift : tvm.te.Tensor
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Shift tensor, 1-D of size channel number
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Returns
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-------
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Output : tvm.te.Tensor
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Output tensor, layout is NCHW
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"""
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return te.compute(
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Input.shape, lambda b, c, i, j: Input[b, c, i, j] * Scale[c] + Shift[c], name="ScaleShift"
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)
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@tvm.te.tag_scope(tag=tag.BROADCAST)
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def scale_shift_nhwc(Input, Scale, Shift):
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"""Batch normalization operator in inference.
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Parameters
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----------
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Input : tvm.te.Tensor
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4-D input tensor, NHWC layout [batch, height, width, channel]
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Scale : tvm.te.Tensor
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Scale tensor, 1-D of size channel number
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Shift : tvm.te.Tensor
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Shift tensor, 1-D of size channel number
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Returns
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-------
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Output : tvm.te.Tensor
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Output tensor, layout is NHWC
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"""
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return te.compute(
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Input.shape, lambda b, i, j, c: Input[b, i, j, c] * Scale[c] + Shift[c], name="ScaleShift"
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)
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@tvm.te.tag_scope(tag=tag.BROADCAST)
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def scale_shift_nchwc(Input, Scale, Shift):
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"""Batch normalization operator in inference.
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Parameters
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----------
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Input : tvm.te.Tensor
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5-D input tensor, NCHWc layout [batch, channel_chunk, height, width, channel_block]
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Scale : tvm.te.Tensor
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Scale tensor, 2-D of size [channel_chunk, channel_block]
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Shift : tvm.te.Tensor
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Shift tensor, 2-D of size [channel_chunk, channel_block]
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Returns
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-------
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Output : tvm.te.Tensor
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Output tensor, layout is NHWC
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"""
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return te.compute(
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Input.shape,
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lambda b, cc, i, j, cb: Input[b, cc, i, j, cb] * Scale[cc, cb] + Shift[cc, cb],
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name="ScaleShift",
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)
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