wehub-resource-sync
89 lines
2.9 KiB
Python
89 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
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"""TVM operator space_to_depth compute."""
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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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def space_to_depth(data, block_size, layout="NCHW"):
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"""Perform space to depth transformation on the data
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Parameters
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----------
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data : tvm.te.Tensor
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4-D tensor in either NCHW or NHWC layout.
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block_size : int
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Size of blocks to decompose into channel dimension.
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layout : string
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Either NCHW or NHWC, indicating data layout.
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Returns
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-------
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output : tvm.te.Tensor
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Output of shape [N, C * block_size**2, H / block_size, W / block_size]
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"""
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if layout == "NCHW":
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in_n, in_c, in_h, in_w = data.shape
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output_shape = [
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in_n,
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in_c * block_size * block_size,
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tvm.tirx.truncdiv(in_h, block_size),
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tvm.tirx.truncdiv(in_w, block_size),
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]
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elif layout == "NHWC":
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in_n, in_h, in_w, in_c = data.shape
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output_shape = [
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in_n,
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tvm.tirx.truncdiv(in_h, block_size),
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tvm.tirx.truncdiv(in_w, block_size),
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in_c * block_size * block_size,
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]
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else:
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raise ValueError("Only NCHW and NHWC layouts are currently supported.")
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def _get_indices(*indices):
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if layout == "NCHW":
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n, c, y, x = indices
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elif layout == "NHWC":
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n, y, x, c = indices
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return n, c, y, x
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def _get_pixel(n, c, y, x):
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block_offset = tvm.tirx.truncdiv(c, in_c)
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channel_idx = tvm.tirx.truncmod(c, in_c)
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x_idx = tvm.tirx.truncmod(block_offset, block_size)
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y_idx = tvm.tirx.truncdiv(block_offset, block_size)
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if layout == "NCHW":
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output = data(n, channel_idx, y_idx + (y * block_size), x_idx + (x * block_size))
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else:
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output = data(n, y_idx + (y * block_size), x_idx + (x * block_size), channel_idx)
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return output
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def _compute(*indices):
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n, c, y, x = _get_indices(*indices)
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return _get_pixel(n, c, y, x)
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return te.compute(output_shape, _compute, name="space_to_depth", tag=tag.INJECTIVE)
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