wehub-resource-sync
311 lines
7.9 KiB
Python
311 lines
7.9 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, unused-variable
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"""NN operator common utilities"""
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import tvm
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from ..utils import get_const_int
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def infer_pad(data, data_pad):
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"""Infer the padding from stages in reverse.
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Parameters
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----------
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data : Tensor
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data stage.
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data_pad : Tensor
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pad stage.
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Returns
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-------
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hpad : int
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padding size on height
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wpad : int
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padding size on width
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"""
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if data_pad is None:
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return 0, 0
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_, _, IH, IW = data.shape
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_, _, TH, TW = data_pad.shape
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hpad = (TH - IH) // 2
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wpad = (TW - IW) // 2
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return get_const_int(hpad), get_const_int(wpad)
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def infer_pad3d(data, data_pad, layout):
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"""Infer the padding from stages in reverse.
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Parameters
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----------
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data : Tensor
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data stage.
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data_pad : Tensor
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pad stage.
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Returns
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-------
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dpad : int
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padding depth
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hpad : int
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padding height
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wpad : int
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padding width
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"""
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if data_pad is None:
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return 0, 0, 0
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if layout == "NDHWC":
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_, ID, IH, IW, _ = data.shape
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_, TD, TH, TW, _ = data_pad.shape
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elif layout == "NCDHW":
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_, _, ID, IH, IW = data.shape
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_, _, TD, TH, TW = data_pad.shape
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else:
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raise ValueError(f"Layout {layout} is not supported")
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dpad = TD - ID
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hpad = TH - IH
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wpad = TW - IW
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return get_const_int(dpad), get_const_int(hpad), get_const_int(wpad)
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def infer_stride(data, kernel, out):
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"""Infer the stride from stages in reverse.
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Parameters
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----------
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data : Tensor
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data stage.
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kernel : Tensor
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kernel stage.
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out : Tensor
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output stage.
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Returns
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-------
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hstride : int
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stride size on height
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wstride : int
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stride size on width
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"""
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_, _, IH, IW = data.shape
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_, _, KH, KW = kernel.shape
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_, _, OH, OW = out.shape
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hstride = (IH - KH) // tvm.te.max(OH - 1, 1) + tvm.tirx.Select(OH == 1, 1, 0)
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wstride = (IW - KW) // tvm.te.max(OW - 1, 1) + tvm.tirx.Select(OW == 1, 1, 0)
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return get_const_int(hstride), get_const_int(wstride)
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def get_pad_tuple(padding, kernel):
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"""Common code to get the pad option
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Parameters
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----------
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padding : int or str
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Padding size, or ['VALID', 'SAME']
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kernel : tuple of int
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Conv kernel size
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Returns
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-------
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pad_top : int
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Padding size on top
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pad_left : int
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Padding size on left
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pad_down : int
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Padding size on down.
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pad_right : int
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Padding size on right.
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"""
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# compute the padding size
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if isinstance(padding, tuple | list):
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if len(padding) == 2:
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pad_h = padding[0] * 2
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pad_w = padding[1] * 2
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elif len(padding) == 4:
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return padding[0], padding[1], padding[2], padding[3]
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else:
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raise ValueError("Size of padding can only be 2 or 4")
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elif isinstance(padding, int):
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pad_h = pad_w = padding * 2
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elif padding == "VALID":
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pad_h = 0
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pad_w = 0
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elif padding == "SAME":
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pad_h = kernel[0] - 1
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pad_w = kernel[1] - 1
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else:
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raise ValueError(f"Unknown padding option {padding}")
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pad_top = (pad_h + 1) // 2
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pad_left = (pad_w + 1) // 2
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return pad_top, pad_left, pad_h - pad_top, pad_w - pad_left
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def get_pad_tuple_generic(padding, kernel):
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"""Common code to get the pad option
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Parameters
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----------
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padding : int or str
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Padding size, or ['VALID', 'SAME']
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kernel : tuple of int
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Conv kernel size
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Returns
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-------
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pad_top : int
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Padding size on top
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pad_down : int
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Padding size on down.
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pad_left : int
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Padding size on left
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pad_right : int
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Padding size on right.
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"""
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# compute the padding size
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if isinstance(padding, tuple | list):
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if len(padding) == len(kernel):
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pad_dimensions = [p * 2 for p in padding]
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elif len(padding) == len(kernel) * 2:
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return (
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[padding[i] for i in range(len(kernel))],
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[padding[len(kernel) + i] for i in range(len(kernel))],
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)
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else:
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raise ValueError("Size of padding can only be len(kernel) or len(kernel) * 2")
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elif isinstance(padding, int):
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pad_dimensions = [padding * 2 for _ in range(len(kernel))]
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elif padding == "VALID":
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pad_dimensions = [0 for _ in range(len(kernel))]
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elif padding == "SAME":
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pad_dimensions = [k - 1 for k in kernel]
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else:
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raise ValueError(f"Unknown padding option {padding}")
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pad_begin = [(p + 1) // 2 for p in pad_dimensions]
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return [pad_begin, [pd - pb for pb, pd in zip(pad_begin, pad_dimensions)]]
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def get_pad_tuple3d(padding, kernel):
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"""Common code to get the pad option
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Parameters
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----------
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padding : int or str
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Padding size, or ['VALID', 'SAME']
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kernel : tuple of int
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Conv kernel size
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Returns
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-------
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pad_front : int
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Padding size on front.
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pad_top : int
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Padding size on top
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pad_left : int
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Padding size on left
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pad_back : int
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Padding size on back.
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pad_down : int
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Padding size on down.
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pad_right : int
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Padding size on right.
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"""
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# compute the padding size
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if isinstance(padding, tuple | list):
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if len(padding) == 3:
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pad_d = padding[0] * 2
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pad_h = padding[1] * 2
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pad_w = padding[2] * 2
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elif len(padding) == 6:
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return padding[0], padding[1], padding[2], padding[3], padding[4], padding[5]
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else:
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raise ValueError("Size of padding can only be 3 or 6")
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elif isinstance(padding, int):
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pad_d = pad_w = pad_h = padding * 2
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elif padding == "VALID":
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pad_h = 0
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pad_w = 0
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pad_d = 0
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elif padding == "SAME":
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pad_d = kernel[0] - 1
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pad_h = kernel[1] - 1
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pad_w = kernel[2] - 1
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else:
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raise ValueError(f"Unknown padding option {padding}")
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pad_top = (pad_h + 1) // 2
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pad_left = (pad_w + 1) // 2
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pad_front = (pad_d + 1) // 2
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return pad_front, pad_top, pad_left, pad_d - pad_front, pad_h - pad_top, pad_w - pad_left
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def get_pad_tuple1d(padding, kernel):
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"""Common code to get the pad option
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Parameters
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----------
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padding : int or str
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Padding size, or ['VALID', 'SAME']
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kernel : tuple of int
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Conv kernel size
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Returns
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-------
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pad_left : int
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Padding size on left
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pad_right : int
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Padding size on right.
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"""
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# compute the padding size
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if isinstance(padding, tuple | list):
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if len(padding) == 1:
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pad_w = padding[0] * 2
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elif len(padding) == 2:
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return padding[0], padding[1]
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else:
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raise ValueError("Size of padding can only be 2 or 4")
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elif isinstance(padding, int):
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pad_w = padding * 2
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elif padding == "VALID":
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pad_w = 0
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elif padding == "SAME":
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pad_w = kernel[0] - 1
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else:
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raise ValueError(f"Unknown padding option {padding}")
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pad_left = (pad_w + 1) // 2
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return pad_left, pad_w - pad_left
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