wehub-resource-sync
160 lines
5.3 KiB
Python
160 lines
5.3 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, line-too-long, unused-variable, too-many-locals, too-many-branches
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# ruff: noqa: F841
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"""Convolution in python"""
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import numpy as np
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import scipy
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from tvm.topi.nn.utils import get_pad_tuple
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def _conv2d_nchw_python(a_np, w_np, stride, padding):
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"""Convolution operator in NCHW layout.
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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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w_np : numpy.ndarray
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4-D with shape [num_filter, in_channel, filter_height, filter_width]
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stride : int or a list/tuple of two ints
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Stride size, or [stride_height, stride_width]
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padding : int or str or a list/tuple of 2 or 4 ints
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Padding size, or ['VALID', 'SAME'], or
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[pad_height, pad_width] for 2 ints, or
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[pad_top, pad_left, pad_bottom, pad_right] for 2 ints
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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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num_filter, _, kernel_h, kernel_w = w_np.shape
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if isinstance(stride, int):
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stride_h = stride_w = stride
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else:
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stride_h, stride_w = stride
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pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel_h, kernel_w))
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pad_h = pad_top + pad_bottom
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pad_w = pad_left + pad_right
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# compute the output shape
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out_channel = num_filter
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out_height = (in_height - kernel_h + pad_h) // stride_h + 1
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out_width = (in_width - kernel_w + pad_w) // stride_w + 1
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b_np = np.zeros((batch, out_channel, out_height, out_width), dtype=a_np.dtype)
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# computation
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for n in range(batch):
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for f in range(out_channel):
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for c in range(in_channel):
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if pad_h > 0 or pad_w > 0:
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apad = np.zeros((in_height + pad_h, in_width + pad_w), dtype=a_np.dtype)
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apad[pad_top : pad_top + in_height, pad_left : pad_left + in_width] = a_np[n, c]
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else:
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apad = a_np[n, c]
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out = _conv2d_hw(apad, w_np[f, c])
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b_np[n, f] += out[::stride_h, ::stride_w]
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return b_np
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def _conv2d_hw(apad, w_np_fc):
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"""2d convolution operator in HW layout.
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This is intended to be used as a subroutine from
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_conv2d_nchw_python. Using scipy.signal.convolve2d directly does
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not work for all dtypes (e.g. float16). Where possible, this
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function uses scipy.signal.convolve2d to take advantage of
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compiled scipy routines, falling back to an explicit loop only
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where needed
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Parameters
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----------
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a_np : numpy.ndarray
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2-D with shape [in_height, in_width]
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w_np : 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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"""
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try:
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return scipy.signal.convolve2d(apad, np.rot90(np.rot90(w_np_fc)), mode="valid")
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except ValueError:
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pass
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assert len(apad.shape) == len(w_np_fc.shape) == 2
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dtype = apad.dtype
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in_height, in_width = apad.shape
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kernel_h, kernel_w = w_np_fc.shape
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output_shape = [a_dim - w_dim + 1 for a_dim, w_dim in zip(apad.shape, w_np_fc.shape)]
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output = np.zeros(output_shape, dtype=apad.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(apad[y : y + kernel_h, x : x + kernel_w] * w_np_fc)
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return output
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def conv2d_nchw_python(a_np, w_np, stride, padding, groups=1):
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"""Convolution operator in NCHW layout.
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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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w_np : numpy.ndarray
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4-D with shape [num_filter, in_channel // groups, filter_height, filter_width]
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stride : int or a list/tuple of two ints
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Stride size, or [stride_height, stride_width]
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padding : int or str or a list/tuple of 2 or 4 ints
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Padding size, or ['VALID', 'SAME'], or
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[pad_height, pad_width] for 2 ints, or
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[pad_top, pad_left, pad_bottom, pad_right] for 2 ints
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groups : int
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Number of groups
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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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a_slices = np.array_split(a_np, groups, axis=1)
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w_slices = np.array_split(w_np, groups, axis=0)
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b_slices = [
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_conv2d_nchw_python(a_slice, w_slice, stride, padding)
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for a_slice, w_slice in zip(a_slices, w_slices)
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]
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b_np = np.concatenate(b_slices, axis=1)
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return b_np
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