wehub-resource-sync
111 lines
3.4 KiB
Python
111 lines
3.4 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=unused-variable, invalid-name
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"""1D convolution in python"""
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import numpy as np
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from tvm.topi.nn.utils import get_pad_tuple1d
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def dilate_np(x, dilation):
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"""1D dilation using numpy
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Parameters
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----------
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x : numpy.ndarray
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Array to dilate with shape [batch, in_channel, in_width]
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dilation : int
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dilation rate of output
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Returns
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-------
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out : numpy.ndarray
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Dilated output with shape [batch, in_channel, (in_width - 1) * dilation + 1]
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"""
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irange = range(len(x) - 1)
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for d in range(dilation - 1):
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indices = [(d + 1) * (i + 1) for i in irange]
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x = np.insert(x, indices, 0)
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return x
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def group_conv1d_ncw_python(a_np, w_np, stride, padding, dilation, groups):
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"Grouped version of `conv1d_ncw_python`, see that for documentation"
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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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conv1d_ncw_python(a_slice, w_slice, stride, padding, dilation)
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for a_slice, w_slice in zip(a_slices, w_slices)
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]
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return np.concatenate(b_slices, axis=1)
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def conv1d_ncw_python(a_np, w_np, stride, padding, dilation):
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"""1D convolution operator in NCW layout
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Parameters
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----------
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a_np : numpy.ndarray
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3-D with shape [batch, in_channel, in_width]
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w_np : numpy.ndarray
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3-D with shape [num_filter, in_channel, filter_width]
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stride : int
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Stride size
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padding : int, tuple, or str
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Single int for padding size or tuple of (left, right) padding
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or a string in ['VALID', 'SAME']
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dilation : int
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Dilation rate of the kernel
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groups : int
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Number of groups in the convolution
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Returns
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-------
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b_np : numpy.ndarray
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3-D with shape [batch, out_channel, out_width]
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"""
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batch, in_c, in_w = a_np.shape
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out_c, _, filter_w = w_np.shape
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if isinstance(stride, tuple | list):
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stride = stride[0]
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if isinstance(dilation, tuple | list):
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dilation = dilation[0]
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dilated_filter_w = (filter_w - 1) * dilation + 1
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pad_left, pad_right = get_pad_tuple1d(padding, (dilated_filter_w,))
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out_w = ((in_w - dilated_filter_w + pad_left + pad_right) // stride) + 1
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padded_a_np = np.zeros((batch, in_c, in_w + pad_left + pad_right))
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padded_a_np[:, :, pad_left : (in_w + pad_left)] = a_np
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b_np = np.zeros((batch, out_c, out_w))
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for n in range(batch):
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for f in range(out_c):
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for c in range(in_c):
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out = np.convolve(
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padded_a_np[n, c], np.flip(dilate_np(w_np[f, c], dilation)), mode="valid"
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)
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b_np[n, f] += out[::stride]
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return b_np
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