wehub-resource-sync
98 lines
3.6 KiB
Python
98 lines
3.6 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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"""Convolution 3D in python"""
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import numpy as np
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import scipy.signal
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from tvm.topi.nn.utils import get_pad_tuple3d
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def _conv3d_ncdhw_python(a_np, w_np, stride, padding):
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batch, in_channel, in_depth, in_height, in_width = a_np.shape
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num_filter, _, kernel_d, kernel_h, kernel_w = w_np.shape
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if isinstance(stride, int):
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stride_d = stride_h = stride_w = stride
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else:
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stride_d, stride_h, stride_w = stride
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pad_front, pad_top, pad_left, pad_back, pad_bottom, pad_right = get_pad_tuple3d(
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padding, (kernel_d, kernel_h, kernel_w)
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)
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pad_d = pad_front + pad_back
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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_depth = (in_depth - kernel_d + pad_d) // stride_d + 1
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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_depth, out_height, out_width))
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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_d > 0 or pad_h > 0 or pad_w > 0:
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apad = np.zeros((in_depth + pad_d, in_height + pad_h, in_width + pad_w))
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apad[
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pad_front : pad_front + in_depth,
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pad_top : pad_top + in_height,
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pad_left : pad_left + in_width,
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] = a_np[n, c]
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else:
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apad = a_np[n, c]
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out = scipy.signal.convolve(apad, np.flip(w_np[f, c]), mode="valid")
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b_np[n, f] += out[::stride_d, ::stride_h, ::stride_w]
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return b_np
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def conv3d_ncdhw_python(a_np, w_np, stride, padding, groups=1):
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"""Convolution operator in NCDHW layout.
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Parameters
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----------
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a_np : numpy.ndarray
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5-D with shape [batch, in_channel, in_depth, in_height, in_width]
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w_np : numpy.ndarray
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5-D with shape [num_filter, in_channel, filter_depth, filter_height, filter_width]
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stride : int or a list/tuple of three ints
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Stride size, or [stride_depth, stride_height, stride_width]
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padding : int or str or a list/tuple of three ints
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Padding size, or ['VALID', 'SAME'], or [pad_depth, pad_height, pad_width]
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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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5-D with shape [batch, out_channel, out_depth, 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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_conv3d_ncdhw_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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