wehub-resource-sync
182 lines
6.2 KiB
Python
182 lines
6.2 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, too-many-locals, too-many-arguments
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"""Deformable convolution in python"""
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import itertools
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import math
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import numpy as np
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from tvm.topi.nn.utils import get_pad_tuple
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def deformable_conv2d_nchw_python(
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a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
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):
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"""Deformable 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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offset_np : numpy.ndarray
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4-D with shape [batch, deformable_groups * filter_height * filter_width * 2,
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out_height, out_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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dilation : int or a list/tuple of two ints
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Dilation size, or [dilate_height, dilate_width]
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deformable_groups : int
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Number of deformable groups
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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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batch, in_channel, in_height, in_width = a_np.shape
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out_channel, _, kernel_h, kernel_w = w_np.shape
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out_height, out_width = offset_np.shape[-2:]
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dtype = a_np.dtype
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ic_per_dgroup = in_channel // deformable_groups
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assert groups == 1, "deformable_conv2d_nchw_python does not support groups > 1"
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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, _, _ = get_pad_tuple(padding, (kernel_h, kernel_w))
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if isinstance(dilation, int):
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dilation_h = dilation_w = dilation
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else:
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dilation_h, dilation_w = dilation
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def _bilinear(n, c, h, w):
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y_low = math.floor(h)
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x_low = math.floor(w)
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y_high = y_low + 1
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x_high = x_low + 1
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wy_h = h - y_low
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wx_h = w - x_low
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wy_l = 1 - wy_h
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wx_l = 1 - wx_h
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val = 0
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for wx, xp in zip((wx_l, wx_h), (x_low, x_high)):
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for wy, yp in zip((wy_l, wy_h), (y_low, y_high)):
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if 0 <= yp < in_height and 0 <= xp < in_width:
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val += wx * wy * a_np[n, c, yp, xp]
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return val
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a_deform = np.zeros((batch, in_channel, out_height, out_width, kernel_h, kernel_w), dtype=dtype)
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for n, h, w in itertools.product(range(batch), range(out_height), range(out_width)):
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offset = offset_np[n, :, h, w].reshape(deformable_groups, kernel_h, kernel_w, 2)
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in_h = h * stride_h - pad_top
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in_w = w * stride_w - pad_left
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index_h_base, index_w_base = np.meshgrid(
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np.arange(in_h, in_h + kernel_h * dilation_h, dilation_h, dtype=offset_np.dtype),
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np.arange(in_w, in_w + kernel_w * dilation_w, dilation_w, dtype=offset_np.dtype),
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indexing="ij",
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)
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for c, kh, kw in itertools.product(range(in_channel), range(kernel_h), range(kernel_w)):
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dg = c // ic_per_dgroup
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index_h = index_h_base + offset[dg, ..., 0]
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index_w = index_w_base + offset[dg, ..., 1]
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y, x = index_h[kh, kw], index_w[kh, kw]
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if y < 0 or y >= in_height or x < 0 or x >= in_width:
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continue
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a_deform[n, c, h, w, kh, kw] = _bilinear(n, c, y, x)
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b_np = np.zeros((batch, out_channel, out_height, out_width), dtype=dtype)
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for n, c, f, h, w in itertools.product(
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range(batch), range(in_channel), range(out_channel), range(out_height), range(out_width)
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):
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b_np[n, f, h, w] += np.tensordot(a_deform[n, c, h, w], w_np[f, c])
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return b_np
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def deformable_conv2d_nhwc_python(
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a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
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):
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"""Deformable convolution operator in NHWC 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_height, in_width, in_channel]
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offset_np : numpy.ndarray
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4-D with shape [batch, out_height, out_width,
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deformable_groups * filter_height * filter_width * 2]
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w_np : numpy.ndarray
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4-D with shape [filter_height, filter_width, in_channel, num_filter]
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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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dilation : int or a list/tuple of two ints
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Dilation size, or [dilate_height, dilate_width]
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deformable_groups : int
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Number of deformable groups
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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_np = np.transpose(a_np, [0, 3, 1, 2]) # NHWC -> NCHW
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offset_np = np.transpose(offset_np, [0, 3, 1, 2]) # NHWC -> NCHW
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w_np = np.transpose(w_np, [3, 2, 0, 1]) # HWIO -> OIHW
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b_np = deformable_conv2d_nchw_python(
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a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
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)
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b_np = np.transpose(b_np, [0, 2, 3, 1]) # NCHW -> NHWC
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return b_np
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