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Improve x86 roi align #3296
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
# pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, undefined-variable, too-many-nested-blocks, too-many-branches, too-many-statements | ||
"""Non-maximum suppression operator for intel cpu""" | ||
import tvm | ||
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from tvm import hybrid | ||
from ..vision.rcnn import roi_align_nchw | ||
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@hybrid.script | ||
def roi_align_nchw_ir(data, rois, pooled_size, spatial_scale, sample_ratio): | ||
"""Hybrid routing fo ROI align operator in NCHW layout. | ||
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Parameters | ||
---------- | ||
data : tvm.Tensor or numpy NDArray | ||
4-D with shape [batch, channel, height, width] | ||
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rois : tvm.Tensor or numpy NDArray | ||
2-D with shape [num_roi, 5]. The last dimension should be in format of | ||
[batch_index, w_start, h_start, w_end, h_end] | ||
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pooled_size : tvm ConsExpr | ||
[out_height, out_width] | ||
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spatial_scale : tvm.const | ||
Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal | ||
of total stride in convolutional layers, which should be in range (0.0, 1.0] | ||
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sample_ratio : tvm.const | ||
Sampling ratio of ROI align, using adaptive size by default. | ||
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Returns | ||
------- | ||
output : tvm.Tensor or numpy NDArray | ||
4-D with shape [num_roi, channel, pooled_size, pooled_size] | ||
""" | ||
channels = data.shape[1] | ||
height = data.shape[2] | ||
width = data.shape[3] | ||
num_rois = rois.shape[0] | ||
pooled_size_h = pooled_size[0] | ||
pooled_size_w = pooled_size[1] | ||
output = output_tensor((num_rois, channels, pooled_size_h, pooled_size_w), data.dtype) | ||
max_num_pc_index = height * width * pooled_size_h * pooled_size_w | ||
w_pc = allocate((num_rois, max_num_pc_index, 4), data.dtype) | ||
pos_pc = allocate((num_rois, max_num_pc_index, 4), "int32") | ||
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for n in parallel(num_rois): | ||
roi_batch_index = int32(rois[n, 0]) | ||
roi_start_w = rois[n, 1] * spatial_scale | ||
roi_start_h = rois[n, 2] * spatial_scale | ||
roi_end_w = rois[n, 3] * spatial_scale | ||
roi_end_h = rois[n, 4] * spatial_scale | ||
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roi_h = max(roi_end_h - roi_start_h, 1.0) | ||
roi_w = max(roi_end_w - roi_start_w, 1.0) | ||
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bin_h = roi_h / pooled_size_h | ||
bin_w = roi_w / pooled_size_w | ||
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roi_bin_grid_h = sample_ratio | ||
roi_bin_grid_w = roi_bin_grid_h | ||
div_h = roi_h / pooled_size_h | ||
div_w = roi_w / pooled_size_w | ||
rounded_div_h = int32(div_h) * 1.0 | ||
rounded_div_w = int32(div_w) * 1.0 | ||
if sample_ratio <= 0: | ||
# Cannot use ceil function since hybrid script | ||
# doesn't support Call as indexing | ||
roi_bin_grid_h = int32(div_h) | ||
roi_bin_grid_w = int32(div_w) | ||
if rounded_div_h < div_h: | ||
roi_bin_grid_h += 1 | ||
if rounded_div_w < div_w: | ||
roi_bin_grid_w += 1 | ||
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count = roi_bin_grid_h * roi_bin_grid_w | ||
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# Pre-calculate indices and weights shared by all channels. | ||
# This is the key point of optimization. | ||
pre_calc_index = 0 | ||
iy_upper = roi_bin_grid_h | ||
ix_upper = roi_bin_grid_w | ||
for ph in range(pooled_size_h): | ||
for pw in range(pooled_size_w): | ||
for iy in range(iy_upper): | ||
yy = roi_start_h + ph * bin_h + (iy + 0.5) * bin_h / roi_bin_grid_h | ||
for ix in range(ix_upper): | ||
xx = roi_start_w + pw * bin_w + (ix + 0.5) * bin_w / roi_bin_grid_w | ||
x = xx | ||
y = yy | ||
if y < -1.0 or y > height or x < -1.0 or x > width: | ||
for i in range(4): | ||
w_pc[n, pre_calc_index, i] = 0.0 | ||
pos_pc[n, pre_calc_index, i] = 0 | ||
else: | ||
if y < 0.0: | ||
y = 0.0 | ||
if x < 0.0: | ||
x = 0.0 | ||
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y_low = int32(y) | ||
x_low = int32(x) | ||
x_high = x_low + 1 | ||
y_high = y_low + 1 | ||
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if y_low >= height - 1: | ||
y_high = height - 1 | ||
y_low = y_high | ||
y = float32(y_low) | ||
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if x_low >= width - 1: | ||
x_high = width - 1 | ||
x_low = x_high | ||
x = float32(x_low) | ||
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ly = y - y_low | ||
lx = x - x_low | ||
hy = 1.0 - ly | ||
hx = 1.0 - lx | ||
w1 = hy * hx | ||
w2 = hy * lx | ||
w3 = ly * hx | ||
w4 = ly * lx | ||
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pos_pc[n, pre_calc_index, 0] = x_low | ||
pos_pc[n, pre_calc_index, 1] = x_high | ||
pos_pc[n, pre_calc_index, 2] = y_low | ||
pos_pc[n, pre_calc_index, 3] = y_high | ||
w_pc[n, pre_calc_index, 0] = w1 | ||
w_pc[n, pre_calc_index, 1] = w2 | ||
w_pc[n, pre_calc_index, 2] = w3 | ||
w_pc[n, pre_calc_index, 3] = w4 | ||
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pre_calc_index += 1 | ||
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for c in range(channels): | ||
pre_calc_index = 0 | ||
for ph in range(pooled_size_h): | ||
for pw in range(pooled_size_w): | ||
output_val = 0.0 | ||
for iy in range(roi_bin_grid_h): | ||
for ix in range(roi_bin_grid_w): | ||
output_val += w_pc[n, pre_calc_index, 0] \ | ||
* data[roi_batch_index, c, | ||
pos_pc[n, pre_calc_index, 2], | ||
pos_pc[n, pre_calc_index, 0]] \ | ||
+ w_pc[n, pre_calc_index, 1] \ | ||
* data[roi_batch_index, c, | ||
pos_pc[n, pre_calc_index, 2], | ||
pos_pc[n, pre_calc_index, 1]] \ | ||
+ w_pc[n, pre_calc_index, 2] \ | ||
* data[roi_batch_index, c, | ||
pos_pc[n, pre_calc_index, 3], | ||
pos_pc[n, pre_calc_index, 0]] \ | ||
+ w_pc[n, pre_calc_index, 3] \ | ||
* data[roi_batch_index, c, | ||
pos_pc[n, pre_calc_index, 3], | ||
pos_pc[n, pre_calc_index, 1]] | ||
pre_calc_index += 1 | ||
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output_val /= count | ||
output[n, c, ph, pw] = output_val | ||
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return output | ||
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@roi_align_nchw.register("cpu") | ||
def roi_align_nchw_cpu(data, rois, pooled_size, spatial_scale, sample_ratio=-1): | ||
"""ROI align operator in NCHW layout. | ||
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Parameters | ||
---------- | ||
data : tvm.Tensor | ||
4-D with shape [batch, channel, height, width] | ||
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rois : tvm.Tensor | ||
2-D with shape [num_roi, 5]. The last dimension should be in format of | ||
[batch_index, w_start, h_start, w_end, h_end] | ||
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pooled_size : int or list/tuple of two ints | ||
output size, or [out_height, out_width] | ||
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spatial_scale : float | ||
Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal | ||
of total stride in convolutional layers, which should be in range (0.0, 1.0] | ||
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sample_ratio : int | ||
Optional sampling ratio of ROI align, using adaptive size by default. | ||
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Returns | ||
------- | ||
output : tvm.Tensor | ||
4-D with shape [num_roi, channel, pooled_size, pooled_size] | ||
""" | ||
if not isinstance(pooled_size, (tuple, list)): | ||
pooled_size = (pooled_size, pooled_size) | ||
pooled_size = tvm.convert(pooled_size) | ||
spatial_scale = tvm.const(spatial_scale, "float32") | ||
sample_ratio = tvm.const(sample_ratio, "int32") | ||
return roi_align_nchw_ir(data, rois, pooled_size, spatial_scale, sample_ratio) |
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cc @were