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135 lines
5.1 KiB
135 lines
5.1 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import math
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import numpy as np
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import unittest
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from op_test import OpTest
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class TestPSROIPoolOp(OpTest):
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def set_data(self):
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self.init_test_case()
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self.make_rois()
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self.calc_psroi_pool()
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self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
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self.attrs = {
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'output_channels': self.output_channels,
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'spatial_scale': self.spatial_scale,
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'pooled_height': self.pooled_height,
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'pooled_width': self.pooled_width
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}
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self.outputs = {'Out': self.outs}
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def init_test_case(self):
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self.batch_size = 3
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self.channels = 3 * 2 * 2
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self.height = 6
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self.width = 4
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self.x_dim = [self.batch_size, self.channels, self.height, self.width]
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self.spatial_scale = 1.0 / 4.0
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self.output_channels = 3
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self.pooled_height = 2
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self.pooled_width = 2
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self.x = np.random.random(self.x_dim).astype('float32')
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def make_rois(self):
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rois = []
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self.rois_lod = [[]]
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for bno in range(self.batch_size):
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self.rois_lod[0].append(bno + 1)
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for i in range(bno + 1):
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x1 = np.random.random_integers(
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0, self.width // self.spatial_scale - self.pooled_width)
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y1 = np.random.random_integers(
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0, self.height // self.spatial_scale - self.pooled_height)
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x2 = np.random.random_integers(x1 + self.pooled_width,
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self.width // self.spatial_scale)
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y2 = np.random.random_integers(
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y1 + self.pooled_height, self.height // self.spatial_scale)
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roi = [bno, x1, y1, x2, y2]
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rois.append(roi)
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self.rois_num = len(rois)
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self.rois = np.array(rois).astype('float32')
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def calc_psroi_pool(self):
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output_shape = (self.rois_num, self.output_channels, self.pooled_height,
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self.pooled_width)
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out_data = np.zeros(output_shape)
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for i in range(self.rois_num):
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roi = self.rois[i]
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roi_batch_id = int(roi[0])
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roi_start_w = round(roi[1]) * self.spatial_scale
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roi_start_h = round(roi[2]) * self.spatial_scale
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roi_end_w = (round(roi[3]) + 1.) * self.spatial_scale
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roi_end_h = (round(roi[4]) + 1.) * self.spatial_scale
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roi_height = max(roi_end_h - roi_start_h, 0.1)
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roi_width = max(roi_end_w - roi_start_w, 0.1)
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bin_size_h = roi_height / float(self.pooled_height)
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bin_size_w = roi_width / float(self.pooled_width)
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x_i = self.x[roi_batch_id]
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for c in range(self.output_channels):
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for ph in range(self.pooled_height):
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for pw in range(self.pooled_width):
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hstart = int(
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math.floor(float(ph) * bin_size_h + roi_start_h))
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wstart = int(
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math.floor(float(pw) * bin_size_w + roi_start_w))
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hend = int(
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math.ceil(
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float(ph + 1) * bin_size_h + roi_start_h))
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wend = int(
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math.ceil(
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float(pw + 1) * bin_size_w + roi_start_w))
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hstart = min(max(hstart, 0), self.height)
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hend = min(max(hend, 0), self.height)
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wstart = min(max(wstart, 0), self.width)
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wend = min(max(wend, 0), self.width)
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c_in = (c * self.pooled_height + ph
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) * self.pooled_width + pw
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is_empty = (hend <= hstart) or (wend <= wstart)
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out_sum = 0.
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for ih in range(hstart, hend):
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for iw in range(wstart, wend):
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out_sum += x_i[c_in, ih, iw]
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bin_area = (hend - hstart) * (wend - wstart)
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out_data[i, c, ph, pw] = 0. if is_empty else (
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out_sum / float(bin_area))
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self.outs = out_data.astype('float32')
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def setUp(self):
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self.op_type = 'psroi_pool'
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self.set_data()
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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if __name__ == '__main__':
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unittest.main()
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