# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed 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. import math import unittest import numpy as np from op_test import OpTest import paddle class TestROIAlignOp(OpTest): def set_data(self): self.init_test_case() self.make_rois() self.calc_roi_align() self.inputs = { 'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod), 'RoisNum': self.boxes_num, } self.attrs = { 'spatial_scale': self.spatial_scale, 'pooled_height': self.pooled_height, 'pooled_width': self.pooled_width, 'sampling_ratio': self.sampling_ratio, 'aligned': self.aligned, } self.outputs = {'Out': self.out_data} def init_test_case(self): self.batch_size = 3 self.channels = 3 self.height = 8 self.width = 6 # n, c, h, w self.x_dim = (self.batch_size, self.channels, self.height, self.width) self.spatial_scale = 1.0 / 2.0 self.pooled_height = 2 self.pooled_width = 2 self.sampling_ratio = -1 self.aligned = False self.x = np.random.random(self.x_dim).astype('float64') def pre_calc( self, x_i, roi_xmin, roi_ymin, roi_bin_grid_h, roi_bin_grid_w, bin_size_h, bin_size_w, ): count = roi_bin_grid_h * roi_bin_grid_w bilinear_pos = np.zeros( [self.channels, self.pooled_height, self.pooled_width, count, 4], np.float64, ) bilinear_w = np.zeros( [self.pooled_height, self.pooled_width, count, 4], np.float64 ) for ph in range(self.pooled_width): for pw in range(self.pooled_height): c = 0 for iy in range(roi_bin_grid_h): y = ( roi_ymin + ph * bin_size_h + (iy + 0.5) * bin_size_h / roi_bin_grid_h ) for ix in range(roi_bin_grid_w): x = ( roi_xmin + pw * bin_size_w + (ix + 0.5) * bin_size_w / roi_bin_grid_w ) if ( y < -1.0 or y > self.height or x < -1.0 or x > self.width ): continue if y <= 0: y = 0 if x <= 0: x = 0 y_low = int(y) x_low = int(x) if y_low >= self.height - 1: y = y_high = y_low = self.height - 1 else: y_high = y_low + 1 if x_low >= self.width - 1: x = x_high = x_low = self.width - 1 else: x_high = x_low + 1 ly = y - y_low lx = x - x_low hy = 1 - ly hx = 1 - lx for ch in range(self.channels): bilinear_pos[ch, ph, pw, c, 0] = x_i[ ch, y_low, x_low ] bilinear_pos[ch, ph, pw, c, 1] = x_i[ ch, y_low, x_high ] bilinear_pos[ch, ph, pw, c, 2] = x_i[ ch, y_high, x_low ] bilinear_pos[ch, ph, pw, c, 3] = x_i[ ch, y_high, x_high ] bilinear_w[ph, pw, c, 0] = hy * hx bilinear_w[ph, pw, c, 1] = hy * lx bilinear_w[ph, pw, c, 2] = ly * hx bilinear_w[ph, pw, c, 3] = ly * lx c = c + 1 return bilinear_pos, bilinear_w def calc_roi_align(self): self.out_data = np.zeros( ( self.rois_num, self.channels, self.pooled_height, self.pooled_width, ) ).astype('float64') offset = 0.5 if self.aligned else 0.0 for i in range(self.rois_num): roi = self.rois[i] roi_batch_id = int(roi[0]) x_i = self.x[roi_batch_id] roi_xmin = roi[1] * self.spatial_scale - offset roi_ymin = roi[2] * self.spatial_scale - offset roi_xmax = roi[3] * self.spatial_scale - offset roi_ymax = roi[4] * self.spatial_scale - offset roi_width = roi_xmax - roi_xmin roi_height = roi_ymax - roi_ymin if not self.aligned: roi_width = max(roi_width, 1) roi_height = max(roi_height, 1) bin_size_h = float(roi_height) / float(self.pooled_height) bin_size_w = float(roi_width) / float(self.pooled_width) roi_bin_grid_h = ( self.sampling_ratio if self.sampling_ratio > 0 else math.ceil(roi_height / self.pooled_height) ) roi_bin_grid_w = ( self.sampling_ratio if self.sampling_ratio > 0 else math.ceil(roi_width / self.pooled_width) ) count = max(int(roi_bin_grid_h * roi_bin_grid_w), 1) pre_size = count * self.pooled_width * self.pooled_height bilinear_pos, bilinear_w = self.pre_calc( x_i, roi_xmin, roi_ymin, int(roi_bin_grid_h), int(roi_bin_grid_w), bin_size_h, bin_size_w, ) for ch in range(self.channels): align_per_bin = (bilinear_pos[ch] * bilinear_w).sum(axis=-1) output_val = align_per_bin.mean(axis=-1) self.out_data[i, ch, :, :] = output_val def make_rois(self): rois = [] self.rois_lod = [[]] for bno in range(self.batch_size): self.rois_lod[0].append(bno + 1) for i in range(bno + 1): x1 = np.random.random_integers( 0, self.width // self.spatial_scale - self.pooled_width ) y1 = np.random.random_integers( 0, self.height // self.spatial_scale - self.pooled_height ) x2 = np.random.random_integers( x1 + self.pooled_width, self.width // self.spatial_scale ) y2 = np.random.random_integers( y1 + self.pooled_height, self.height // self.spatial_scale ) roi = [bno, x1, y1, x2, y2] rois.append(roi) self.rois_num = len(rois) self.rois = np.array(rois).astype("float64") self.boxes_num = np.array( [bno + 1 for bno in range(self.batch_size)] ).astype('int32') def setUp(self): self.op_type = "roi_align" self.python_api = lambda x, boxes, boxes_num, pooled_height, pooled_width, spatial_scale, sampling_ratio, aligned: paddle.vision.ops.roi_align( x, boxes, boxes_num, (pooled_height, pooled_width), spatial_scale, sampling_ratio, aligned, ) self.set_data() def test_check_output(self): self.check_output(check_eager=True) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=True) class TestROIAlignInLodOp(TestROIAlignOp): def set_data(self): self.init_test_case() self.make_rois() self.calc_roi_align() seq_len = self.rois_lod[0] self.inputs = { 'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod), 'RoisNum': np.asarray(seq_len).astype('int32'), } self.attrs = { 'spatial_scale': self.spatial_scale, 'pooled_height': self.pooled_height, 'pooled_width': self.pooled_width, 'sampling_ratio': self.sampling_ratio, 'aligned': self.aligned, } self.outputs = {'Out': self.out_data} class TestROIAlignOpWithAligned(TestROIAlignOp): def init_test_case(self): self.batch_size = 3 self.channels = 3 self.height = 8 self.width = 6 # n, c, h, w self.x_dim = (self.batch_size, self.channels, self.height, self.width) self.spatial_scale = 1.0 / 2.0 self.pooled_height = 2 self.pooled_width = 2 self.sampling_ratio = -1 self.aligned = True self.x = np.random.random(self.x_dim).astype('float64') if __name__ == '__main__': unittest.main()