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