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- # 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 get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op_test_xpu import XPUOpTest
-
- import paddle
-
- paddle.enable_static()
-
-
- class XPUTestPriorBoxOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'prior_box'
- self.use_dynamic_create_class = False
-
- class TestPriorBoxOp(XPUOpTest):
- def setUp(self):
- self.op_type = "prior_box"
- self.use_xpu = True
- self.dtype = self.in_type
- self.set_data()
-
- def set_data(self):
- self.init_test_params()
- self.init_test_input()
- self.init_test_output()
- self.inputs = {'Input': self.input, 'Image': self.image}
-
- self.attrs = {
- 'min_sizes': self.min_sizes,
- 'aspect_ratios': self.aspect_ratios,
- 'variances': self.variances,
- 'flip': self.flip,
- 'clip': self.clip,
- 'min_max_aspect_ratios_order': self.min_max_aspect_ratios_order,
- 'step_w': self.step_w,
- 'step_h': self.step_h,
- 'offset': self.offset,
- }
- if len(self.max_sizes) > 0:
- self.attrs['max_sizes'] = self.max_sizes
-
- self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
-
- def test_check_output(self):
- place = paddle.XPUPlace(0)
- self.check_output_with_place(place)
-
- def set_max_sizes(self):
- max_sizes = [5, 10]
- self.max_sizes = np.array(max_sizes).astype('float32').tolist()
-
- def set_min_max_aspect_ratios_order(self):
- self.min_max_aspect_ratios_order = False
-
- def init_test_params(self):
- self.layer_w = 32
- self.layer_h = 32
-
- self.image_w = 40
- self.image_h = 40
-
- self.step_w = float(self.image_w) / float(self.layer_w)
- self.step_h = float(self.image_h) / float(self.layer_h)
-
- self.input_channels = 2
- self.image_channels = 3
- self.batch_size = 10
-
- self.min_sizes = [2, 4]
- self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
- self.set_max_sizes()
- self.aspect_ratios = [2.0, 3.0]
- self.flip = True
- self.set_min_max_aspect_ratios_order()
- self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
- self.aspect_ratios = np.array(
- self.aspect_ratios, dtype=np.float64
- ).flatten()
- self.variances = [0.1, 0.1, 0.2, 0.2]
- self.variances = np.array(
- self.variances, dtype=np.float64
- ).flatten()
-
- self.clip = True
- self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
- if len(self.max_sizes) > 0:
- self.num_priors += len(self.max_sizes)
- self.offset = 0.5
-
- def init_test_input(self):
- self.image = np.random.random(
- (
- self.batch_size,
- self.image_channels,
- self.image_w,
- self.image_h,
- )
- ).astype(self.dtype)
-
- self.input = np.random.random(
- (
- self.batch_size,
- self.input_channels,
- self.layer_w,
- self.layer_h,
- )
- ).astype(self.dtype)
-
- def init_test_output(self):
- out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
- out_boxes = np.zeros(out_dim).astype(self.dtype)
- out_var = np.zeros(out_dim).astype(self.dtype)
-
- idx = 0
- for h in range(self.layer_h):
- for w in range(self.layer_w):
- c_x = (w + self.offset) * self.step_w
- c_y = (h + self.offset) * self.step_h
- idx = 0
- for s in range(len(self.min_sizes)):
- min_size = self.min_sizes[s]
- if not self.min_max_aspect_ratios_order:
- # rest of priors
- for r in range(len(self.real_aspect_ratios)):
- ar = self.real_aspect_ratios[r]
- c_w = min_size * math.sqrt(ar) / 2
- c_h = (min_size / math.sqrt(ar)) / 2
- out_boxes[h, w, idx, :] = [
- (c_x - c_w) / self.image_w,
- (c_y - c_h) / self.image_h,
- (c_x + c_w) / self.image_w,
- (c_y + c_h) / self.image_h,
- ]
- idx += 1
-
- if len(self.max_sizes) > 0:
- max_size = self.max_sizes[s]
- # second prior: aspect_ratio = 1,
- c_w = c_h = math.sqrt(min_size * max_size) / 2
- out_boxes[h, w, idx, :] = [
- (c_x - c_w) / self.image_w,
- (c_y - c_h) / self.image_h,
- (c_x + c_w) / self.image_w,
- (c_y + c_h) / self.image_h,
- ]
- idx += 1
- else:
- c_w = c_h = min_size / 2.0
- out_boxes[h, w, idx, :] = [
- (c_x - c_w) / self.image_w,
- (c_y - c_h) / self.image_h,
- (c_x + c_w) / self.image_w,
- (c_y + c_h) / self.image_h,
- ]
- idx += 1
- if len(self.max_sizes) > 0:
- max_size = self.max_sizes[s]
- # second prior: aspect_ratio = 1,
- c_w = c_h = math.sqrt(min_size * max_size) / 2
- out_boxes[h, w, idx, :] = [
- (c_x - c_w) / self.image_w,
- (c_y - c_h) / self.image_h,
- (c_x + c_w) / self.image_w,
- (c_y + c_h) / self.image_h,
- ]
- idx += 1
-
- # rest of priors
- for r in range(len(self.real_aspect_ratios)):
- ar = self.real_aspect_ratios[r]
- if abs(ar - 1.0) < 1e-6:
- continue
- c_w = min_size * math.sqrt(ar) / 2
- c_h = (min_size / math.sqrt(ar)) / 2
- out_boxes[h, w, idx, :] = [
- (c_x - c_w) / self.image_w,
- (c_y - c_h) / self.image_h,
- (c_x + c_w) / self.image_w,
- (c_y + c_h) / self.image_h,
- ]
- idx += 1
-
- # clip the prior's coordinate such that it is within[0, 1]
- if self.clip:
- out_boxes = np.clip(out_boxes, 0.0, 1.0)
- # set the variance.
- out_var = np.tile(
- self.variances, (self.layer_h, self.layer_w, self.num_priors, 1)
- )
- self.out_boxes = out_boxes.astype(self.dtype)
- self.out_var = out_var.astype(self.dtype)
-
- class TestPriorBoxOpWithoutMaxSize(TestPriorBoxOp):
- def set_max_sizes(self):
- self.max_sizes = []
-
- class TestPriorBoxOpWithSpecifiedOutOrder(TestPriorBoxOp):
- def set_min_max_aspect_ratios_order(self):
- self.min_max_aspect_ratios_order = True
-
-
- support_types = get_xpu_op_support_types('prior_box')
- for stype in support_types:
- create_test_class(globals(), XPUTestPriorBoxOp, stype)
-
- if __name__ == '__main__':
- unittest.main()
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