paddlepaddle--paddle
wehub-resource-sync
320 行
11 KiB
Python
320 行
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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def python_prior_box(
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input,
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image,
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min_sizes,
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max_sizes=None,
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aspect_ratios=[1.0],
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variances=[0.1, 0.1, 0.2, 0.2],
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flip=False,
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clip=False,
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step_w=0,
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step_h=0,
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offset=0.5,
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min_max_aspect_ratios_order=False,
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name=None,
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):
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return paddle.vision.ops.prior_box(
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input,
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image,
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min_sizes=min_sizes,
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max_sizes=max_sizes,
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aspect_ratios=aspect_ratios,
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variance=variances,
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flip=flip,
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clip=clip,
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steps=[step_w, step_h],
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offset=offset,
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name=name,
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min_max_aspect_ratios_order=min_max_aspect_ratios_order,
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)
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class TestPriorBoxOp(OpTest):
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def set_data(self):
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self.init_test_params()
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self.init_test_input()
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self.init_test_output()
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self.inputs = {'Input': self.input, 'Image': self.image}
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self.attrs = {
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'min_sizes': self.min_sizes,
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'aspect_ratios': self.aspect_ratios,
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'variances': self.variances,
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'flip': self.flip,
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'clip': self.clip,
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'step_w': self.step_w,
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'step_h': self.step_h,
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'offset': self.offset,
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'min_max_aspect_ratios_order': self.min_max_aspect_ratios_order,
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}
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if len(self.max_sizes) > 0:
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self.attrs['max_sizes'] = self.max_sizes
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self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def setUp(self):
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self.op_type = "prior_box"
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self.python_api = python_prior_box
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self.set_data()
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def set_max_sizes(self):
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max_sizes = [5, 10]
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self.max_sizes = np.array(max_sizes).astype('float32').tolist()
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def set_min_max_aspect_ratios_order(self):
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self.min_max_aspect_ratios_order = False
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def init_test_params(self):
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self.layer_w = 32
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self.layer_h = 32
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self.image_w = 40
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self.image_h = 40
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self.step_w = float(self.image_w) / float(self.layer_w)
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self.step_h = float(self.image_h) / float(self.layer_h)
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self.input_channels = 2
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self.image_channels = 3
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self.batch_size = 10
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self.min_sizes = [2, 4]
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self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
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self.set_max_sizes()
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self.aspect_ratios = [2.0, 3.0]
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self.flip = True
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self.set_min_max_aspect_ratios_order()
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self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
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self.variances = [0.1, 0.1, 0.2, 0.2]
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self.variances = np.array(self.variances, dtype=np.float64).flatten()
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self.clip = True
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self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
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if len(self.max_sizes) > 0:
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self.num_priors += len(self.max_sizes)
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self.offset = 0.5
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def init_test_input(self):
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self.image = np.random.random(
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(self.batch_size, self.image_channels, self.image_w, self.image_h)
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).astype('float32')
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self.input = np.random.random(
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(self.batch_size, self.input_channels, self.layer_w, self.layer_h)
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).astype('float32')
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def init_test_output(self):
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out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
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out_boxes = np.zeros(out_dim).astype('float32')
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out_var = np.zeros(out_dim).astype('float32')
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idx = 0
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for h in range(self.layer_h):
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for w in range(self.layer_w):
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c_x = (w + self.offset) * self.step_w
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c_y = (h + self.offset) * self.step_h
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idx = 0
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for s in range(len(self.min_sizes)):
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min_size = self.min_sizes[s]
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if not self.min_max_aspect_ratios_order:
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# rest of priors
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for r in range(len(self.real_aspect_ratios)):
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ar = self.real_aspect_ratios[r]
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c_w = min_size * math.sqrt(ar) / 2
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c_h = (min_size / math.sqrt(ar)) / 2
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out_boxes[h, w, idx, :] = [
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(c_x - c_w) / self.image_w,
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(c_y - c_h) / self.image_h,
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(c_x + c_w) / self.image_w,
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(c_y + c_h) / self.image_h,
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]
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idx += 1
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if len(self.max_sizes) > 0:
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max_size = self.max_sizes[s]
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# second prior: aspect_ratio = 1,
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c_w = c_h = math.sqrt(min_size * max_size) / 2
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out_boxes[h, w, idx, :] = [
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(c_x - c_w) / self.image_w,
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(c_y - c_h) / self.image_h,
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(c_x + c_w) / self.image_w,
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(c_y + c_h) / self.image_h,
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]
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idx += 1
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else:
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c_w = c_h = min_size / 2.0
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out_boxes[h, w, idx, :] = [
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(c_x - c_w) / self.image_w,
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(c_y - c_h) / self.image_h,
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(c_x + c_w) / self.image_w,
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(c_y + c_h) / self.image_h,
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]
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idx += 1
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if len(self.max_sizes) > 0:
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max_size = self.max_sizes[s]
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# second prior: aspect_ratio = 1,
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c_w = c_h = math.sqrt(min_size * max_size) / 2
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out_boxes[h, w, idx, :] = [
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(c_x - c_w) / self.image_w,
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(c_y - c_h) / self.image_h,
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(c_x + c_w) / self.image_w,
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(c_y + c_h) / self.image_h,
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]
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idx += 1
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# rest of priors
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for r in range(len(self.real_aspect_ratios)):
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ar = self.real_aspect_ratios[r]
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if abs(ar - 1.0) < 1e-6:
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continue
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c_w = min_size * math.sqrt(ar) / 2
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c_h = (min_size / math.sqrt(ar)) / 2
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out_boxes[h, w, idx, :] = [
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(c_x - c_w) / self.image_w,
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(c_y - c_h) / self.image_h,
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(c_x + c_w) / self.image_w,
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(c_y + c_h) / self.image_h,
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]
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idx += 1
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# clip the prior's coordinate such that it is within[0, 1]
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if self.clip:
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out_boxes = np.clip(out_boxes, 0.0, 1.0)
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# set the variance.
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out_var = np.tile(
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self.variances, (self.layer_h, self.layer_w, self.num_priors, 1)
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)
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self.out_boxes = out_boxes.astype('float32')
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self.out_var = out_var.astype('float32')
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class TestPriorBoxOpWithoutMaxSize(TestPriorBoxOp):
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def set_max_sizes(self):
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self.max_sizes = []
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class TestPriorBoxOpWithSpecifiedOutOrder(TestPriorBoxOp):
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def set_min_max_aspect_ratios_order(self):
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self.min_max_aspect_ratios_order = True
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class TestPriorBoxOp_ZeroSize(TestPriorBoxOp):
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def init_test_params(self):
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self.__class__.op_type = "prior_box"
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self.layer_w = 0
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self.layer_h = 0
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self.image_w = 40
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self.image_h = 40
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self.step_w = (
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float(self.image_w) / float(self.layer_w) if self.layer_w > 0 else 0
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)
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self.step_h = (
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float(self.image_h) / float(self.layer_h) if self.layer_h > 0 else 0
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)
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self.input_channels = 2
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self.image_channels = 3
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self.batch_size = 10
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self.min_sizes = [2, 4]
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self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
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self.set_max_sizes()
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self.aspect_ratios = [2.0, 3.0]
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self.flip = True
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self.set_min_max_aspect_ratios_order()
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self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
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self.variances = [0.1, 0.1, 0.2, 0.2]
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self.variances = np.array(self.variances, dtype=np.float64).flatten()
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self.clip = True
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self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
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if len(self.max_sizes) > 0:
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self.num_priors += len(self.max_sizes)
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self.offset = 0.5
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class TestPriorBoxAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(678)
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self.input_np = np.random.rand(2, 10, 32, 32).astype('float32')
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self.image_np = np.random.rand(2, 10, 40, 40).astype('float32')
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self.min_sizes = [2.0, 4.0]
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def test_dygraph_with_static(self):
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paddle.enable_static()
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input = paddle.static.data(
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name='input', shape=[2, 10, 32, 32], dtype='float32'
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)
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image = paddle.static.data(
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name='image', shape=[2, 10, 40, 40], dtype='float32'
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)
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box, var = paddle.vision.ops.prior_box(
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input=input,
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image=image,
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min_sizes=self.min_sizes,
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clip=True,
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flip=True,
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)
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exe = paddle.static.Executor()
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box_np, var_np = exe.run(
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paddle.static.default_main_program(),
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feed={
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'input': self.input_np,
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'image': self.image_np,
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},
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fetch_list=[box, var],
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)
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paddle.disable_static()
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inputs_dy = paddle.to_tensor(self.input_np)
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image_dy = paddle.to_tensor(self.image_np)
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box_dy, var_dy = paddle.vision.ops.prior_box(
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input=inputs_dy,
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image=image_dy,
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min_sizes=self.min_sizes,
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clip=True,
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flip=True,
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)
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box_dy_np = box_dy.numpy()
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var_dy_np = var_dy.numpy()
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np.testing.assert_allclose(box_np, box_dy_np)
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np.testing.assert_allclose(var_np, var_dy_np)
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paddle.enable_static()
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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