forked from PulseFocusPlatform/PulseFocusPlatform
836 lines
30 KiB
Python
836 lines
30 KiB
Python
# Copyright (c) 2020 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 os, sys
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# add python path of PadleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4)))
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if parent_path not in sys.path:
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sys.path.append(parent_path)
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.dygraph import base
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import ppdet.modeling.ops as ops
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from ppdet.modeling.tests.test_base import LayerTest
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def make_rois(h, w, rois_num, output_size):
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rois = np.zeros((0, 4)).astype('float32')
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for roi_num in rois_num:
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roi = np.zeros((roi_num, 4)).astype('float32')
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roi[:, 0] = np.random.randint(0, h - output_size[0], size=roi_num)
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roi[:, 1] = np.random.randint(0, w - output_size[1], size=roi_num)
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roi[:, 2] = np.random.randint(roi[:, 0] + output_size[0], h)
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roi[:, 3] = np.random.randint(roi[:, 1] + output_size[1], w)
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rois = np.vstack((rois, roi))
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return rois
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def softmax(x):
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# clip to shiftx, otherwise, when calc loss with
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# log(exp(shiftx)), may get log(0)=INF
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shiftx = (x - np.max(x)).clip(-64.)
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exps = np.exp(shiftx)
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return exps / np.sum(exps)
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class TestCollectFpnProposals(LayerTest):
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def test_collect_fpn_proposals(self):
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multi_bboxes_np = []
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multi_scores_np = []
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rois_num_per_level_np = []
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for i in range(4):
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bboxes_np = np.random.rand(5, 4).astype('float32')
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scores_np = np.random.rand(5, 1).astype('float32')
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rois_num = np.array([2, 3]).astype('int32')
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multi_bboxes_np.append(bboxes_np)
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multi_scores_np.append(scores_np)
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rois_num_per_level_np.append(rois_num)
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with self.static_graph():
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multi_bboxes = []
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multi_scores = []
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rois_num_per_level = []
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for i in range(4):
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bboxes = paddle.static.data(
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name='rois' + str(i),
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shape=[5, 4],
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dtype='float32',
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lod_level=1)
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scores = paddle.static.data(
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name='scores' + str(i),
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shape=[5, 1],
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dtype='float32',
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lod_level=1)
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rois_num = paddle.static.data(
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name='rois_num' + str(i), shape=[None], dtype='int32')
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multi_bboxes.append(bboxes)
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multi_scores.append(scores)
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rois_num_per_level.append(rois_num)
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fpn_rois, rois_num = ops.collect_fpn_proposals(
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multi_bboxes,
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multi_scores,
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2,
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5,
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10,
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rois_num_per_level=rois_num_per_level)
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feed = {}
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for i in range(4):
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feed['rois' + str(i)] = multi_bboxes_np[i]
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feed['scores' + str(i)] = multi_scores_np[i]
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feed['rois_num' + str(i)] = rois_num_per_level_np[i]
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fpn_rois_stat, rois_num_stat = self.get_static_graph_result(
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feed=feed, fetch_list=[fpn_rois, rois_num], with_lod=True)
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fpn_rois_stat = np.array(fpn_rois_stat)
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rois_num_stat = np.array(rois_num_stat)
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with self.dynamic_graph():
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multi_bboxes_dy = []
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multi_scores_dy = []
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rois_num_per_level_dy = []
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for i in range(4):
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bboxes_dy = base.to_variable(multi_bboxes_np[i])
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scores_dy = base.to_variable(multi_scores_np[i])
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rois_num_dy = base.to_variable(rois_num_per_level_np[i])
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multi_bboxes_dy.append(bboxes_dy)
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multi_scores_dy.append(scores_dy)
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rois_num_per_level_dy.append(rois_num_dy)
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fpn_rois_dy, rois_num_dy = ops.collect_fpn_proposals(
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multi_bboxes_dy,
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multi_scores_dy,
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2,
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5,
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10,
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rois_num_per_level=rois_num_per_level_dy)
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fpn_rois_dy = fpn_rois_dy.numpy()
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rois_num_dy = rois_num_dy.numpy()
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self.assertTrue(np.array_equal(fpn_rois_stat, fpn_rois_dy))
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self.assertTrue(np.array_equal(rois_num_stat, rois_num_dy))
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def test_collect_fpn_proposals_error(self):
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def generate_input(bbox_type, score_type, name):
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multi_bboxes = []
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multi_scores = []
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for i in range(4):
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bboxes = paddle.static.data(
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name='rois' + name + str(i),
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shape=[10, 4],
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dtype=bbox_type,
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lod_level=1)
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scores = paddle.static.data(
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name='scores' + name + str(i),
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shape=[10, 1],
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dtype=score_type,
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lod_level=1)
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multi_bboxes.append(bboxes)
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multi_scores.append(scores)
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return multi_bboxes, multi_scores
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with self.static_graph():
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bbox1 = paddle.static.data(
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name='rois', shape=[5, 10, 4], dtype='float32', lod_level=1)
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score1 = paddle.static.data(
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name='scores', shape=[5, 10, 1], dtype='float32', lod_level=1)
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bbox2, score2 = generate_input('int32', 'float32', '2')
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self.assertRaises(
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TypeError,
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ops.collect_fpn_proposals,
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multi_rois=bbox1,
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multi_scores=score1,
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min_level=2,
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max_level=5,
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post_nms_top_n=2000)
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self.assertRaises(
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TypeError,
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ops.collect_fpn_proposals,
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multi_rois=bbox2,
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multi_scores=score2,
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min_level=2,
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max_level=5,
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post_nms_top_n=2000)
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paddle.disable_static()
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class TestDistributeFpnProposals(LayerTest):
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def test_distribute_fpn_proposals(self):
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rois_np = np.random.rand(10, 4).astype('float32')
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rois_num_np = np.array([4, 6]).astype('int32')
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with self.static_graph():
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rois = paddle.static.data(
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name='rois', shape=[10, 4], dtype='float32')
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rois_num = paddle.static.data(
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name='rois_num', shape=[None], dtype='int32')
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multi_rois, restore_ind, rois_num_per_level = ops.distribute_fpn_proposals(
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fpn_rois=rois,
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min_level=2,
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max_level=5,
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refer_level=4,
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refer_scale=224,
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rois_num=rois_num)
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fetch_list = multi_rois + [restore_ind] + rois_num_per_level
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output_stat = self.get_static_graph_result(
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feed={'rois': rois_np,
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'rois_num': rois_num_np},
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fetch_list=fetch_list,
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with_lod=True)
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output_stat_np = []
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for output in output_stat:
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output_np = np.array(output)
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if len(output_np) > 0:
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output_stat_np.append(output_np)
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with self.dynamic_graph():
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rois_dy = base.to_variable(rois_np)
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rois_num_dy = base.to_variable(rois_num_np)
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multi_rois_dy, restore_ind_dy, rois_num_per_level_dy = ops.distribute_fpn_proposals(
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fpn_rois=rois_dy,
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min_level=2,
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max_level=5,
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refer_level=4,
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refer_scale=224,
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rois_num=rois_num_dy)
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output_dy = multi_rois_dy + [restore_ind_dy] + rois_num_per_level_dy
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output_dy_np = []
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for output in output_dy:
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output_np = output.numpy()
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if len(output_np) > 0:
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output_dy_np.append(output_np)
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for res_stat, res_dy in zip(output_stat_np, output_dy_np):
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self.assertTrue(np.array_equal(res_stat, res_dy))
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def test_distribute_fpn_proposals_error(self):
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with self.static_graph():
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fpn_rois = paddle.static.data(
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name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
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self.assertRaises(
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TypeError,
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ops.distribute_fpn_proposals,
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fpn_rois=fpn_rois,
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min_level=2,
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max_level=5,
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refer_level=4,
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refer_scale=224)
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paddle.disable_static()
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class TestROIAlign(LayerTest):
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def test_roi_align(self):
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b, c, h, w = 2, 12, 20, 20
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inputs_np = np.random.rand(b, c, h, w).astype('float32')
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rois_num = [4, 6]
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output_size = (7, 7)
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rois_np = make_rois(h, w, rois_num, output_size)
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rois_num_np = np.array(rois_num).astype('int32')
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with self.static_graph():
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inputs = paddle.static.data(
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name='inputs', shape=[b, c, h, w], dtype='float32')
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rois = paddle.static.data(
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name='rois', shape=[10, 4], dtype='float32')
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rois_num = paddle.static.data(
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name='rois_num', shape=[None], dtype='int32')
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output = ops.roi_align(
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input=inputs,
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rois=rois,
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output_size=output_size,
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rois_num=rois_num)
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output_np, = self.get_static_graph_result(
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feed={
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'inputs': inputs_np,
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'rois': rois_np,
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'rois_num': rois_num_np
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},
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fetch_list=output,
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with_lod=False)
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with self.dynamic_graph():
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inputs_dy = base.to_variable(inputs_np)
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rois_dy = base.to_variable(rois_np)
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rois_num_dy = base.to_variable(rois_num_np)
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output_dy = ops.roi_align(
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input=inputs_dy,
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rois=rois_dy,
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output_size=output_size,
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rois_num=rois_num_dy)
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output_dy_np = output_dy.numpy()
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self.assertTrue(np.array_equal(output_np, output_dy_np))
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def test_roi_align_error(self):
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with self.static_graph():
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inputs = paddle.static.data(
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name='inputs', shape=[2, 12, 20, 20], dtype='float32')
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rois = paddle.static.data(
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name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
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self.assertRaises(
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TypeError,
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ops.roi_align,
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input=inputs,
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rois=rois,
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output_size=(7, 7))
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paddle.disable_static()
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class TestROIPool(LayerTest):
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def test_roi_pool(self):
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b, c, h, w = 2, 12, 20, 20
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inputs_np = np.random.rand(b, c, h, w).astype('float32')
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rois_num = [4, 6]
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output_size = (7, 7)
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rois_np = make_rois(h, w, rois_num, output_size)
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rois_num_np = np.array(rois_num).astype('int32')
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with self.static_graph():
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inputs = paddle.static.data(
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name='inputs', shape=[b, c, h, w], dtype='float32')
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rois = paddle.static.data(
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name='rois', shape=[10, 4], dtype='float32')
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rois_num = paddle.static.data(
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name='rois_num', shape=[None], dtype='int32')
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output, _ = ops.roi_pool(
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input=inputs,
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rois=rois,
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output_size=output_size,
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rois_num=rois_num)
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output_np, = self.get_static_graph_result(
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feed={
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'inputs': inputs_np,
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'rois': rois_np,
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'rois_num': rois_num_np
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},
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fetch_list=[output],
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with_lod=False)
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with self.dynamic_graph():
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inputs_dy = base.to_variable(inputs_np)
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rois_dy = base.to_variable(rois_np)
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rois_num_dy = base.to_variable(rois_num_np)
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output_dy, _ = ops.roi_pool(
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input=inputs_dy,
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rois=rois_dy,
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output_size=output_size,
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rois_num=rois_num_dy)
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output_dy_np = output_dy.numpy()
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self.assertTrue(np.array_equal(output_np, output_dy_np))
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def test_roi_pool_error(self):
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with self.static_graph():
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inputs = paddle.static.data(
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name='inputs', shape=[2, 12, 20, 20], dtype='float32')
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rois = paddle.static.data(
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name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
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self.assertRaises(
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TypeError,
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ops.roi_pool,
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input=inputs,
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rois=rois,
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output_size=(7, 7))
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paddle.disable_static()
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class TestIoUSimilarity(LayerTest):
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def test_iou_similarity(self):
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b, c, h, w = 2, 12, 20, 20
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inputs_np = np.random.rand(b, c, h, w).astype('float32')
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output_size = (7, 7)
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x_np = make_rois(h, w, [20], output_size)
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y_np = make_rois(h, w, [10], output_size)
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with self.static_graph():
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x = paddle.static.data(name='x', shape=[20, 4], dtype='float32')
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y = paddle.static.data(name='y', shape=[10, 4], dtype='float32')
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iou = ops.iou_similarity(x=x, y=y)
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iou_np, = self.get_static_graph_result(
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feed={
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'x': x_np,
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'y': y_np,
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}, fetch_list=[iou], with_lod=False)
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with self.dynamic_graph():
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x_dy = base.to_variable(x_np)
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y_dy = base.to_variable(y_np)
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iou_dy = ops.iou_similarity(x=x_dy, y=y_dy)
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iou_dy_np = iou_dy.numpy()
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self.assertTrue(np.array_equal(iou_np, iou_dy_np))
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class TestBipartiteMatch(LayerTest):
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def test_bipartite_match(self):
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distance = np.random.random((20, 10)).astype('float32')
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with self.static_graph():
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x = paddle.static.data(name='x', shape=[20, 10], dtype='float32')
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match_indices, match_dist = ops.bipartite_match(
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x, match_type='per_prediction', dist_threshold=0.5)
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match_indices_np, match_dist_np = self.get_static_graph_result(
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feed={'x': distance, },
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fetch_list=[match_indices, match_dist],
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with_lod=False)
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with self.dynamic_graph():
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x_dy = base.to_variable(distance)
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match_indices_dy, match_dist_dy = ops.bipartite_match(
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x_dy, match_type='per_prediction', dist_threshold=0.5)
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match_indices_dy_np = match_indices_dy.numpy()
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match_dist_dy_np = match_dist_dy.numpy()
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self.assertTrue(np.array_equal(match_indices_np, match_indices_dy_np))
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self.assertTrue(np.array_equal(match_dist_np, match_dist_dy_np))
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class TestYoloBox(LayerTest):
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def test_yolo_box(self):
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# x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
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np_x = np.random.random([1, 30, 7, 7]).astype('float32')
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np_origin_shape = np.array([[608, 608]], dtype='int32')
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class_num = 10
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conf_thresh = 0.01
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downsample_ratio = 32
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scale_x_y = 1.2
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# static
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with self.static_graph():
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# x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
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x = paddle.static.data(
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name='x', shape=[1, 30, 7, 7], dtype='float32')
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origin_shape = paddle.static.data(
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name='origin_shape', shape=[1, 2], dtype='int32')
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boxes, scores = ops.yolo_box(
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x,
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origin_shape, [10, 13, 30, 13],
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class_num,
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conf_thresh,
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downsample_ratio,
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scale_x_y=scale_x_y)
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boxes_np, scores_np = self.get_static_graph_result(
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feed={
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'x': np_x,
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'origin_shape': np_origin_shape,
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},
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fetch_list=[boxes, scores],
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with_lod=False)
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# dygraph
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with self.dynamic_graph():
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x_dy = fluid.layers.assign(np_x)
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origin_shape_dy = fluid.layers.assign(np_origin_shape)
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boxes_dy, scores_dy = ops.yolo_box(
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x_dy,
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origin_shape_dy, [10, 13, 30, 13],
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10,
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0.01,
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32,
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scale_x_y=scale_x_y)
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|
|
boxes_dy_np = boxes_dy.numpy()
|
|
scores_dy_np = scores_dy.numpy()
|
|
|
|
self.assertTrue(np.array_equal(boxes_np, boxes_dy_np))
|
|
self.assertTrue(np.array_equal(scores_np, scores_dy_np))
|
|
|
|
def test_yolo_box_error(self):
|
|
with self.static_graph():
|
|
# x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
|
|
x = paddle.static.data(
|
|
name='x', shape=[1, 30, 7, 7], dtype='float32')
|
|
origin_shape = paddle.static.data(
|
|
name='origin_shape', shape=[1, 2], dtype='int32')
|
|
|
|
self.assertRaises(
|
|
TypeError,
|
|
ops.yolo_box,
|
|
x,
|
|
origin_shape, [10, 13, 30, 13],
|
|
10.123,
|
|
0.01,
|
|
32,
|
|
scale_x_y=1.2)
|
|
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestPriorBox(LayerTest):
|
|
def test_prior_box(self):
|
|
input_np = np.random.rand(2, 10, 32, 32).astype('float32')
|
|
image_np = np.random.rand(2, 10, 40, 40).astype('float32')
|
|
min_sizes = [2, 4]
|
|
with self.static_graph():
|
|
input = paddle.static.data(
|
|
name='input', shape=[2, 10, 32, 32], dtype='float32')
|
|
image = paddle.static.data(
|
|
name='image', shape=[2, 10, 40, 40], dtype='float32')
|
|
|
|
box, var = ops.prior_box(
|
|
input=input,
|
|
image=image,
|
|
min_sizes=min_sizes,
|
|
clip=True,
|
|
flip=True)
|
|
box_np, var_np = self.get_static_graph_result(
|
|
feed={
|
|
'input': input_np,
|
|
'image': image_np,
|
|
},
|
|
fetch_list=[box, var],
|
|
with_lod=False)
|
|
|
|
with self.dynamic_graph():
|
|
inputs_dy = base.to_variable(input_np)
|
|
image_dy = base.to_variable(image_np)
|
|
|
|
box_dy, var_dy = ops.prior_box(
|
|
input=inputs_dy,
|
|
image=image_dy,
|
|
min_sizes=min_sizes,
|
|
clip=True,
|
|
flip=True)
|
|
box_dy_np = box_dy.numpy()
|
|
var_dy_np = var_dy.numpy()
|
|
|
|
self.assertTrue(np.array_equal(box_np, box_dy_np))
|
|
self.assertTrue(np.array_equal(var_np, var_dy_np))
|
|
|
|
def test_prior_box_error(self):
|
|
with self.static_graph():
|
|
input = paddle.static.data(
|
|
name='input', shape=[2, 10, 32, 32], dtype='int32')
|
|
image = paddle.static.data(
|
|
name='image', shape=[2, 10, 40, 40], dtype='int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
ops.prior_box,
|
|
input=input,
|
|
image=image,
|
|
min_sizes=[2, 4],
|
|
clip=True,
|
|
flip=True)
|
|
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestMulticlassNms(LayerTest):
|
|
def test_multiclass_nms(self):
|
|
boxes_np = np.random.rand(10, 81, 4).astype('float32')
|
|
scores_np = np.random.rand(10, 81).astype('float32')
|
|
rois_num_np = np.array([2, 8]).astype('int32')
|
|
with self.static_graph():
|
|
boxes = paddle.static.data(
|
|
name='bboxes',
|
|
shape=[None, 81, 4],
|
|
dtype='float32',
|
|
lod_level=1)
|
|
scores = paddle.static.data(
|
|
name='scores', shape=[None, 81], dtype='float32', lod_level=1)
|
|
rois_num = paddle.static.data(
|
|
name='rois_num', shape=[None], dtype='int32')
|
|
|
|
output = ops.multiclass_nms(
|
|
bboxes=boxes,
|
|
scores=scores,
|
|
background_label=0,
|
|
score_threshold=0.5,
|
|
nms_top_k=400,
|
|
nms_threshold=0.3,
|
|
keep_top_k=200,
|
|
normalized=False,
|
|
return_index=True,
|
|
rois_num=rois_num)
|
|
out_np, index_np, nms_rois_num_np = self.get_static_graph_result(
|
|
feed={
|
|
'bboxes': boxes_np,
|
|
'scores': scores_np,
|
|
'rois_num': rois_num_np
|
|
},
|
|
fetch_list=output,
|
|
with_lod=True)
|
|
out_np = np.array(out_np)
|
|
index_np = np.array(index_np)
|
|
nms_rois_num_np = np.array(nms_rois_num_np)
|
|
|
|
with self.dynamic_graph():
|
|
boxes_dy = base.to_variable(boxes_np)
|
|
scores_dy = base.to_variable(scores_np)
|
|
rois_num_dy = base.to_variable(rois_num_np)
|
|
|
|
out_dy, index_dy, nms_rois_num_dy = ops.multiclass_nms(
|
|
bboxes=boxes_dy,
|
|
scores=scores_dy,
|
|
background_label=0,
|
|
score_threshold=0.5,
|
|
nms_top_k=400,
|
|
nms_threshold=0.3,
|
|
keep_top_k=200,
|
|
normalized=False,
|
|
return_index=True,
|
|
rois_num=rois_num_dy)
|
|
out_dy_np = out_dy.numpy()
|
|
index_dy_np = index_dy.numpy()
|
|
nms_rois_num_dy_np = nms_rois_num_dy.numpy()
|
|
|
|
self.assertTrue(np.array_equal(out_np, out_dy_np))
|
|
self.assertTrue(np.array_equal(index_np, index_dy_np))
|
|
self.assertTrue(np.array_equal(nms_rois_num_np, nms_rois_num_dy_np))
|
|
|
|
def test_multiclass_nms_error(self):
|
|
with self.static_graph():
|
|
boxes = paddle.static.data(
|
|
name='bboxes', shape=[81, 4], dtype='float32', lod_level=1)
|
|
scores = paddle.static.data(
|
|
name='scores', shape=[81], dtype='float32', lod_level=1)
|
|
rois_num = paddle.static.data(
|
|
name='rois_num', shape=[40, 41], dtype='int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
ops.multiclass_nms,
|
|
boxes=boxes,
|
|
scores=scores,
|
|
background_label=0,
|
|
score_threshold=0.5,
|
|
nms_top_k=400,
|
|
nms_threshold=0.3,
|
|
keep_top_k=200,
|
|
normalized=False,
|
|
return_index=True,
|
|
rois_num=rois_num)
|
|
|
|
|
|
class TestMatrixNMS(LayerTest):
|
|
def test_matrix_nms(self):
|
|
N, M, C = 7, 1200, 21
|
|
BOX_SIZE = 4
|
|
nms_top_k = 400
|
|
keep_top_k = 200
|
|
score_threshold = 0.01
|
|
post_threshold = 0.
|
|
|
|
scores_np = np.random.random((N * M, C)).astype('float32')
|
|
scores_np = np.apply_along_axis(softmax, 1, scores_np)
|
|
scores_np = np.reshape(scores_np, (N, M, C))
|
|
scores_np = np.transpose(scores_np, (0, 2, 1))
|
|
|
|
boxes_np = np.random.random((N, M, BOX_SIZE)).astype('float32')
|
|
boxes_np[:, :, 0:2] = boxes_np[:, :, 0:2] * 0.5
|
|
boxes_np[:, :, 2:4] = boxes_np[:, :, 2:4] * 0.5 + 0.5
|
|
|
|
with self.static_graph():
|
|
boxes = paddle.static.data(
|
|
name='boxes', shape=[N, M, BOX_SIZE], dtype='float32')
|
|
scores = paddle.static.data(
|
|
name='scores', shape=[N, C, M], dtype='float32')
|
|
out, index, _ = ops.matrix_nms(
|
|
bboxes=boxes,
|
|
scores=scores,
|
|
score_threshold=score_threshold,
|
|
post_threshold=post_threshold,
|
|
nms_top_k=nms_top_k,
|
|
keep_top_k=keep_top_k,
|
|
return_index=True)
|
|
out_np, index_np = self.get_static_graph_result(
|
|
feed={'boxes': boxes_np,
|
|
'scores': scores_np},
|
|
fetch_list=[out, index],
|
|
with_lod=True)
|
|
|
|
with self.dynamic_graph():
|
|
boxes_dy = base.to_variable(boxes_np)
|
|
scores_dy = base.to_variable(scores_np)
|
|
|
|
out_dy, index_dy, _ = ops.matrix_nms(
|
|
bboxes=boxes_dy,
|
|
scores=scores_dy,
|
|
score_threshold=score_threshold,
|
|
post_threshold=post_threshold,
|
|
nms_top_k=nms_top_k,
|
|
keep_top_k=keep_top_k,
|
|
return_index=True)
|
|
out_dy_np = out_dy.numpy()
|
|
index_dy_np = index_dy.numpy()
|
|
|
|
self.assertTrue(np.array_equal(out_np, out_dy_np))
|
|
self.assertTrue(np.array_equal(index_np, index_dy_np))
|
|
|
|
def test_matrix_nms_error(self):
|
|
with self.static_graph():
|
|
bboxes = paddle.static.data(
|
|
name='bboxes', shape=[7, 1200, 4], dtype='float32')
|
|
scores = paddle.static.data(
|
|
name='data_error', shape=[7, 21, 1200], dtype='int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
ops.matrix_nms,
|
|
bboxes=bboxes,
|
|
scores=scores,
|
|
score_threshold=0.01,
|
|
post_threshold=0.,
|
|
nms_top_k=400,
|
|
keep_top_k=200,
|
|
return_index=True)
|
|
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestBoxCoder(LayerTest):
|
|
def test_box_coder(self):
|
|
|
|
prior_box_np = np.random.random((81, 4)).astype('float32')
|
|
prior_box_var_np = np.random.random((81, 4)).astype('float32')
|
|
target_box_np = np.random.random((20, 81, 4)).astype('float32')
|
|
|
|
# static
|
|
with self.static_graph():
|
|
prior_box = paddle.static.data(
|
|
name='prior_box', shape=[81, 4], dtype='float32')
|
|
prior_box_var = paddle.static.data(
|
|
name='prior_box_var', shape=[81, 4], dtype='float32')
|
|
target_box = paddle.static.data(
|
|
name='target_box', shape=[20, 81, 4], dtype='float32')
|
|
|
|
boxes = ops.box_coder(
|
|
prior_box=prior_box,
|
|
prior_box_var=prior_box_var,
|
|
target_box=target_box,
|
|
code_type="decode_center_size",
|
|
box_normalized=False)
|
|
|
|
boxes_np, = self.get_static_graph_result(
|
|
feed={
|
|
'prior_box': prior_box_np,
|
|
'prior_box_var': prior_box_var_np,
|
|
'target_box': target_box_np,
|
|
},
|
|
fetch_list=[boxes],
|
|
with_lod=False)
|
|
|
|
# dygraph
|
|
with self.dynamic_graph():
|
|
prior_box_dy = base.to_variable(prior_box_np)
|
|
prior_box_var_dy = base.to_variable(prior_box_var_np)
|
|
target_box_dy = base.to_variable(target_box_np)
|
|
|
|
boxes_dy = ops.box_coder(
|
|
prior_box=prior_box_dy,
|
|
prior_box_var=prior_box_var_dy,
|
|
target_box=target_box_dy,
|
|
code_type="decode_center_size",
|
|
box_normalized=False)
|
|
|
|
boxes_dy_np = boxes_dy.numpy()
|
|
|
|
self.assertTrue(np.array_equal(boxes_np, boxes_dy_np))
|
|
|
|
def test_box_coder_error(self):
|
|
with self.static_graph():
|
|
prior_box = paddle.static.data(
|
|
name='prior_box', shape=[81, 4], dtype='int32')
|
|
prior_box_var = paddle.static.data(
|
|
name='prior_box_var', shape=[81, 4], dtype='float32')
|
|
target_box = paddle.static.data(
|
|
name='target_box', shape=[20, 81, 4], dtype='float32')
|
|
|
|
self.assertRaises(TypeError, ops.box_coder, prior_box,
|
|
prior_box_var, target_box)
|
|
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestGenerateProposals(LayerTest):
|
|
def test_generate_proposals(self):
|
|
scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
|
|
bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32')
|
|
im_shape_np = np.array([[8, 8], [6, 6]]).astype('float32')
|
|
anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4),
|
|
[4, 4, 3, 4]).astype('float32')
|
|
variances_np = np.ones((4, 4, 3, 4)).astype('float32')
|
|
|
|
with self.static_graph():
|
|
scores = paddle.static.data(
|
|
name='scores', shape=[2, 3, 4, 4], dtype='float32')
|
|
bbox_deltas = paddle.static.data(
|
|
name='bbox_deltas', shape=[2, 12, 4, 4], dtype='float32')
|
|
im_shape = paddle.static.data(
|
|
name='im_shape', shape=[2, 2], dtype='float32')
|
|
anchors = paddle.static.data(
|
|
name='anchors', shape=[4, 4, 3, 4], dtype='float32')
|
|
variances = paddle.static.data(
|
|
name='var', shape=[4, 4, 3, 4], dtype='float32')
|
|
rois, roi_probs, rois_num = ops.generate_proposals(
|
|
scores,
|
|
bbox_deltas,
|
|
im_shape,
|
|
anchors,
|
|
variances,
|
|
pre_nms_top_n=10,
|
|
post_nms_top_n=5,
|
|
return_rois_num=True)
|
|
rois_stat, roi_probs_stat, rois_num_stat = self.get_static_graph_result(
|
|
feed={
|
|
'scores': scores_np,
|
|
'bbox_deltas': bbox_deltas_np,
|
|
'im_shape': im_shape_np,
|
|
'anchors': anchors_np,
|
|
'var': variances_np
|
|
},
|
|
fetch_list=[rois, roi_probs, rois_num],
|
|
with_lod=True)
|
|
|
|
with self.dynamic_graph():
|
|
scores_dy = base.to_variable(scores_np)
|
|
bbox_deltas_dy = base.to_variable(bbox_deltas_np)
|
|
im_shape_dy = base.to_variable(im_shape_np)
|
|
anchors_dy = base.to_variable(anchors_np)
|
|
variances_dy = base.to_variable(variances_np)
|
|
rois, roi_probs, rois_num = ops.generate_proposals(
|
|
scores_dy,
|
|
bbox_deltas_dy,
|
|
im_shape_dy,
|
|
anchors_dy,
|
|
variances_dy,
|
|
pre_nms_top_n=10,
|
|
post_nms_top_n=5,
|
|
return_rois_num=True)
|
|
rois_dy = rois.numpy()
|
|
roi_probs_dy = roi_probs.numpy()
|
|
rois_num_dy = rois_num.numpy()
|
|
|
|
self.assertTrue(np.array_equal(np.array(rois_stat), rois_dy))
|
|
self.assertTrue(np.array_equal(np.array(roi_probs_stat), roi_probs_dy))
|
|
self.assertTrue(np.array_equal(np.array(rois_num_stat), rois_num_dy))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|