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813 lines
30 KiB
813 lines
30 KiB
# 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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from __future__ import print_function
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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from paddle.fluid.layers import detection
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from paddle.fluid.framework import Program, program_guard
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import unittest
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class TestDetection(unittest.TestCase):
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def test_detection_output(self):
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program = Program()
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with program_guard(program):
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pb = layers.data(
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name='prior_box',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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pbv = layers.data(
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name='prior_box_var',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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loc = layers.data(
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name='target_box',
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shape=[2, 10, 4],
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append_batch_size=False,
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dtype='float32')
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scores = layers.data(
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name='scores',
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shape=[2, 10, 20],
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append_batch_size=False,
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dtype='float32')
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out = layers.detection_output(
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scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
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out2, index = layers.detection_output(
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scores=scores,
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loc=loc,
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prior_box=pb,
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prior_box_var=pbv,
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return_index=True)
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self.assertIsNotNone(out)
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self.assertIsNotNone(out2)
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self.assertIsNotNone(index)
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self.assertEqual(out.shape[-1], 6)
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print(str(program))
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def test_box_coder_api(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[4], dtype='float32')
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y = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
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bcoder = layers.box_coder(
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prior_box=x,
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prior_box_var=[0.1, 0.2, 0.1, 0.2],
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target_box=y,
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code_type='encode_center_size')
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self.assertIsNotNone(bcoder)
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print(str(program))
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def test_box_coder_error(self):
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program = Program()
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with program_guard(program):
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x1 = fluid.data(name='x1', shape=[10, 4], dtype='int32')
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y1 = fluid.data(
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name='y1', shape=[10, 4], dtype='float32', lod_level=1)
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x2 = fluid.data(name='x2', shape=[10, 4], dtype='float32')
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y2 = fluid.data(
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name='y2', shape=[10, 4], dtype='int32', lod_level=1)
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self.assertRaises(
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TypeError,
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layers.box_coder,
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prior_box=x1,
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prior_box_var=[0.1, 0.2, 0.1, 0.2],
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target_box=y1,
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code_type='encode_center_size')
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self.assertRaises(
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TypeError,
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layers.box_coder,
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prior_box=x2,
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prior_box_var=[0.1, 0.2, 0.1, 0.2],
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target_box=y2,
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code_type='encode_center_size')
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def test_detection_api(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[4], dtype='float32')
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y = layers.data(name='y', shape=[4], dtype='float32')
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z = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
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iou = layers.iou_similarity(x=x, y=y)
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bcoder = layers.box_coder(
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prior_box=x,
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prior_box_var=y,
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target_box=z,
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code_type='encode_center_size')
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self.assertIsNotNone(iou)
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self.assertIsNotNone(bcoder)
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matched_indices, matched_dist = layers.bipartite_match(iou)
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self.assertIsNotNone(matched_indices)
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self.assertIsNotNone(matched_dist)
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gt = layers.data(
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name='gt', shape=[1, 1], dtype='int32', lod_level=1)
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trg, trg_weight = layers.target_assign(
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gt, matched_indices, mismatch_value=0)
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self.assertIsNotNone(trg)
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self.assertIsNotNone(trg_weight)
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gt2 = layers.data(
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name='gt2', shape=[10, 4], dtype='float32', lod_level=1)
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trg, trg_weight = layers.target_assign(
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gt2, matched_indices, mismatch_value=0)
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self.assertIsNotNone(trg)
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self.assertIsNotNone(trg_weight)
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print(str(program))
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def test_ssd_loss(self):
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program = Program()
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with program_guard(program):
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pb = layers.data(
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name='prior_box',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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pbv = layers.data(
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name='prior_box_var',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
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scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
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gt_box = layers.data(
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name='gt_box', shape=[4], lod_level=1, dtype='float32')
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gt_label = layers.data(
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name='gt_label', shape=[1], lod_level=1, dtype='int32')
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loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
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self.assertIsNotNone(loss)
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self.assertEqual(loss.shape[-1], 1)
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print(str(program))
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class TestPriorBox(unittest.TestCase):
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def test_prior_box(self):
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program = Program()
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with program_guard(program):
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data_shape = [3, 224, 224]
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images = fluid.layers.data(
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name='pixel', shape=data_shape, dtype='float32')
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conv1 = fluid.layers.conv2d(images, 3, 3, 2)
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box, var = layers.prior_box(
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input=conv1,
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image=images,
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min_sizes=[100.0],
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aspect_ratios=[1.],
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flip=True,
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clip=True)
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assert len(box.shape) == 4
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assert box.shape == var.shape
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assert box.shape[3] == 4
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class TestPriorBox2(unittest.TestCase):
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def test_prior_box(self):
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program = Program()
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with program_guard(program):
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data_shape = [None, 3, None, None]
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images = fluid.data(name='pixel', shape=data_shape, dtype='float32')
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conv1 = fluid.layers.conv2d(images, 3, 3, 2)
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box, var = layers.prior_box(
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input=conv1,
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image=images,
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min_sizes=[100.0],
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aspect_ratios=[1.],
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flip=True,
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clip=True)
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assert len(box.shape) == 4
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assert box.shape == var.shape
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assert box.shape[3] == 4
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class TestDensityPriorBox(unittest.TestCase):
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def test_density_prior_box(self):
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program = Program()
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with program_guard(program):
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data_shape = [3, 224, 224]
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images = fluid.layers.data(
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name='pixel', shape=data_shape, dtype='float32')
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conv1 = fluid.layers.conv2d(images, 3, 3, 2)
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box, var = layers.density_prior_box(
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input=conv1,
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image=images,
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densities=[3, 4],
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fixed_sizes=[50., 60.],
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fixed_ratios=[1.0],
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clip=True)
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assert len(box.shape) == 4
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assert box.shape == var.shape
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assert box.shape[-1] == 4
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class TestAnchorGenerator(unittest.TestCase):
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def test_anchor_generator(self):
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data_shape = [3, 224, 224]
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images = fluid.layers.data(
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name='pixel', shape=data_shape, dtype='float32')
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conv1 = fluid.layers.conv2d(images, 3, 3, 2)
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anchor, var = fluid.layers.anchor_generator(
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input=conv1,
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anchor_sizes=[64, 128, 256, 512],
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aspect_ratios=[0.5, 1.0, 2.0],
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variance=[0.1, 0.1, 0.2, 0.2],
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stride=[16.0, 16.0],
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offset=0.5)
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assert len(anchor.shape) == 4
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assert anchor.shape == var.shape
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assert anchor.shape[3] == 4
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class TestGenerateProposalLabels(unittest.TestCase):
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def test_generate_proposal_labels(self):
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program = Program()
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with program_guard(program):
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rpn_rois = layers.data(
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name='rpn_rois',
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shape=[4, 4],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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gt_classes = layers.data(
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name='gt_classes',
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shape=[6],
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dtype='int32',
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lod_level=1,
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append_batch_size=False)
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is_crowd = layers.data(
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name='is_crowd',
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shape=[6],
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dtype='int32',
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lod_level=1,
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append_batch_size=False)
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gt_boxes = layers.data(
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name='gt_boxes',
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shape=[6, 4],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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im_info = layers.data(
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name='im_info',
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shape=[1, 3],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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class_nums = 5
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outs = fluid.layers.generate_proposal_labels(
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rpn_rois=rpn_rois,
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gt_classes=gt_classes,
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is_crowd=is_crowd,
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gt_boxes=gt_boxes,
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im_info=im_info,
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batch_size_per_im=2,
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fg_fraction=0.5,
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fg_thresh=0.5,
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bg_thresh_hi=0.5,
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bg_thresh_lo=0.0,
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bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
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class_nums=class_nums)
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rois = outs[0]
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labels_int32 = outs[1]
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bbox_targets = outs[2]
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bbox_inside_weights = outs[3]
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bbox_outside_weights = outs[4]
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assert rois.shape[1] == 4
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assert rois.shape[0] == labels_int32.shape[0]
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assert rois.shape[0] == bbox_targets.shape[0]
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assert rois.shape[0] == bbox_inside_weights.shape[0]
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assert rois.shape[0] == bbox_outside_weights.shape[0]
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assert bbox_targets.shape[1] == 4 * class_nums
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assert bbox_inside_weights.shape[1] == 4 * class_nums
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assert bbox_outside_weights.shape[1] == 4 * class_nums
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class TestGenerateMaskLabels(unittest.TestCase):
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def test_generate_mask_labels(self):
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program = Program()
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with program_guard(program):
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im_info = layers.data(
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name='im_info',
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shape=[1, 3],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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gt_classes = layers.data(
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name='gt_classes',
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shape=[2, 1],
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dtype='int32',
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lod_level=1,
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append_batch_size=False)
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is_crowd = layers.data(
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name='is_crowd',
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shape=[2, 1],
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dtype='int32',
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lod_level=1,
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append_batch_size=False)
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gt_segms = layers.data(
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name='gt_segms',
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shape=[20, 2],
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dtype='float32',
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lod_level=3,
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append_batch_size=False)
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rois = layers.data(
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name='rois',
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shape=[4, 4],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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labels_int32 = layers.data(
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name='labels_int32',
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shape=[4, 1],
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dtype='int32',
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lod_level=1,
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append_batch_size=False)
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num_classes = 5
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resolution = 14
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outs = fluid.layers.generate_mask_labels(
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im_info=im_info,
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gt_classes=gt_classes,
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is_crowd=is_crowd,
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gt_segms=gt_segms,
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rois=rois,
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labels_int32=labels_int32,
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num_classes=num_classes,
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resolution=resolution)
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mask_rois, roi_has_mask_int32, mask_int32 = outs
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assert mask_rois.shape[1] == 4
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assert mask_int32.shape[1] == num_classes * resolution * resolution
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class TestMultiBoxHead(unittest.TestCase):
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def test_multi_box_head(self):
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data_shape = [3, 224, 224]
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mbox_locs, mbox_confs, box, var = self.multi_box_head_output(data_shape)
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assert len(box.shape) == 2
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assert box.shape == var.shape
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assert box.shape[1] == 4
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assert mbox_locs.shape[1] == mbox_confs.shape[1]
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def multi_box_head_output(self, data_shape):
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images = fluid.layers.data(
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name='pixel', shape=data_shape, dtype='float32')
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conv1 = fluid.layers.conv2d(images, 3, 3, 2)
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conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
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conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
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conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
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conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)
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mbox_locs, mbox_confs, box, var = layers.multi_box_head(
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inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
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image=images,
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num_classes=21,
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min_ratio=20,
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max_ratio=90,
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aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
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base_size=300,
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offset=0.5,
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flip=True,
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clip=True)
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return mbox_locs, mbox_confs, box, var
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class TestDetectionMAP(unittest.TestCase):
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def test_detection_map(self):
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program = Program()
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with program_guard(program):
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detect_res = layers.data(
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name='detect_res',
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shape=[10, 6],
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append_batch_size=False,
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dtype='float32')
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label = layers.data(
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name='label',
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shape=[10, 6],
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append_batch_size=False,
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dtype='float32')
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map_out = detection.detection_map(detect_res, label, 21)
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self.assertIsNotNone(map_out)
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self.assertEqual(map_out.shape, (1, ))
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print(str(program))
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class TestRpnTargetAssign(unittest.TestCase):
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def test_rpn_target_assign(self):
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program = Program()
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with program_guard(program):
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bbox_pred_shape = [10, 50, 4]
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cls_logits_shape = [10, 50, 2]
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anchor_shape = [50, 4]
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bbox_pred = layers.data(
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name='bbox_pred',
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shape=bbox_pred_shape,
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append_batch_size=False,
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dtype='float32')
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cls_logits = layers.data(
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name='cls_logits',
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shape=cls_logits_shape,
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append_batch_size=False,
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dtype='float32')
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anchor_box = layers.data(
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name='anchor_box',
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shape=anchor_shape,
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append_batch_size=False,
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dtype='float32')
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anchor_var = layers.data(
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name='anchor_var',
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shape=anchor_shape,
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append_batch_size=False,
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dtype='float32')
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gt_boxes = layers.data(
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name='gt_boxes', shape=[4], lod_level=1, dtype='float32')
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is_crowd = layers.data(
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name='is_crowd',
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shape=[1, 10],
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dtype='int32',
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lod_level=1,
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append_batch_size=False)
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im_info = layers.data(
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name='im_info',
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shape=[1, 3],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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outs = layers.rpn_target_assign(
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bbox_pred=bbox_pred,
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cls_logits=cls_logits,
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anchor_box=anchor_box,
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anchor_var=anchor_var,
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gt_boxes=gt_boxes,
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is_crowd=is_crowd,
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im_info=im_info,
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rpn_batch_size_per_im=256,
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rpn_straddle_thresh=0.0,
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rpn_fg_fraction=0.5,
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rpn_positive_overlap=0.7,
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rpn_negative_overlap=0.3,
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use_random=False)
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pred_scores = outs[0]
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pred_loc = outs[1]
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tgt_lbl = outs[2]
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tgt_bbox = outs[3]
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bbox_inside_weight = outs[4]
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self.assertIsNotNone(pred_scores)
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self.assertIsNotNone(pred_loc)
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self.assertIsNotNone(tgt_lbl)
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self.assertIsNotNone(tgt_bbox)
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self.assertIsNotNone(bbox_inside_weight)
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assert pred_scores.shape[1] == 1
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assert pred_loc.shape[1] == 4
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assert pred_loc.shape[1] == tgt_bbox.shape[1]
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print(str(program))
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class TestGenerateProposals(unittest.TestCase):
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def test_generate_proposals(self):
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program = Program()
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with program_guard(program):
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data_shape = [20, 64, 64]
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images = fluid.layers.data(
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name='images', shape=data_shape, dtype='float32')
|
|
im_info = fluid.layers.data(
|
|
name='im_info', shape=[3], dtype='float32')
|
|
anchors, variances = fluid.layers.anchor_generator(
|
|
name='anchor_generator',
|
|
input=images,
|
|
anchor_sizes=[32, 64],
|
|
aspect_ratios=[1.0],
|
|
variance=[0.1, 0.1, 0.2, 0.2],
|
|
stride=[16.0, 16.0],
|
|
offset=0.5)
|
|
num_anchors = anchors.shape[2]
|
|
scores = fluid.layers.data(
|
|
name='scores', shape=[num_anchors, 8, 8], dtype='float32')
|
|
bbox_deltas = fluid.layers.data(
|
|
name='bbox_deltas',
|
|
shape=[num_anchors * 4, 8, 8],
|
|
dtype='float32')
|
|
rpn_rois, rpn_roi_probs = fluid.layers.generate_proposals(
|
|
name='generate_proposals',
|
|
scores=scores,
|
|
bbox_deltas=bbox_deltas,
|
|
im_info=im_info,
|
|
anchors=anchors,
|
|
variances=variances,
|
|
pre_nms_top_n=6000,
|
|
post_nms_top_n=1000,
|
|
nms_thresh=0.5,
|
|
min_size=0.1,
|
|
eta=1.0)
|
|
self.assertIsNotNone(rpn_rois)
|
|
self.assertIsNotNone(rpn_roi_probs)
|
|
print(rpn_rois.shape)
|
|
|
|
|
|
class TestYoloDetection(unittest.TestCase):
|
|
def test_yolov3_loss(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
|
|
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
|
|
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
|
|
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
|
|
loss = layers.yolov3_loss(
|
|
x,
|
|
gt_box,
|
|
gt_label, [10, 13, 30, 13], [0, 1],
|
|
10,
|
|
0.7,
|
|
32,
|
|
gt_score=gt_score,
|
|
use_label_smooth=False)
|
|
|
|
self.assertIsNotNone(loss)
|
|
|
|
def test_yolo_box(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
|
|
img_size = layers.data(name='img_size', shape=[2], dtype='int32')
|
|
boxes, scores = layers.yolo_box(x, img_size, [10, 13, 30, 13], 10,
|
|
0.01, 32)
|
|
self.assertIsNotNone(boxes)
|
|
self.assertIsNotNone(scores)
|
|
|
|
def test_yolov3_loss_with_scale(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
|
|
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
|
|
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
|
|
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
|
|
loss = layers.yolov3_loss(
|
|
x,
|
|
gt_box,
|
|
gt_label, [10, 13, 30, 13], [0, 1],
|
|
10,
|
|
0.7,
|
|
32,
|
|
gt_score=gt_score,
|
|
use_label_smooth=False,
|
|
scale_x_y=1.2)
|
|
|
|
self.assertIsNotNone(loss)
|
|
|
|
def test_yolo_box_with_scale(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
|
|
img_size = layers.data(name='img_size', shape=[2], dtype='int32')
|
|
boxes, scores = layers.yolo_box(
|
|
x, img_size, [10, 13, 30, 13], 10, 0.01, 32, scale_x_y=1.2)
|
|
self.assertIsNotNone(boxes)
|
|
self.assertIsNotNone(scores)
|
|
|
|
|
|
class TestBoxClip(unittest.TestCase):
|
|
def test_box_clip(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
input_box = layers.data(
|
|
name='input_box', shape=[7, 4], dtype='float32', lod_level=1)
|
|
im_info = layers.data(name='im_info', shape=[3], dtype='float32')
|
|
out = layers.box_clip(input_box, im_info)
|
|
self.assertIsNotNone(out)
|
|
|
|
|
|
class TestMulticlassNMS(unittest.TestCase):
|
|
def test_multiclass_nms(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
bboxes = layers.data(
|
|
name='bboxes', shape=[-1, 10, 4], dtype='float32')
|
|
scores = layers.data(name='scores', shape=[-1, 10], dtype='float32')
|
|
output = layers.multiclass_nms(bboxes, scores, 0.3, 400, 200, 0.7)
|
|
self.assertIsNotNone(output)
|
|
|
|
def test_multiclass_nms_error(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
bboxes1 = fluid.data(
|
|
name='bboxes1', shape=[10, 10, 4], dtype='int32')
|
|
scores1 = fluid.data(
|
|
name='scores1', shape=[10, 10], dtype='float32')
|
|
bboxes2 = fluid.data(
|
|
name='bboxes2', shape=[10, 10, 4], dtype='float32')
|
|
scores2 = fluid.data(name='scores2', shape=[10, 10], dtype='int32')
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.multiclass_nms,
|
|
bboxes=bboxes1,
|
|
scores=scores1,
|
|
score_threshold=0.5,
|
|
nms_top_k=400,
|
|
keep_top_k=200)
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.multiclass_nms,
|
|
bboxes=bboxes2,
|
|
scores=scores2,
|
|
score_threshold=0.5,
|
|
nms_top_k=400,
|
|
keep_top_k=200)
|
|
|
|
|
|
class TestMulticlassNMS2(unittest.TestCase):
|
|
def test_multiclass_nms2(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
bboxes = layers.data(
|
|
name='bboxes', shape=[-1, 10, 4], dtype='float32')
|
|
scores = layers.data(name='scores', shape=[-1, 10], dtype='float32')
|
|
output = fluid.contrib.multiclass_nms2(bboxes, scores, 0.3, 400,
|
|
200, 0.7)
|
|
output2, index = fluid.contrib.multiclass_nms2(
|
|
bboxes, scores, 0.3, 400, 200, 0.7, return_index=True)
|
|
self.assertIsNotNone(output)
|
|
self.assertIsNotNone(output2)
|
|
self.assertIsNotNone(index)
|
|
|
|
|
|
class TestCollectFpnPropsals(unittest.TestCase):
|
|
def test_collect_fpn_proposals(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
multi_bboxes = []
|
|
multi_scores = []
|
|
for i in range(4):
|
|
bboxes = layers.data(
|
|
name='rois' + str(i),
|
|
shape=[10, 4],
|
|
dtype='float32',
|
|
lod_level=1,
|
|
append_batch_size=False)
|
|
scores = layers.data(
|
|
name='scores' + str(i),
|
|
shape=[10, 1],
|
|
dtype='float32',
|
|
lod_level=1,
|
|
append_batch_size=False)
|
|
multi_bboxes.append(bboxes)
|
|
multi_scores.append(scores)
|
|
fpn_rois = layers.collect_fpn_proposals(multi_bboxes, multi_scores,
|
|
2, 5, 10)
|
|
self.assertIsNotNone(fpn_rois)
|
|
|
|
def test_collect_fpn_proposals_error(self):
|
|
def generate_input(bbox_type, score_type, name):
|
|
multi_bboxes = []
|
|
multi_scores = []
|
|
for i in range(4):
|
|
bboxes = fluid.data(
|
|
name='rois' + name + str(i),
|
|
shape=[10, 4],
|
|
dtype=bbox_type,
|
|
lod_level=1)
|
|
scores = fluid.data(
|
|
name='scores' + name + str(i),
|
|
shape=[10, 1],
|
|
dtype=score_type,
|
|
lod_level=1)
|
|
multi_bboxes.append(bboxes)
|
|
multi_scores.append(scores)
|
|
return multi_bboxes, multi_scores
|
|
|
|
program = Program()
|
|
with program_guard(program):
|
|
bbox1 = fluid.data(
|
|
name='rois', shape=[5, 10, 4], dtype='float32', lod_level=1)
|
|
score1 = fluid.data(
|
|
name='scores', shape=[5, 10, 1], dtype='float32', lod_level=1)
|
|
bbox2, score2 = generate_input('int32', 'float32', '2')
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.collect_fpn_proposals,
|
|
multi_rois=bbox1,
|
|
multi_scores=score1,
|
|
min_level=2,
|
|
max_level=5,
|
|
post_nms_top_n=2000)
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.collect_fpn_proposals,
|
|
multi_rois=bbox2,
|
|
multi_scores=score2,
|
|
min_level=2,
|
|
max_level=5,
|
|
post_nms_top_n=2000)
|
|
|
|
|
|
class TestDistributeFpnProposals(unittest.TestCase):
|
|
def test_distribute_fpn_proposals(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
fpn_rois = fluid.layers.data(
|
|
name='data', shape=[4], dtype='float32', lod_level=1)
|
|
multi_rois, restore_ind = layers.distribute_fpn_proposals(
|
|
fpn_rois=fpn_rois,
|
|
min_level=2,
|
|
max_level=5,
|
|
refer_level=4,
|
|
refer_scale=224)
|
|
self.assertIsNotNone(multi_rois)
|
|
self.assertIsNotNone(restore_ind)
|
|
|
|
def test_distribute_fpn_proposals_error(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
fpn_rois = fluid.data(
|
|
name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.distribute_fpn_proposals,
|
|
fpn_rois=fpn_rois,
|
|
min_level=2,
|
|
max_level=5,
|
|
refer_level=4,
|
|
refer_scale=224)
|
|
|
|
|
|
class TestBoxDecoderAndAssign(unittest.TestCase):
|
|
def test_box_decoder_and_assign(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
pb = fluid.data(name='prior_box', shape=[None, 4], dtype='float32')
|
|
pbv = fluid.data(name='prior_box_var', shape=[4], dtype='float32')
|
|
loc = fluid.data(
|
|
name='target_box', shape=[None, 4 * 81], dtype='float32')
|
|
scores = fluid.data(
|
|
name='scores', shape=[None, 81], dtype='float32')
|
|
decoded_box, output_assign_box = fluid.layers.box_decoder_and_assign(
|
|
pb, pbv, loc, scores, 4.135)
|
|
self.assertIsNotNone(decoded_box)
|
|
self.assertIsNotNone(output_assign_box)
|
|
|
|
def test_box_decoder_and_assign_error(self):
|
|
def generate_input(pb_type, pbv_type, loc_type, score_type, name):
|
|
pb = fluid.data(
|
|
name='prior_box' + name, shape=[None, 4], dtype=pb_type)
|
|
pbv = fluid.data(
|
|
name='prior_box_var' + name, shape=[4], dtype=pbv_type)
|
|
loc = fluid.data(
|
|
name='target_box' + name, shape=[None, 4 * 81], dtype=loc_type)
|
|
scores = fluid.data(
|
|
name='scores' + name, shape=[None, 81], dtype=score_type)
|
|
return pb, pbv, loc, scores
|
|
|
|
program = Program()
|
|
with program_guard(program):
|
|
pb1, pbv1, loc1, scores1 = generate_input('int32', 'float32',
|
|
'float32', 'float32', '1')
|
|
pb2, pbv2, loc2, scores2 = generate_input('float32', 'float32',
|
|
'int32', 'float32', '2')
|
|
pb3, pbv3, loc3, scores3 = generate_input('float32', 'float32',
|
|
'float32', 'int32', '3')
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.box_decoder_and_assign,
|
|
prior_box=pb1,
|
|
prior_box_var=pbv1,
|
|
target_box=loc1,
|
|
box_score=scores1,
|
|
box_clip=4.0)
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.box_decoder_and_assign,
|
|
prior_box=pb2,
|
|
prior_box_var=pbv2,
|
|
target_box=loc2,
|
|
box_score=scores2,
|
|
box_clip=4.0)
|
|
self.assertRaises(
|
|
TypeError,
|
|
layers.box_decoder_and_assign,
|
|
prior_box=pb3,
|
|
prior_box_var=pbv3,
|
|
target_box=loc3,
|
|
box_score=scores3,
|
|
box_clip=4.0)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|