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135 lines
5.0 KiB
135 lines
5.0 KiB
# Copyright (c) 2019 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 numpy as np
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import unittest
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from py_precise_roi_pool import PyPrRoIPool
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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestPRROIPoolOp(OpTest):
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def set_data(self):
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self.init_test_case()
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self.make_rois()
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self.prRoIPool = PyPrRoIPool()
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self.outs = self.prRoIPool.compute(
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self.x, self.rois, self.output_channels, self.spatial_scale,
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self.pooled_height, self.pooled_width).astype('float32')
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self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
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self.attrs = {
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'output_channels': self.output_channels,
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'spatial_scale': self.spatial_scale,
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'pooled_height': self.pooled_height,
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'pooled_width': self.pooled_width
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}
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self.outputs = {'Out': self.outs}
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def init_test_case(self):
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self.batch_size = 3
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self.channels = 3 * 2 * 2
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self.height = 6
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self.width = 4
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self.x_dim = [self.batch_size, self.channels, self.height, self.width]
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self.spatial_scale = 1.0 / 4.0
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self.output_channels = self.channels
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self.pooled_height = 2
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self.pooled_width = 2
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self.x = np.random.random(self.x_dim).astype('float32')
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def make_rois(self):
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rois = []
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self.rois_lod = [[]]
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for bno in range(self.batch_size):
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self.rois_lod[0].append(bno + 1)
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for i in range(bno + 1):
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x1 = np.random.uniform(
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0, self.width // self.spatial_scale - self.pooled_width)
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y1 = np.random.uniform(
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0, self.height // self.spatial_scale - self.pooled_height)
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x2 = np.random.uniform(x1 + self.pooled_width,
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self.width // self.spatial_scale)
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y2 = np.random.uniform(y1 + self.pooled_height,
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self.height // self.spatial_scale)
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roi = [bno, x1, y1, x2, y2]
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rois.append(roi)
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self.rois_num = len(rois)
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self.rois = np.array(rois).astype('float32')
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def setUp(self):
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self.op_type = 'prroi_pool'
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self.set_data()
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def test_check_output(self):
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self.check_output()
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def test_backward(self):
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for place in self._get_places():
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self._get_gradient(['X'], place, ["Out"], None)
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def run_net(self, place):
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with program_guard(Program(), Program()):
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x = fluid.layers.data(
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name="X",
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shape=[self.channels, self.height, self.width],
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dtype="float32")
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rois = fluid.layers.data(
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name="ROIs", shape=[4], dtype="float32", lod_level=1)
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output = fluid.layers.prroi_pool(x, rois, 0.25, 2, 2)
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loss = fluid.layers.mean(output)
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optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
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optimizer.minimize(loss)
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input_x = fluid.create_lod_tensor(self.x, [], place)
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input_rois = fluid.create_lod_tensor(self.rois[:, 1:5],
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self.rois_lod, place)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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exe.run(fluid.default_main_program(),
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{'X': input_x,
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"ROIs": input_rois})
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def test_net(self):
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places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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self.run_net(place)
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def test_errors(self):
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with program_guard(Program(), Program()):
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x = fluid.layers.data(
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name="x", shape=[245, 30, 30], dtype="float32")
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rois = fluid.layers.data(
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name="rois", shape=[4], dtype="float32", lod_level=1)
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# spatial_scale must be float type
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self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7,
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7)
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# pooled_height must be int type
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self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25,
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0.7, 7)
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# pooled_width must be int type
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self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25,
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7, 0.7)
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if __name__ == '__main__':
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unittest.main()
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