# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import unittest from py_precise_roi_pool import PyPrRoIPool from op_test import OpTest import paddle import paddle.fluid as fluid from paddle.fluid import Program, program_guard class TestPRROIPoolOp(OpTest): def set_data(self): self.init_test_case() self.make_rois() self.prRoIPool = PyPrRoIPool() self.outs = self.prRoIPool.compute(self.x, self.rois, self.output_channels, self.spatial_scale, self.pooled_height, self.pooled_width).astype('float32') self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)} self.attrs = { 'output_channels': self.output_channels, 'spatial_scale': self.spatial_scale, 'pooled_height': self.pooled_height, 'pooled_width': self.pooled_width } self.outputs = {'Out': self.outs} def init_test_case(self): self.batch_size = 3 self.channels = 3 * 2 * 2 self.height = 12 self.width = 16 self.x_dim = [self.batch_size, self.channels, self.height, self.width] self.spatial_scale = 1.0 / 2.0 self.output_channels = self.channels self.pooled_height = 4 self.pooled_width = 4 self.x = np.random.random(self.x_dim).astype('float32') def make_rois(self): rois = [] self.rois_lod = [[]] for bno in range(self.batch_size): self.rois_lod[0].append(bno + 1) for i in range(bno + 1): x1 = np.random.uniform( 0, self.width // self.spatial_scale - self.pooled_width) y1 = np.random.uniform( 0, self.height // self.spatial_scale - self.pooled_height) x2 = np.random.uniform(x1 + self.pooled_width, self.width // self.spatial_scale) y2 = np.random.uniform(y1 + self.pooled_height, self.height // self.spatial_scale) roi = [bno, x1, y1, x2, y2] rois.append(roi) self.rois_num = len(rois) self.rois = np.array(rois).astype('float32') def setUp(self): self.op_type = 'prroi_pool' self.set_data() def test_check_output(self): self.check_output(check_eager=True) def test_backward(self): places = [fluid.CPUPlace()] if fluid.core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: self.check_grad_with_place(place, ['X'], 'Out', check_eager=True) def run_net(self, place): with program_guard(Program(), Program()): x = fluid.layers.data( name="X", shape=[self.channels, self.height, self.width], dtype="float32") rois = fluid.layers.data(name="ROIs", shape=[4], dtype="float32", lod_level=1) output = fluid.layers.prroi_pool(x, rois, 0.25, 2, 2) loss = paddle.mean(output) optimizer = fluid.optimizer.SGD(learning_rate=1e-3) optimizer.minimize(loss) input_x = fluid.create_lod_tensor(self.x, [], place) input_rois = fluid.create_lod_tensor(self.rois[:, 1:5], self.rois_lod, place) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) exe.run(fluid.default_main_program(), { 'X': input_x, "ROIs": input_rois }) def test_net(self): places = [fluid.CPUPlace()] if fluid.core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: self.run_net(place) def test_errors(self): with program_guard(Program(), Program()): x = fluid.layers.data(name="x", shape=[245, 30, 30], dtype="float32") rois = fluid.layers.data(name="rois", shape=[4], dtype="float32", lod_level=1) # spatial_scale must be float type self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7, 7) # pooled_height must be int type self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25, 0.7, 7) # pooled_width must be int type self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25, 7, 0.7) class TestPRROIPoolOpTensorRoIs(OpTest): def set_data(self): self.init_test_case() self.make_rois() self.prRoIPool = PyPrRoIPool() self.outs = self.prRoIPool.compute(self.x, self.rois, self.output_channels, self.spatial_scale, self.pooled_height, self.pooled_width).astype('float32') self.rois_index = np.array(self.rois_lod).reshape([-1]).astype(np.int64) self.inputs = { 'X': self.x, 'ROIs': self.rois[:, 1:5], 'BatchRoINums': self.rois_index } self.attrs = { 'output_channels': self.output_channels, 'spatial_scale': self.spatial_scale, 'pooled_height': self.pooled_height, 'pooled_width': self.pooled_width } self.outputs = {'Out': self.outs} def init_test_case(self): self.batch_size = 3 self.channels = 3 * 2 * 2 self.height = 12 self.width = 16 self.x_dim = [self.batch_size, self.channels, self.height, self.width] self.spatial_scale = 1.0 / 2.0 self.output_channels = self.channels self.pooled_height = 4 self.pooled_width = 4 self.x = np.random.random(self.x_dim).astype('float32') def make_rois(self): rois = [] self.rois_lod = [] for bno in range(self.batch_size): self.rois_lod.append(bno + 1) for i in range(bno + 1): x1 = np.random.uniform( 0, self.width // self.spatial_scale - self.pooled_width) y1 = np.random.uniform( 0, self.height // self.spatial_scale - self.pooled_height) x2 = np.random.uniform(x1 + self.pooled_width, self.width // self.spatial_scale) y2 = np.random.uniform(y1 + self.pooled_height, self.height // self.spatial_scale) roi = [bno, x1, y1, x2, y2] rois.append(roi) self.rois_num = len(rois) self.rois = np.array(rois).astype('float32') def setUp(self): self.op_type = 'prroi_pool' self.set_data() def test_check_output(self): self.check_output(check_eager=True) def test_backward(self): places = [fluid.CPUPlace()] if fluid.core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: self.check_grad_with_place(place, ['X'], 'Out', check_eager=True) def run_net(self, place): with program_guard(Program(), Program()): x = fluid.layers.data( name="X", shape=[self.channels, self.height, self.width], dtype="float32") rois = fluid.layers.data(name="ROIs", shape=[4], dtype="float32") rois_index = fluid.layers.data(name='rois_idx', shape=[], dtype="int64") output = fluid.layers.prroi_pool(x, rois, 0.25, 2, 2, batch_roi_nums=rois_index) loss = paddle.mean(output) optimizer = fluid.optimizer.SGD(learning_rate=1e-3) optimizer.minimize(loss) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) exe.run(fluid.default_main_program(), { 'X': self.x, "ROIs": self.rois[:, 1:5], "rois_idx": self.rois_index }) def test_net(self): places = [fluid.CPUPlace()] if fluid.core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: self.run_net(place) def test_errors(self): with program_guard(Program(), Program()): x = fluid.layers.data(name="x", shape=[245, 30, 30], dtype="float32") rois = fluid.layers.data(name="rois", shape=[4], dtype="float32", lod_level=1) # spatial_scale must be float type self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 2, 7, 7) # pooled_height must be int type self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25, 0.7, 7) # pooled_width must be int type self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25, 7, 0.7) def test_bad_x(): x = fluid.layers.data(name='data1', shape=[2, 3, 16, 16], dtype='int64', append_batch_size=False) label = fluid.layers.data(name='label1', shape=[2, 4], dtype='float32', lod_level=1, append_batch_size=False) output = fluid.layers.prroi_pool(x, label, 0.25, 2, 2) self.assertRaises(TypeError, test_bad_x) def test_bad_y(): x = fluid.layers.data(name='data2', shape=[2, 3, 16, 16], dtype='float32', append_batch_size=False) label = [[1, 2, 3, 4], [2, 3, 4, 5]] output = fluid.layers.prroi_pool(x, label, 0.25, 2, 2) self.assertRaises(TypeError, test_bad_y) if __name__ == '__main__': import paddle paddle.enable_static() unittest.main()