# Copyright (c) 2018 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 unittest import paddle.fluid as fluid import paddle.v2 as paddle import numpy as np class TestDataBalance(unittest.TestCase): def prepare_data(self): def fake_data_generator(): for n in xrange(self.total_ins_num): yield np.ones((3, 4)) * n, n # Prepare data with fluid.program_guard(fluid.Program(), fluid.Program()): reader = paddle.batch( fake_data_generator, batch_size=self.batch_size) feeder = fluid.DataFeeder( feed_list=[ fluid.layers.data( name='image', shape=[3, 4], dtype='float32'), fluid.layers.data( name='label', shape=[1], dtype='int64'), ], place=fluid.CPUPlace()) self.num_batches = fluid.recordio_writer.convert_reader_to_recordio_file( self.data_file_name, reader, feeder) def prepare_lod_data(self): def fake_data_generator(): for n in xrange(1, self.total_ins_num + 1): d1 = (np.ones((n, 3)) * n).astype('float32') d2 = (np.array(n).reshape((1, 1))).astype('int32') yield d1, d2 # Prepare lod data with fluid.program_guard(fluid.Program(), fluid.Program()): with fluid.recordio_writer.create_recordio_writer( filename=self.lod_data_file_name) as writer: eof = False generator = fake_data_generator() while (not eof): data_batch = [ np.array([]).reshape((0, 3)), np.array([]).reshape( (0, 1)) ] lod = [0] for _ in xrange(self.batch_size): try: ins = generator.next() except StopIteration: eof = True break for i, d in enumerate(ins): data_batch[i] = np.concatenate( (data_batch[i], d), axis=0) lod.append(lod[-1] + ins[0].shape[0]) if data_batch[0].shape[0] > 0: for i, d in enumerate(data_batch): t = fluid.LoDTensor() t.set(data_batch[i], fluid.CPUPlace()) if i == 0: t.set_lod([lod]) writer.append_tensor(t) writer.complete_append_tensor() def setUp(self): self.use_cuda = fluid.core.is_compiled_with_cuda() self.data_file_name = './data_balance_test.recordio' self.lod_data_file_name = './data_balance_with_lod_test.recordio' self.total_ins_num = 50 self.batch_size = 10 self.prepare_data() self.prepare_lod_data() def main(self): main_prog = fluid.Program() startup_prog = fluid.Program() with fluid.program_guard(main_prog, startup_prog): data_reader = fluid.layers.io.open_files( filenames=[self.data_file_name], shapes=[[-1, 3, 4], [-1, 1]], lod_levels=[0, 0], dtypes=['float32', 'int64']) if self.use_cuda: data_reader = fluid.layers.double_buffer(data_reader) image, label = fluid.layers.read_file(data_reader) place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) parallel_exe = fluid.ParallelExecutor( use_cuda=self.use_cuda, main_program=main_prog) if (parallel_exe.device_count > self.batch_size): print("WARNING: Unittest TestDataBalance skipped. \ For the result is not correct when device count \ is larger than batch size.") exit(0) fetch_list = [image.name, label.name] data_appeared = [False] * self.total_ins_num while (True): try: image_val, label_val = parallel_exe.run(fetch_list, return_numpy=True) except fluid.core.EOFException: break ins_num = image_val.shape[0] broadcasted_label = np.ones( (ins_num, 3, 4)) * label_val.reshape((ins_num, 1, 1)) self.assertEqual(image_val.all(), broadcasted_label.all()) for l in label_val: self.assertFalse(data_appeared[l[0]]) data_appeared[l[0]] = True for i in data_appeared: self.assertTrue(i) def main_lod(self): main_prog = fluid.Program() startup_prog = fluid.Program() with fluid.program_guard(main_prog, startup_prog): data_reader = fluid.layers.io.open_files( filenames=[self.lod_data_file_name], shapes=[[-1, 3], [-1, 1]], lod_levels=[1, 0], dtypes=['float32', 'int32'], thread_num=1) ins, label = fluid.layers.read_file(data_reader) place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) parallel_exe = fluid.ParallelExecutor( use_cuda=self.use_cuda, main_program=main_prog) if (parallel_exe.device_count > self.batch_size): print("WARNING: Unittest TestDataBalance skipped. \ For the result is not correct when device count \ is larger than batch size.") exit(0) fetch_list = [ins.name, label.name] data_appeared = [False] * self.total_ins_num while (True): try: ins_tensor, label_tensor = parallel_exe.run( fetch_list, return_numpy=False) except fluid.core.EOFException: break ins_val = np.array(ins_tensor) label_val = np.array(label_tensor) ins_lod = ins_tensor.lod()[0] self.assertEqual(ins_val.shape[1], 3) self.assertEqual(label_val.shape[1], 1) self.assertEqual(len(ins_lod) - 1, label_val.shape[0]) for i in range(0, len(ins_lod) - 1): ins_elem = ins_val[ins_lod[i]:ins_lod[i + 1]][:] label_elem = label_val[i][0] self.assertEqual(ins_elem.all(), label_elem.all()) self.assertFalse(data_appeared[int(label_elem - 1)]) data_appeared[int(label_elem - 1)] = True for i in data_appeared: self.assertTrue(i) def test_all(self): self.main() self.main_lod()