# 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. from __future__ import print_function import unittest import numpy import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core from paddle.fluid.executor import Executor from paddle.fluid import framework from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell from paddle.fluid.layers import rnn as dynamic_rnn from paddle.fluid import contrib from paddle.fluid.contrib.layers import basic_lstm import paddle.fluid.layers.utils as utils import numpy as np class TestLSTMCell(unittest.TestCase): def setUp(self): self.batch_size = 4 self.input_size = 16 self.hidden_size = 16 def test_run(self): inputs = fluid.data( name='inputs', shape=[None, self.input_size], dtype='float32') pre_hidden = fluid.data( name='pre_hidden', shape=[None, self.hidden_size], dtype='float32') pre_cell = fluid.data( name='pre_cell', shape=[None, self.hidden_size], dtype='float32') cell = LSTMCell(self.hidden_size) lstm_hidden_new, lstm_states_new = cell(inputs, [pre_hidden, pre_cell]) lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit( "basicLSTM", self.hidden_size, None, None, None, None, 1.0, "float32") lstm_hidden, lstm_cell = lstm_unit(inputs, pre_hidden, pre_cell) if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = Executor(place) exe.run(framework.default_startup_program()) inputs_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32') pre_hidden_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32') pre_cell_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32') param_names = [[ "LSTMCell/BasicLSTMUnit_0.w_0", "basicLSTM/BasicLSTMUnit_0.w_0" ], ["LSTMCell/BasicLSTMUnit_0.b_0", "basicLSTM/BasicLSTMUnit_0.b_0"]] for names in param_names: param = np.array(fluid.global_scope().find_var(names[0]).get_tensor( )) param = np.random.uniform( -0.1, 0.1, size=param.shape).astype('float32') fluid.global_scope().find_var(names[0]).get_tensor().set(param, place) fluid.global_scope().find_var(names[1]).get_tensor().set(param, place) out = exe.run(feed={ 'inputs': inputs_np, 'pre_hidden': pre_hidden_np, 'pre_cell': pre_cell_np }, fetch_list=[lstm_hidden_new, lstm_hidden]) self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0)) class TestGRUCell(unittest.TestCase): def setUp(self): self.batch_size = 4 self.input_size = 16 self.hidden_size = 16 def test_run(self): inputs = fluid.data( name='inputs', shape=[None, self.input_size], dtype='float32') pre_hidden = layers.data( name='pre_hidden', shape=[None, self.hidden_size], append_batch_size=False, dtype='float32') cell = GRUCell(self.hidden_size) gru_hidden_new, _ = cell(inputs, pre_hidden) gru_unit = contrib.layers.rnn_impl.BasicGRUUnit( "basicGRU", self.hidden_size, None, None, None, None, "float32") gru_hidden = gru_unit(inputs, pre_hidden) if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = Executor(place) exe.run(framework.default_startup_program()) inputs_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32') pre_hidden_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32') param_names = [ ["GRUCell/BasicGRUUnit_0.w_0", "basicGRU/BasicGRUUnit_0.w_0"], ["GRUCell/BasicGRUUnit_0.w_1", "basicGRU/BasicGRUUnit_0.w_1"], ["GRUCell/BasicGRUUnit_0.b_0", "basicGRU/BasicGRUUnit_0.b_0"], ["GRUCell/BasicGRUUnit_0.b_1", "basicGRU/BasicGRUUnit_0.b_1"] ] for names in param_names: param = np.array(fluid.global_scope().find_var(names[0]).get_tensor( )) param = np.random.uniform( -0.1, 0.1, size=param.shape).astype('float32') fluid.global_scope().find_var(names[0]).get_tensor().set(param, place) fluid.global_scope().find_var(names[1]).get_tensor().set(param, place) out = exe.run(feed={'inputs': inputs_np, 'pre_hidden': pre_hidden_np}, fetch_list=[gru_hidden_new, gru_hidden]) self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0)) class TestRnn(unittest.TestCase): def setUp(self): self.batch_size = 4 self.input_size = 16 self.hidden_size = 16 self.seq_len = 4 def test_run(self): inputs_basic_lstm = fluid.data( name='inputs_basic_lstm', shape=[None, None, self.input_size], dtype='float32') sequence_length = fluid.data( name="sequence_length", shape=[None], dtype='int64') inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2]) cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn") output, final_state = dynamic_rnn( cell=cell, inputs=inputs_dynamic_rnn, sequence_length=sequence_length, is_reverse=False) output_new = layers.transpose(output, perm=[1, 0, 2]) rnn_out, last_hidden, last_cell = basic_lstm(inputs_basic_lstm, None, None, self.hidden_size, num_layers=1, \ batch_first = False, bidirectional=False, sequence_length=sequence_length, forget_bias = 1.0) if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = Executor(place) exe.run(framework.default_startup_program()) inputs_basic_lstm_np = np.random.uniform( -0.1, 0.1, (self.seq_len, self.batch_size, self.input_size)).astype('float32') sequence_length_np = np.ones( self.batch_size, dtype='int64') * self.seq_len inputs_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32') pre_hidden_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32') pre_cell_np = np.random.uniform( -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32') param_names = [[ "LSTMCell_for_rnn/BasicLSTMUnit_0.w_0", "basic_lstm_layers_0/BasicLSTMUnit_0.w_0" ], [ "LSTMCell_for_rnn/BasicLSTMUnit_0.b_0", "basic_lstm_layers_0/BasicLSTMUnit_0.b_0" ]] for names in param_names: param = np.array(fluid.global_scope().find_var(names[0]).get_tensor( )) param = np.random.uniform( -0.1, 0.1, size=param.shape).astype('float32') fluid.global_scope().find_var(names[0]).get_tensor().set(param, place) fluid.global_scope().find_var(names[1]).get_tensor().set(param, place) out = exe.run(feed={ 'inputs_basic_lstm': inputs_basic_lstm_np, 'sequence_length': sequence_length_np, 'inputs': inputs_np, 'pre_hidden': pre_hidden_np, 'pre_cell': pre_cell_np }, fetch_list=[output_new, rnn_out]) self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4)) class TestRnnUtil(unittest.TestCase): """ Test cases for rnn apis' utility methods for coverage. """ def test_case(self): inputs = {"key1": 1, "key2": 2} func = lambda x: x + 1 outputs = utils.map_structure(func, inputs) utils.assert_same_structure(inputs, outputs) try: inputs["key3"] = 3 utils.assert_same_structure(inputs, outputs) except ValueError as identifier: pass if __name__ == '__main__': unittest.main()