# 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. from __future__ import print_function import unittest import os import six import numpy as np import paddle.fluid.core as core import paddle.fluid as fluid import warnings import paddle.fluid.executor as executor import paddle.fluid.layers as layers import paddle.fluid.optimizer as optimizer from paddle.fluid.compiler import CompiledProgram from paddle.fluid.framework import Program, program_guard from paddle.fluid.io import save_inference_model, load_inference_model, save_persistables from paddle.fluid.transpiler import memory_optimize class TestBook(unittest.TestCase): class InferModel(object): def __init__(self, list): self.program = list[0] self.feed_var_names = list[1] self.fetch_vars = list[2] def test_fit_line_inference_model(self): MODEL_DIR = "./tmp/inference_model" UNI_MODEL_DIR = "./tmp/inference_model1" init_program = Program() program = Program() with program_guard(program, init_program): x = layers.data(name='x', shape=[2], dtype='float32') y = layers.data(name='y', shape=[1], dtype='float32') y_predict = layers.fc(input=x, size=1, act=None) cost = layers.square_error_cost(input=y_predict, label=y) avg_cost = layers.mean(cost) sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001) sgd_optimizer.minimize(avg_cost, init_program) place = core.CPUPlace() exe = executor.Executor(place) exe.run(init_program, feed={}, fetch_list=[]) for i in six.moves.xrange(100): tensor_x = np.array( [[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32") tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32") exe.run(program, feed={'x': tensor_x, 'y': tensor_y}, fetch_list=[avg_cost]) # Separated model and unified model save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program) save_inference_model(UNI_MODEL_DIR, ["x", "y"], [avg_cost], exe, program, 'model', 'params') main_program = program.clone()._prune_with_input( feeded_var_names=["x", "y"], targets=[avg_cost]) params_str = save_persistables(exe, None, main_program, None) expected = exe.run(program, feed={'x': tensor_x, 'y': tensor_y}, fetch_list=[avg_cost])[0] six.moves.reload_module(executor) # reload to build a new scope model_0 = self.InferModel(load_inference_model(MODEL_DIR, exe)) with open(os.path.join(UNI_MODEL_DIR, 'model'), "rb") as f: model_str = f.read() model_1 = self.InferModel( load_inference_model(None, exe, model_str, params_str)) for model in [model_0, model_1]: outs = exe.run(model.program, feed={ model.feed_var_names[0]: tensor_x, model.feed_var_names[1]: tensor_y }, fetch_list=model.fetch_vars) actual = outs[0] self.assertEqual(model.feed_var_names, ["x", "y"]) self.assertEqual(len(model.fetch_vars), 1) print("fetch %s" % str(model.fetch_vars[0])) self.assertEqual(expected, actual) self.assertRaises(ValueError, fluid.io.load_inference_model, None, exe, model_str, None) class TestSaveInferenceModel(unittest.TestCase): def test_save_inference_model(self): MODEL_DIR = "./tmp/inference_model2" init_program = Program() program = Program() # fake program without feed/fetch with program_guard(program, init_program): x = layers.data(name='x', shape=[2], dtype='float32') y = layers.data(name='y', shape=[1], dtype='float32') y_predict = layers.fc(input=x, size=1, act=None) cost = layers.square_error_cost(input=y_predict, label=y) avg_cost = layers.mean(cost) place = core.CPUPlace() exe = executor.Executor(place) exe.run(init_program, feed={}, fetch_list=[]) save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program) def test_save_inference_model_with_auc(self): MODEL_DIR = "./tmp/inference_model4" init_program = Program() program = Program() # fake program without feed/fetch with program_guard(program, init_program): x = layers.data(name='x', shape=[2], dtype='float32') y = layers.data(name='y', shape=[1], dtype='int32') predict = fluid.layers.fc(input=x, size=2, act='softmax') acc = fluid.layers.accuracy(input=predict, label=y) auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict, label=y) cost = fluid.layers.cross_entropy(input=predict, label=y) avg_cost = fluid.layers.mean(x=cost) place = core.CPUPlace() exe = executor.Executor(place) exe.run(init_program, feed={}, fetch_list=[]) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program) expected_warn = "please ensure that you have set the auc states to zeros before saving inference model" self.assertTrue(len(w) > 0) self.assertTrue(expected_warn == str(w[0].message)) class TestInstance(unittest.TestCase): def test_save_inference_model(self): MODEL_DIR = "./tmp/inference_model3" init_program = Program() program = Program() # fake program without feed/fetch with program_guard(program, init_program): x = layers.data(name='x', shape=[2], dtype='float32') y = layers.data(name='y', shape=[1], dtype='float32') y_predict = layers.fc(input=x, size=1, act=None) cost = layers.square_error_cost(input=y_predict, label=y) avg_cost = layers.mean(cost) place = core.CPUPlace() exe = executor.Executor(place) exe.run(init_program, feed={}, fetch_list=[]) # will print warning message cp_prog = CompiledProgram(program).with_data_parallel( loss_name=avg_cost.name) save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, cp_prog) self.assertRaises(TypeError, save_inference_model, [MODEL_DIR, ["x", "y"], [avg_cost], [], cp_prog]) if __name__ == '__main__': unittest.main()