# 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 os import importlib import tempfile import numpy as np import paddle.fluid.core as core import paddle.fluid as fluid import warnings import paddle 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 paddle.enable_static() class InferModel(object): def __init__(self, list): self.program = list[0] self.feed_var_names = list[1] self.fetch_vars = list[2] class TestBook(unittest.TestCase): def test_fit_line_inference_model(self): root_path = tempfile.TemporaryDirectory() MODEL_DIR = os.path.join(root_path.name, "inference_model") UNI_MODEL_DIR = os.path.join(root_path.name, "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 = paddle.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 range(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] importlib.reload(executor) # reload to build a new scope model_0 = 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 = 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) root_path.cleanup() self.assertRaises(ValueError, fluid.io.load_inference_model, None, exe, model_str, None) class TestSaveInferenceModel(unittest.TestCase): def test_save_inference_model(self): root_path = tempfile.TemporaryDirectory() MODEL_DIR = os.path.join(root_path.name, "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 = paddle.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) root_path.cleanup() def test_save_inference_model_with_auc(self): root_path = tempfile.TemporaryDirectory() MODEL_DIR = os.path.join(root_path.name, "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 = paddle.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) root_path.cleanup() 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): root_path = tempfile.TemporaryDirectory() MODEL_DIR = os.path.join(root_path.name, "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 = paddle.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]) root_path.cleanup() class TestSaveInferenceModelNew(unittest.TestCase): def test_save_and_load_inference_model(self): root_path = tempfile.TemporaryDirectory() MODEL_DIR = os.path.join(root_path.name, "inference_model5") init_program = fluid.default_startup_program() program = fluid.default_main_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 = paddle.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=[]) tensor_x = np.array([[1, 1], [1, 2], [5, 2]]).astype("float32") tensor_y = np.array([[-2], [-3], [-7]]).astype("float32") for i in range(3): exe.run(program, feed={ 'x': tensor_x, 'y': tensor_y }, fetch_list=[avg_cost]) self.assertRaises(ValueError, paddle.static.save_inference_model, None, ['x', 'y'], [avg_cost], exe) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR + "/", [x, y], [avg_cost], exe) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR, ['x', 'y'], [avg_cost], exe) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR, 'x', [avg_cost], exe) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR, [x, y], ['avg_cost'], exe) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR, [x, y], 'avg_cost', exe) model_path = MODEL_DIR + "_isdir.pdmodel" os.makedirs(model_path) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR + "_isdir", [x, y], [avg_cost], exe) os.rmdir(model_path) params_path = MODEL_DIR + "_isdir.pdmodel" os.makedirs(params_path) self.assertRaises(ValueError, paddle.static.save_inference_model, MODEL_DIR + "_isdir", [x, y], [avg_cost], exe) os.rmdir(params_path) paddle.static.io.save_inference_model(MODEL_DIR, [x, y], [avg_cost], exe) self.assertTrue(os.path.exists(MODEL_DIR + ".pdmodel")) self.assertTrue(os.path.exists(MODEL_DIR + ".pdiparams")) expected = exe.run(program, feed={ 'x': tensor_x, 'y': tensor_y }, fetch_list=[avg_cost])[0] importlib.reload(executor) # reload to build a new scope self.assertRaises(ValueError, paddle.static.load_inference_model, None, exe) self.assertRaises(ValueError, paddle.static.load_inference_model, MODEL_DIR + "/", exe) self.assertRaises(ValueError, paddle.static.load_inference_model, [MODEL_DIR], exe) self.assertRaises(ValueError, paddle.static.load_inference_model, MODEL_DIR, exe, pserver_endpoints=None) self.assertRaises(ValueError, paddle.static.load_inference_model, MODEL_DIR, exe, unsupported_param=None) self.assertRaises((TypeError, ValueError), paddle.static.load_inference_model, None, exe, model_filename="illegal", params_filename="illegal") model = InferModel(paddle.static.io.load_inference_model( MODEL_DIR, exe)) root_path.cleanup() 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) self.assertEqual(expected, actual) # test save_to_file content type should be bytes self.assertRaises(ValueError, paddle.static.io.save_to_file, '', 123) # test _get_valid_program self.assertRaises(TypeError, paddle.static.io._get_valid_program, 0) p = Program() cp = CompiledProgram(p) paddle.static.io._get_valid_program(cp) self.assertTrue(paddle.static.io._get_valid_program(cp) is p) cp._program = None self.assertRaises(TypeError, paddle.static.io._get_valid_program, cp) def test_serialize_program_and_persistables(self): init_program = fluid.default_startup_program() program = fluid.default_main_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 = paddle.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=[]) tensor_x = np.array([[1, 1], [1, 2], [5, 2]]).astype("float32") tensor_y = np.array([[-2], [-3], [-7]]).astype("float32") for i in range(3): exe.run(program, feed={ 'x': tensor_x, 'y': tensor_y }, fetch_list=[avg_cost]) # test if return type of serialize_program is bytes res1 = paddle.static.io.serialize_program([x, y], [avg_cost]) self.assertTrue(isinstance(res1, bytes)) # test if return type of serialize_persistables is bytes res2 = paddle.static.io.serialize_persistables([x, y], [avg_cost], exe) self.assertTrue(isinstance(res2, bytes)) # test if variables in program is empty res = paddle.static.io._serialize_persistables(Program(), None) self.assertEqual(res, None) self.assertRaises(TypeError, paddle.static.io.deserialize_persistables, None, None, None) def test_normalize_program(self): init_program = fluid.default_startup_program() program = fluid.default_main_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 = paddle.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=[]) tensor_x = np.array([[1, 1], [1, 2], [5, 2]]).astype("float32") tensor_y = np.array([[-2], [-3], [-7]]).astype("float32") for i in range(3): exe.run(program, feed={ 'x': tensor_x, 'y': tensor_y }, fetch_list=[avg_cost]) # test if return type of serialize_program is bytes res = paddle.static.normalize_program(program, [x, y], [avg_cost]) self.assertTrue(isinstance(res, Program)) # test program type self.assertRaises(TypeError, paddle.static.normalize_program, None, [x, y], [avg_cost]) # test feed_vars type self.assertRaises(TypeError, paddle.static.normalize_program, program, 'x', [avg_cost]) # test fetch_vars type self.assertRaises(TypeError, paddle.static.normalize_program, program, [x, y], 'avg_cost') class TestLoadInferenceModelError(unittest.TestCase): def test_load_model_not_exist(self): place = core.CPUPlace() exe = executor.Executor(place) self.assertRaises(ValueError, load_inference_model, './test_not_exist_dir', exe) if __name__ == '__main__': unittest.main()