# 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 sys sys.path.append("../") import unittest import paddle import paddle.fluid as fluid from paddleslim.quant import quant_aware, convert sys.path.append("../demo") from models import MobileNet from layers import conv_bn_layer import paddle.dataset.mnist as reader from paddle.fluid.framework import IrGraph from paddle.fluid import core import numpy as np class TestQuantAwareCase1(unittest.TestCase): def get_model(self): image = fluid.layers.data( name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) startup_prog = fluid.default_startup_program() train_prog = fluid.default_main_program() return startup_prog, train_prog def get_op_number(self, prog): graph = IrGraph(core.Graph(prog.desc), for_test=False) quant_op_nums = 0 op_nums = 0 for op in graph.all_op_nodes(): if op.name() in ['conv2d', 'depthwise_conv2d', 'mul']: op_nums += 1 elif 'fake_' in op.name(): quant_op_nums += 1 return op_nums, quant_op_nums def test_quant_op(self): startup_prog, train_prog = self.get_model() place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( ) else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) config_1 = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'], } quant_prog_1 = quant_aware( train_prog, place, config=config_1, for_test=True) op_nums_1, quant_op_nums_1 = self.get_op_number(quant_prog_1) convert_prog_1 = convert(quant_prog_1, place, config=config_1) convert_op_nums_1, convert_quant_op_nums_1 = self.get_op_number( convert_prog_1) config_1['not_quant_pattern'] = ['last_fc'] quant_prog_2 = quant_aware( train_prog, place, config=config_1, for_test=True) op_nums_2, quant_op_nums_2 = self.get_op_number(quant_prog_2) convert_prog_2 = convert(quant_prog_2, place, config=config_1) convert_op_nums_2, convert_quant_op_nums_2 = self.get_op_number( convert_prog_2) self.assertTrue(op_nums_1 == op_nums_2) # test quant_aware op numbers self.assertTrue(op_nums_1 * 4 == quant_op_nums_1) # test convert op numbers self.assertTrue(convert_op_nums_1 * 2 == convert_quant_op_nums_1) # test skip_quant self.assertTrue(quant_op_nums_1 - 4 == quant_op_nums_2) self.assertTrue(convert_quant_op_nums_1 - 2 == convert_quant_op_nums_2) class TestQuantAwareCase2(unittest.TestCase): def test_accuracy(self): image = fluid.layers.data( name='image', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') model = MobileNet() out = model.net(input=image, class_dim=10) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) optimizer = fluid.optimizer.Momentum( momentum=0.9, learning_rate=0.01, regularization=fluid.regularizer.L2Decay(4e-5)) optimizer.minimize(avg_cost) main_prog = fluid.default_main_program() val_prog = main_prog.clone(for_test=True) place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( ) else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) feeder = fluid.DataFeeder([image, label], place, program=main_prog) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=64) eval_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64) def train(program): iter = 0 for data in train_reader(): cost, top1, top5 = exe.run( program, feed=feeder.feed(data), fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) def test(program): iter = 0 result = [[], [], []] for data in train_reader(): cost, top1, top5 = exe.run( program, feed=feeder.feed(data), fetch_list=[avg_cost, acc_top1, acc_top5]) iter += 1 if iter % 100 == 0: print( 'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'. format(iter, cost, top1, top5)) result[0].append(cost) result[1].append(top1) result[2].append(top5) print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format( np.mean(result[0]), np.mean(result[1]), np.mean(result[2]))) return np.mean(result[1]), np.mean(result[2]) train(main_prog) top1_1, top5_1 = test(main_prog) config = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'], } quant_train_prog = quant_aware( main_prog, place, config, for_test=False) quant_eval_prog = quant_aware(val_prog, place, config, for_test=True) train(quant_train_prog) quant_eval_prog = convert(quant_eval_prog, place, config) top1_2, top5_2 = test(quant_eval_prog) # values before quantization and after quantization should be close print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1)) print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2)) if __name__ == '__main__': unittest.main()