# 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 numpy as np import sys sys.path.append("../") import unittest import logging import paddle import paddle.nn as nn import paddle.fluid as fluid from paddle.fluid.optimizer import AdamOptimizer from paddle.fluid.dygraph.container import Sequential from paddle.fluid.dygraph.nn import Conv2D from paddle.fluid.dygraph.nn import Pool2D from paddle.fluid.dygraph.nn import Linear from paddle.fluid.log_helper import get_logger from paddleslim.quant import quant_aware _logger = get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') class ImperativeLenet(nn.Layer): def __init__(self, num_classes=10, classifier_activation='softmax'): super(ImperativeLenet, self).__init__() self.features = Sequential( Conv2D( num_channels=1, num_filters=6, filter_size=3, stride=1, padding=1), Pool2D( pool_size=2, pool_type='max', pool_stride=2), Conv2D( num_channels=6, num_filters=16, filter_size=5, stride=1, padding=0), Pool2D( pool_size=2, pool_type='max', pool_stride=2)) self.fc = Sequential( Linear( input_dim=400, output_dim=120), Linear( input_dim=120, output_dim=84), Linear( input_dim=84, output_dim=num_classes, act=classifier_activation)) def forward(self, inputs): x = self.features(inputs) x = fluid.layers.flatten(x, 1) x = self.fc(x) return x class TestImperativeQatDefaultConfig(unittest.TestCase): """ QAT = quantization-aware training This test case uses defualt quantization config, weight_quantize_type is channel_wise_abs_max """ def test_qat_acc(self): with fluid.dygraph.guard(): lenet = ImperativeLenet() quant_lenet = quant_aware(lenet) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=32, drop_last=True) test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=32) def train(model): adam = AdamOptimizer( learning_rate=0.001, parameter_list=model.parameters()) epoch_num = 1 for epoch in range(epoch_num): model.train() for batch_id, data in enumerate(train_reader()): x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(-1, 1) img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) out = model(img) acc = fluid.layers.accuracy(out, label) loss = fluid.layers.cross_entropy(out, label) avg_loss = fluid.layers.mean(loss) avg_loss.backward() adam.minimize(avg_loss) model.clear_gradients() if batch_id % 100 == 0: _logger.info( "Train | At epoch {} step {}: loss = {:}, acc= {:}". format(epoch, batch_id, avg_loss.numpy(), acc.numpy())) def test(model): model.eval() avg_acc = [[], []] for batch_id, data in enumerate(test_reader()): x_data = np.array([x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(-1, 1) img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) out = model(img) acc_top1 = fluid.layers.accuracy( input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy( input=out, label=label, k=5) avg_acc[0].append(acc_top1.numpy()) avg_acc[1].append(acc_top5.numpy()) if batch_id % 100 == 0: _logger.info( "Test | step {}: acc1 = {:}, acc5 = {:}".format( batch_id, acc_top1.numpy(), acc_top5.numpy())) _logger.info("Test |Average: acc_top1 {}, acc_top5 {}".format( np.mean(avg_acc[0]), np.mean(avg_acc[1]))) return np.mean(avg_acc[0]), np.mean(avg_acc[1]) train(lenet) top1_1, top5_1 = test(lenet) quant_lenet.__init__() train(quant_lenet) top1_2, top5_2 = test(quant_lenet) # values before quantization and after quantization should be close _logger.info("Before quantization: top1: {}, top5: {}".format( top1_1, top5_1)) _logger.info("After quantization: top1: {}, top5: {}".format( top1_2, top5_2)) class TestImperativeQatUserDefineConfig(unittest.TestCase): """ QAT = quantization-aware training This test case is for testing user defined quantization config. """ def test_qat_acc(self): with fluid.dygraph.guard(): lenet = ImperativeLenet() quant_config = { 'weight_quantize_type': 'abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantizable_layer_type': ['Conv2D', 'Linear'] } quant_lenet = quant_aware(lenet, quant_config) train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=32, drop_last=True) test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=32) def train(model): adam = AdamOptimizer( learning_rate=0.001, parameter_list=model.parameters()) epoch_num = 1 for epoch in range(epoch_num): model.train() for batch_id, data in enumerate(train_reader()): x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(-1, 1) img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) out = model(img) acc = fluid.layers.accuracy(out, label) loss = fluid.layers.cross_entropy(out, label) avg_loss = fluid.layers.mean(loss) avg_loss.backward() adam.minimize(avg_loss) model.clear_gradients() if batch_id % 100 == 0: _logger.info( "Train | At epoch {} step {}: loss = {:}, acc= {:}". format(epoch, batch_id, avg_loss.numpy(), acc.numpy())) def test(model): model.eval() avg_acc = [[], []] for batch_id, data in enumerate(test_reader()): x_data = np.array([x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array( [x[1] for x in data]).astype('int64').reshape(-1, 1) img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) out = model(img) acc_top1 = fluid.layers.accuracy( input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy( input=out, label=label, k=5) avg_acc[0].append(acc_top1.numpy()) avg_acc[1].append(acc_top5.numpy()) if batch_id % 100 == 0: _logger.info( "Test | step {}: acc1 = {:}, acc5 = {:}".format( batch_id, acc_top1.numpy(), acc_top5.numpy())) _logger.info("Test |Average: acc_top1 {}, acc_top5 {}".format( np.mean(avg_acc[0]), np.mean(avg_acc[1]))) return np.mean(avg_acc[0]), np.mean(avg_acc[1]) train(lenet) top1_1, top5_1 = test(lenet) quant_lenet.__init__() train(quant_lenet) top1_2, top5_2 = test(quant_lenet) # values before quantization and after quantization should be close _logger.info("Before quantization: top1: {}, top5: {}".format( top1_1, top5_1)) _logger.info("After quantization: top1: {}, top5: {}".format( top1_2, top5_2)) if __name__ == '__main__': unittest.main()