# 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 os import numpy as np import random import unittest import logging import paddle import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.optimizer import AdamOptimizer from paddle.fluid.framework import IrGraph from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass 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 os.environ["CPU_NUM"] = "1" if core.is_compiled_with_cuda(): fluid.set_flags({"FLAGS_cudnn_deterministic": True}) _logger = get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') def StaticLenet(data, num_classes=10, classifier_activation='softmax'): conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1") conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2") fc_w1_attr = fluid.ParamAttr(name="fc_w_1") fc_w2_attr = fluid.ParamAttr(name="fc_w_2") fc_w3_attr = fluid.ParamAttr(name="fc_w_3") conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1") conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2") fc_b1_attr = fluid.ParamAttr(name="fc_b_1") fc_b2_attr = fluid.ParamAttr(name="fc_b_2") fc_b3_attr = fluid.ParamAttr(name="fc_b_3") conv1 = fluid.layers.conv2d( data, num_filters=6, filter_size=3, stride=1, padding=1, param_attr=conv2d_w1_attr, bias_attr=conv2d_b1_attr) pool1 = fluid.layers.pool2d( conv1, pool_size=2, pool_type='max', pool_stride=2) conv2 = fluid.layers.conv2d( pool1, num_filters=16, filter_size=5, stride=1, padding=0, param_attr=conv2d_w2_attr, bias_attr=conv2d_b2_attr) pool2 = fluid.layers.pool2d( conv2, pool_size=2, pool_type='max', pool_stride=2) fc1 = fluid.layers.fc(input=pool2, size=120, param_attr=fc_w1_attr, bias_attr=fc_b1_attr) fc2 = fluid.layers.fc(input=fc1, size=84, param_attr=fc_w2_attr, bias_attr=fc_b2_attr) fc3 = fluid.layers.fc(input=fc2, size=num_classes, act=classifier_activation, param_attr=fc_w3_attr, bias_attr=fc_b3_attr) return fc3 class ImperativeLenet(fluid.dygraph.Layer): def __init__(self, num_classes=10, classifier_activation='softmax'): super(ImperativeLenet, self).__init__() conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1") conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2") fc_w1_attr = fluid.ParamAttr(name="fc_w_1") fc_w2_attr = fluid.ParamAttr(name="fc_w_2") fc_w3_attr = fluid.ParamAttr(name="fc_w_3") conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1") conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2") fc_b1_attr = fluid.ParamAttr(name="fc_b_1") fc_b2_attr = fluid.ParamAttr(name="fc_b_2") fc_b3_attr = fluid.ParamAttr(name="fc_b_3") self.features = Sequential( Conv2D( num_channels=1, num_filters=6, filter_size=3, stride=1, padding=1, param_attr=conv2d_w1_attr, bias_attr=conv2d_b1_attr), Pool2D( pool_size=2, pool_type='max', pool_stride=2), Conv2D( num_channels=6, num_filters=16, filter_size=5, stride=1, padding=0, param_attr=conv2d_w2_attr, bias_attr=conv2d_b2_attr), Pool2D( pool_size=2, pool_type='max', pool_stride=2)) self.fc = Sequential( Linear( input_dim=400, output_dim=120, param_attr=fc_w1_attr, bias_attr=fc_b1_attr), Linear( input_dim=120, output_dim=84, param_attr=fc_w2_attr, bias_attr=fc_b2_attr), Linear( input_dim=84, output_dim=num_classes, act=classifier_activation, param_attr=fc_w3_attr, bias_attr=fc_b3_attr)) def forward(self, inputs): x = self.features(inputs) x = fluid.layers.flatten(x, 1) x = self.fc(x) return x class TestImperativeQat(unittest.TestCase): """ QAT = quantization-aware training """ def test_qat_save(self): imperative_qat = ImperativeQuantAware( weight_quantize_type='abs_max', activation_quantize_type='moving_average_abs_max') with fluid.dygraph.guard(): lenet = ImperativeLenet() imperative_qat.quantize(lenet) adam = AdamOptimizer( learning_rate=0.001, parameter_list=lenet.parameters()) 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) epoch_num = 1 for epoch in range(epoch_num): lenet.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 = lenet(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) lenet.clear_gradients() if batch_id % 100 == 0: _logger.info( "Train | At epoch {} step {}: loss = {:}, acc= {:}". format(epoch, batch_id, avg_loss.numpy(), acc.numpy())) lenet.eval() 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 = lenet(img) acc_top1 = fluid.layers.accuracy( input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy( input=out, label=label, k=5) if batch_id % 100 == 0: _logger.info( "Test | At epoch {} step {}: acc1 = {:}, acc5 = {:}". format(epoch, batch_id, acc_top1.numpy(), acc_top5.numpy())) # save weights model_dict = lenet.state_dict() fluid.save_dygraph(model_dict, "save_temp") # test the correctness of `save_quantized_model` data = next(test_reader()) test_data = np.array([x[0].reshape(1, 28, 28) for x in data]).astype('float32') test_img = fluid.dygraph.to_variable(test_data) lenet.eval() before_save = lenet(test_img) # save inference quantized model path = "./mnist_infer_model" imperative_qat.save_quantized_model( dirname=path, model=lenet, input_shape=[(1, 28, 28)], input_dtype=['float32'], feed=[0], fetch=[0]) if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = fluid.Executor(place) [inference_program, feed_target_names, fetch_targets] = ( fluid.io.load_inference_model( dirname=path, executor=exe)) after_save, = exe.run(inference_program, feed={feed_target_names[0]: test_data}, fetch_list=fetch_targets) self.assertTrue( np.allclose(after_save, before_save.numpy()), msg='Failed to save the inference quantized model.') if __name__ == '__main__': unittest.main()