# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # #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. # # Based on: # -------------------------------------------------------- # DARTS # Copyright (c) 2018, Hanxiao Liu. # Licensed under the Apache License, Version 2.0; # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function from learning_rate import cosine_decay import numpy as np import argparse from model import NetworkCIFAR as Network import reader import sys import os import time import logging import genotypes import paddle.fluid as fluid import shutil import utils import cPickle as cp parser = argparse.ArgumentParser("cifar") parser.add_argument( '--data', type=str, default='./dataset/cifar/cifar-10-batches-py/', help='location of the data corpus') parser.add_argument('--batch_size', type=int, default=96, help='batch size') parser.add_argument( '--learning_rate', type=float, default=0.025, help='init learning rate') parser.add_argument('--momentum', type=float, default=0.9, help='momentum') parser.add_argument( '--weight_decay', type=float, default=3e-4, help='weight decay') parser.add_argument( '--report_freq', type=float, default=50, help='report frequency') parser.add_argument( '--epochs', type=int, default=600, help='num of training epochs') parser.add_argument( '--init_channels', type=int, default=36, help='num of init channels') parser.add_argument( '--layers', type=int, default=20, help='total number of layers') parser.add_argument( '--model_path', type=str, default='saved_models', help='path to save the model') parser.add_argument( '--auxiliary', action='store_true', default=False, help='use auxiliary tower') parser.add_argument( '--auxiliary_weight', type=float, default=0.4, help='weight for auxiliary loss') parser.add_argument( '--cutout', action='store_true', default=False, help='use cutout') parser.add_argument( '--cutout_length', type=int, default=16, help='cutout length') parser.add_argument( '--drop_path_prob', type=float, default=0.2, help='drop path probability') parser.add_argument('--save', type=str, default='EXP', help='experiment name') parser.add_argument( '--arch', type=str, default='DARTS', help='which architecture to use') parser.add_argument( '--grad_clip', type=float, default=5, help='gradient clipping') parser.add_argument( '--lr_exp_decay', action='store_true', default=False, help='use exponential_decay learning_rate') parser.add_argument('--mix_alpha', type=float, default=0.5, help='mixup alpha') parser.add_argument( '--lrc_loss_lambda', default=0, type=float, help='lrc_loss_lambda') parser.add_argument( '--loss_type', default=1, type=float, help='loss_type 0: cross entropy 1: multi margin loss 2: max margin loss') args = parser.parse_args() CIFAR_CLASSES = 10 dataset_train_size = 50000 image_size = 32 def main(): image_shape = [3, image_size, image_size] devices = os.getenv("CUDA_VISIBLE_DEVICES") or "" devices_num = len(devices.split(",")) logging.info("args = %s", args) genotype = eval("genotypes.%s" % args.arch) model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype) steps_one_epoch = dataset_train_size / (devices_num * args.batch_size) train(model, args, image_shape, steps_one_epoch) def build_program(main_prog, startup_prog, args, is_train, model, im_shape, steps_one_epoch): out = [] with fluid.program_guard(main_prog, startup_prog): py_reader = model.build_input(im_shape, args.batch_size, is_train) if is_train: with fluid.unique_name.guard(): loss = model.train_model(py_reader, args.init_channels, args.auxiliary, args.auxiliary_weight, args.batch_size, args.lrc_loss_lambda) optimizer = fluid.optimizer.Momentum( learning_rate=cosine_decay(args.learning_rate, \ args.epochs, steps_one_epoch), regularization=fluid.regularizer.L2Decay(\ args.weight_decay), momentum=args.momentum) optimizer.minimize(loss) out = [py_reader, loss] else: with fluid.unique_name.guard(): loss, acc_1, acc_5 = model.test_model(py_reader, args.init_channels) out = [py_reader, loss, acc_1, acc_5] return out def train(model, args, im_shape, steps_one_epoch): train_startup_prog = fluid.Program() test_startup_prog = fluid.Program() train_prog = fluid.Program() test_prog = fluid.Program() train_py_reader, loss_train = build_program(train_prog, train_startup_prog, args, True, model, im_shape, steps_one_epoch) test_py_reader, loss_test, acc_1, acc_5 = build_program( test_prog, test_startup_prog, args, False, model, im_shape, steps_one_epoch) test_prog = test_prog.clone(for_test=True) place = fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(train_startup_prog) exe.run(test_startup_prog) exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = 1 train_exe = fluid.ParallelExecutor( main_program=train_prog, use_cuda=True, loss_name=loss_train.name, exec_strategy=exec_strategy) train_reader = reader.train10(args) test_reader = reader.test10(args) train_py_reader.decorate_paddle_reader(train_reader) test_py_reader.decorate_paddle_reader(test_reader) fluid.clip.set_gradient_clip(fluid.clip.GradientClipByNorm(args.grad_clip)) fluid.memory_optimize(fluid.default_main_program()) def save_model(postfix, main_prog): model_path = os.path.join(args.model_path, postfix) if os.path.isdir(model_path): shutil.rmtree(model_path) fluid.io.save_persistables(exe, model_path, main_program=main_prog) def test(epoch_id): test_fetch_list = [loss_test, acc_1, acc_5] objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() test_py_reader.start() test_start_time = time.time() step_id = 0 try: while True: prev_test_start_time = test_start_time test_start_time = time.time() loss_test_v, acc_1_v, acc_5_v = exe.run( test_prog, fetch_list=test_fetch_list) objs.update(np.array(loss_test_v), args.batch_size) top1.update(np.array(acc_1_v), args.batch_size) top5.update(np.array(acc_5_v), args.batch_size) if step_id % args.report_freq == 0: print("Epoch {}, Step {}, acc_1 {}, acc_5 {}, time {}". format(epoch_id, step_id, np.array(acc_1_v), np.array(acc_5_v), test_start_time - prev_test_start_time)) step_id += 1 except fluid.core.EOFException: test_py_reader.reset() print("Epoch {0}, top1 {1}, top5 {2}".format(epoch_id, top1.avg, top5.avg)) train_fetch_list = [loss_train] epoch_start_time = time.time() for epoch_id in range(args.epochs): model.drop_path_prob = args.drop_path_prob * epoch_id / args.epochs train_py_reader.start() epoch_end_time = time.time() if epoch_id > 0: print("Epoch {}, total time {}".format(epoch_id - 1, epoch_end_time - epoch_start_time)) epoch_start_time = epoch_end_time epoch_end_time start_time = time.time() step_id = 0 try: while True: prev_start_time = start_time start_time = time.time() loss_v, = train_exe.run( fetch_list=[v.name for v in train_fetch_list]) print("Epoch {}, Step {}, loss {}, time {}".format(epoch_id, step_id, \ np.array(loss_v).mean(), start_time-prev_start_time)) step_id += 1 sys.stdout.flush() except fluid.core.EOFException: train_py_reader.reset() if epoch_id % 50 == 0 or epoch_id == args.epochs - 1: save_model(str(epoch_id), train_prog) test(epoch_id) if __name__ == '__main__': main()