from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import math import logging import paddle import argparse import functools import numpy as np import paddle.fluid as fluid sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir) import models from utility import add_arguments, print_arguments, _download, _decompress from paddleslim.dist import merge, l2_loss, soft_label_loss, fsp_loss logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') _logger = logging.getLogger(__name__) _logger.setLevel(logging.INFO) parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('batch_size', int, 64, "Minibatch size.") add_arg('use_gpu', bool, True, "Whether to use GPU or not.") add_arg('save_inference', bool, False, "Whether to save inference model.") add_arg('total_images', int, 1281167, "Training image number.") add_arg('image_shape', str, "3,224,224", "Input image size") add_arg('lr', float, 0.1, "The learning rate used to fine-tune pruned model.") add_arg('lr_strategy', str, "piecewise_decay", "The learning rate decay strategy.") add_arg('l2_decay', float, 3e-5, "The l2_decay parameter.") add_arg('momentum_rate', float, 0.9, "The value of momentum_rate.") add_arg('num_epochs', int, 120, "The number of total epochs.") add_arg('data', str, "imagenet", "Which data to use. 'cifar10' or 'imagenet'") add_arg('log_period', int, 20, "Log period in batches.") add_arg('model', str, "MobileNet", "Set the network to use.") add_arg('pretrained_model', str, None, "Whether to use pretrained model.") add_arg('teacher_model', str, "ResNet50_vd", "Set the teacher network to use.") add_arg('teacher_pretrained_model', str, "./ResNet50_vd_pretrained", "Whether to use pretrained model.") parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step") # yapf: enable model_list = [m for m in dir(models) if "__" not in m] def piecewise_decay(args): if args.use_gpu: devices_num = fluid.core.get_cuda_device_count() else: devices_num = int(os.environ.get('CPU_NUM', 1)) step = int( math.ceil(float(args.total_images) / args.batch_size) / devices_num) bd = [step * e for e in args.step_epochs] lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)] learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr) optimizer = fluid.optimizer.Momentum( learning_rate=learning_rate, momentum=args.momentum_rate, regularization=fluid.regularizer.L2Decay(args.l2_decay)) return learning_rate, optimizer def cosine_decay(args): if cfg.use_gpu: devices_num = fluid.core.get_cuda_device_count() else: devices_num = int(os.environ.get('CPU_NUM', 1)) step = int( math.ceil(float(args.total_images) / args.batch_size) / devices_num) learning_rate = fluid.layers.cosine_decay( learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs) optimizer = fluid.optimizer.Momentum( learning_rate=learning_rate, momentum=args.momentum_rate, regularization=fluid.regularizer.L2Decay(args.l2_decay)) return learning_rate, optimizer def create_optimizer(args): if args.lr_strategy == "piecewise_decay": return piecewise_decay(args) elif args.lr_strategy == "cosine_decay": return cosine_decay(args) def compress(args): if args.data == "cifar10": import paddle.dataset.cifar as reader train_reader = reader.train10() val_reader = reader.test10() class_dim = 10 image_shape = "3,32,32" elif args.data == "imagenet": import imagenet_reader as reader train_reader = reader.train() val_reader = reader.val() class_dim = 1000 image_shape = "3,224,224" else: raise ValueError("{} is not supported.".format(args.data)) image_shape = [int(m) for m in image_shape.split(",")] assert args.model in model_list, "{} is not in lists: {}".format( args.model, model_list) student_program = fluid.Program() s_startup = fluid.Program() with fluid.program_guard(student_program, s_startup): with fluid.unique_name.guard(): image = fluid.layers.data( name='image', shape=image_shape, dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') train_loader = fluid.io.DataLoader.from_generator( feed_list=[image, label], capacity=64, use_double_buffer=True, iterable=True) valid_loader = fluid.io.DataLoader.from_generator( feed_list=[image, label], capacity=64, use_double_buffer=True, iterable=True) # model definition model = models.__dict__[args.model]() out = model.net(input=image, class_dim=class_dim) 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) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) train_reader = paddle.fluid.io.batch( train_reader, batch_size=args.batch_size, drop_last=True) val_reader = paddle.fluid.io.batch( val_reader, batch_size=args.batch_size, drop_last=True) val_program = student_program.clone(for_test=True) places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places() train_loader.set_sample_list_generator(train_reader, places) valid_loader.set_sample_list_generator(val_reader, place) teacher_model = models.__dict__[args.teacher_model]() # define teacher program teacher_program = fluid.Program() t_startup = fluid.Program() with fluid.program_guard(teacher_program, t_startup): with fluid.unique_name.guard(): image = fluid.layers.data( name='image', shape=image_shape, dtype='float32') predict = teacher_model.net(image, class_dim=class_dim) exe.run(t_startup) if not os.path.exists(args.teacher_pretrained_model): _download( 'http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar', '.') _decompress('./ResNet50_vd_pretrained.tar') assert args.teacher_pretrained_model and os.path.exists( args.teacher_pretrained_model ), "teacher_pretrained_model should be set when teacher_model is not None." def if_exist(var): exist = os.path.exists( os.path.join(args.teacher_pretrained_model, var.name)) if args.data == "cifar10" and (var.name == 'fc_0.w_0' or var.name == 'fc_0.b_0'): exist = False return exist fluid.io.load_vars( exe, args.teacher_pretrained_model, main_program=teacher_program, predicate=if_exist) data_name_map = {'image': 'image'} merge(teacher_program, student_program, data_name_map, place) with fluid.program_guard(student_program, s_startup): distill_loss = soft_label_loss("teacher_fc_0.tmp_0", "fc_0.tmp_0", student_program) loss = avg_cost + distill_loss lr, opt = create_optimizer(args) opt.minimize(loss) exe.run(s_startup) build_strategy = fluid.BuildStrategy() build_strategy.fuse_all_reduce_ops = False parallel_main = fluid.CompiledProgram(student_program).with_data_parallel( loss_name=loss.name, build_strategy=build_strategy) for epoch_id in range(args.num_epochs): for step_id, data in enumerate(train_loader): lr_np, loss_1, loss_2, loss_3 = exe.run( parallel_main, feed=data, fetch_list=[ lr.name, loss.name, avg_cost.name, distill_loss.name ]) if step_id % args.log_period == 0: _logger.info( "train_epoch {} step {} lr {:.6f}, loss {:.6f}, class loss {:.6f}, distill loss {:.6f}". format(epoch_id, step_id, lr_np[0], loss_1[0], loss_2[0], loss_3[0])) val_acc1s = [] val_acc5s = [] for step_id, data in enumerate(valid_loader): val_loss, val_acc1, val_acc5 = exe.run( val_program, data, fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name]) val_acc1s.append(val_acc1) val_acc5s.append(val_acc5) if step_id % args.log_period == 0: _logger.info( "valid_epoch {} step {} loss {:.6f}, top1 {:.6f}, top5 {:.6f}". format(epoch_id, step_id, val_loss[0], val_acc1[0], val_acc5[0])) if args.save_inference: fluid.io.save_inference_model( os.path.join("./saved_models", str(epoch_id)), ["image"], [out], exe, student_program) _logger.info("epoch {} top1 {:.6f}, top5 {:.6f}".format( epoch_id, np.mean(val_acc1s), np.mean(val_acc5s))) def main(): args = parser.parse_args() print_arguments(args) compress(args) if __name__ == '__main__': main()