diff --git a/demo/distillation/train.py b/demo/distillation/train.py new file mode 100644 index 0000000000000000000000000000000000000000..7f389168440a59f0872d44ab6e62f262e373f6f0 --- /dev/null +++ b/demo/distillation/train.py @@ -0,0 +1,238 @@ +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.append(sys.path[0] + "/../") +import models +import imagenet_reader as reader +from utility import add_arguments, print_arguments +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*4, "Minibatch size.") +add_arg('use_gpu', bool, True, "Whether to use GPU or not.") +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, "mnist", "Which data to use. 'mnist' 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", "Set the teacher network to use.") +add_arg('teacher_pretrained_model', str, "../pretrain/ResNet50_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): + step = int(math.ceil(float(args.total_images) / args.batch_size)) + 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 optimizer + + +def cosine_decay(args): + step = int(math.ceil(float(args.total_images) / args.batch_size)) + 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 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 == "mnist": + import paddle.dataset.mnist as reader + train_reader = reader.train() + val_reader = reader.test() + class_dim = 10 + image_shape = "1,28,28" + 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) + #print("="*50+"student_model_params"+"="*50) + #for v in student_program.list_vars(): + # print(v.name, v.shape) + + place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() + exe = fluid.Executor(place) + + train_reader = paddle.batch( + train_reader, batch_size=args.batch_size, drop_last=True) + val_reader = paddle.batch( + val_reader, batch_size=args.batch_size, drop_last=True) + val_program = student_program.clone(for_test=True) + + places = fluid.cuda_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() + teacher_scope = fluid.Scope() + with fluid.scope_guard(teacher_scope): + 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) + + #print("="*50+"teacher_model_params"+"="*50) + #for v in teacher_program.list_vars(): + # print(v.name, v.shape) + + exe.run(t_startup) + 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): + return os.path.exists( + os.path.join(args.teacher_pretrained_model, var.name) + ) and var.name != 'conv1_weights' and var.name != 'fc_0.w_0' and var.name != 'fc_0.b_0' + + fluid.io.load_vars( + exe, + args.teacher_pretrained_model, + main_program=teacher_program, + predicate=if_exist) + + data_name_map = {'image': 'image'} + main = merge( + teacher_program, + student_program, + data_name_map, + place, + teacher_scope=teacher_scope) + + #print("="*50+"teacher_vars"+"="*50) + #for v in teacher_program.list_vars(): + # if '_generated_var' not in v.name and 'fetch' not in v.name and 'feed' not in v.name: + # print(v.name, v.shape) + #return + + with fluid.program_guard(main, s_startup): + l2_loss_v = l2_loss("teacher_fc_0.tmp_0", "fc_0.tmp_0", main) + fsp_loss_v = fsp_loss("teacher_res2a_branch2a.conv2d.output.1.tmp_0", + "teacher_res3a_branch2a.conv2d.output.1.tmp_0", + "depthwise_conv2d_1.tmp_0", "conv2d_3.tmp_0", + main) + loss = avg_cost + l2_loss_v + fsp_loss_v + 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(main).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): + loss_1, loss_2, loss_3, loss_4 = exe.run( + parallel_main, + feed=data, + fetch_list=[ + loss.name, avg_cost.name, l2_loss_v.name, fsp_loss_v.name + ]) + if step_id % args.log_period == 0: + _logger.info( + "train_epoch {} step {} loss {:.6f}, class loss {:.6f}, l2 loss {:.6f}, fsp loss {:.6f}". + format(epoch_id, step_id, loss_1[0], loss_2[0], loss_3[0], + loss_4[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])) + _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()