import os import sys import logging import paddle import argparse import functools import math import time import numpy as np import paddle.fluid as fluid sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir) from paddleslim.prune import AutoPruner from paddleslim.common import get_logger from paddleslim.analysis import flops import models from utility import add_arguments, print_arguments _logger = get_logger(__name__, level=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('model', str, "MobileNet", "The target model.") add_arg('pretrained_model', str, "../pretrained_model/MobileNetV1_pretrained", "Whether to use pretrained model.") 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('total_images', int, 1281167, "The number of total training images.") parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step") add_arg('config_file', str, None, "The config file for compression with yaml format.") add_arg('data', str, "imagenet", "Which data to use. 'mnist' or 'imagenet'") add_arg('log_period', int, 10, "Log period in batches.") add_arg('test_period', int, 10, "Test period in epoches.") # 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): train_reader = None test_reader = None 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) image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') # 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) val_program = fluid.default_main_program().clone(for_test=True) opt = create_optimizer(args) opt.minimize(avg_cost) place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) if args.pretrained_model: def if_exist(var): return os.path.exists(os.path.join(args.pretrained_model, var.name)) fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist) val_reader = paddle.fluid.io.batch(val_reader, batch_size=args.batch_size) train_reader = paddle.fluid.io.batch( train_reader, batch_size=args.batch_size, drop_last=True) train_feeder = feeder = fluid.DataFeeder([image, label], place) val_feeder = feeder = fluid.DataFeeder( [image, label], place, program=val_program) def test(epoch, program): batch_id = 0 acc_top1_ns = [] acc_top5_ns = [] for data in val_reader(): start_time = time.time() acc_top1_n, acc_top5_n = exe.run( program, feed=train_feeder.feed(data), fetch_list=[acc_top1.name, acc_top5.name]) end_time = time.time() if batch_id % args.log_period == 0: _logger.info( "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}". format(epoch, batch_id, np.mean(acc_top1_n), np.mean(acc_top5_n), end_time - start_time)) acc_top1_ns.append(np.mean(acc_top1_n)) acc_top5_ns.append(np.mean(acc_top5_n)) batch_id += 1 _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format( epoch, np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns)))) return np.mean(np.array(acc_top1_ns)) def train(epoch, program): build_strategy = fluid.BuildStrategy() exec_strategy = fluid.ExecutionStrategy() train_program = fluid.compiler.CompiledProgram( program).with_data_parallel( loss_name=avg_cost.name, build_strategy=build_strategy, exec_strategy=exec_strategy) batch_id = 0 for data in train_reader(): start_time = time.time() loss_n, acc_top1_n, acc_top5_n = exe.run( train_program, feed=train_feeder.feed(data), fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name]) end_time = time.time() loss_n = np.mean(loss_n) acc_top1_n = np.mean(acc_top1_n) acc_top5_n = np.mean(acc_top5_n) if batch_id % args.log_period == 0: _logger.info( "epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {}; time: {}". format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n, end_time - start_time)) batch_id += 1 params = [] for param in fluid.default_main_program().global_block().all_parameters(): if "_sep_weights" in param.name: params.append(param.name) pruner = AutoPruner( val_program, fluid.global_scope(), place, params=params, init_ratios=[0.33] * len(params), pruned_flops=0.5, pruned_latency=None, server_addr=("", 0), init_temperature=100, reduce_rate=0.85, max_try_times=300, max_client_num=10, search_steps=100, max_ratios=0.9, min_ratios=0., is_server=True, key="auto_pruner") while True: pruned_program, pruned_val_program = pruner.prune( fluid.default_main_program(), val_program) for i in range(1): train(i, pruned_program) score = test(0, pruned_val_program) pruner.reward(score) def main(): args = parser.parse_args() print_arguments(args) compress(args) if __name__ == '__main__': main()