import os import sys import logging import paddle import argparse import functools import time import numpy as np import paddle.fluid as fluid from paddleslim.prune.unstructured_pruner import UnstructuredPruner from paddleslim.common import get_logger sys.path.append(os.path.join(os.path.dirname("__file__"), os.path.pardir)) import models from utility import add_arguments, print_arguments import paddle.vision.transforms as T _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, "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, "cosine_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('threshold', float, 1e-5, "The threshold to set zeros, the abs(weights) lower than which will be zeros.") add_arg('pruning_mode', str, 'ratio', "the pruning mode: whether by ratio or by threshold.") add_arg('ratio', float, 0.5, "The ratio to set zeros, the smaller portion will be zeros.") add_arg('num_epochs', int, 120, "The number of total epochs.") parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step") add_arg('data', str, "mnist", "Which data to use. 'mnist' or 'imagenet'.") add_arg('log_period', int, 100, "Log period in batches.") add_arg('test_period', int, 10, "Test period in epoches.") add_arg('model_path', str, "./models", "The path to save model.") add_arg('model_period', int, 10, "The period to save model in epochs.") add_arg('resume_epoch', int, -1, "The epoch to resume training.") # yapf: enable model_list = models.__all__ def piecewise_decay(args, step_per_epoch): bd = [step_per_epoch * e for e in args.step_epochs] lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)] learning_rate = paddle.optimizer.lr.PiecewiseDecay(boundaries=bd, values=lr) optimizer = paddle.optimizer.Momentum( learning_rate=learning_rate, momentum=args.momentum_rate, weight_decay=paddle.regularizer.L2Decay(args.l2_decay)) return optimizer, learning_rate def cosine_decay(args, step_per_epoch): learning_rate = paddle.optimizer.lr.CosineAnnealingDecay( learning_rate=args.lr, T_max=args.num_epochs * step_per_epoch) optimizer = paddle.optimizer.Momentum( learning_rate=learning_rate, momentum=args.momentum_rate, weight_decay=paddle.regularizer.L2Decay(args.l2_decay)) return optimizer, learning_rate def create_optimizer(args, step_per_epoch): if args.lr_strategy == "piecewise_decay": return piecewise_decay(args, step_per_epoch) elif args.lr_strategy == "cosine_decay": return cosine_decay(args, step_per_epoch) def compress(args): train_reader = None test_reader = None if args.data == "mnist": transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])]) train_dataset = paddle.vision.datasets.MNIST( mode='train', backend="cv2", transform=transform) val_dataset = paddle.vision.datasets.MNIST( mode='test', backend="cv2", transform=transform) class_dim = 10 image_shape = "1,28,28" args.pretrained_model = False elif args.data == "imagenet": import imagenet_reader as reader train_dataset = reader.ImageNetDataset(data_dir='/data', mode='train') val_dataset = reader.ImageNetDataset(data_dir='/data', mode='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) places = paddle.static.cuda_places( ) if args.use_gpu else paddle.static.cpu_places() place = places[0] exe = paddle.static.Executor(place) image = paddle.static.data( name='image', shape=[None] + image_shape, dtype='float32') label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') batch_size_per_card = int(args.batch_size / len(places)) train_loader = paddle.io.DataLoader( train_dataset, places=places, feed_list=[image, label], drop_last=True, batch_size=batch_size_per_card, shuffle=True, return_list=False, use_shared_memory=True, num_workers=32) valid_loader = paddle.io.DataLoader( val_dataset, places=place, feed_list=[image, label], drop_last=False, return_list=False, use_shared_memory=True, batch_size=batch_size_per_card, shuffle=False) step_per_epoch = int(np.ceil(len(train_dataset) * 1. / args.batch_size)) # model definition model = models.__dict__[args.model]() out = model.net(input=image, class_dim=class_dim) cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) val_program = paddle.static.default_main_program().clone(for_test=True) opt, learning_rate = create_optimizer(args, step_per_epoch) opt.minimize(avg_cost) pruner = UnstructuredPruner( paddle.static.default_main_program(), mode=args.pruning_mode, ratio=args.ratio, threshold=args.threshold, place=place) exe.run(paddle.static.default_startup_program()) if args.pretrained_model: def if_exist(var): return os.path.exists(os.path.join(args.pretrained_model, var.name)) _logger.info("Load pretrained model from {}".format( args.pretrained_model)) # NOTE: We are using fluid.io.load_vars() because the pretrained model is from an older version which requires this API. # Please consider using paddle.static.load(program, model_path) when possible paddle.fluid.io.load_vars( exe, args.pretrained_model, predicate=if_exist) def test(epoch, program): acc_top1_ns = [] acc_top5_ns = [] _logger.info("The current density of the inference model is {}%".format( round(100 * UnstructuredPruner.total_sparse( paddle.static.default_main_program()), 2))) for batch_id, data in enumerate(valid_loader): start_time = time.time() acc_top1_n, acc_top5_n = exe.run( program, feed={ "image": data[0].get('image'), "label": data[0].get('label') }, 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)) _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)))) def train(epoch, program): for batch_id, data in enumerate(train_loader): start_time = time.time() loss_n, acc_top1_n, acc_top5_n = exe.run( train_program, feed={ "image": data[0].get('image'), "label": data[0].get('label') }, 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[{}] lr: {:.6f} - loss: {}; acc_top1: {}; acc_top5: {}; time: {}". format(epoch, batch_id, learning_rate.get_lr(), loss_n, acc_top1_n, acc_top5_n, end_time - start_time)) learning_rate.step() pruner.step() batch_id += 1 build_strategy = paddle.static.BuildStrategy() exec_strategy = paddle.static.ExecutionStrategy() train_program = paddle.static.CompiledProgram( paddle.static.default_main_program()).with_data_parallel( loss_name=avg_cost.name, build_strategy=build_strategy, exec_strategy=exec_strategy) for i in range(args.resume_epoch + 1, args.num_epochs): train(i, train_program) _logger.info("The current density of the pruned model is: {}%".format( round(100 * UnstructuredPruner.total_sparse( paddle.static.default_main_program()), 2))) if i % args.test_period == 0: pruner.update_params() test(i, val_program) if i > args.resume_epoch and i % args.model_period == 0: pruner.update_params() # NOTE: We are using fluid.io.save_params() because the pretrained model is from an older version which requires this API. # Please consider using paddle.static.save(program, model_path) as long as it becomes possible. fluid.io.save_params(executor=exe, dirname=args.model_path) def main(): paddle.enable_static() args = parser.parse_args() print_arguments(args) compress(args) if __name__ == '__main__': main()