train.py

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
from paddleslim.prune import Pruner
from paddleslim.common import get_logger
from paddleslim.analysis import flops
sys.path.append(sys.path[0] + "/../")
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_pretained",                "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, "mnist",                 "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.batch(val_reader, batch_size=args.batch_size)
    train_reader = paddle.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))))

    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)
    _logger.info("fops before pruning: {}".format(
        flops(fluid.default_main_program())))
    pruner = Pruner()
    pruned_val_program = pruner.prune(
        val_program,
        fluid.global_scope(),
        params=params,
        ratios=[0.33] * len(params),
        place=place,
        only_graph=True)

    pruned_program = pruner.prune(
        fluid.default_main_program(),
        fluid.global_scope(),
        params=params,
        ratios=[0.33] * len(params),
        place=place)

    for param in pruned_program[0].global_block().all_parameters():
        if "weights" in param.name:
            print param.name, param.shape
    return
    _logger.info("fops after pruning: {}".format(flops(pruned_program)))

    for i in range(args.num_epochs):
        train(i, pruned_program)
        if i % args.test_period == 0:
            test(i, pruned_val_program)


def main():
    args = parser.parse_args()
    print_arguments(args)
    compress(args)


if __name__ == '__main__':
    main()