vgg16.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""VGG16 benchmark in Fluid"""
from __future__ import print_function

import sys
import time
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import argparse
import functools
import os


def str2bool(v):
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')


parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
    '--batch_size', type=int, default=128, help="Batch size for training.")
parser.add_argument(
    '--learning_rate',
    type=float,
    default=1e-3,
    help="Learning rate for training.")
parser.add_argument('--num_passes', type=int, default=50, help="No. of passes.")
parser.add_argument(
    '--device',
    type=str,
    default='CPU',
    choices=['CPU', 'GPU'],
    help="The device type.")
parser.add_argument(
    '--data_format',
    type=str,
    default='NCHW',
    choices=['NCHW', 'NHWC'],
    help='The data order, now only support NCHW.')
parser.add_argument(
    '--data_set',
    type=str,
    default='cifar10',
    choices=['cifar10', 'flowers'],
    help='Optional dataset for benchmark.')
parser.add_argument(
    '--local',
    type=str2bool,
    default=True,
    help='Whether to run as local mode.')
args = parser.parse_args()


def vgg16_bn_drop(input):
    def conv_block(input, num_filter, groups, dropouts):
        return fluid.nets.img_conv_group(
            input=input,
            pool_size=2,
            pool_stride=2,
            conv_num_filter=[num_filter] * groups,
            conv_filter_size=3,
            conv_act='relu',
            conv_with_batchnorm=True,
            conv_batchnorm_drop_rate=dropouts,
            pool_type='max')

    conv1 = conv_block(input, 64, 2, [0.3, 0])
    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])

    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
    fc1 = fluid.layers.fc(input=drop, size=512, act=None)
    bn = fluid.layers.batch_norm(input=fc1, act='relu')
    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
    fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
    return fc2


def main():
    if args.data_set == "cifar10":
        classdim = 10
        if args.data_format == 'NCHW':
            data_shape = [3, 32, 32]
        else:
            data_shape = [32, 32, 3]
    else:
        classdim = 102
        if args.data_format == 'NCHW':
            data_shape = [3, 224, 224]
        else:
            data_shape = [224, 224, 3]

    # Input data
    images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    # Train program
    net = vgg16_bn_drop(images)
    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
    cost = fluid.layers.cross_entropy(input=predict, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    # Evaluator
    accuracy = fluid.evaluator.Accuracy(input=predict, label=label)

    # inference program
    inference_program = fluid.default_main_program().clone()
    with fluid.program_guard(inference_program):
        test_target = accuracy.metrics + accuracy.states
        inference_program = fluid.io.get_inference_program(test_target)

    # Optimization
    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
    optimize_ops, params_grads = optimizer.minimize(avg_cost)

    # Initialize executor
    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
    exe = fluid.Executor(place)

    # test
    def test(exe):
        accuracy.reset(exe)
        for batch_id, data in enumerate(test_reader()):
            img_data = np.array(map(lambda x: x[0].reshape(data_shape),
                                    data)).astype("float32")
            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
            y_data = y_data.reshape([-1, 1])

            exe.run(inference_program,
                    feed={"pixel": img_data,
                          "label": y_data})

        return accuracy.eval(exe)

    def train_loop(exe, trainer_prog):
        iters = 0
        ts = time.time()
        for pass_id in range(args.num_passes):
            # train
            start_time = time.time()
            num_samples = 0
            accuracy.reset(exe)
            for batch_id, data in enumerate(train_reader()):
                ts = time.time()
                img_data = np.array(
                    map(lambda x: x[0].reshape(data_shape), data)).astype(
                        "float32")
                y_data = np.array(map(lambda x: x[1], data)).astype("int64")
                y_data = y_data.reshape([-1, 1])

                loss, acc = exe.run(trainer_prog,
                                    feed={"pixel": img_data,
                                          "label": y_data},
                                    fetch_list=[avg_cost] + accuracy.metrics)
                iters += 1
                num_samples += len(data)
                print(
                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
                    % (pass_id, iters, loss, acc, time.time() - ts)
                )  # The accuracy is the accumulation of batches, but not the current batch.

            pass_elapsed = time.time() - start_time
            pass_train_acc = accuracy.eval(exe)
            pass_test_acc = test(exe)
            print(
                "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
                % (pass_id, num_samples / pass_elapsed, pass_train_acc,
                   pass_test_acc))

    if args.local:
        # Parameter initialization
        exe.run(fluid.default_startup_program())

        # data reader
        train_reader = paddle.batch(
            paddle.reader.shuffle(
                paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
                else paddle.dataset.flowers.train(),
                buf_size=5120),
            batch_size=args.batch_size)
        test_reader = paddle.batch(
            paddle.dataset.cifar.test10()
            if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
            batch_size=args.batch_size)
        train_loop(exe, fluid.default_main_program())
    else:
        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # all pserver endpoints
        eplist = []
        for ip in pserver_ips.split(","):
            eplist.append(':'.join([ip, "6174"]))
        pserver_endpoints = ",".join(eplist)
        print("pserver endpoints: ", pserver_endpoints)
        trainers = int(os.getenv("TRAINERS"))  # total trainer count
        current_endpoint = os.getenv(
            "POD_IP") + ":6174"  # current pserver endpoint
        training_role = os.getenv(
            "TRAINING_ROLE",
            "TRAINER")  # get the training role: trainer/pserver
        t = fluid.DistributeTranspiler()
        t.transpile(
            optimize_ops,
            params_grads,
            pservers=pserver_endpoints,
            trainers=trainers)

        if training_role == "PSERVER":
            if not current_endpoint:
                print("need env SERVER_ENDPOINT")
                exit(1)
            pserver_prog = t.get_pserver_program(current_endpoint)
            pserver_startup = t.get_startup_program(current_endpoint,
                                                    pserver_prog)
            print("starting server side startup")
            exe.run(pserver_startup)
            print("starting parameter server...")
            exe.run(pserver_prog)
        elif training_role == "TRAINER":
            # Parameter initialization
            exe.run(fluid.default_startup_program())

            # data reader
            train_reader = paddle.batch(
                paddle.reader.shuffle(
                    paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
                    else paddle.dataset.flowers.train(),
                    buf_size=5120),
                batch_size=args.batch_size)
            test_reader = paddle.batch(
                paddle.dataset.cifar.test10() if args.data_set == 'cifar10' else
                paddle.dataset.flowers.test(),
                batch_size=args.batch_size)

            trainer_prog = t.get_trainer_program()
            feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
            # TODO(typhoonzero): change trainer startup program to fetch parameters from pserver
            exe.run(fluid.default_startup_program())
            train_loop(exe, trainer_prog)
        else:
            print("environment var TRAINER_ROLE should be TRAINER os PSERVER")


def print_arguments():
    print('-----------  Configuration Arguments -----------')
    for arg, value in sorted(vars(args).iteritems()):
        print('%s: %s' % (arg, value))
    print('------------------------------------------------')


if __name__ == "__main__":
    print_arguments()
    main()