pipeline_train.py

#  Copyright (c) 2019 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.

import numpy as np
import copy
import pickle
import os
from functools import partial
import logging
import time
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import argparse
import random
import sys
import math

logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger("fluid")
logger.setLevel(logging.INFO)

is_profile = False


def parse_args():
    parser = argparse.ArgumentParser("Resnet with pipelie parallel.")
    parser.add_argument(
        '--batch_size', type=int, default=100, help='input batch size')
    parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
    return parser.parse_args()


def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
                  act=None):
    conv = fluid.layers.conv2d(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
        stride=stride,
        padding=(filter_size - 1) // 2,
        groups=groups,
        act=None,
        bias_attr=False)
    return fluid.layers.batch_norm(
        input=conv,
        act=act, )


def shortcut(input, ch_out, stride, is_first):
    ch_in = input.shape[1]
    if ch_in != ch_out or stride != 1 or is_first == True:
        return conv_bn_layer(input, ch_out, 1, stride)
    else:
        return input


def bottleneck_block(input, num_filters, stride):
    conv0 = conv_bn_layer(
        input=input, num_filters=num_filters, filter_size=1, act='relu')
    conv1 = conv_bn_layer(
        input=conv0,
        num_filters=num_filters,
        filter_size=3,
        stride=stride,
        act='relu')
    conv2 = conv_bn_layer(
        input=conv1, num_filters=num_filters * 4, filter_size=1, act=None)

    short = shortcut(input, num_filters * 4, stride, is_first=False)

    return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')


def basic_block(input, num_filters, stride, is_first):
    conv0 = conv_bn_layer(
        input=input,
        num_filters=num_filters,
        filter_size=3,
        act='relu',
        stride=stride)
    conv1 = conv_bn_layer(
        input=conv0, num_filters=num_filters, filter_size=3, act=None)
    short = shortcut(input, num_filters, stride, is_first)
    return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')


def network(input, layers=50, class_dim=1000):
    supported_layers = [18, 34, 50, 101, 152]
    assert layers in supported_layers
    depth = None
    if layers == 18:
        depth = [2, 2, 2, 2]
    elif layers == 34 or layers == 50:
        depth = [3, 4, 6, 3]
    elif layers == 101:
        depth = [3, 4, 23, 3]
    elif layers == 152:
        depth = [3, 8, 36, 3]
    num_filters = [64, 128, 256, 512]
    with fluid.device_guard("gpu:0"):
        conv = conv_bn_layer(
            input=input, num_filters=64, filter_size=7, stride=2, act='relu')
        conv = fluid.layers.pool2d(
            input=conv,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')
    if layers >= 50:
        for block in range(len(depth)):
            with fluid.device_guard("gpu:1"):
                for i in range(depth[block]):
                    conv = bottleneck_block(
                        input=conv,
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1)

        with fluid.device_guard("gpu:2"):
            pool = fluid.layers.pool2d(
                input=conv, pool_size=7, pool_type='avg', global_pooling=True)
            stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
            out = fluid.layers.fc(
                input=pool,
                size=class_dim,
                param_attr=fluid.param_attr.ParamAttr(
                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
    else:
        for block in range(len(depth)):
            with fluid.device_guard("gpu:1"):
                for i in range(depth[block]):
                    conv = basic_block(
                        input=conv,
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1,
                        is_first=block == i == 0)
        with fluid.device_guard("gpu:2"):
            pool = fluid.layers.pool2d(
                input=conv, pool_size=7, pool_type='avg', global_pooling=True)
            stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
            out = fluid.layers.fc(
                input=pool,
                size=class_dim,
                param_attr=fluid.param_attr.ParamAttr(
                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
    return out


def train():
    args = parse_args()
    lr = args.lr

    with fluid.device_guard("gpu:0"):
        image = fluid.layers.data(
            name="image", shape=[3, 224, 224], dtype="float32")
        label = fluid.layers.data(name="label", shape=[1], dtype="int64")
        data_loader = fluid.io.DataLoader.from_generator(
            feed_list=[image, label],
            capacity=64,
            use_double_buffer=True,
            iterable=False)
        fc = build_network(image, layers=50)

    with fluid.device_guard("gpu:3"):
        out, prob = fluid.layers.softmax_with_cross_entropy(
            logits=fc, label=label, return_softmax=True)
        loss = fluid.layers.mean(out)
        acc_top1 = fluid.layers.accuracy(input=prob, label=label, k=1)
        acc_top5 = fluid.layers.accuracy(input=prob, label=label, k=5)

    optimizer = fluid.optimizer.SGD(lr)
    optimizer = fluid.optimizer.PipelineOptimizer(optimizer, num_microbatches=2)
    optimizer.minimize(loss)

    def train_reader():
        for _ in range(4000):
            img = np.random.random(size=[3, 224, 224]).astype('float32')
            label = np.random.random(size=[1]).astype('int64')
            yield img, label

    data_loader.set_sample_generator(train_reader, batch_size=args.batch_size)

    place = fluid.CUDAPlace(0)
    exe = fluid.Executor(place)

    exe.run(fluid.default_startup_program())

    data_loader.start()
    logger.info("begin training...")
    exe.train_from_dataset(fluid.default_main_program(), debug=is_profile)


if __name__ == "__main__":
    train()