resnet.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import functools
import numpy as np
import time

import cProfile, pstats, StringIO

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler


def parse_args():
    parser = argparse.ArgumentParser('Convolution model benchmark.')
    parser.add_argument(
        '--model',
        type=str,
        choices=['resnet_imagenet', 'resnet_cifar10'],
        default='resnet_imagenet',
        help='The model architecture.')
    parser.add_argument(
        '--batch_size', type=int, default=32, help='The minibatch size.')
    parser.add_argument(
        '--use_fake_data',
        action='store_true',
        help='use real data or fake data')
    parser.add_argument(
        '--skip_batch_num',
        type=int,
        default=5,
        help='The first num of minibatch num to skip, for better performance test'
    )
    parser.add_argument(
        '--iterations', type=int, default=80, help='The number of minibatches.')
    parser.add_argument(
        '--pass_num', type=int, default=100, help='The number of passes.')
    parser.add_argument(
        '--data_format',
        type=str,
        default='NCHW',
        choices=['NCHW', 'NHWC'],
        help='The data data_format, now only support NCHW.')
    parser.add_argument(
        '--device',
        type=str,
        default='GPU',
        choices=['CPU', 'GPU'],
        help='The device type.')
    parser.add_argument(
        '--data_set',
        type=str,
        default='flowers',
        choices=['cifar10', 'flowers'],
        help='Optional dataset for benchmark.')
    parser.add_argument(
        '--infer_only', action='store_true', help='If set, run forward only.')
    parser.add_argument(
        '--use_cprof', action='store_true', help='If set, use cProfile.')
    parser.add_argument(
        '--use_nvprof',
        action='store_true',
        help='If set, use nvprof for CUDA.')
    parser.add_argument(
        '--with_test',
        action='store_true',
        help='If set, test the testset during training.')
    args = parser.parse_args()
    return args


def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
    conv1 = fluid.layers.conv2d(
        input=input,
        filter_size=filter_size,
        num_filters=ch_out,
        stride=stride,
        padding=padding,
        act=None,
        bias_attr=False)
    return fluid.layers.batch_norm(input=conv1, act=act)


def shortcut(input, ch_out, stride):
    ch_in = input.shape[1] if args.data_format == 'NCHW' else input.shape[-1]
    if ch_in != ch_out:
        return conv_bn_layer(input, ch_out, 1, stride, 0, None)
    else:
        return input


def basicblock(input, ch_out, stride):
    short = shortcut(input, ch_out, stride)
    conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
    conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
    return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')


def bottleneck(input, ch_out, stride):
    short = shortcut(input, ch_out * 4, stride)
    conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
    conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
    conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
    return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')


def layer_warp(block_func, input, ch_out, count, stride):
    res_out = block_func(input, ch_out, stride)
    for i in range(1, count):
        res_out = block_func(res_out, ch_out, 1)
    return res_out


def resnet_imagenet(input, class_dim, depth=50, data_format='NCHW'):

    cfg = {
        18: ([2, 2, 2, 1], basicblock),
        34: ([3, 4, 6, 3], basicblock),
        50: ([3, 4, 6, 3], bottleneck),
        101: ([3, 4, 23, 3], bottleneck),
        152: ([3, 8, 36, 3], bottleneck)
    }
    stages, block_func = cfg[depth]
    conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
    pool1 = fluid.layers.pool2d(
        input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
    res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
    res2 = layer_warp(block_func, res1, 128, stages[1], 2)
    res3 = layer_warp(block_func, res2, 256, stages[2], 2)
    res4 = layer_warp(block_func, res3, 512, stages[3], 2)
    pool2 = fluid.layers.pool2d(
        input=res4,
        pool_size=7,
        pool_type='avg',
        pool_stride=1,
        global_pooling=True)
    out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
    return out


def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
    assert (depth - 2) % 6 == 0

    n = (depth - 2) // 6

    conv1 = conv_bn_layer(
        input=input, ch_out=16, filter_size=3, stride=1, padding=1)
    res1 = layer_warp(basicblock, conv1, 16, n, 1)
    res2 = layer_warp(basicblock, res1, 32, n, 2)
    res3 = layer_warp(basicblock, res2, 64, n, 2)
    pool = fluid.layers.pool2d(
        input=res3, pool_size=8, pool_type='avg', pool_stride=1)
    out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
    return out


def run_benchmark(model, args):
    if args.use_cprof:
        pr = cProfile.Profile()
        pr.enable()

    if args.data_set == "cifar10":
        class_dim = 10
        if args.data_format == 'NCHW':
            dshape = [3, 32, 32]
        else:
            dshape = [32, 32, 3]
    else:
        class_dim = 102
        if args.data_format == 'NCHW':
            dshape = [3, 224, 224]
        else:
            dshape = [224, 224, 3]

    input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    predict = model(input, class_dim)
    cost = fluid.layers.cross_entropy(input=predict, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
    batch_acc = fluid.layers.accuracy(
        input=predict, label=label, total=batch_size_tensor)

    inference_program = fluid.default_main_program().clone()
    with fluid.program_guard(inference_program):
        inference_program = fluid.io.get_inference_program(
            target_vars=[batch_acc, batch_size_tensor])

    optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
    opts = optimizer.minimize(avg_cost)

    fluid.memory_optimize(fluid.default_main_program())

    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)

    def test(exe):
        test_accuracy = fluid.average.WeightedAverage()
        for batch_id, data in enumerate(test_reader()):
            img_data = np.array(map(lambda x: x[0].reshape(dshape),
                                    data)).astype("float32")
            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
            y_data = y_data.reshape([-1, 1])

            acc, weight = exe.run(inference_program,
                                  feed={"data": img_data,
                                        "label": y_data},
                                  fetch_list=[batch_acc, batch_size_tensor])
            test_accuracy.add(value=acc, weight=weight)

        return test_accuracy.eval()

    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    accuracy = fluid.average.WeightedAverage()
    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
    if args.use_fake_data:
        data = train_reader().next()
        image = np.array(map(lambda x: x[0].reshape(dshape), data)).astype(
            'float32')
        label = np.array(map(lambda x: x[1], data)).astype('int64')
        label = label.reshape([-1, 1])

    iters, num_samples, start_time = 0, 0, time.time()
    for pass_id in range(args.pass_num):
        accuracy.reset()
        train_accs = []
        train_losses = []
        for batch_id, data in enumerate(train_reader()):
            if iters == args.skip_batch_num:
                start_time = time.time()
                num_samples = 0
            if iters == args.iterations:
                break
            if not args.use_fake_data:
                image = np.array(map(lambda x: x[0].reshape(dshape),
                                     data)).astype('float32')
                label = np.array(map(lambda x: x[1], data)).astype('int64')
                label = label.reshape([-1, 1])
            loss, acc, weight = train_exe.run(
                feed={'data': image,
                      'label': label},
                fetch_list=[
                    avg_cost.name, batch_acc.name, batch_size_tensor.name
                ])
            iters += 1
            num_samples += len(label)
            accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
            loss = np.mean(np.array(loss))
            acc = np.mean(np.array(acc))
            train_losses.append(loss)
            train_accs.append(acc)
            print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
                  (pass_id, iters, loss, acc))
        print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
              (pass_id, np.mean(train_losses), np.mean(train_accs)))
        train_elapsed = time.time() - start_time
        examples_per_sec = num_samples / train_elapsed
        print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
              (num_samples, train_elapsed, examples_per_sec))
        # evaluation
        if args.with_test:
            pass_test_acc = test(exe)
        exit(0)


def print_arguments(args):
    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
                                vars(args)['device'] == 'GPU')
    print('----------- resnet Configuration Arguments -----------')
    for arg, value in sorted(vars(args).iteritems()):
        print('%s: %s' % (arg, value))
    print('------------------------------------------------')


if __name__ == '__main__':
    model_map = {
        'resnet_imagenet': resnet_imagenet,
        'resnet_cifar10': resnet_cifar10
    }
    args = parse_args()
    print_arguments(args)
    if args.data_format == 'NHWC':
        raise ValueError('Only support NCHW data_format now.')
    if args.use_nvprof and args.device == 'GPU':
        with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
            run_benchmark(model_map[args.model], args)
    else:
        run_benchmark(model_map[args.model], args)