train.py

# Copyright (c) 2019 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.

import numpy as np
import argparse
import ast
import paddle
import paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.dygraph.base import to_variable

from paddle.fluid import framework

import math
import sys

IMAGENET1000 = 1281167
base_lr = 0.1
momentum_rate = 0.9
l2_decay = 1e-4


def parse_args():
    parser = argparse.ArgumentParser("Training for Resnet.")
    parser.add_argument(
        "--use_data_parallel",
        type=ast.literal_eval,
        default=False,
        help="The flag indicating whether to use data parallel mode to train the model."
    )
    parser.add_argument(
        "-e", "--epoch", default=120, type=int, help="set epoch")
    parser.add_argument(
        "-b", "--batch_size", default=32, type=int, help="set epoch")
    parser.add_argument("--ce", action="store_true", help="run ce")
    args = parser.parse_args()
    return args


args = parse_args()
batch_size = args.batch_size


def optimizer_setting():

    total_images = IMAGENET1000

    step = int(math.ceil(float(total_images) / batch_size))

    epochs = [30, 60, 90]
    bd = [step * e for e in epochs]

    lr = []
    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
    optimizer = fluid.optimizer.Momentum(
        learning_rate=fluid.layers.piecewise_decay(
            boundaries=bd, values=lr),
        momentum=momentum_rate,
        regularization=fluid.regularizer.L2Decay(l2_decay))

    return optimizer


class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self,
                 name_scope,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None):
        super(ConvBNLayer, self).__init__(name_scope)

        self._conv = Conv2D(
            self.full_name(),
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=False)

        self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)

        return y


class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self,
                 name_scope,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True):
        super(BottleneckBlock, self).__init__(name_scope)

        self.conv0 = ConvBNLayer(
            self.full_name(),
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act='relu')
        self.conv1 = ConvBNLayer(
            self.full_name(),
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu')
        self.conv2 = ConvBNLayer(
            self.full_name(),
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None)

        if not shortcut:
            self.short = ConvBNLayer(
                self.full_name(),
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                stride=stride)

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 4

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = fluid.layers.elementwise_add(x=short, y=conv2)

        layer_helper = LayerHelper(self.full_name(), act='relu')
        return layer_helper.append_activation(y)


class ResNet(fluid.dygraph.Layer):
    def __init__(self, name_scope, layers=50, class_dim=102):
        super(ResNet, self).__init__(name_scope)

        self.layers = layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if 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]

        self.conv = ConvBNLayer(
            self.full_name(),
            num_channels=3,
            num_filters=64,
            filter_size=7,
            stride=2,
            act='relu')
        self.pool2d_max = Pool2D(
            self.full_name(),
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')

        self.bottleneck_block_list = []
        num_channels = 64
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                bottleneck_block = self.add_sublayer(
                    'bb_%d_%d' % (block, i),
                    BottleneckBlock(
                        self.full_name(),
                        num_channels=num_channels,
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1,
                        shortcut=shortcut))
                num_channels = bottleneck_block._num_channels_out
                self.bottleneck_block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = Pool2D(
            self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)

        import math
        stdv = 1.0 / math.sqrt(2048 * 1.0)

        self.out = FC(self.full_name(),
                      size=class_dim,
                      act='softmax',
                      param_attr=fluid.param_attr.ParamAttr(
                          initializer=fluid.initializer.Uniform(-stdv, stdv)))

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
        for bottleneck_block in self.bottleneck_block_list:
            y = bottleneck_block(y)
        y = self.pool2d_avg(y)
        y = self.out(y)
        return y


def eval(model, data):

    model.eval()
    total_loss = 0.0
    total_acc1 = 0.0
    total_acc5 = 0.0
    total_sample = 0
    for batch_id, data in enumerate(data()):
        dy_x_data = np.array(
            [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
        if len(np.array([x[1] for x in data]).astype('int64')) != batch_size:
            continue
        y_data = np.array([x[1] for x in data]).astype('int64').reshape(
            batch_size, 1)

        img = to_variable(dy_x_data)
        label = to_variable(y_data)
        label.stop_gradient = True

        out = model(img)
        #loss = fluid.layers.cross_entropy(input=out, label=label)
        #avg_loss = fluid.layers.mean(x=loss)

        acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
        acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)

        #dy_out = avg_loss.numpy()

        #total_loss += dy_out
        total_acc1 += acc_top1.numpy()
        total_acc5 += acc_top5.numpy()
        total_sample += 1

        # print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
        if batch_id % 10 == 0:
            print("test | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                  ( batch_id, total_loss / total_sample, \
                   total_acc1 / total_sample, total_acc5 / total_sample))
    if args.ce:
        print("kpis\ttest_acc1\t%0.3f" % (total_acc1 / total_sample))
        print("kpis\ttest_acc5\t%0.3f" % (total_acc5 / total_sample))
        print("kpis\ttest_loss\t%0.3f" % (total_loss / total_sample))
    print("final eval loss %0.3f acc1 %0.3f acc5 %0.3f" % \
          (total_loss / total_sample, \
           total_acc1 / total_sample, total_acc5 / total_sample))


def train_resnet():
    epoch = args.epoch
    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
        if args.use_data_parallel else fluid.CUDAPlace(0)
    with fluid.dygraph.guard(place):
        if args.ce:
            print("ce mode")
            seed = 33
            np.random.seed(seed)
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed

        if args.use_data_parallel:
            strategy = fluid.dygraph.parallel.prepare_context()

        resnet = ResNet("resnet")
        optimizer = optimizer_setting()

        if args.use_data_parallel:
            resnet = fluid.dygraph.parallel.DataParallel(resnet, strategy)

        train_reader = paddle.batch(
            paddle.dataset.flowers.train(use_xmap=False), batch_size=batch_size)
        if args.use_data_parallel:
            train_reader = fluid.contrib.reader.distributed_batch_reader(
                train_reader)

        test_reader = paddle.batch(
            paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size)

        #file_name = './model/epoch_0.npz'
        #model_data = np.load( file_name )

        for eop in range(epoch):

            resnet.train()
            total_loss = 0.0
            total_acc1 = 0.0
            total_acc5 = 0.0
            total_sample = 0

            #dict_state = resnet.state_dict()

            #resnet.load_dict( model_data )

            print("load finished")

            for batch_id, data in enumerate(train_reader()):
                dy_x_data = np.array(
                    [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
                if len(np.array([x[1]
                                 for x in data]).astype('int64')) != batch_size:
                    continue
                y_data = np.array([x[1] for x in data]).astype('int64').reshape(
                    -1, 1)

                img = to_variable(dy_x_data)
                label = to_variable(y_data)
                label.stop_gradient = True

                out = resnet(img)
                loss = fluid.layers.cross_entropy(input=out, label=label)
                avg_loss = fluid.layers.mean(x=loss)

                acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
                acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)

                dy_out = avg_loss.numpy()

                if args.use_data_parallel:
                    avg_loss = resnet.scale_loss(avg_loss)
                    avg_loss.backward()
                    resnet.apply_collective_grads()
                else:
                    avg_loss.backward()

                optimizer.minimize(avg_loss)
                resnet.clear_gradients()

                total_loss += dy_out
                total_acc1 += acc_top1.numpy()
                total_acc5 += acc_top5.numpy()
                total_sample += 1
                #print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
                if batch_id % 10 == 0:
                    print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                           ( eop, batch_id, total_loss / total_sample, \
                             total_acc1 / total_sample, total_acc5 / total_sample))

            if args.ce:
                print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
                print("kpis\ttrain_acc5\t%0.3f" % (total_acc5 / total_sample))
                print("kpis\ttrain_loss\t%0.3f" % (total_loss / total_sample))
            print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                  (eop, batch_id, total_loss / total_sample, \
                   total_acc1 / total_sample, total_acc5 / total_sample))
            resnet.eval()
            eval(resnet, test_reader)

            save_parameters = (not args.use_data_parallel) or (
                args.use_data_parallel and
                fluid.dygraph.parallel.Env().local_rank == 0)
            if save_parameters:
                fluid.save_dygraph(resnet.state_dict(),
                                                'resnet_params')


if __name__ == '__main__':

    train_resnet()