train.py

# Copyright (c) 2020 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 argparse
import os

from paddle.fluid.dygraph.base import to_variable
import numpy as np
import paddle.fluid as fluid

from datasets import Dataset
import transforms as T
import models
import utils.logging as logging
from utils import get_environ_info
from utils import load_pretrained_model
from val import evaluate


def parse_args():
    parser = argparse.ArgumentParser(description='Model training')

    # params of model
    parser.add_argument('--model_name',
                        dest='model_name',
                        help="Model type for traing, which is one of ('UNet')",
                        type=str,
                        default='UNet')

    # params of dataset
    parser.add_argument('--data_dir',
                        dest='data_dir',
                        help='The root directory of dataset',
                        type=str)
    parser.add_argument('--train_list',
                        dest='train_list',
                        help='Train list file of dataset',
                        type=str)
    parser.add_argument('--val_list',
                        dest='val_list',
                        help='Val list file of dataset',
                        type=str,
                        default=None)
    parser.add_argument('--num_classes',
                        dest='num_classes',
                        help='Number of classes',
                        type=int,
                        default=2)

    # params of training
    parser.add_argument("--input_size",
                        dest="input_size",
                        help="The image size for net inputs.",
                        nargs=2,
                        default=[512, 512],
                        type=int)
    parser.add_argument('--num_epochs',
                        dest='num_epochs',
                        help='Number epochs for training',
                        type=int,
                        default=100)
    parser.add_argument('--batch_size',
                        dest='batch_size',
                        help='Mini batch size',
                        type=int,
                        default=2)
    parser.add_argument('--learning_rate',
                        dest='learning_rate',
                        help='Learning rate',
                        type=float,
                        default=0.01)
    parser.add_argument('--pretrained_model',
                        dest='pretrained_model',
                        help='The path of pretrianed weight',
                        type=str,
                        default=None)
    parser.add_argument('--save_interval_epochs',
                        dest='save_interval_epochs',
                        help='The interval epochs for save a model snapshot',
                        type=int,
                        default=5)
    parser.add_argument('--save_dir',
                        dest='save_dir',
                        help='The directory for saving the model snapshot',
                        type=str,
                        default='./output')

    return parser.parse_args()


def train(model,
          train_dataset,
          eval_dataset=None,
          optimizer=None,
          save_dir='output',
          num_epochs=100,
          batch_size=2,
          pretrained_model=None,
          save_interval_epochs=1,
          num_classes=None):
    if not os.path.isdir(save_dir):
        if os.path.exists(save_dir):
            os.remove(save_dir)
        os.makedirs(save_dir)

    load_pretrained_model(model, pretrained_model)

    data_generator = train_dataset.generator(batch_size=batch_size,
                                             drop_last=True)
    num_steps_each_epoch = train_dataset.num_samples // args.batch_size

    for epoch in range(num_epochs):
        for step, data in enumerate(data_generator()):
            images = np.array([d[0] for d in data])
            labels = np.array([d[2] for d in data]).astype('int64')
            images = to_variable(images)
            labels = to_variable(labels)
            loss = model(images, labels, mode='train')
            loss.backward()
            optimizer.minimize(loss)
            logging.info("[TRAIN] Epoch={}/{}, Step={}/{}, loss={}".format(
                epoch + 1, num_epochs, step + 1, num_steps_each_epoch,
                loss.numpy()))

        if (
                epoch + 1
        ) % save_interval_epochs == 0 or num_steps_each_epoch == num_epochs - 1:
            current_save_dir = os.path.join(save_dir,
                                            "epoch_{}".format(epoch + 1))
            if not os.path.isdir(current_save_dir):
                os.makedirs(current_save_dir)
            fluid.save_dygraph(model.state_dict(),
                               os.path.join(current_save_dir, 'model'))

            if eval_dataset is not None:
                model.eval()
                evaluate(model,
                         eval_dataset,
                         model_dir=current_save_dir,
                         num_classes=num_classes,
                         batch_size=batch_size,
                         ignore_index=model.ignore_index,
                         epoch_id=epoch + 1)
                model.train()


def main(args):
    with fluid.dygraph.guard(places):
        # Creat dataset reader
        train_transforms = T.Compose([
            T.Resize(args.input_size),
            T.RandomHorizontalFlip(),
            T.Normalize()
        ])
        train_dataset = Dataset(data_dir=args.data_dir,
                                file_list=args.train_list,
                                transforms=train_transforms,
                                num_workers='auto',
                                buffer_size=100,
                                parallel_method='thread',
                                shuffle=True)
        if args.val_list is not None:
            eval_transforms = T.Compose(
                [T.Resize(args.input_size),
                 T.Normalize()])
            eval_dataset = Dataset(data_dir=args.data_dir,
                                   file_list=args.val_list,
                                   transforms=eval_transforms,
                                   num_workers='auto',
                                   buffer_size=100,
                                   parallel_method='thread',
                                   shuffle=False)

        if args.model_name == 'UNet':
            model = models.UNet(num_classes=args.num_classes, ignore_index=255)

        # Creat optimizer
        num_steps_each_epoch = train_dataset.num_samples // args.batch_size
        decay_step = args.num_epochs * num_steps_each_epoch
        lr_decay = fluid.layers.polynomial_decay(args.learning_rate,
                                                 decay_step,
                                                 end_learning_rate=0,
                                                 power=0.9)
        optimizer = fluid.optimizer.Momentum(
            lr_decay,
            momentum=0.9,
            parameter_list=model.parameters(),
            regularization=fluid.regularizer.L2Decay(regularization_coeff=4e-5))

        train(model,
              train_dataset,
              eval_dataset,
              optimizer,
              save_dir=args.save_dir,
              num_epochs=args.num_epochs,
              batch_size=args.batch_size,
              pretrained_model=args.pretrained_model,
              save_interval_epochs=args.save_interval_epochs,
              num_classes=args.num_classes)


if __name__ == '__main__':
    args = parse_args()
    env_info = get_environ_info()
    if env_info['place'] == 'cpu':
        places = fluid.CPUPlace()
    else:
        places = fluid.CUDAPlace(0)
    main(args)