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.
from __future__ import print_function

import os
import sys
import random
import numpy as np

import argparse
import functools

import paddle
import paddle.fluid as fluid

from paddle.static import InputSpec as Input
from paddle.vision.transforms import BatchCompose

from utility import add_arguments, print_arguments
from utility import SeqAccuracy, LoggerCallBack
from seq2seq_attn import Seq2SeqAttModel, WeightCrossEntropy
import data

parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size',        int,   32,           "Minibatch size.")
add_arg('epoch',             int,   30,           "Epoch number.")
add_arg('num_workers',       int,   0,            "workers number.")
add_arg('lr',                float, 0.001,        "Learning rate.")
add_arg('lr_decay_strategy', str,   "",           "Learning rate decay strategy.")
add_arg('checkpoint_path',   str,   "checkpoint", "The directory the model to be saved to.")
add_arg('train_images',      str,   None,         "The directory of images to be used for training.")
add_arg('train_list',        str,   None,         "The list file of images to be used for training.")
add_arg('test_images',       str,   None,         "The directory of images to be used for test.")
add_arg('test_list',         str,   None,         "The list file of images to be used for training.")
add_arg('resume_path',       str,   None,         "The init model file of directory.")
add_arg('use_gpu',           bool,  True,         "Whether use GPU to train.")
# model hyper paramters
add_arg('encoder_size',      int,   200,     "Encoder size.")
add_arg('decoder_size',      int,   128,     "Decoder size.")
add_arg('embedding_dim',     int,   128,     "Word vector dim.")
add_arg('num_classes',       int,   95,     "Number classes.")
add_arg('gradient_clip',     float, 5.0,     "Gradient clip value.")
add_arg('dynamic',           bool,  False,      "Whether to use dygraph.")
# yapf: enable


def main(FLAGS):
    device = paddle.set_device("gpu" if FLAGS.use_gpu else "cpu")
    fluid.enable_dygraph(device) if FLAGS.dynamic else None

    # yapf: disable
    inputs = [
        Input([None,1,48,384], "float32", name="pixel"),
        Input([None, None], "int64", name="label_in"),
    ]
    labels = [
        Input([None, None], "int64", name="label_out"),
        Input([None, None], "float32", name="mask"),
    ]
    # yapf: enable

    model = paddle.Model(
        Seq2SeqAttModel(
            encoder_size=FLAGS.encoder_size,
            decoder_size=FLAGS.decoder_size,
            emb_dim=FLAGS.embedding_dim,
            num_classes=FLAGS.num_classes),
        inputs,
        labels)

    lr = FLAGS.lr
    if FLAGS.lr_decay_strategy == "piecewise_decay":
        learning_rate = fluid.layers.piecewise_decay(
            [200000, 250000], [lr, lr * 0.1, lr * 0.01])
    else:
        learning_rate = lr
    grad_clip = fluid.clip.GradientClipByGlobalNorm(FLAGS.gradient_clip)
    optimizer = fluid.optimizer.Adam(
        learning_rate=learning_rate,
        parameter_list=model.parameters(),
        grad_clip=grad_clip)

    model.prepare(optimizer, WeightCrossEntropy(), SeqAccuracy())

    train_dataset = data.train()
    train_collate_fn = BatchCompose(
        [data.Resize(), data.Normalize(), data.PadTarget()])
    train_sampler = data.BatchSampler(
        train_dataset, batch_size=FLAGS.batch_size, shuffle=True)
    train_loader = fluid.io.DataLoader(
        train_dataset,
        batch_sampler=train_sampler,
        places=device,
        num_workers=FLAGS.num_workers,
        return_list=True,
        collate_fn=train_collate_fn)
    test_dataset = data.test()
    test_collate_fn = BatchCompose(
        [data.Resize(), data.Normalize(), data.PadTarget()])
    test_sampler = data.BatchSampler(
        test_dataset,
        batch_size=FLAGS.batch_size,
        drop_last=False,
        shuffle=False)
    test_loader = fluid.io.DataLoader(
        test_dataset,
        batch_sampler=test_sampler,
        places=device,
        num_workers=0,
        return_list=True,
        collate_fn=test_collate_fn)

    model.fit(train_data=train_loader,
              eval_data=test_loader,
              epochs=FLAGS.epoch,
              save_dir=FLAGS.checkpoint_path,
              callbacks=[LoggerCallBack(10, 2, FLAGS.batch_size)])


if __name__ == '__main__':
    FLAGS = parser.parse_args()
    print_arguments(FLAGS)
    main(FLAGS)