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 pgl
import model# import LabelGraphGCN
from pgl import data_loader
from pgl.utils.logger import log
import paddle.fluid as fluid
import numpy as np
import time
import argparse
from build_model import build_model
import yaml
from easydict import EasyDict as edict



def load(name):
    if name == 'cora':
        dataset = data_loader.CoraDataset()
    elif name == "pubmed":
        dataset = data_loader.CitationDataset("pubmed", symmetry_edges=False)
    elif name == "citeseer":
        dataset = data_loader.CitationDataset("citeseer", symmetry_edges=False)
    else:
        raise ValueError(name + " dataset doesn't exists")
    return dataset


def main(args, config):
    dataset = load(args.dataset)

    indegree = dataset.graph.indegree()
    norm = np.zeros_like(indegree, dtype="float32")
    norm[indegree > 0] = np.power(indegree[indegree > 0], -0.5)
    dataset.graph.node_feat["norm"] = np.expand_dims(norm, -1)

    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
    train_program = fluid.default_main_program()
    startup_program = fluid.default_startup_program()

    with fluid.program_guard(train_program, startup_program):
        with fluid.unique_name.guard():
            gw, loss, acc = build_model(dataset,
                                config=config,
                                phase="train",
                                main_prog=train_program)

    test_program = fluid.Program()
    with fluid.program_guard(test_program, startup_program):
        with fluid.unique_name.guard():
            _gw, v_loss, v_acc = build_model(dataset,
                config=config,
                phase="test",
                main_prog=test_program)
    test_program = test_program.clone(for_test=True)

    exe = fluid.Executor(place)
    exe.run(startup_program)


    train_index = dataset.train_index
    train_label = np.expand_dims(dataset.y[train_index], -1)
    train_index = np.expand_dims(train_index, -1)
    log.info("Number of Train %s" % len(train_index))

    val_index = dataset.val_index
    val_label = np.expand_dims(dataset.y[val_index], -1)
    val_index = np.expand_dims(val_index, -1)

    test_index = dataset.test_index
    test_label = np.expand_dims(dataset.y[test_index], -1)
    test_index = np.expand_dims(test_index, -1)

    dur = []
    cal_val_acc = []
    cal_test_acc = []
 
    for epoch in range(300):
        if epoch >= 3:
            t0 = time.time()
        feed_dict = gw.to_feed(dataset.graph)
        feed_dict["node_index"] = np.array(train_index, dtype="int64")
        feed_dict["node_label"] = np.array(train_label, dtype="int64")
        train_loss, train_acc = exe.run(train_program,
                                        feed=feed_dict,
                                        fetch_list=[loss, acc],
                                        return_numpy=True)

        if epoch >= 3:
            time_per_epoch = 1.0 * (time.time() - t0)
            dur.append(time_per_epoch)

        feed_dict = gw.to_feed(dataset.graph)
        feed_dict["node_index"] = np.array(val_index, dtype="int64")
        feed_dict["node_label"] = np.array(val_label, dtype="int64")
        val_loss, val_acc = exe.run(test_program,
                                    feed=feed_dict,
                                    fetch_list=[v_loss, v_acc],
                                    return_numpy=True)

        val_loss = val_loss[0]
        val_acc = val_acc[0]
        cal_val_acc.append(val_acc)

        feed_dict["node_index"] = np.array(test_index, dtype="int64")
        feed_dict["node_label"] = np.array(test_label, dtype="int64")
        test_loss, test_acc = exe.run(test_program,
                                  feed=feed_dict,
                                  fetch_list=[v_loss, v_acc],
                                  return_numpy=True)

        test_loss = test_loss[0]
        test_acc = test_acc[0]
        cal_test_acc.append(test_acc)
        if epoch % 10 == 0:
            log.info("Epoch %d " % epoch +
                 "Train Loss: %f " % train_loss + "Train Acc: %f " % train_acc
                 + "Val Loss: %f " % val_loss + "Val Acc: %f " % val_acc
                 +" Test Loss: %f " % test_loss + " Test Acc: %f " % test_acc)
     
    cal_val_acc = np.array(cal_val_acc)
    log.info("Model: %s Best Test Accuracy: %f" % (config.model_name,
                  cal_test_acc[np.argmax(cal_val_acc)]))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Benchmarking Citation Network')
    parser.add_argument(
        "--dataset", type=str, default="cora", help="dataset (cora, pubmed)")
    parser.add_argument("--use_cuda", action='store_true', help="use_cuda")
    parser.add_argument("--conf", type=str, help="config file for models")
    args = parser.parse_args()
    config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
    log.info(args)
    main(args, config)