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94 lines
3.5 KiB
94 lines
3.5 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import time
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import pytest
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import numpy as np
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from mindspore import context
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from mindspore import Tensor
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from model_zoo.official.gnn.gcn.src.gcn import GCN
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from model_zoo.official.gnn.gcn.src.metrics import LossAccuracyWrapper, TrainNetWrapper
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from model_zoo.official.gnn.gcn.src.config import ConfigGCN
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from model_zoo.official.gnn.gcn.src.dataset import get_adj_features_labels, get_mask
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DATA_DIR = '/home/workspace/mindspore_dataset/cora/cora_mr/cora_mr'
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TRAIN_NODE_NUM = 140
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EVAL_NODE_NUM = 500
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TEST_NODE_NUM = 1000
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SEED = 20
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_gcn():
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print("test_gcn begin")
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np.random.seed(SEED)
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend", save_graphs=False)
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config = ConfigGCN()
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config.dropout = 0.0
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adj, feature, label_onehot, _ = get_adj_features_labels(DATA_DIR)
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nodes_num = label_onehot.shape[0]
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train_mask = get_mask(nodes_num, 0, TRAIN_NODE_NUM)
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eval_mask = get_mask(nodes_num, TRAIN_NODE_NUM, TRAIN_NODE_NUM + EVAL_NODE_NUM)
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test_mask = get_mask(nodes_num, nodes_num - TEST_NODE_NUM, nodes_num)
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class_num = label_onehot.shape[1]
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input_dim = feature.shape[1]
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gcn_net = GCN(config, input_dim, class_num)
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gcn_net.add_flags_recursive(fp16=True)
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adj = Tensor(adj)
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feature = Tensor(feature)
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eval_net = LossAccuracyWrapper(gcn_net, label_onehot, eval_mask, config.weight_decay)
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test_net = LossAccuracyWrapper(gcn_net, label_onehot, test_mask, config.weight_decay)
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train_net = TrainNetWrapper(gcn_net, label_onehot, train_mask, config)
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loss_list = []
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for epoch in range(config.epochs):
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t = time.time()
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train_net.set_train()
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train_result = train_net(adj, feature)
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train_loss = train_result[0].asnumpy()
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train_accuracy = train_result[1].asnumpy()
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eval_net.set_train(False)
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eval_result = eval_net(adj, feature)
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eval_loss = eval_result[0].asnumpy()
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eval_accuracy = eval_result[1].asnumpy()
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loss_list.append(eval_loss)
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print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(train_loss),
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"train_acc=", "{:.5f}".format(train_accuracy), "val_loss=", "{:.5f}".format(eval_loss),
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"val_acc=", "{:.5f}".format(eval_accuracy), "time=", "{:.5f}".format(time.time() - t))
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if epoch > config.early_stopping and loss_list[-1] > np.mean(loss_list[-(config.early_stopping+1):-1]):
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print("Early stopping...")
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break
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test_net.set_train(False)
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test_result = test_net(adj, feature)
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test_loss = test_result[0].asnumpy()
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test_accuracy = test_result[1].asnumpy()
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print("Test set results:", "loss=", "{:.5f}".format(test_loss),
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"accuracy=", "{:.5f}".format(test_accuracy))
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assert test_accuracy > 0.812
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