Add GCN to model zoo

model_zoo/gcn/README.md

+# GCN Example
+ 
+## Description
+ 
+This is an example of training GCN with Cora and Citeseer dataset in MindSpore.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the dataset Cora or Citeseer provided by /kimiyoung/planetoid from github.
+ 
+> Place the dataset to any path you want, the folder should include files as follows(we use Cora dataset as an example):
+ 
+```
+.
+└─data
+    ├─ind.cora.allx
+    ├─ind.cora.ally
+    ├─ind.cora.graph
+    ├─ind.cora.test.index
+    ├─ind.cora.tx
+    ├─ind.cora.ty
+    ├─ind.cora.x
+    └─ind.cora.y
+```
+
+> Generate dataset in mindrecord format for cora or citeseer.
+>> Usage
+```buildoutcfg
+cd ./scripts
+# SRC_PATH is the dataset file path you downloaded, DATASET_NAME is cora or citeseer
+sh run_process_data.sh [SRC_PATH] [DATASET_NAME]
+```
+
+>> Launch
+```
+#Generate dataset in mindrecord format for cora
+sh run_process_data.sh cora
+#Generate dataset in mindrecord format for citeseer
+sh run_process_data.sh citeseer
+```
+
+## Structure
+ 
+```shell
+.
+└─gcn      
+  ├─README.md
+  ├─scripts 
+  | ├─run_process_data.sh  # Generate dataset in mindrecord format
+  | └─run_train.sh         # Launch training   
+  |
+  ├─src
+  | ├─config.py            # Parameter configuration
+  | ├─dataset.py           # Data preprocessin
+  | ├─gcn.py               # GCN backbone
+  | └─metrics.py           # Loss and accuracy
+  |
+  └─train.py               # Train net
+```
+ 
+## Parameter configuration
+ 
+Parameters for training can be set in config.py.
+ 
+```
+"learning_rate": 0.01,            # Learning rate
+"epochs": 200,                    # Epoch sizes for training
+"hidden1": 16,                    # Hidden size for the first graph convolution layer
+"dropout": 0.5,                   # Dropout ratio for the first graph convolution layer
+"weight_decay": 5e-4,             # Weight decay for the parameter of the first graph convolution layer
+"early_stopping": 10,             # Tolerance for early stopping
+```
+
+## Running the example
+
+### Train
+ 
+#### Usage
+
+```
+# run train with cora or citeseer dataset, DATASET_NAME is cora or citeseer
+sh run_train.sh [DATASET_NAME]
+```
+ 
+#### Launch
+ 
+```bash
+sh run_train.sh cora
+```
+ 
+#### Result
+ 
+Training result will be stored in the scripts path, whose folder name begins with "train". You can find the result like the followings in log.
+
+ 
+```
+Epoch: 0001 train_loss= 1.95373 train_acc= 0.09286 val_loss= 1.95075 val_acc= 0.20200 time= 7.25737
+Epoch: 0002 train_loss= 1.94812 train_acc= 0.32857 val_loss= 1.94717 val_acc= 0.34000 time= 0.00438
+Epoch: 0003 train_loss= 1.94249 train_acc= 0.47857 val_loss= 1.94337 val_acc= 0.43000 time= 0.00428
+Epoch: 0004 train_loss= 1.93550 train_acc= 0.55000 val_loss= 1.93957 val_acc= 0.46400 time= 0.00421
+Epoch: 0005 train_loss= 1.92617 train_acc= 0.67143 val_loss= 1.93558 val_acc= 0.45400 time= 0.00430
+...
+Epoch: 0196 train_loss= 0.60326 train_acc= 0.97857 val_loss= 1.05155 val_acc= 0.78200 time= 0.00418
+Epoch: 0197 train_loss= 0.60377 train_acc= 0.97143 val_loss= 1.04940 val_acc= 0.78000 time= 0.00418
+Epoch: 0198 train_loss= 0.60680 train_acc= 0.95000 val_loss= 1.04847 val_acc= 0.78000 time= 0.00414
+Epoch: 0199 train_loss= 0.61920 train_acc= 0.96429 val_loss= 1.04797 val_acc= 0.78400 time= 0.00413
+Epoch: 0200 train_loss= 0.57948 train_acc= 0.96429 val_loss= 1.04753 val_acc= 0.78600 time= 0.00415
+Optimization Finished!
+Test set results: cost= 1.00983 accuracy= 0.81300 time= 0.39083
+...
+```

model_zoo/gcn/scripts/run_process_data.sh

+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [ $# != 2 ]
+then 
+    echo "Usage: sh run_train.sh [SRC_PATH] [DATASET_NAME]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+SRC_PATH=$(get_real_path $1)
+echo $SRC_PATH
+
+DATASET_NAME=$2
+echo $DATASET_NAME
+
+if [ ! -d data_mr ]; then
+  mkdir data_mr
+else
+  echo data_mr exist
+fi
+MINDRECORD_PATH=`pwd`/data_mr
+
+rm -f $MINDRECORD_PATH/*
+
+cd ../../../example/graph_to_mindrecord || exit
+
+python writer.py --mindrecord_script $DATASET_NAME \
+--mindrecord_file "$MINDRECORD_PATH/$DATASET_NAME" \
+--mindrecord_partitions 1 \
+--mindrecord_header_size_by_bit 18 \
+--mindrecord_page_size_by_bit 20 \
+--graph_api_args "$SRC_PATH"
+
+cd - || exit

model_zoo/gcn/scripts/run_train.sh

+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [ $# != 1 ]
+then 
+    echo "Usage: sh run_train.sh [DATASET_NAME]"
+exit 1
+fi
+
+DATASET_NAME=$1
+echo $DATASET_NAME
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export RANK_SIZE=$DEVICE_NUM
+export DEVICE_ID=0
+export RANK_ID=0
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ../*.py ./train
+cp *.sh ./train
+cp -r ../src ./train
+cd ./train || exit
+env > env.log
+echo "start training for device $DEVICE_ID"
+
+
+if [ $DATASET_NAME == cora ]
+then
+    python train.py --data_dir=../data_mr/$DATASET_NAME --train_nodes_num=140 &> log &
+fi
+
+if [ $DATASET_NAME == citeseer ]
+then
+    python train.py --data_dir=../data_mr/$DATASET_NAME --train_nodes_num=120 &> log &
+fi
+cd ..
+

model_zoo/gcn/src/config.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+network config setting, will be used in train.py
+"""
+
+
+class ConfigGCN():
+    learning_rate = 0.01
+    epochs = 200
+    hidden1 = 16
+    dropout = 0.5
+    weight_decay = 5e-4
+    early_stopping = 10

model_zoo/gcn/src/dataset.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+create adjacency matrix, node features, labels, and mask for training.
+"""
+import numpy as np
+import scipy.sparse as sp
+import mindspore.dataset as ds
+
+
+def normalize_adj(adj):
+    """Symmetrically normalize adjacency matrix."""
+    rowsum = np.array(adj.sum(1))
+    d_inv_sqrt = np.power(rowsum, -0.5).flatten()
+    d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
+    d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
+    return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()
+
+
+def get_adj_features_labels(data_dir):
+    """Get adjacency matrix, node features and labels from dataset."""
+    g = ds.GraphData(data_dir)
+    nodes = g.get_all_nodes(0)
+    nodes_list = nodes.tolist()
+    row_tensor = g.get_node_feature(nodes_list, [1, 2])
+    features = row_tensor[0]
+    labels = row_tensor[1]
+
+    nodes_num = labels.shape[0]
+    class_num = labels.max() + 1
+    labels_onehot = np.eye(nodes_num, class_num)[labels].astype(np.float32)
+
+    neighbor = g.get_all_neighbors(nodes_list, 0)
+    node_map = {node_id: index for index, node_id in enumerate(nodes_list)}
+    adj = np.zeros([nodes_num, nodes_num], dtype=np.float32)
+    for index, value in np.ndenumerate(neighbor):
+        # The first column of neighbor is node_id, second column to last column are neighbors of the first column.
+        # So we only care index[1] > 1.
+        # If the node does not have that many neighbors, -1 is padded. So if value < 0, we will not deal with it.
+        if value >= 0 and index[1] > 0:
+            adj[node_map[neighbor[index[0], 0]], node_map[value]] = 1
+    adj = sp.coo_matrix(adj)
+    adj = adj + adj.T.multiply(adj.T > adj) + sp.eye(nodes_num)
+    nor_adj = normalize_adj(adj)
+    nor_adj = np.array(nor_adj.todense())
+    return nor_adj, features, labels_onehot
+
+
+def get_mask(total, begin, end):
+    """Generate mask."""
+    mask = np.zeros([total]).astype(np.float32)
+    mask[begin:end] = 1
+    return mask

model_zoo/gcn/src/gcn.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""GCN."""
+import numpy as np
+from mindspore import nn
+from mindspore.common.parameter import ParameterTuple
+from mindspore.ops import composite as C
+from mindspore.ops import functional as F
+from mindspore.ops import operations as P
+from mindspore import Tensor
+from mindspore.nn.layer.activation import get_activation
+from src.metrics import Loss, Accuracy
+
+
+def glorot(shape):
+    init_range = np.sqrt(6.0/(shape[0]+shape[1]))
+    initial = np.random.uniform(-init_range, init_range, shape).astype(np.float32)
+    return Tensor(initial)
+
+
+class GraphConvolution(nn.Cell):
+    """
+    GCN graph convolution layer.
+
+    Args:
+        feature_in_dim (int): The input feature dimension.
+        feature_out_dim (int): The output feature dimension.
+        dropout_ratio (float): Dropout ratio for the dropout layer. Default: None.
+        activation (str): Activation function applied to the output of the layer, eg. 'relu'. Default: None.
+
+    Inputs:
+        - **adj** (Tensor) - Tensor of shape :math:`(N, N)`.
+        - **input_feature** (Tensor) - Tensor of shape :math:`(N, C)`.
+
+    Outputs:
+        Tensor, output tensor.
+    """
+
+    def __init__(self,
+                 feature_in_dim,
+                 feature_out_dim,
+                 dropout_ratio=None,
+                 activation=None):
+        super(GraphConvolution, self).__init__()
+        self.in_dim = feature_in_dim
+        self.out_dim = feature_out_dim
+        self.weight_init = glorot([self.out_dim, self.in_dim])
+        self.fc = nn.Dense(self.in_dim,
+                           self.out_dim,
+                           weight_init=self.weight_init,
+                           has_bias=False)
+        self.dropout_ratio = dropout_ratio
+        if self.dropout_ratio is not None:
+            self.dropout = nn.Dropout(keep_prob=1-self.dropout_ratio)
+        self.dropout_flag = self.dropout_ratio is not None
+        self.activation = get_activation(activation)
+        self.activation_flag = self.activation is not None
+        self.matmul = P.MatMul()
+
+    def construct(self, adj, input_feature):
+        dropout = input_feature
+        if self.dropout_flag:
+            dropout = self.dropout(dropout)
+
+        fc = self.fc(dropout)
+        output_feature = self.matmul(adj, fc)
+
+        if self.activation_flag:
+            output_feature = self.activation(output_feature)
+        return output_feature
+
+
+class GCN(nn.Cell):
+    """
+    GCN architecture.
+
+    Args:
+        config (ConfigGCN): Configuration for GCN.
+        adj (numpy.ndarray): Numbers of block in different layers.
+        feature (numpy.ndarray): Input channel in each layer.
+        output_dim (int): The number of output channels, equal to classes num.
+    """
+
+    def __init__(self, config, adj, feature, output_dim):
+        super(GCN, self).__init__()
+        self.adj = Tensor(adj)
+        self.feature = Tensor(feature)
+        input_dim = feature.shape[1]
+        self.layer0 = GraphConvolution(input_dim, config.hidden1, activation="relu", dropout_ratio=config.dropout)
+        self.layer1 = GraphConvolution(config.hidden1, output_dim, dropout_ratio=None)
+
+    def construct(self):
+        output0 = self.layer0(self.adj, self.feature)
+        output1 = self.layer1(self.adj, output0)
+        return output1
+
+
+class LossAccuracyWrapper(nn.Cell):
+    """
+    Wraps the GCN model with loss and accuracy cell.
+
+    Args:
+        network (Cell): GCN network.
+        label (numpy.ndarray): Dataset labels.
+        mask (numpy.ndarray): Mask for training, evaluation or test.
+        weight_decay (float): Weight decay parameter for weight of the first convolution layer.
+    """
+
+    def __init__(self, network, label, mask, weight_decay):
+        super(LossAccuracyWrapper, self).__init__()
+        self.network = network
+        self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
+        self.accuracy = Accuracy(label, mask)
+
+    def construct(self):
+        preds = self.network()
+        loss = self.loss(preds)
+        accuracy = self.accuracy(preds)
+        return loss, accuracy
+
+
+class LossWrapper(nn.Cell):
+    """
+    Wraps the GCN model with loss.
+
+    Args:
+        network (Cell): GCN network.
+        label (numpy.ndarray): Dataset labels.
+        mask (numpy.ndarray): Mask for training.
+        weight_decay (float): Weight decay parameter for weight of the first convolution layer.
+    """
+
+    def __init__(self, network, label, mask, weight_decay):
+        super(LossWrapper, self).__init__()
+        self.network = network
+        self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
+
+    def construct(self):
+        preds = self.network()
+        loss = self.loss(preds)
+        return loss
+
+
+class TrainOneStepCell(nn.Cell):
+    r"""
+    Network training package class.
+
+    Wraps the network with an optimizer. The resulting Cell be trained without inputs.
+    Backward graph will be created in the construct function to do parameter updating. Different
+    parallel modes are available to run the training.
+
+    Args:
+        network (Cell): The training network.
+        optimizer (Cell): Optimizer for updating the weights.
+        sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
+
+    Outputs:
+        Tensor, a scalar Tensor with shape :math:`()`.
+
+    Examples:
+        >>> net = Net()
+        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
+        >>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> loss_net = nn.WithLossCell(net, loss_fn)
+        >>> train_net = nn.TrainOneStepCell(loss_net, optim)
+    """
+
+    def __init__(self, network, optimizer, sens=1.0):
+        super(TrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.add_flags(defer_inline=True)
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.sens = sens
+
+    def construct(self):
+        weights = self.weights
+        loss = self.network()
+        sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
+        grads = self.grad(self.network, weights)(sens)
+        return F.depend(loss, self.optimizer(grads))
+
+
+class TrainNetWrapper(nn.Cell):
+    """
+    Wraps the GCN model with optimizer.
+
+    Args:
+        network (Cell): GCN network.
+        label (numpy.ndarray): Dataset labels.
+        mask (numpy.ndarray): Mask for training, evaluation or test.
+        config (ConfigGCN): Configuration for GCN.
+    """
+
+    def __init__(self, network, label, mask, config):
+        super(TrainNetWrapper, self).__init__(auto_prefix=True)
+        self.network = network
+        loss_net = LossWrapper(network, label, mask, config.weight_decay)
+        optimizer = nn.Adam(loss_net.trainable_params(),
+                            learning_rate=config.learning_rate)
+        self.loss_train_net = TrainOneStepCell(loss_net, optimizer)
+        self.accuracy = Accuracy(label, mask)
+
+    def construct(self):
+        loss = self.loss_train_net()
+        accuracy = self.accuracy(self.network())
+        return loss, accuracy

model_zoo/gcn/src/metrics.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Loss and accuracy."""
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.common import dtype as mstype
+from mindspore.ops import operations as P
+
+
+class Loss(nn.Cell):
+    """Softmax cross-entropy loss with masking."""
+    def __init__(self, label, mask, weight_decay, param):
+        super(Loss, self).__init__()
+        self.label = Tensor(label)
+        self.mask = Tensor(mask)
+        self.loss = P.SoftmaxCrossEntropyWithLogits()
+        self.one = Tensor(1.0, mstype.float32)
+        self.zero = Tensor(0.0, mstype.float32)
+        self.mean = P.ReduceMean()
+        self.cast = P.Cast()
+        self.l2_loss = P.L2Loss()
+        self.reduce_sum = P.ReduceSum()
+        self.weight_decay = weight_decay
+        self.param = param
+
+    def construct(self, preds):
+        param = self.l2_loss(self.param)
+        loss = self.weight_decay * param
+        preds = self.cast(preds, mstype.float32)
+        loss = loss + self.loss(preds, self.label)[0]
+        mask = self.cast(self.mask, mstype.float32)
+        mask_reduce = self.mean(mask)
+        mask = mask / mask_reduce
+        loss = loss * mask
+        loss = self.mean(loss)
+        return loss
+
+
+class Accuracy(nn.Cell):
+    """Accuracy with masking."""
+    def __init__(self, label, mask):
+        super(Accuracy, self).__init__()
+        self.label = Tensor(label)
+        self.mask = Tensor(mask)
+        self.equal = P.Equal()
+        self.argmax = P.Argmax()
+        self.cast = P.Cast()
+        self.mean = P.ReduceMean()
+
+    def construct(self, preds):
+        preds = self.cast(preds, mstype.float32)
+        correct_prediction = self.equal(self.argmax(preds), self.argmax(self.label))
+        accuracy_all = self.cast(correct_prediction, mstype.float32)
+        mask = self.cast(self.mask, mstype.float32)
+        mask_reduce = self.mean(mask)
+        mask = mask / mask_reduce
+        accuracy_all *= mask
+        return self.mean(accuracy_all)

model_zoo/gcn/train.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""
+GCN training script.
+"""
+
+import time
+import argparse
+
+import numpy as np
+from mindspore import context
+
+from src.gcn import GCN, LossAccuracyWrapper, TrainNetWrapper
+from src.config import ConfigGCN
+from src.dataset import get_adj_features_labels, get_mask
+
+
+def train():
+    """Train model."""
+    parser = argparse.ArgumentParser(description='GCN')
+    parser.add_argument('--data_dir', type=str, default='./data/cora/cora_mr', help='Dataset directory')
+    parser.add_argument('--seed', type=int, default=123, help='Random seed')
+    parser.add_argument('--train_nodes_num', type=int, default=140, help='Nodes numbers for training')
+    parser.add_argument('--eval_nodes_num', type=int, default=500, help='Nodes numbers for evaluation')
+    parser.add_argument('--test_nodes_num', type=int, default=1000, help='Nodes numbers for test')
+    args_opt = parser.parse_args()
+
+    np.random.seed(args_opt.seed)
+    context.set_context(mode=context.GRAPH_MODE,
+                        device_target="Ascend", save_graphs=False)
+    config = ConfigGCN()
+    adj, feature, label = get_adj_features_labels(args_opt.data_dir)
+
+    nodes_num = label.shape[0]
+    train_mask = get_mask(nodes_num, 0, args_opt.train_nodes_num)
+    eval_mask = get_mask(nodes_num, args_opt.train_nodes_num, args_opt.train_nodes_num + args_opt.eval_nodes_num)
+    test_mask = get_mask(nodes_num, nodes_num - args_opt.test_nodes_num, nodes_num)
+
+    class_num = label.shape[1]
+    gcn_net = GCN(config, adj, feature, class_num)
+    gcn_net.add_flags_recursive(fp16=True)
+
+    eval_net = LossAccuracyWrapper(gcn_net, label, eval_mask, config.weight_decay)
+    test_net = LossAccuracyWrapper(gcn_net, label, test_mask, config.weight_decay)
+    train_net = TrainNetWrapper(gcn_net, label, train_mask, config)
+
+    loss_list = []
+    for epoch in range(config.epochs):
+        t = time.time()
+
+        train_net.set_train()
+        train_result = train_net()
+        train_loss = train_result[0].asnumpy()
+        train_accuracy = train_result[1].asnumpy()
+
+        eval_net.set_train(False)
+        eval_result = eval_net()
+        eval_loss = eval_result[0].asnumpy()
+        eval_accuracy = eval_result[1].asnumpy()
+
+        loss_list.append(eval_loss)
+        print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(train_loss),
+              "train_acc=", "{:.5f}".format(train_accuracy), "val_loss=", "{:.5f}".format(eval_loss),
+              "val_acc=", "{:.5f}".format(eval_accuracy), "time=", "{:.5f}".format(time.time() - t))
+
+        if epoch > config.early_stopping and loss_list[-1] > np.mean(loss_list[-(config.early_stopping+1):-1]):
+            print("Early stopping...")
+            break
+
+    t_test = time.time()
+    test_net.set_train(False)
+    test_result = test_net()
+    test_loss = test_result[0].asnumpy()
+    test_accuracy = test_result[1].asnumpy()
+    print("Test set results:", "loss=", "{:.5f}".format(test_loss),
+          "accuracy=", "{:.5f}".format(test_accuracy), "time=", "{:.5f}".format(time.time() - t_test))
+
+
+if __name__ == '__main__':
+    train()

tests/st/gnn/gcn/src/config.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
+
 class ConfigGCN():
     learning_rate = 0.01
     epochs = 200

tests/st/gnn/gcn/test_gcn.py

@@ -58,10 +58,12 @@ def test_gcn():
     for epoch in range(config.epochs):
         t = time.time()
 
+        train_net.set_train()
         train_result = train_net()
         train_loss = train_result[0].asnumpy()
         train_accuracy = train_result[1].asnumpy()
 
+        eval_net.set_train(False)
         eval_result = eval_net()
         eval_loss = eval_result[0].asnumpy()
         eval_accuracy = eval_result[1].asnumpy()
@@ -75,6 +77,7 @@ def test_gcn():
             print("Early stopping...")
             break
 
+    test_net.set_train(False)
     test_result = test_net()
     test_loss = test_result[0].asnumpy()
     test_accuracy = test_result[1].asnumpy()