add gcn training scripts

tests/st/gnn/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.
+# ============================================================================
+
+class ConfigGCN():
+    learning_rate = 0.01
+    epochs = 200
+    hidden1 = 16
+    dropout = 0.0
+    weight_decay = 5e-4
+    early_stopping = 10

tests/st/gnn/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.
+# ============================================================================
+
+import numpy as np
+import scipy.sparse as sp
+import mindspore.dataset as ds
+
+
+def normalize_adj(adj):
+    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):
+    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):
+    mask = np.zeros([total]).astype(np.float32)
+    mask[begin:end] = 1
+    return mask

tests/st/gnn/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.
+# ============================================================================
+
+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):
+    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):
+    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):
+    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):
+    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):
+    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

tests/st/gnn/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.
+# ============================================================================
+
+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):
+    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):
+    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)

tests/st/gnn/gcn/test_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.
+# ============================================================================
+
+import time
+import pytest
+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
+
+
+DATA_DIR = '/home/workspace/mindspore_dataset/cora/cora_mr/cora_mr'
+TRAIN_NODE_NUM = 140
+EVAL_NODE_NUM = 500
+TEST_NODE_NUM = 1000
+SEED = 20
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_gcn():
+    print("test_gcn begin")
+    np.random.seed(SEED)
+    context.set_context(mode=context.GRAPH_MODE,
+                        device_target="Ascend", save_graphs=True)
+    config = ConfigGCN()
+    adj, feature, label = get_adj_features_labels(DATA_DIR)
+
+    nodes_num = label.shape[0]
+    train_mask = get_mask(nodes_num, 0, TRAIN_NODE_NUM)
+    eval_mask = get_mask(nodes_num, TRAIN_NODE_NUM, TRAIN_NODE_NUM + EVAL_NODE_NUM)
+    test_mask = get_mask(nodes_num, nodes_num - TEST_NODE_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_result = train_net()
+        train_loss = train_result[0].asnumpy()
+        train_accuracy = train_result[1].asnumpy()
+
+        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
+
+    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))
+    assert test_accuracy > 0.812