add hub config

model_zoo/official/gnn/bgcf/mindspore_hub_conf.py

@@ -0,0 +1,25 @@
+# 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.
+# ============================================================================
+"""hub config."""
+from src.bgcf import BGCF
+
+def bgcf(*args, **kwargs):
+    return BGCF(*args, **kwargs)
+
+
+def create_network(name, *args, **kwargs):
+    if name == "bgcf":
+        return bgcf(*args, **kwargs)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/gnn/gat/mindspore_hub_conf.py

@@ -0,0 +1,25 @@
+# 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.
+# ============================================================================
+"""hub config."""
+from src.gat import GAT
+
+def gat(*args, **kwargs):
+    return GAT(*args, **kwargs)
+
+
+def create_network(name, *args, **kwargs):
+    if name == "gat":
+        return gat(*args, **kwargs)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/gnn/gcn/mindspore_hub_conf.py

@@ -0,0 +1,25 @@
+# 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.
+# ============================================================================
+"""hub config."""
+from src.gcn import GCN
+
+def gcn(*args, **kwargs):
+    return GCN(*args, **kwargs)
+
+
+def create_network(name, *args, **kwargs):
+    if name == "gcn":
+        return gcn(*args, **kwargs)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/gnn/gcn/src/metrics.py

@@ -25,7 +25,7 @@ from mindspore.ops import functional as F
 class Loss(nn.Cell):
     """Softmax cross-entropy loss with masking."""
     def __init__(self, label, mask, weight_decay, param):
-        super(Loss, self).__init__()
+        super(Loss, self).__init__(auto_prefix=False)
         self.label = Tensor(label)
         self.mask = Tensor(mask)
         self.loss = P.SoftmaxCrossEntropyWithLogits()
@@ -55,7 +55,7 @@ class Loss(nn.Cell):
 class Accuracy(nn.Cell):
     """Accuracy with masking."""
     def __init__(self, label, mask):
-        super(Accuracy, self).__init__()
+        super(Accuracy, self).__init__(auto_prefix=False)
         self.label = Tensor(label)
         self.mask = Tensor(mask)
         self.equal = P.Equal()
@@ -86,7 +86,7 @@ class LossAccuracyWrapper(nn.Cell):
     """
 
     def __init__(self, network, label, mask, weight_decay):
-        super(LossAccuracyWrapper, self).__init__()
+        super(LossAccuracyWrapper, self).__init__(auto_prefix=False)
         self.network = network
         self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
         self.accuracy = Accuracy(label, mask)
@@ -110,7 +110,7 @@ class LossWrapper(nn.Cell):
     """
 
     def __init__(self, network, label, mask, weight_decay):
-        super(LossWrapper, self).__init__()
+        super(LossWrapper, self).__init__(auto_prefix=False)
         self.network = network
         self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
 
@@ -174,7 +174,7 @@ class TrainNetWrapper(nn.Cell):
     """
 
     def __init__(self, network, label, mask, config):
-        super(TrainNetWrapper, self).__init__(auto_prefix=True)
+        super(TrainNetWrapper, self).__init__(auto_prefix=False)
         self.network = network
         loss_net = LossWrapper(network, label, mask, config.weight_decay)
         optimizer = nn.Adam(loss_net.trainable_params(),

model_zoo/official/gnn/gcn/train.py

@@ -16,7 +16,7 @@
 """
 GCN training script.
 """
-
+import os
 import time
 import argparse
 import ast
@@ -27,6 +27,7 @@ from matplotlib import animation
 from sklearn import manifold
 from mindspore import context
 from mindspore.common import set_seed
+from mindspore.train.serialization import save_checkpoint, load_checkpoint
 
 from src.gcn import GCN
 from src.metrics import LossAccuracyWrapper, TrainNetWrapper
@@ -55,6 +56,8 @@ def train():
     parser.add_argument('--test_nodes_num', type=int, default=1000, help='Nodes numbers for test')
     parser.add_argument('--save_TSNE', type=ast.literal_eval, default=False, help='Whether to save t-SNE graph')
     args_opt = parser.parse_args()
+    if not os.path.exists("ckpts"):
+        os.mkdir("ckpts")
 
     set_seed(args_opt.seed)
     context.set_context(mode=context.GRAPH_MODE,
@@ -72,7 +75,6 @@ def train():
     gcn_net.add_flags_recursive(fp16=True)
 
     eval_net = LossAccuracyWrapper(gcn_net, label_onehot, eval_mask, config.weight_decay)
-    test_net = LossAccuracyWrapper(gcn_net, label_onehot, test_mask, config.weight_decay)
     train_net = TrainNetWrapper(gcn_net, label_onehot, train_mask, config)
 
     loss_list = []
@@ -112,7 +114,12 @@ def train():
         if epoch > config.early_stopping and loss_list[-1] > np.mean(loss_list[-(config.early_stopping+1):-1]):
             print("Early stopping...")
             break
+    save_checkpoint(gcn_net, "ckpts/gcn.ckpt")
+    gcn_net_test = GCN(config, adj, feature, class_num)
+    load_checkpoint("ckpts/gcn.ckpt", net=gcn_net_test)
+    gcn_net_test.add_flags_recursive(fp16=True)
 
+    test_net = LossAccuracyWrapper(gcn_net_test, label_onehot, test_mask, config.weight_decay)
     t_test = time.time()
     test_net.set_train(False)
     test_result = test_net()