export script for gcn and gat

mindspore/ops/operations/_quant_ops.py

@@ -210,9 +210,9 @@ class FakeQuantWithMinMaxVars(PrimitiveWithInfer):
             raise ValueError(f"For '{self.name}', the shape of \'min\' cannot broadcast to the shape of \'x\'.")
 
     def infer_shape(self, x_shape, min_shape, max_shape):
-        validator.check_integer("x rank", len(x_shape), 1, Rel.GE, self.name)
+        validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
         validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
-        validator.check_integer("min shape", len(min_shape), 1, Rel.EQ, self.name)
+        validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
         self.check_broadcast(min_shape, x_shape)
         return x_shape
 
@@ -273,10 +273,10 @@ class FakeQuantWithMinMaxVarsGradient(PrimitiveWithInfer):
             raise ValueError(f"For '{self.name}', the shape of \'min\' cannot broadcast to the shape of \'x\'.")
 
     def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
-        validator.check_integer("x rank", len(x_shape), 1, Rel.GE, self.name)
+        validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
         validator.check("dout shape", dout_shape, "x shape", x_shape, Rel.EQ, self.name)
         validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
-        validator.check_integer("min shape", len(min_shape), 1, Rel.EQ, self.name)
+        validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
         self.check_broadcast(min_shape, x_shape)
         return x_shape, min_shape, max_shape
 
@@ -325,9 +325,9 @@ class FakeQuantWithMinMaxVarsPerChannel(PrimitiveWithInfer):
             'narrow_range', narrow_range, (bool,), self.name)
 
     def infer_shape(self, x_shape, min_shape, max_shape):
-        validator.check_integer("x rank", len(x_shape), 1, Rel.GE, self.name)
+        validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
         validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
-        validator.check_integer("min shape", len(min_shape), 1, Rel.EQ, self.name)
+        validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
         validator.check("min shape", min_shape[0], "x shape", x_shape[-1], Rel.EQ, self.name)
         return x_shape
 
@@ -382,10 +382,10 @@ class FakeQuantWithMinMaxVarsPerChannelGradient(PrimitiveWithInfer):
             'narrow_range', narrow_range, (bool,), self.name)
 
     def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
-        validator.check_integer("x rank", len(x_shape), 1, Rel.GE, self.name)
+        validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
         validator.check("dout shape", dout_shape, "x shape", x_shape, Rel.EQ, self.name)
         validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
-        validator.check_integer("min shape", len(min_shape), 1, Rel.EQ, self.name)
+        validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
         validator.check("min shape", min_shape[0], "x shape", x_shape[-1], Rel.EQ, self.name)
         return x_shape, min_shape, max_shape
 

model_zoo/official/gnn/gat/export.py

@@ -0,0 +1,64 @@
+# 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.
+# ============================================================================
+"""export checkpoint file into air models"""
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+from mindspore.train.serialization import load_checkpoint, export
+
+from src.gat import GAT
+from src.config import GatConfig
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='GAT_export')
+    parser.add_argument('--ckpt_file', type=str, default='./ckpts/gat.ckpt', help='GAT ckpt file.')
+    parser.add_argument('--output_file', type=str, default='gat.air', help='GAT output air name.')
+    parser.add_argument('--dataset', type=str, default='cora', help='GAT dataset name.')
+    args_opt = parser.parse_args()
+
+    if args_opt.dataset == "citeseer":
+        feature_size = [1, 3312, 3703]
+        biases_size = [1, 3312, 3312]
+        num_classes = 6
+    else:
+        feature_size = [1, 2708, 1433]
+        biases_size = [1, 2708, 2708]
+        num_classes = 7
+
+    hid_units = GatConfig.hid_units
+    n_heads = GatConfig.n_heads
+
+    feature = np.random.uniform(0.0, 1.0, size=feature_size).astype(np.float32)
+    biases = np.random.uniform(0.0, 1.0, size=biases_size).astype(np.float64)
+
+    feature_size = feature.shape[2]
+    num_nodes = feature.shape[1]
+
+    gat_net = GAT(feature_size,
+                  num_classes,
+                  num_nodes,
+                  hid_units,
+                  n_heads,
+                  attn_drop=0.0,
+                  ftr_drop=0.0)
+
+    gat_net.set_train(False)
+    load_checkpoint(args_opt.ckpt_file, net=gat_net)
+    gat_net.add_flags_recursive(fp16=True)
+
+    export(gat_net, Tensor(feature), Tensor(biases), file_name=args_opt.output_file, file_format="AIR")

model_zoo/official/gnn/gcn/export.py

@@ -0,0 +1,53 @@
+# 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.
+# ============================================================================
+"""export checkpoint file into air models"""
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+from mindspore.train.serialization import load_checkpoint, export
+
+from src.gcn import GCN
+from src.config import ConfigGCN
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='GCN_export')
+    parser.add_argument('--ckpt_file', type=str, default='', help='GCN ckpt file.')
+    parser.add_argument('--output_file', type=str, default='gcn.air', help='GCN output air name.')
+    parser.add_argument('--dataset', type=str, default='cora', help='GCN dataset name.')
+    args_opt = parser.parse_args()
+
+    config = ConfigGCN()
+
+    if args_opt.dataset == "cora":
+        input_dim = 1433
+        class_num = 7
+        adj = Tensor(np.zeros((2708, 2708), np.float64))
+        feature = Tensor(np.zeros((2708, 1433), np.float32))
+    else:
+        input_dim = 3703
+        class_num = 6
+        adj = Tensor(np.zeros((3312, 3312), np.float64))
+        feature = Tensor(np.zeros((3312, 3703), np.float32))
+
+    gcn_net = GCN(config, input_dim, class_num)
+
+    gcn_net.set_train(False)
+    load_checkpoint(args_opt.ckpt_file, net=gcn_net)
+    gcn_net.add_flags_recursive(fp16=True)
+
+    export(gcn_net, adj, feature, file_name=args_opt.output_file, file_format="AIR")