add bert_thor hub file

model_zoo/official/cv/resnet_thor/mindspore_hub_conf.py

@@ -0,0 +1,21 @@
+# 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.resnet_thor import resnet50
+
+def create_network(name, *args, **kwargs):
+    if name == 'resnet50_thor':
+        return resnet50(*args, **kwargs)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/resnet_thor/src/resnet_thor.py

@@ -273,7 +273,8 @@ class ResNet(nn.Cell):
                  damping,
                  loss_scale,
                  frequency,
-                 batch_size):
+                 batch_size,
+                 include_top=True):
         super(ResNet, self).__init__()
 
         if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
@@ -321,11 +322,12 @@ class ResNet(nn.Cell):
                                        loss_scale=loss_scale,
                                        frequency=frequency,
                                        batch_size=batch_size)
-
-        self.mean = P.ReduceMean(keep_dims=True)
-        self.flatten = nn.Flatten()
-        self.end_point = _fc(out_channels[3], num_classes, damping=damping, loss_scale=loss_scale,
-                             frequency=frequency, batch_size=batch_size)
+        self.include_top = include_top
+        if self.include_top:
+            self.mean = P.ReduceMean(keep_dims=True)
+            self.flatten = nn.Flatten()
+            self.end_point = _fc(out_channels[3], num_classes, damping=damping, loss_scale=loss_scale,
+                                 frequency=frequency, batch_size=batch_size)
 
     def _make_layer(self, block, layer_num, in_channel, out_channel, stride,
                     damping, loss_scale, frequency, batch_size):
@@ -371,6 +373,9 @@ class ResNet(nn.Cell):
         c4 = self.layer3(c3)
         c5 = self.layer4(c4)
 
+        if not self.include_top:
+            return x
+
         out = self.mean(c5, (2, 3))
         out = self.flatten(out)
         out = self.end_point(out)
@@ -378,7 +383,7 @@ class ResNet(nn.Cell):
         return out
 
 
-def resnet50(class_num=10, damping=0.03, loss_scale=1, frequency=278, batch_size=32):
+def resnet50(class_num=10, damping=0.03, loss_scale=1, frequency=278, batch_size=32, include_top=True):
     """
     Get ResNet50 neural network.
 
@@ -400,4 +405,5 @@ def resnet50(class_num=10, damping=0.03, loss_scale=1, frequency=278, batch_size
                   damping,
                   loss_scale,
                   frequency,
-                  batch_size)
+                  batch_size,
+                  include_top)

model_zoo/official/nlp/bert_thor/mindspore_hub_conf.py

@@ -0,0 +1,49 @@
+# 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.
+# ============================================================================
+'''
+Bert hub interface for bert_thor
+'''
+from src.bert_model import BertModel
+from src.bert_model import BertConfig
+import mindspore.common.dtype as mstype
+bert_net_cfg = BertConfig(
+    batch_size=12,
+    seq_length=512,
+    vocab_size=30522,
+    hidden_size=1024,
+    num_hidden_layers=24,
+    num_attention_heads=16,
+    intermediate_size=4096,
+    hidden_act="gelu",
+    hidden_dropout_prob=0.1,
+    attention_probs_dropout_prob=0.1,
+    max_position_embeddings=512,
+    type_vocab_size=2,
+    initializer_range=0.02,
+    use_relative_positions=False,
+    input_mask_from_dataset=True,
+    token_type_ids_from_dataset=True,
+    dtype=mstype.float32,
+    compute_type=mstype.float16,
+    enable_fused_layernorm=True
+)
+def create_network(name, *args, **kwargs):
+    '''
+    Create bert network for bert_thor.
+    '''
+    if name == 'bert_thor':
+        is_training = kwargs.get("is_training", default=False)
+        return BertModel(bert_net_cfg, is_training, *args)
+    raise NotImplementedError(f"{name} is not implemented in the repo")