add moboilenetv3 and resnext50 hub

model_zoo/official/cv/mobilenetv2/README.md

@@ -77,6 +77,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
   │   ├──utils.py       # utils to load ckpt_file for fine tune or incremental learn
   ├── train.py      # training script
   ├── eval.py       # evaluation script
+  ├── mindspore_hub_conf.py       #  mindspore hub interface
 ```
 
 ## [Training process](#contents)

model_zoo/official/cv/mobilenetv2/src/models.py

@@ -119,7 +119,7 @@ def load_ckpt(network, pretrain_ckpt_path, trainable=True):
         for param in network.get_parameters():
             param.requires_grad = False
 
-def define_net(config, is_training):
+def define_net(config, is_training=True):
     backbone_net = MobileNetV2Backbone()
     activation = config.activation if not is_training else "None"
     head_net = MobileNetV2Head(input_channel=backbone_net.out_channels,

model_zoo/official/cv/mobilenetv3/Readme.md

@@ -69,6 +69,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
   │   ├──mobilenetV3.py      # MobileNetV3 architecture
   ├── train.py      # training script
   ├── eval.py       #  evaluation script
+  ├── mindspore_hub_conf.py       #  mindspore hub interface
 ```
 
 ## [Training process](#contents)

model_zoo/official/cv/mobilenetv3/eval.py

@@ -42,7 +42,7 @@ if __name__ == '__main__':
         raise ValueError("Unsupported device_target.")
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-    net = mobilenet_v3_large(num_classes=config.num_classes)
+    net = mobilenet_v3_large(num_classes=config.num_classes, activation="Softmax")
 
     dataset = create_dataset(dataset_path=args_opt.dataset_path,
                              do_train=False,

model_zoo/official/cv/mobilenetv3/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.mobilenetV3 import mobilenet_v3_large, mobilenet_v3_small
+
+def create_network(name, *args, **kwargs):
+    if name == "mobilenetv3_large":
+        net = mobilenet_v3_large(*args, **kwargs)
+    elif name == "mobilenetv3_small":
+        net = mobilenet_v3_small(*args, **kwargs)
+    else:
+        raise NotImplementedError(f"{name} is not implemented in the repo")
+    return net

model_zoo/official/cv/mobilenetv3/src/mobilenetV3.py

@@ -246,7 +246,8 @@ class MobileNetV3(nn.Cell):
         >>> MobileNetV3(num_classes=1000)
     """
 
-    def __init__(self, model_cfgs, num_classes=1000, multiplier=1., final_drop=0., round_nearest=8):
+    def __init__(self, model_cfgs, num_classes=1000, multiplier=1., final_drop=0.,
+                 round_nearest=8, include_top=True, activation="None"):
         super(MobileNetV3, self).__init__()
         self.cfgs = model_cfgs['cfg']
         self.inplanes = 16
@@ -285,19 +286,34 @@ class MobileNetV3(nn.Cell):
 
         # make it nn.CellList
         self.features = nn.SequentialCell(self.features)
-        self.output = nn.Conv2d(in_channels=model_cfgs['cls_ch_expand'],
-                                out_channels=num_classes,
-                                kernel_size=1, has_bias=True, pad_mode='pad')
-        self.squeeze = P.Squeeze(axis=(2, 3))
+        self.include_top = include_top
+        self.need_activation = False
+        if self.include_top:
+            self.output = nn.Conv2d(in_channels=model_cfgs['cls_ch_expand'],
+                                    out_channels=num_classes,
+                                    kernel_size=1, has_bias=True, pad_mode='pad')
+            self.squeeze = P.Squeeze(axis=(2, 3))
+            if activation != "None":
+                self.need_activation = True
+                if activation == "Sigmoid":
+                    self.activation = P.Sigmoid()
+                elif activation == "Softmax":
+                    self.activation = P.Softmax()
+                else:
+                    raise NotImplementedError(f"The activation {activation} not in [Sigmoid, Softmax].")
 
         self._initialize_weights()
 
     def construct(self, x):
         x = self.features(x)
-        x = self.output(x)
-        x = self.squeeze(x)
+        if self.include_top:
+            x = self.output(x)
+            x = self.squeeze(x)
+            if self.need_activation:
+                x = self.activation(x)
         return x
 
+
     def _make_layer(self, kernel_size, exp_ch, out_channel, use_se, act_func, stride=1):
         mid_planes = exp_ch
         out_planes = out_channel

model_zoo/official/cv/resnext50/README.md

@@ -96,7 +96,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     ├─warmup_cosine_annealing.py      # learning rate each step
     ├─warmup_step_lr.py               # warmup step learning rate
   ├─eval.py                           # eval net
-  └─train.py                          # train net
+  ├──train.py                         # train net
+  ├──mindspore_hub_conf.py            #  mindspore hub interface
   
 ```
 

model_zoo/official/cv/resnext50/eval.py

@@ -201,7 +201,7 @@ def test(cloud_args=None):
                                             max_epoch=1, rank=args.rank, group_size=args.group_size,
                                             mode='eval')
         eval_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
-        network = get_network(args.backbone, args.num_classes, platform=args.platform)
+        network = get_network(args.backbone, num_classes=args.num_classes, platform=args.platform)
         if network is None:
             raise NotImplementedError('not implement {}'.format(args.backbone))
 

model_zoo/official/cv/resnext50/mindspore_hub_conf.py

@@ -0,0 +1,22 @@
+# 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.image_classification import get_network
+
+def create_network(name, *args, **kwargs):
+    if name == "renext50":
+        get_network("renext50", *args, **kwargs)
+        return net
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/resnext50/src/image_classification.py

@@ -31,31 +31,46 @@ class ImageClassificationNetwork(nn.Cell):
     Returns:
         Tensor, output tensor.
     """
-    def __init__(self, backbone, head):
+    def __init__(self, backbone, head, include_top=True, activation="None"):
         super(ImageClassificationNetwork, self).__init__()
         self.backbone = backbone
-        self.head = head
+        self.include_top = include_top
+        self.need_activation = False
+        if self.include_top:
+            self.head = head
+            if activation != "None":
+                self.need_activation = True
+                if activation == "Sigmoid":
+                    self.activation = P.Sigmoid()
+                elif activation == "Softmax":
+                    self.activation = P.Softmax()
+                else:
+                    raise NotImplementedError(f"The activation {activation} not in [Sigmoid, Softmax].")
 
     def construct(self, x):
         x = self.backbone(x)
-        x = self.head(x)
+        if self.include_top:
+            x = self.head(x)
+            if self.need_activation:
+                x = self.activation(x)
         return x
 
+
 class Resnet(ImageClassificationNetwork):
     """
     Resnet architecture.
     Args:
         backbone_name (string): backbone.
-        num_classes (int): number of classes.
+        num_classes (int): number of classes, Default is 1000.
     Returns:
         Resnet.
     """
-    def __init__(self, backbone_name, num_classes, platform="Ascend"):
+    def __init__(self, backbone_name, num_classes=1000, platform="Ascend", include_top=True, activation="None"):
         self.backbone_name = backbone_name
         backbone = backbones.__dict__[self.backbone_name](platform=platform)
         out_channels = backbone.get_out_channels()
         head = heads.CommonHead(num_classes=num_classes, out_channels=out_channels)
-        super(Resnet, self).__init__(backbone, head)
+        super(Resnet, self).__init__(backbone, head, include_top, activation)
 
         default_recurisive_init(self)
 
@@ -79,7 +94,7 @@ class Resnet(ImageClassificationNetwork):
 
 
 
-def get_network(backbone_name, num_classes, platform="Ascend"):
+def get_network(backbone_name, **kwargs):
     if backbone_name in ['resnext50']:
-        return Resnet(backbone_name, num_classes, platform)
+        return Resnet(backbone_name, **kwargs)
     return None

model_zoo/official/cv/resnext50/train.py

@@ -213,7 +213,7 @@ def train(cloud_args=None):
     # network
     args.logger.important_info('start create network')
     # get network and init
-    network = get_network(args.backbone, args.num_classes, platform=args.platform)
+    network = get_network(args.backbone, num_classes=args.num_classes, platform=args.platform)
     if network is None:
         raise NotImplementedError('not implement {}'.format(args.backbone))
 

model_zoo/official/cv/ssd/README.md

@@ -114,7 +114,8 @@ sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
       ├─ lr_schedule.py           ## learning ratio generator
       └─ ssd.py                   ## ssd architecture
     ├─ eval.py                    ## eval scripts
-    └─ train.py                   ## train scripts
+    ├─ train.py                   ## train scripts
+    ├── mindspore_hub_conf.py       #  mindspore hub interface
 ```
 
 ## [Script Parameters](#contents)