add mobilenetv2 and ssd hub

model_zoo/official/cv/mobilenetv2/mindspore_hub_conf.py

@@ -0,0 +1,32 @@
+# 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.mobilenetV2 import MobileNetV2Backbone, MobileNetV2Head, mobilenet_v2
+
+def create_network(name, *args, **kwargs):
+    if name == "mobilenetv2":
+        backbone_net = MobileNetV2Backbone()
+        include_top = kwargs["include_top"]
+        if include_top is None:
+            include_top = True
+        if include_top:
+            activation = kwargs["activation"]
+            head_net = MobileNetV2Head(input_channel=backbone_net.out_channels,
+                                       num_classes=int(kwargs["num_classes"]),
+                                       activation=activation)
+            net = mobilenet_v2(backbone_net, head_net)
+            return net
+        return backbone_net
+    raise NotImplementedError(f"{name} is not implemented in the repo")

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

@@ -48,6 +48,7 @@ def train_parse_args():
         for fine tune or incremental learning')
     train_parser.add_argument('--run_distribute', type=ast.literal_eval, default=True, help='Run distribute')
     train_args = train_parser.parse_args()
+    train_args.is_training = True
     return train_args
 
 def eval_parse_args():
@@ -61,5 +62,6 @@ def eval_parse_args():
         for incremental learning')
     eval_parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='If run distribute in GPU.')
     eval_args = eval_parser.parse_args()
+    eval_args.is_training = False
     return eval_args
     

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

@@ -38,7 +38,8 @@ def set_config(args):
         "keep_checkpoint_max": 20,
         "save_checkpoint_path": "./",
         "platform": args.platform,
-        "run_distribute": False
+        "run_distribute": False,
+        "activation": "Softmax"
     })
     config_gpu = ed({
         "num_classes": 1000,
@@ -60,7 +61,8 @@ def set_config(args):
         "save_checkpoint_path": "./",
         "platform": args.platform,
         "ccl": "nccl",
-        "run_distribute": args.run_distribute
+        "run_distribute": args.run_distribute,
+        "activation": "Softmax"
     })
     config_ascend = ed({
         "num_classes": 1000,
@@ -85,7 +87,8 @@ def set_config(args):
         "device_id": int(os.getenv('DEVICE_ID', '0')),
         "rank_id": int(os.getenv('RANK_ID', '0')),
         "rank_size": int(os.getenv('RANK_SIZE', '1')),
-        "run_distribute": int(os.getenv('RANK_SIZE', '1')) > 1.
+        "run_distribute": int(os.getenv('RANK_SIZE', '1')) > 1.,
+        "activation": "Softmax"
     })
     config = ed({"CPU": config_cpu,
                  "GPU": config_gpu,

model_zoo/official/cv/mobilenetv2/src/mobilenetV2.py

@@ -242,16 +242,25 @@ class MobileNetV2Head(nn.Cell):
         >>> MobileNetV2(num_classes=1000)
     """
 
-    def __init__(self, input_channel=1280, num_classes=1000, has_dropout=False):
+    def __init__(self, input_channel=1280, num_classes=1000, has_dropout=False, activation="None"):
         super(MobileNetV2Head, self).__init__()
         # mobilenet head
         head = ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes, has_bias=True)] if not has_dropout else
                 [GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(input_channel, num_classes, has_bias=True)])
         self.head = nn.SequentialCell(head)
+        self.need_activation = True
+        if activation == "Sigmoid":
+            self.activation = P.Sigmoid()
+        elif activation == "Softmax":
+            self.activation = P.Softmax()
+        else:
+            self.need_activation = False
         self._initialize_weights()
 
     def construct(self, x):
         x = self.head(x)
+        if self.need_activation:
+            x = self.activation(x)
         return x
 
     def _initialize_weights(self):

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

@@ -121,7 +121,7 @@ def load_ckpt(network, pretrain_ckpt_path, trainable=True):
 
 def define_net(config):
     backbone_net = MobileNetV2Backbone()
+    activation = config.activation if not args.is_training else "None"
     head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes)
-    net = mobilenet_v2(backbone_net, head_net)
+    net = mobilenet_v2(backbone_net, head_net, activation=activation)
-
     return backbone_net, head_net, net

model_zoo/official/cv/resnet/README.md

@@ -158,7 +158,7 @@ Parameters for both training and evaluation can be set in config.py.
 "epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
 "pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
 "save_checkpoint": True,          # whether save checkpoint or not
-"save_checkpoint_steps": 195,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
+"save_checkpoint_epochs": 5,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last step
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
 "save_checkpoint_path": "./",     # path to save checkpoint
 "warmup_epochs": 5,               # number of warmup epoch
@@ -179,15 +179,16 @@ Parameters for both training and evaluation can be set in config.py.
 "epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
 "pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
 "save_checkpoint": True,          # whether save checkpoint or not
-"save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
+"save_checkpoint_epochs": 5,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
 "save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
 "warmup_epochs": 0,               # number of warmup epoch
-"lr_decay_mode": "cosine",        # decay mode for generating learning rate
+"lr_decay_mode": "Linear",        # decay mode for generating learning rate
 "label_smooth": True,             # label smooth
 "label_smooth_factor": 0.1,       # label smooth factor
 "lr_init": 0,                     # initial learning rate
 "lr_max": 0.1,                    # maximum learning rate
+"lr_end": 0.0,                    # minimum learning rate
 ```
 
 - Config for ResNet101, ImageNet2012 dataset
@@ -201,7 +202,7 @@ Parameters for both training and evaluation can be set in config.py.
 "epoch_size": 120,                # epoch size for training
 "pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
 "save_checkpoint": True,          # whether save checkpoint or not
-"save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
+"save_checkpoint_epochs": 5,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
 "keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
 "save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
 "warmup_epochs": 0,               # number of warmup epoch

model_zoo/official/cv/ssd/mindspore_hub_conf.py

@@ -0,0 +1,24 @@
+# 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.ssd import SSD300, ssd_mobilenet_v2
+from src.config import config
+
+def create_network(name, *args, **kwargs):
+    if name == "ssd300":
+        backbone = ssd_mobilenet_v2()
+        ssd = SSD300(backbone=backbone, config=config, *args, **kwargs)
+        return ssd
+    raise NotImplementedError(f"{name} is not implemented in the repo")