Backbone network for RetinaNet

collections/nemo_cv/nemo_cv/modules/README_RetinaNet.md

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+
+# The docker image that is tested working with RetinaNet is nvcr.io/nvidia/pytorch:19.09-py3
+
+# To run RetinaNet Nemo Wrapper follow the below steps:
+
+docker run -it --rm --ipc=host --gpus all -v {path to the project}:/workspace nvcr.io/nvidia/pytorch:19.09-py3
+
+pip install --no-cache-dir git+https://github.com/nvidia/retinanet-examples
+
+git clone https://github.com/NVIDIA/NeMo.git 
+
+cd NeMo/nemo
+# Change the version of Pytorch in Nemo/nemo/setup.py to 'torch==1.2.0'
+
+python setup install
+
+cd ..
+cd collections/nemo_cv/nemo_cv/modules/
+
+python pascal_retinanet.py train model_mydataset.pth --backbone ResNet18FPN --classes 20 --iters 10000 --val-iters 1000 --lr 0.0005 --resize 512 --jitter 480 640 --images /workspace/PASCAL_VOC/JPEGImages/ --annotations /workspace/PASCAL_VOC/pascal_train2012.json --val-annotations /workspace/PASCAL_VOC/pascal_val2012.json

collections/nemo_cv/nemo_cv/modules/pascal_retinanet.py

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+# Copyright (C) , NVIDIA INC. All Rights Reserved.
+#
+# 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.
+
+__author__ = "Hussein Al-barazanchi"
+
+
+import sys
+from retinanet_module import *
+
+
+def main(args=None):
+    'Entry point for the retinanet command'
+
+    args = parse(args or sys.argv[1:])
+
+    detector = RetinaNet(args)
+    detector.execute(args)
+
+if __name__ == '__main__':
+    main()

collections/nemo_cv/nemo_cv/modules/retinanet_module.py

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+# Copyright (C) , NVIDIA INC. All Rights Reserved.
+#
+# 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.
+
+__author__ = "Hussein Al-barazanchi"
+
+
+import torch
+
+from retinanet.model import Model
+from retinanet.main import parse, load_model, worker
+
+from nemo.backends.pytorch.nm import TrainableNM
+
+
+class RetinaNet(TrainableNM):
+    """
+        Wrapper class around the RetinaNet model.
+    """
+
+    @staticmethod
+    def create_ports():
+
+        return None, None
+
+    def __init__(self, args):
+        """
+        Creates the RetinaNet model.
+
+        Args:
+            num_classes: Number of output classes of the model.
+            pretrained: use weights of model pretrained on COCO train2017.
+        """
+
+        super().__init__()
+
+        # Create
+        self.model, self.state = load_model(args, verbose=True)
+        if self.model: 
+            self.model.share_memory()
+
+    def forward(self, images, bounding_boxes, targets):
+        pass
+
+    def execute(self, args):
+
+        world = torch.cuda.device_count()
+        if args.command == 'export' or world <= 1:
+            worker(0, args, 1, self.model, self.state)
+        else:
+            torch.multiprocessing.spawn(worker, args=(args, world, self.model, self.state), nprocs=world)