update utils.create_pretrained()

Dockerfile

 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
 FROM nvcr.io/nvidia/pytorch:20.03-py3
+RUN pip install -U gsutil
 
 # Create working directory
 RUN mkdir -p /usr/src/app
 
 # Install dependencies (pip or conda)
 #RUN pip install -r requirements.txt
-RUN pip install -U gsutil
 
 # Copy weights
 #RUN python3 -c "from models import *; \
 
 # Bash into running container
 # sudo docker container exec -it ba65811811ab bash
-# python -c "from utils.utils import *; create_backbone('weights/last.pt')" && gsutil cp weights/backbone.pt gs://*
+# python -c "from utils.utils import *; create_pretrained('weights/last.pt')" && gsutil cp weights/pretrained.pt gs://*
 
 # Bash into stopped container
 # sudo docker commit 6d525e299258 user/test_image && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh user/test_image

utils/utils.py

     print('Optimizer stripped from %s' % f)
 
 
-def create_backbone(f='weights/best.pt', s='weights/backbone.pt'):  # from utils.utils import *; create_backbone()
-    # create backbone 's' from 'f'
+def create_pretrained(f='weights/best.pt', s='weights/pretrained.pt'):  # from utils.utils import *; create_pretrained()
+    # create pretrained checkpoint 's' from 'f' (create_pretrained(x, x) for x in glob.glob('./*.pt'))
     device = torch.device('cpu')
     x = torch.load(s, map_location=device)
 
     for p in x['model'].parameters():
         p.requires_grad = True
     torch.save(x, s)
-    print('%s modified for backbone use and saved as %s' % (f, s))
+    print('%s saved as pretrained checkpoint %s' % (f, s))
 
 
 def coco_class_count(path='../coco/labels/train2014/'):