Cant resume training with detectron2

Hi, I am training a model on Faster R CNN architecture. For the first session I used the below config:
```
def get_train_cfg(config_file_path, checkpoint_url, train_dataset_name, test_dataset_name, num_classes, device, output_dir):

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(config_file_path))
cfg.MODEL_WEIGHTS = model_zoo.get_checkpoint_url(checkpoint_url)
cfg.DATASETS.TRAIN = (train_dataset_name,)
cfg.DATASETS.TEST = (train_dataset_name,)

cfg.DATALOADER.NUM_WORKERS = 2
cfg.SOLVER.IMS_PER_BATCH = 1
cfg.SOLVER.BASE_LR = 0.0001
cfg.SOLVER.MAX_ITER = 16000
cfg.SOLVER.STEPS = []

cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes
cfg.MODEL.DEVICE = device
cfg.OUTPUT_DIR = output_dir

return cfg
```
I want to continue my training. I have `last_checkpoint`, `metrics.json`, `cfg.pickle`, and `model_final.pth`.

## Instructions To Reproduce the Issue:
Below is the link of my notebook
[Notebook](https://www.kaggle.com/meetog/detectron2-test)

## Expected behavior:
The training should start at 16001 iterations and the total loss at 16000 iterations was about 0.8. But the learning rate didn't change from 0.0001 from 0 to 16000 iterations. When I continue training by `resume_or_load(resume=True)` The below error is shown

## Error:
```
[12/04 05:54:36 d2.data.datasets.coco]: Loaded 381 images in COCO format from ../input/cascade-rcnn/train.json
[12/04 05:54:36 d2.data.build]: Removed 1 images with no usable annotations. 380 images left.
[12/04 05:54:36 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[12/04 05:54:36 d2.data.build]: Using training sampler TrainingSampler
[12/04 05:54:36 d2.data.common]: Serializing 380 elements to byte tensors and concatenating them all ...
[12/04 05:54:36 d2.data.common]: Serialized dataset takes 2.23 MiB
[12/04 05:54:37 d2.engine.hooks]: Loading scheduler from state_dict ...
[12/04 05:54:37 d2.engine.train_loop]: Starting training from iteration 16000
[12/04 05:54:37 d2.engine.hooks]: Total training time: 0:00:00 (0:00:00 on hooks)
[12/04 05:54:37 d2.data.datasets.coco]: Loaded 381 images in COCO format from ../input/cascade-rcnn/train.json
[12/04 05:54:38 d2.data.dataset_mapper]: [DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[12/04 05:54:38 d2.data.common]: Serializing 381 elements to byte tensors and concatenating them all ...
[12/04 05:54:38 d2.data.common]: Serialized dataset takes 2.23 MiB
WARNING [12/04 05:54:38 d2.engine.defaults]: No evaluator found. Use DefaultTrainer.test(evaluators=), or implement its build_evaluator method.
[12/04 05:54:38 d2.utils.events]: iter: 16001 lr: N/A max_mem: 1627M
```
It shows `lr: N/A`
Why is that?

## My Environment:
Python: 3.7.10,
Detectron2 : 0.6,
Torch : 1.9.1cu101