Resource exhausted: OOM when allocating tensor with shape[32,960,10,10]

Error got during object detection training with ssd_mobilenet_v2_quantized_300x300_coco model.
I am running below command to start the training:
python ../../models/research/object_detection/model_main.py --pipeline_config_path=./ssd_mobilenet_v2_quantized_300x300_coco.config --model_dir=./training/ --num_train_steps=2000000 --sample_1_of_n_eval_examples=1 --alsologtostderr
Training was going fine till 47900 steps, after that i got error:

File "/home/saini/.virtualenvs/cv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1370, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
  (0) Resource exhausted: OOM when allocating tensor with shape[32,960,10,10] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node gradients/AddN_162-1-TransposeNHWCToNCHW-LayoutOptimizer}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

	 [[Loss/Cast_232/_16919]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

  (1) Resource exhausted: OOM when allocating tensor with shape[32,960,10,10] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
	 [[{{node gradients/AddN_162-1-TransposeNHWCToNCHW-LayoutOptimizer}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

0 successful operations.
0 derived errors ignored.

OS: Ubuntu 18.04
Tensorflow: 1.14.0 GPU
CUDA: 10.0
CUDNN:: 7.6
Batch Size: 32

Follow changes i have made in default TF object detection API:

model_lib.py
tf.estimator.EvalSpec(
            name=eval_spec_name,
            input_fn=eval_input_fn,
            steps=None,
            throttle_secs = 172800,
            exporters=exporter))
eval.proto
optional uint32 eval_interval_secs = 3 [default = 172800]; # default = 600
model_main.py
config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir, save_checkpoints_steps=5000)