Structured pruning for yolov5s model

[Imran686]

I did unstructured pruning for yolov5s and saved it as onnx but it did not give good improvements in terms of inference speed and model size. I would like to do structured pruning using sparseML library. How that could be done? I tried using the modifier on the following link: https://github.com/neuralmagic/sparseml/blob/main/src/sparseml/pytorch/sparsification/pruning/modifier_pruning_structured.py.

I created a .yaml file as follows:

Structured pruning

modifiers:

• !StructuredPruningModifier
  param_groups = [
  # Convolutional layers
  {
  'name': 'conv',
  'params': [
  'model.model.model.0.conv.weight', 'model.model.model.1.conv.weight',
  'model.model.model.2.cv1.conv.weight', 'model.model.model.2.cv2.conv.weight',
  ........
  ],
  'prune_ratio': 0.5 # Example pruning ratio for conv layers
  },
  # Batch normalization layer
  {
  'name': 'bn',
  'params': [
  'model.model.model.0.bn.weight', 'model.model.model.0.bn.bias',
  'model.model.model.1.bn.weight', 'model.model.model.1.bn.bias',
  ........
  ],
  'prune_ratio': 0.3 # Example pruning ratio for batch norm layers
  }
  ]
  mask_type: filter
  init_sparsity: 0.05
  final_sparsity: 0.8
  start_epoch: 0.0
  end_epoch: 10.0
  update_frequency: 1.0
  params: ALL_PRUNABLE
  leave_enabled: True
  inter_func: cubic

But I am unable to apply it to my model. The following code I used for fine tuning unstructured pruned model:

!sparseml.yolov5.train
--weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none
--recipe zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned75_quant-none
--teacher-weights zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/base-none
--data coco128.yaml
--hyp hyps/hyp.scratch-low.yaml --cfg yolov5s.yaml --patience 0 --gradient-accum-steps 4

How would I do the same in the case of structured pruning?