Skip to content

Latest commit

 

History

History
285 lines (257 loc) · 8.34 KB

yolov3-spp.test.md

File metadata and controls

285 lines (257 loc) · 8.34 KB
Raw

General Epoch/LR variables

num_epochs: &num_epochs 5 init_lr: &init_lr 0.0032

Pruning Epoch/LR variables

pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032 pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1 pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985

Quantization Epoch/LR variables

quantization_start_epoch: &quantization_start_epoch 4 quantization_init_lr: &quantization_init_lr 0.0032

Block pruning management variables

pruning_start_epoch: &pruning_start_epoch 0 pruning_end_epoch: &pruning_end_epoch 2 pruning_update_frequency: &pruning_update_frequency 0.5 init_sparsity: &init_sparsity 0.05 mask_type: &mask_type [1, 4]

prune_none_target_sparsity: &prune_none_target_sparsity 0.4 prune_low_target_sparsity: &prune_low_target_sparsity 0.75 prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8 prune_high_target_sparsity: &prune_high_target_sparsity 0.85

Modifiers

training_modifiers:

  • !EpochRangeModifier start_epoch: 0.0 end_epoch: *num_epochs

pruning phase

  • !SetLearningRateModifier start_epoch: 0.0 learning_rate: *init_lr

pruning recovery phase

  • !LearningRateModifier constant_logging: False end_epoch: *quantization_start_epoch init_lr: *pruning_recovery_start_lr lr_class: StepLR lr_kwargs: { 'step_size': *pruning_recovery_lr_step_size, 'gamma': *pruning_recovery_lr_gamma } start_epoch: *pruning_end_epoch update_frequency: -1.0

  • !SetWeightDecayModifier start_epoch: *pruning_end_epoch weight_decay: 0.0

quantization aware training phase

  • !SetLearningRateModifier start_epoch: *quantization_start_epoch learning_rate: *quantization_init_lr

pruning_modifiers:

  • !GMPruningModifier params:

    • model.16.conv.weight
    • model.19.cv1.conv.weight
    • model.26.cv2.conv.weight init_sparsity: *init_sparsity final_sparsity: *prune_none_target_sparsity mask_type: *mask_type start_epoch: *pruning_start_epoch end_epoch: *pruning_end_epoch update_frequency: *pruning_update_frequency

  • !GMPruningModifier params:

    • model.12.cv1.conv.weight
    • model.14.conv.weight
    • model.2.cv1.conv.weight
    • model.20.cv2.conv.weight
    • model.22.conv.weight
    • model.26.cv1.conv.weight
    • model.27.1.cv1.conv.weight
    • model.28.m.0.weight
    • model.28.m.2.weight
    • model.4.0.cv1.conv.weight
    • model.6.1.cv1.conv.weight
    • model.6.2.cv1.conv.weight
    • model.6.3.cv1.conv.weight
    • model.6.4.cv1.conv.weight
    • model.6.5.cv1.conv.weight
    • model.6.6.cv1.conv.weight
    • model.6.7.cv1.conv.weight
    • model.8.0.cv1.conv.weight
    • model.8.1.cv1.conv.weight
    • model.8.2.cv1.conv.weight
    • model.8.3.cv1.conv.weight
    • model.8.4.cv1.conv.weight
    • model.8.5.cv1.conv.weight
    • model.8.6.cv1.conv.weight
    • model.8.7.cv1.conv.weight init_sparsity: *init_sparsity final_sparsity: *prune_low_target_sparsity mask_type: *mask_type start_epoch: *pruning_start_epoch end_epoch: *pruning_end_epoch update_frequency: *pruning_update_frequency

  • !GMPruningModifier params:

    • model.1.conv.weight
    • model.10.0.cv1.conv.weight
    • model.10.1.cv1.conv.weight
    • model.10.2.cv1.conv.weight
    • model.10.3.cv1.conv.weight
    • model.11.cv1.conv.weight
    • model.12.cv2.conv.weight
    • model.19.cv2.conv.weight
    • model.2.cv2.conv.weight
    • model.27.0.cv1.conv.weight
    • model.27.0.cv2.conv.weight
    • model.27.1.cv2.conv.weight
    • model.28.m.1.weight
    • model.3.conv.weight
    • model.4.0.cv2.conv.weight
    • model.4.1.cv1.conv.weight
    • model.4.1.cv2.conv.weight
    • model.5.conv.weight
    • model.6.0.cv1.conv.weight
    • model.6.0.cv2.conv.weight
    • model.6.1.cv2.conv.weight
    • model.6.2.cv2.conv.weight
    • model.6.3.cv2.conv.weight init_sparsity: *init_sparsity final_sparsity: *prune_mid_target_sparsity mask_type: *mask_type start_epoch: *pruning_start_epoch end_epoch: *pruning_end_epoch update_frequency: *pruning_update_frequency

  • !GMPruningModifier params:

    • model.10.0.cv2.conv.weight
    • model.10.1.cv2.conv.weight
    • model.10.2.cv2.conv.weight
    • model.10.3.cv2.conv.weight
    • model.11.cv2.conv.weight
    • model.13.conv.weight
    • model.15.conv.weight
    • model.20.cv1.conv.weight
    • model.21.conv.weight
    • model.23.conv.weight
    • model.6.4.cv2.conv.weight
    • model.6.5.cv2.conv.weight
    • model.6.6.cv2.conv.weight
    • model.6.7.cv2.conv.weight
    • model.7.conv.weight
    • model.8.0.cv2.conv.weight
    • model.8.1.cv2.conv.weight
    • model.8.2.cv2.conv.weight
    • model.8.3.cv2.conv.weight
    • model.8.4.cv2.conv.weight
    • model.8.5.cv2.conv.weight
    • model.8.6.cv2.conv.weight
    • model.8.7.cv2.conv.weight
    • model.9.conv.weight init_sparsity: *init_sparsity final_sparsity: *prune_high_target_sparsity mask_type: *mask_type start_epoch: *pruning_start_epoch end_epoch: *pruning_end_epoch update_frequency: *pruning_update_frequency

quantization_modifiers:

  • !QuantizationModifier start_epoch: *quantization_start_epoch submodules:
    • model.0
    • model.1
    • model.2
    • model.3
    • model.4
    • model.5
    • model.6
    • model.7
    • model.8
    • model.9
    • model.10
    • model.11
    • model.12
    • model.13
    • model.14
    • model.15
    • model.16
    • model.17
    • model.18
    • model.19
    • model.20
    • model.21
    • model.22
    • model.23
    • model.24
    • model.25
    • model.26
    • model.27

YOLOv3-SPP Test Recipe

This is a test recipe useful for quickly evaluating the time and resources needed for pruning and quantizing a model. In addition, it offers a quick integration tests pathway. This recipe creates a sparse-quantized YOLOv3-SPP model that will not be accurate. Use the following SparseML integration with ultralytics/yolov3 to run.

Note that half-precision, EMA, and pickling are not supported for quantization. Therefore, once quantization is run, all three will be disabled for the training pipeline. This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.

Weights and Biases

Training

To set up the training environment, follow the instructions on the integration README. Using the given training script from the yolov3 directory the following command can be used to launch this recipe.
The contents of the hyp.pruned_quantized.yaml hyperparameters file is given below. Adjust the script command for your GPU device setup. Ultralytics supports both DataParallel and DDP.

script command:

python train.py \
    --recipe ../recipes/yolov3-spp.test.md \
    --weights PRETRAINED_WEIGHTS \
    --data voc.yaml \
    --hyp ../data/hyp.pruned_quantized.short.yaml \
    --name yolov3-spp-lrelu-pruned-quantized

hyp.prune_quantized.yaml:

lr0: 0.0
lrf: 0.0
momentum: 0.843
weight_decay: 0.00036
warmup_epochs: 40.0
warmup_momentum: 0.5
warmup_bias_lr: 0.05
box: 0.0296
cls: 0.243
cls_pw: 0.631
obj: 0.301
obj_pw: 0.911
iou_t: 0.2
anchor_t: 2.91
fl_gamma: 0.0
hsv_h: 0.0138
hsv_s: 0.664
hsv_v: 0.464
degrees: 0.373
translate: 0.245
scale: 0.898
shear: 0.602
perspective: 0.0
flipud: 0.00856
fliplr: 0.5
mosaic: 1.0
mixup: 0.243