add quantized model

recipes/yolov5s.quantized.md

@@ -1,60 +1,17 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
 ---
 # General Hyperparams
-num_epochs: &num_epochs 302
-init_lr: &init_lr 0.01
-final_lr: &final_lr 0.002
-weights_warmup_lr: &weights_warmup_lr 0
-biases_warmup_lr: &biases_warmup_lr 0.1
+num_epochs: &num_epochs 2
 quantization_lr: &quantization_lr 0.000002
 
 
 # Quantization Params
-quantization_start_epoch: &quantization_start_epoch 300
+quantization_start_epoch: &quantization_start_epoch 0
 
 training_modifiers:
   - !EpochRangeModifier
     start_epoch: 0
     end_epoch: *num_epochs
 
-  - !LearningRateFunctionModifier
-    start_epoch: 3
-    end_epoch: *num_epochs
-    lr_func: cosine
-    init_lr: *init_lr
-    final_lr: *final_lr
-
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: 3
-    lr_func: linear
-    init_lr: *weights_warmup_lr
-    final_lr: *init_lr
-    param_groups: [0, 1]
-
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: 3
-    lr_func: linear
-    init_lr: *biases_warmup_lr
-    final_lr: *init_lr
-    param_groups: [2]
-
   - !SetLearningRateModifier
     start_epoch: *quantization_start_epoch
     learning_rate: *quantization_lr
@@ -64,29 +21,4 @@ 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' ]
----
-
-# YOLOv5s Pruned Quantized
-
-This recipe creates a sparse, [YOLOv5s](https://github.com/ultralytics/yolov5) model that achieves 94% recovery of its baseline accuracy on the COCO dataset (0.556 mAP@0.5 baseline vs 0.525 mAP@0.5 for this recipe).
-Training was done using 4 GPUs at half precision with a total batch size of 256 using the [SparseML integration with ultralytics/yolov5](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov5).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/README.md).
-Using the given training script from the `yolov5` directory the following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --cfg ../models/yolov5s.yaml \
-    --weights PRETRAINED_WEIGHTS \
-    --data coco.yaml \
-    --hyp data/hyp.scratch.yaml \
-    --recipe ../recipes/yolov5s.pruned_quantized.md \
-```
+---

yolov5-train/yolov5-deepsparse/yolov5s-sgd-quantized/hyp.yaml

@@ -0,0 +1,28 @@
+lr0: 0.01
+lrf: 0.2
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 0.05
+cls: 0.5
+cls_pw: 1.0
+obj: 1.0
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 4.0
+fl_gamma: 0.0
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0

yolov5-train/yolov5-deepsparse/yolov5s-sgd-quantized/opt.yaml

@@ -0,0 +1,44 @@
+weights: yolov5-deepsparse/yolov5s-sgd/weights/best.pt
+cfg: ./models_v5.0/yolov5s.yaml
+data: /home/dnth/Desktop/yolov5-deepsparse-blogpost/yolov5-train/data/pistols.yaml
+hyp: data/hyps/hyp.scratch.yaml
+epochs: 300
+batch_size: 64
+imgsz: 416
+rect: false
+resume: false
+nosave: false
+noval: false
+noautoanchor: false
+evolve: null
+bucket: ''
+cache: null
+image_weights: false
+device: ''
+multi_scale: false
+single_cls: false
+optimizer: SGD
+sync_bn: false
+workers: 8
+project: yolov5-deepsparse
+name: yolov5s-sgd-quantized
+exist_ok: false
+quad: false
+cos_lr: false
+label_smoothing: 0.0
+patience: 100
+freeze:
+- 0
+save_period: -1
+local_rank: -1
+entity: null
+upload_dataset: false
+bbox_interval: -1
+artifact_alias: latest
+recipe: ../recipes/yolov5s.quantized.md
+disable_ema: false
+max_train_steps: -1
+max_eval_steps: -1
+one_shot: false
+num_export_samples: 0
+save_dir: yolov5-deepsparse/yolov5s-sgd-quantized2

yolov5-train/yolov5-deepsparse/yolov5s-sgd-quantized/results.csv

@@ -0,0 +1,3 @@
+               epoch,      train/box_loss,      train/obj_loss,      train/cls_loss,   metrics/precision,      metrics/recall,     metrics/mAP_0.5,metrics/mAP_0.5:0.95,        val/box_loss,        val/obj_loss,        val/cls_loss,               x/lr0,               x/lr1,               x/lr2
+                   0,            0.017206,           0.0087093,                   0,             0.85351,             0.78803,             0.83145,             0.51715,            0.038543,           0.0084712,                   0,               2e-06,               2e-06,               2e-06
+                   1,            0.017049,           0.0087496,                   0,             0.86435,             0.78422,             0.83391,             0.52026,             0.03854,           0.0084568,                   0,               2e-06,               2e-06,               2e-06