Dataset calibration for Custom data on Yolov8 instance segmentation #2752

Priyanshu88 · 2024-06-19T14:54:10Z

Priyanshu88
Jun 19, 2024

I am want to do the nncf quantization for yolov8 instance segmentation model on custom dataset my dataset is in coco format with .yaml file containing the paths and classes. I am using the below code for my quantization but facing problem with it for my custom dataset. I am using the above reference. Please help me correcting this. Thanks!


    def yolo_openvino(self):
        self.model.export(format="openvino", dynamic=True, half=False)
        self.logger.info("Model Exported to ONYX")
        core = Core()
        det_model_path = r"yolov8s-seg_openvino_model/yolov8s-seg.xml"
        print(det_model_path)
        det_ov_model = core.read_model(str(det_model_path))

        CFG_PATH = "yaml_coco_format.yaml"
        seg_validator = self.model.task_map[self.model.task]["validator"](args=CFG_PATH)

        print("seg-validator",seg_validator)
        seg_data_loader = seg_validator.get_dataloader("coco_format_data/")
        quantization_dataset = nncf.Dataset(seg_data_loader, self.transform_fn)
        ignored_scope = nncf.IgnoredScope(
            types=["Multiply", "Subtract", "Sigmoid"],  # ignore operations
            names=[
                "/model.22/dfl/conv/Conv",  # in the post-processing subgraph
                "/model.22/Add",
                "/model.22/Add_1",
                "/model.22/Add_2",
                "/model.22/Add_3",
                "/model.22/Add_4",
                "/model.22/Add_5",
                "/model.22/Add_6",
                "/model.22/Add_7",
                "/model.22/Add_8",
                "/model.22/Add_9",
                "/model.22/Add_10",
            ],
        )
        quantized_det_model = nncf.quantize(
            det_ov_model,
            quantization_dataset,
            preset=nncf.QuantizationPreset.MIXED,
            ignored_scope=ignored_scope,
        )
        int8_model_det_path = f"{self.model_path}/mds.xml"
        serialize(quantized_det_model, str(int8_model_det_path))
        self.logger.info("Model Quantized")
        return quantized_det_model

    def transform_fn(self, data_item):

        input_tensor = np.array(self.obj_transform(data_item[0][0]).numpy())
        return torch.unsqueeze(torch.Tensor(input_tensor), 0)

alexsu52 · 2024-06-20T05:27:58Z

alexsu52
Jun 20, 2024
Maintainer

Hello @Priyanshu88,

What kind of problem did you face?

1 reply

Priyanshu88 Jun 20, 2024
Author

Hi ! @alexsu52 ,
Problem:

Error Traceback (most recent call last):
  File "/NNCF.py", line 246, in compress_model
    model = self.yolo_openvino()
  File "NNCF.py", line 116, in yolo_openvino
    seg_validator = self.model.task_map[self.model.task]["validator"](args=args)
  File "/opt/conda/lib/python3.9/site-packages/ultralytics/models/yolo/segment/val.py", line 33, in __init__
    super().__init__(dataloader, save_dir, pbar, args, _callbacks)
  File "/opt/conda/lib/python3.9/site-packages/ultralytics/models/yolo/detect/val.py", line 34, in __init__
    super().__init__(dataloader, save_dir, pbar, args, _callbacks)
  File "/opt/conda/lib/python3.9/site-packages/ultralytics/engine/validator.py", line 78, in __init__
    self.args = get_cfg(overrides=args)
  File "/opt/conda/lib/python3.9/site-packages/ultralytics/cfg/__init__.py", line 114, in get_cfg
    check_dict_alignment(cfg, overrides)
  File "/opt/conda/lib/python3.9/site-packages/ultralytics/cfg/__init__.py", line 203, in check_dict_alignment
    raise SyntaxError(string + CLI_HELP_MSG) from e
SyntaxError: '�[31m�[1mauto_augment�[0m' is not a valid YOLO argument. Similar arguments are i.e. ['augment=False'].

I have modified the function like below:

def yolo_openvino(self):
        self.model.export(format="openvino", dynamic=True, half=False)
        self.logger.info("Model Exported to ONYX")
        core = Core()
        det_model_path = r"yolov8s-seg_openvino_model/yolov8s-seg.xml"
        print(det_model_path)
        det_ov_model = core.read_model(str(det_model_path))
        args = get_cfg(cfg="default.yaml")
        args.data = str("yaml_coco_format.yaml")

        seg_validator = self.model.task_map[self.model.task]["validator"](args=args)

        print("seg-validator",seg_validator)
        seg_validator.data = check_det_dataset(args.data)
        seg_validator.stride = 32
        seg_data_loader = seg_validator.get_dataloader("coco_format_data/",1)
        seg_validator.is_coco = True
        seg_validator.class_map = coco80_to_coco91_class()
        seg_validator.names = self.model.model.names
        seg_validator.metrics.names = seg_validator.names
        seg_validator.nc = self.model.model.model.model[-1].nc
        seg_validator.nm = 32
        seg_validator.process = ops.process_mask
        seg_validator.plot_masks = []
        quantization_dataset = nncf.Dataset(seg_data_loader, self.transform_fn)
        ignored_scope = nncf.IgnoredScope(
            types=["Multiply", "Subtract", "Sigmoid"],  # ignore operations
            names=[
                "/model.22/dfl/conv/Conv",  # in the post-processing subgraph
                "/model.22/Add",
                "/model.22/Add_1",
                "/model.22/Add_2",
                "/model.22/Add_3",
                "/model.22/Add_4",
                "/model.22/Add_5",
                "/model.22/Add_6",
                "/model.22/Add_7",
                "/model.22/Add_8",
                "/model.22/Add_9",
                "/model.22/Add_10",
            ],
        )
        quantized_det_model = nncf.quantize(
            det_ov_model,
            quantization_dataset,
            preset=nncf.QuantizationPreset.MIXED,
            ignored_scope=ignored_scope,
        )
        int8_model_det_path = f"{self.model_path}/mds.xml"
        serialize(quantized_det_model, str(int8_model_det_path))
        self.logger.info("Model Quantized")
        return quantized_det_model

I have modified the default.yaml taking reference from here

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training

task: segment # (str) YOLO task, i.e. detect, segment, classify, pose
mode: export # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark

# Train settings -------------------------------------------------------------------------------------------------------
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: yaml_coco_format.yaml # (str, optional) path to data file, i.e. coco8.yaml
epochs: 100 # (int) number of epochs to train for
time: # (float, optional) number of hours to train for, overrides epochs if supplied
patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes
save: True # (bool) save train checkpoints and predict results
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
cache: False # (bool) True/ram, disk or False. Use cache for data loading
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
project: # (str, optional) project name
name: # (str, optional) experiment name, results saved to 'project/name' directory
exist_ok: False # (bool) whether to overwrite existing experiment
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
verbose: True # (bool) whether to print verbose output
seed: 0 # (int) random seed for reproducibility
deterministic: True # (bool) whether to enable deterministic mode
single_cls: False # (bool) train multi-class data as single-class
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # (bool) use cosine learning rate scheduler
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
resume: False # (bool) resume training from last checkpoint
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation
overlap_mask: True # (bool) masks should overlap during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # (float) use dropout regularization (classify train only)

# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # (bool) validate/test during training
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # (bool) save results to JSON file
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
max_det: 300 # (int) maximum number of detections per image
half: False # (bool) use half precision (FP16)
dnn: False # (bool) use OpenCV DNN for ONNX inference
plots: True # (bool) save plots and images during train/val

# Predict settings -----------------------------------------------------------------------------------------------------
source: # (str, optional) source directory for images or videos
vid_stride: 1 # (int) video frame-rate stride
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
visualize: False # (bool) visualize model features
augment: False # (bool) apply image augmentation to prediction sources
agnostic_nms: False # (bool) class-agnostic NMS
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks: False # (bool) use high-resolution segmentation masks
embed: # (list[int], optional) return feature vectors/embeddings from given layers

# Visualize settings ---------------------------------------------------------------------------------------------------
show: False # (bool) show predicted images and videos if environment allows
save_frames: False # (bool) save predicted individual video frames
save_txt: False # (bool) save results as .txt file
save_conf: False # (bool) save results with confidence scores
save_crop: False # (bool) save cropped images with results
show_labels: True # (bool) show prediction labels, i.e. 'person'
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
show_boxes: True # (bool) show prediction boxes
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.

# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
keras: False # (bool) use Kera=s
optimize: False # (bool) TorchScript: optimize for mobile
int8: False # (bool) CoreML/TF INT8 quantization
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
simplify: False # (bool) ONNX: simplify model using `onnxslim`
opset: # (int, optional) ONNX: opset version
workspace: 4 # (int) TensorRT: workspace size (GB)
nms: False # (bool) CoreML: add NMS

# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
momentum: 0.937 # (float) SGD momentum/Adam beta1
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
warmup_momentum: 0.8 # (float) warmup initial momentum
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
box: 7.5 # (float) box loss gain
cls: 0.5 # (float) cls loss gain (scale with pixels)
dfl: 1.5 # (float) dfl loss gain
pose: 12.0 # (float) pose loss gain
kobj: 1.0 # (float) keypoint obj loss gain
label_smoothing: 0.0 # (float) label smoothing (fraction)
nbs: 64 # (int) nominal batch size
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
degrees: 0.0 # (float) image rotation (+/- deg)
translate: 0.1 # (float) image translation (+/- fraction)
scale: 0.5 # (float) image scale (+/- gain)
shear: 0.0 # (float) image shear (+/- deg)
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # (float) image flip up-down (probability)
fliplr: 0.5 # (float) image flip left-right (probability)
bgr: 0.0 # (float) image channel BGR (probability)
mosaic: 1.0 # (float) image mosaic (probability)
mixup: 0.0 # (float) image mixup (probability)
copy_paste: 0.0 # (float) segment copy-paste (probability)
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
crop_fraction: 1.0 # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.

# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: yaml_coco_format.yaml # (str, optional) for overriding defaults.yaml

# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]

alexsu52 · 2024-06-20T06:53:52Z

alexsu52
Jun 20, 2024
Maintainer

@Priyanshu88, It looks like the problem is not related to NNCF. I would recommend testing your model on your custom dataset without using NNCF. You can find tutorials and documentation on how to do this in the ultralytics documenation

1 reply

Priyanshu88 Jun 20, 2024
Author

Thank you @alexsu52 for looking into my problem. I did the same and it is fully working but the problem is coming when i try nncf quant, I am following the reference from here. Where the default cfg is this. Now in the code

seg_validator = seg_model.task_map[seg_model.task]["validator"](args=args)
seg_validator.data = check_det_dataset(args.data)
seg_validator.stride = 32
seg_data_loader = seg_validator.get_dataloader(OUT_DIR / "coco/", 1)

seg_validator.is_coco = True
seg_validator.class_map = coco80_to_coco91_class()
seg_validator.names = seg_model.model.names
seg_validator.metrics.names = seg_validator.names
seg_validator.nc = seg_model.model.model[-1].nc
seg_validator.nm = 32
seg_validator.process = ops.process_mask
seg_validator.plot_masks = []

i see that the above adjust lines in model are from coco yaml followed by default yaml. So can you please help me out how will it possible with my dataset and implementation. My coco.yaml is like this:

path: root
train: coco_format_data/images/train2017
val: coco_format_data/images/val2017
nc: 16
names:
  .......................

alexsu52 · 2024-06-20T09:28:18Z

alexsu52
Jun 20, 2024
Maintainer

@Priyanshu88, could you provide the full reproducer with dataset?

1 reply

Priyanshu88 Jun 20, 2024
Author

Sure. Dataset: https://universe.roboflow.com/mj-4iyes/surgical_tool_segmentation_mj
I have done a quick run for you so that it will be understandable for both of us. Notebook:
custom-yolov8-ins.zip

alexsu52 · 2024-06-21T05:46:46Z

alexsu52
Jun 21, 2024
Maintainer

@Priyanshu88, thanks for the provided notebook. I tried to run your notebook and got the following error:

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[6], [line 2](vscode-notebook-cell:?execution_count=6&line=2)
      [1](vscode-notebook-cell:?execution_count=6&line=1) seg_validator = seg_model.task_map[seg_model.task]["validator"](args=args)
----> [2](vscode-notebook-cell:?execution_count=6&line=2) seg_validator.data = check_det_dataset(args.data)
      [3](vscode-notebook-cell:?execution_count=6&line=3) seg_validator.stride = 32
      [4](vscode-notebook-cell:?execution_count=6&line=4) seg_data_loader = seg_validator.get_dataloader("/home/pande/seg-kompress/Kompress/ABC/dataset/root/coco_format_data", 1)

File ~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:269, in check_det_dataset(dataset, autodownload)
    [253](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:253) def check_det_dataset(dataset, autodownload=True):
    [254](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:254)     """
    [255](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:255)     Download, verify, and/or unzip a dataset if not found locally.
    [256](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:256) 
   (...)
    [266](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:266)         (dict): Parsed dataset information and paths.
    [267](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:267)     """
--> [269](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:269)     file = check_file(dataset)
    [271](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:271)     # Download (optional)
    [272](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/data/utils.py:272)     extract_dir = ""

File ~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/utils/checks.py:504, in check_file(file, suffix, download, hard)
    [502](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/utils/checks.py:502) files = glob.glob(str(ROOT / "**" / file), recursive=True) or glob.glob(str(ROOT.parent / file))  # find file
    [503](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/utils/checks.py:503) if not files and hard:
--> [504](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/utils/checks.py:504)     raise FileNotFoundError(f"'{file}' does not exist")
    [505](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/utils/checks.py:505) elif len(files) > 1 and hard:
    [506](https://file+.vscode-resource.vscode-cdn.net/home/susloval/Downloads/~/work/venv/nncf_torch/lib/python3.10/site-packages/ultralytics/utils/checks.py:506)     raise FileNotFoundError(f"Multiple files match '{file}', specify exact path: {files}")

FileNotFoundError: '/home/pande/seg-kompress/Kompress/ABC/dataset/supporting_yaml_coco_format.yaml' does not exist

I didn't find supporting_yaml_coco format.yaml in the zip file. Please provide it to have the exact environment that reproduces your problem.

2 replies

Priyanshu88 Jun 21, 2024
Author

Once you download the dataset in yolov8 format and unzip it, you will find data.yaml file, it is just the renaming of file that i have done. You can provide your path of the local location where you will save the data.

Priyanshu88 Jun 21, 2024
Author

@alexsu52, I have generalized the case for you from dataset download to the whole process, you just need to replace the absolute paths and relative paths according to your and you will be able to run it properly, I have also added how i am trying change the custom data classes names, but i am getting the final .xml output as the coco labels after quantization.
custom-yolov8-ins (2).zip

alexsu52 · 2024-06-21T07:59:24Z

alexsu52
Jun 21, 2024
Maintainer

@Priyanshu88, I managed to run your reproducer and get a quantized model. There weren't any errors. Are you still experiencing the problem you wrote about earlier?

I have also added how i am trying change the custom data classes names, but i am getting the final .xml output as the coco labels after quantization.

I'm sure before quantization you have the same behavior. Probably, you need mapping from model output to your classes in post-processing step.

6 replies

alexsu52 Jun 21, 2024
Maintainer

@Priyanshu88, I just ran your notebooks.

Is it like we first have to do normal training of the model on custom dataset and then perform the nncf quantization with the output trained weights?

This is right option, If you classes are not mapped to coco classes.

Priyanshu88 Jun 21, 2024
Author

@alexsu52 Can you please share the run that you have done, I just want to validate with mine. If you can also validate the final quantized model xml file label as it is same as the custom dataset label, it would be better.

If it is an option, then is there any possibility that i will be able to get the quantized model on my custom dataset and labels with out doing the normal training on custom data?

alexsu52 Jun 25, 2024
Maintainer

The model quantization does not change semantic of inputs and outputs of the models. As prerequisite for model quantization is getting non-quantized model which solve your task.

If it is an option, then is there any possibility that i will be able to get the quantized model on my custom dataset and labels with out doing the normal training on custom data?

This is possible if you can map custom labels to the model classes with which the model was trained.

Priyanshu88 Jun 28, 2024
Author

Have you checked the labels of quantized model as I have shared in the above image and the labels are same as custom dataset labels?

This is possible if you can map custom labels to the model classes with which the model was trained.

Can you please share how using any code reference?

alexsu52 Jul 1, 2024
Maintainer

@Priyanshu88, I did not check correctness your code relative to label mapping. I checked the quantization code which does not use labels as input. If you think that quantization degrades the accuracy of the model, then add code to your notebook that shows it via comparing the results of the fp32 and int8 models and I will look at it.

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Dataset calibration for Custom data on Yolov8 instance segmentation #2752

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