correct albumentation

try to make augmentation on GPU.
saved llm prompt

orangepi5/00 install-os.md

@@ -1,5 +0,0 @@
-1. Download latest release from here https://joshua-riek.github.io/ubuntu-rockchip-download/boards/orangepi-5.html
-   f.e. https://github.com/Joshua-Riek/ubuntu-rockchip/releases/download/v2.3.2/ubuntu-22.04-preinstalled-desktop-arm64-orangepi-5.img.xz
-   but look to the more recent version on ubuntu 22.04
-   
-2. Write the image to the microsd using https://bztsrc.gitlab.io/usbimager/ (sudo ./usbimager on linux) (or use BalenaEtcher)

orangepi5/convert-install.sh

@@ -1,36 +0,0 @@
-mkdir rknn-convert
-cd rknn-convert
-
-# Install converter PT to ONNX
-git clone https://github.com/airockchip/ultralytics_yolov8
-cd ultralytics_yolov8
-sudo apt install python3.12-venv
-python3 -m venv env
-source env/bin/activate
-pip install .
-pip install onnx
-cp ultralytics/cfg/default.yaml ultralytics/cfg/default_backup.yaml
-sed -i -E "s/(model: ).+( #.+)/\1azaion.pt\2/" ultralytics/cfg/default.yaml
-cd ..
-deactivate
-
-# Install converter ONNX to RKNN
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-chmod +x miniconda.sh
-bash miniconda.sh -b -p $HOME/miniconda
-
-source ~/miniconda/bin/activate
-conda create -n toolkit2 -y python=3.11
-conda activate toolkit2
-git clone https://github.com/rockchip-linux/rknn-toolkit2.git
-cd rknn-toolkit2/rknn-toolkit2/packages
-pip install -r requirements_cp311-1.6.0.txt
-pip install rknn_toolkit2-1.6.0+81f21f4d-cp311-cp311-linux_x86_64.whl
-pip install "numpy<2.0"
-cd ../../../
-git clone https://github.com/airockchip/rknn_model_zoo.git
-
-sed -i -E "s#(DATASET_PATH = ').+(')#\1/azaion/data-sample/azaion_subset.txt\2 #" rknn_model_zoo/examples/yolov8/python/convert.py
-
-conda deactivate
-conda deactivate

orangepi5/convert.sh

@@ -1,19 +0,0 @@
-# PT to ONNX
-cd rknn-convert/ultralytics_yolov8/
-cp --verbose /azaion/models/azaion.pt .
-source env/bin/activate
-pip install onnx
-export PYTHONPATH=./
-python ./ultralytics/engine/exporter.py
-cp --verbose azaion.onnx ../
-cd ..
-deactivate
-cp --verbose azaion.onnx /azaion/models/
-
-#  ONNX to RKNN
-source ~/miniconda/bin/activate
-conda activate toolkit2
-cd rknn_model_zoo/examples/yolov8/python
-python convert.py ../../../../azaion.onnx rk3588 i8 /azaion/models/azaion.rknn
-conda deactivate
-conda deactivate

preprocessing-cuda.py

@@ -0,0 +1,171 @@
+import os
+import time
+import numpy as np
+import cv2
+from pathlib import Path
+import concurrent.futures
+
+import nvidia.dali as dali
+import nvidia.dali.fn as fn
+import nvidia.dali.types as types
+
+from constants import (
+    data_images_dir,
+    data_labels_dir,
+    processed_images_dir,
+    processed_labels_dir
+)
+
+# Configurable number of augmentations per image
+NUM_AUGMENTATIONS = 7
+
+
+class DataLoader:
+    def __init__(self, batch_size=32):
+        self.batch_size = batch_size
+        os.makedirs(processed_images_dir, exist_ok=True)
+        os.makedirs(processed_labels_dir, exist_ok=True)
+
+    def _read_labels(self, labels_path):
+        with open(labels_path, 'r') as f:
+            rows = f.readlines()
+            arr = []
+            for row in rows:
+                str_coordinates = row.split(' ')
+                class_num = str_coordinates.pop(0)
+                coordinates = [float(n.replace(',', '.')) for n in str_coordinates]
+                coordinates.append(class_num)
+                arr.append(coordinates)
+            return arr
+
+    def _get_image_label_pairs(self):
+        processed_images = set(f.name for f in os.scandir(processed_images_dir))
+        pairs = []
+
+        for image_file in os.scandir(data_images_dir):
+            if image_file.is_file() and image_file.name not in processed_images:
+                image_path = os.path.join(data_images_dir, image_file.name)
+                labels_path = os.path.join(data_labels_dir, f'{Path(image_path).stem}.txt')
+
+                if os.path.exists(labels_path):
+                    pairs.append((image_path, labels_path))
+
+        return pairs
+
+    def create_dali_pipeline(self, file_paths):
+        @dali.pipeline_def(batch_size=self.batch_size, num_threads=32, device_id=0)
+        def augmentation_pipeline():
+            # Read images
+            jpegs, _ = fn.file_reader(file_root=data_images_dir, file_list=file_paths, random_shuffle=False)
+
+            # Decode images
+            images = fn.decoders.image(jpegs, device='mixed')
+
+            # Random augmentations with GPU acceleration
+            augmented_images = []
+            for _ in range(NUM_AUGMENTATIONS):
+                aug_image = fn.random_resized_crop(images, random_area=(0.8, 1.0))
+
+                # Apply multiple random augmentations
+                aug_image = fn.flip(aug_image, horizontal=fn.random.coin_flip())
+                aug_image = fn.brightness_contrast(
+                    aug_image,
+                    brightness=fn.random.uniform(range=(-0.05, 0.05)),
+                    contrast=fn.random.uniform(range=(-0.05, 0.05))
+                )
+                aug_image = fn.rotate(
+                    aug_image,
+                    angle=fn.random.uniform(range=(-25, 25)),
+                    fill_value=0
+                )
+
+                # Add noise and color jittering
+                aug_image = fn.noise.gaussian(aug_image, mean=0, stddev=fn.random.uniform(range=(0, 0.1)))
+                aug_image = fn.hsv(
+                    aug_image,
+                    hue=fn.random.uniform(range=(-8, 8)),
+                    saturation=fn.random.uniform(range=(-8, 8)),
+                    value=fn.random.uniform(range=(-8, 8))
+                )
+
+                augmented_images.append(aug_image)
+
+            # Also include original image
+            augmented_images.append(images)
+
+            return augmented_images
+
+        return augmentation_pipeline()
+
+    def process_batch(self):
+        image_label_pairs = self._get_image_label_pairs()
+
+        # Create file list for DALI
+        file_list_path = os.path.join(processed_images_dir, 'file_list.txt')
+        with open(file_list_path, 'w') as f:
+            for img_path, _ in image_label_pairs:
+                f.write(f'{img_path}\n')
+
+        # Create DALI pipeline
+        pipeline = self.create_dali_pipeline(file_list_path)
+        pipeline.build()
+
+        # Process images
+        for batch_idx in range(0, len(image_label_pairs), self.batch_size):
+            batch_pairs = image_label_pairs[batch_idx:batch_idx + self.batch_size]
+
+            pipeline.run()
+
+            # Get augmented images
+            for img_idx, (orig_img_path, orig_labels_path) in enumerate(batch_pairs):
+                # Read original labels
+                orig_labels = self._read_labels(orig_labels_path)
+
+                # Write original image and labels
+                self._write_image_and_labels(
+                    pipeline.output[NUM_AUGMENTATIONS][img_idx],
+                    orig_img_path,
+                    orig_labels,
+                    is_original=True
+                )
+
+                # Write augmented images
+                for aug_idx in range(NUM_AUGMENTATIONS):
+                    self._write_image_and_labels(
+                        pipeline.output[aug_idx][img_idx],
+                        orig_img_path,
+                        orig_labels,
+                        aug_idx=aug_idx
+                    )
+
+    def _write_image_and_labels(self, image, orig_img_path, labels, is_original=False, aug_idx=None):
+        path = Path(orig_img_path)
+
+        if is_original:
+            img_name = path.name
+            label_name = f'{path.stem}.txt'
+        else:
+            img_name = f'{path.stem}_{aug_idx + 1}{path.suffix}'
+            label_name = f'{path.stem}_{aug_idx + 1}.txt'
+
+        # Write image
+        img_path = os.path.join(processed_images_dir, img_name)
+        cv2.imencode('.jpg', image.asnumpy())[1].tofile(img_path)
+
+        # Write labels
+        label_path = os.path.join(processed_labels_dir, label_name)
+        with open(label_path, 'w') as f:
+            lines = [f'{ann[4]} {round(ann[0], 5)} {round(ann[1], 5)} {round(ann[2], 5)} {round(ann[3], 5)}\n' for ann in labels]
+            f.writelines(lines)
+
+
+def main():
+    while True:
+        loader = DataLoader()
+        loader.process_batch()
+        print('All processed, waiting for 2 minutes...')
+        time.sleep(120)
+
+
+if __name__ == '__main__':
+    main()

preprocessing.py

@@ -5,11 +5,29 @@ from pathlib import Path
 import albumentations as A
 import cv2
 import numpy as np
+import concurrent.futures
 
 from constants import (data_images_dir, data_labels_dir, processed_images_dir, processed_labels_dir,
                        annotation_classes, checkpoint_file, checkpoint_date_format)
 from dto.imageLabel import ImageLabel
 
+total_files_processed = 0
+transform = A.Compose([
+    # Flips, rotations and brightness
+    A.HorizontalFlip(),
+    A.RandomBrightnessContrast(brightness_limit=(-0.05, 0.05), contrast_limit=(-0.05, 0.05)),
+    A.Affine(p=0.7, scale=(0.8, 1.2), rotate=25, translate_percent=0.1),
+
+    # Weather
+    A.RandomFog(p=0.2, fog_coef_range=(0, 0.3)),
+    A.RandomShadow(p=0.2),
+
+    # Image Quality/Noise
+    A.MotionBlur(p=0.2, blur_limit=(3, 5)),
+
+    # Color Variations
+    A.HueSaturationValue(p=0.3, hue_shift_limit=8, sat_shift_limit=8, val_shift_limit=8)
+], bbox_params=A.BboxParams(format='yolo'))
 
 def correct_bboxes(labels):
     margin = 0.0005
@@ -37,31 +55,18 @@ def correct_bboxes(labels):
 
 
 def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
-    transforms = [
-        A.Compose([A.HorizontalFlip(always_apply=True)],
-                  bbox_params=A.BboxParams(format='yolo', )),
-        A.Compose([A.RandomBrightnessContrast(always_apply=True)],
-                  bbox_params=A.BboxParams(format='yolo')),
-        A.Compose([A.SafeRotate(limit=90, always_apply=True)],
-                  bbox_params=A.BboxParams(format='yolo')),
-        A.Compose([A.SafeRotate(limit=90, always_apply=True),
-                   A.RandomBrightnessContrast(always_apply=True)],
-                  bbox_params=A.BboxParams(format='yolo')),
-        A.Compose([A.ShiftScaleRotate(scale_limit=0.2, always_apply=True),
-                   A.VerticalFlip(always_apply=True), ],
-                  bbox_params=A.BboxParams(format='yolo')),
-        A.Compose([A.ShiftScaleRotate(scale_limit=0.2, always_apply=True)],
-                  bbox_params=A.BboxParams(format='yolo')),
-        A.Compose([A.SafeRotate(limit=90, always_apply=True),
-                   A.RandomBrightnessContrast(always_apply=True)],
-                  bbox_params=A.BboxParams(format='yolo'))
-    ]
-
     results = []
     labels = correct_bboxes(img_ann.labels)
     if len(labels) == 0 and len(img_ann.labels) != 0:
         print('no labels but was!!!')
-    for i, transform in enumerate(transforms):
+    results.append(ImageLabel(
+            image=img_ann.image,
+            labels=img_ann.labels,
+            image_path=os.path.join(processed_images_dir, Path(img_ann.image_path).name),
+            labels_path=os.path.join(processed_labels_dir, Path(img_ann.labels_path).name)
+        )
+    )
+    for i in range(7):
         try:
             res = transform(image=img_ann.image, bboxes=labels)
             path = Path(img_ann.image_path)
@@ -87,7 +92,8 @@ def write_result(img_ann: ImageLabel):
                  img_ann.labels]
         f.writelines(lines)
         f.close()
-    print(f'{img_ann.labels_path} written')
+    global total_files_processed
+    print(f'{total_files_processed}. {img_ann.labels_path} written')
 
 
 def read_labels(labels_path) -> [[]]:
@@ -104,19 +110,10 @@ def read_labels(labels_path) -> [[]]:
         return arr
 
 
-def process_image(img_ann):
-    results = image_processing(img_ann)
-    for res_ann in results:
-        write_result(res_ann)
-    write_result(ImageLabel(
-        image=img_ann.image,
-        labels=img_ann.labels,
-        image_path=os.path.join(processed_images_dir, Path(img_ann.image_path).name),
-        labels_path=os.path.join(processed_labels_dir, Path(img_ann.labels_path).name)
-    ))
-
-
 def preprocess_annotations():
+    global total_files_processed  # Indicate that we're using the global counter
+    total_files_processed = 0
+
     os.makedirs(processed_images_dir, exist_ok=True)
     os.makedirs(processed_labels_dir, exist_ok=True)
 
@@ -126,20 +123,31 @@ def preprocess_annotations():
         for image_file in imd:
             if image_file.is_file() and image_file.name not in processed_images:
                 images.append(image_file)
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        executor.map(process_image_file, images)
 
-    for image_file in images:
+def process_image_file(image_file): # this function will be executed in thread
     try:
         image_path = os.path.join(data_images_dir, image_file.name)
         labels_path = os.path.join(data_labels_dir, f'{Path(image_path).stem}.txt')
         image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
-            process_image(ImageLabel(
+
+        img_ann = ImageLabel(
             image_path=image_path,
             image=image,
             labels_path=labels_path,
             labels=read_labels(labels_path)
-            ))
+        )
+        try:
+            results = image_processing(img_ann)
+            for res_ann in results:
+                write_result(res_ann)
         except Exception as e:
-            print(f'Error appeared {e}')
+            print(e)
+        global total_files_processed
+        total_files_processed += 1
+    except Exception as e:
+        print(f'Error appeared in thread for {image_file.name}: {e}')
 
 
 def main():

prompts/DALI_initial_prompt.txt

@@ -0,0 +1,172 @@
+I have a code for augmenting photos for dataset, I'm using albumentations. The problem is - I have 38k photos and more in a future, and albumentations works on CPU. It's working very slow, around 1800/ hour. I want to use GPU approach for augmentation task, DALI, since it's an original Nvidia implementation.
+Note, it should create 7 augmented images  + original one.
+
+Here is a code I'm using now:
+
+import os.path
+import time
+from datetime import datetime, timedelta
+from pathlib import Path
+import albumentations as A
+import cv2
+import numpy as np
+import concurrent.futures
+
+from constants import (data_images_dir, data_labels_dir, processed_images_dir, processed_labels_dir,
+                       annotation_classes, checkpoint_file, checkpoint_date_format)
+from dto.imageLabel import ImageLabel
+
+total_files_processed = 0
+transform = A.Compose([
+    # Flips, rotations and brightness
+    A.HorizontalFlip(),
+    A.RandomBrightnessContrast(brightness_limit=(-0.05, 0.05), contrast_limit=(-0.05, 0.05)),
+    A.Affine(p=0.7, scale=(0.8, 1.2), rotate=25, translate_percent=0.1),
+
+    # Weather
+    A.RandomFog(p=0.2, fog_coef_range=(0, 0.3)),
+    A.RandomShadow(p=0.2),
+
+    # Image Quality/Noise
+    A.MotionBlur(p=0.2, blur_limit=(3, 5)),
+
+    # Color Variations
+    A.HueSaturationValue(p=0.3, hue_shift_limit=8, sat_shift_limit=8, val_shift_limit=8)
+], bbox_params=A.BboxParams(format='yolo'))
+
+def correct_bboxes(labels):
+    margin = 0.0005
+    min_size = 0.01
+    res = []
+    for bboxes in labels:
+        x = bboxes[0]
+        y = bboxes[1]
+        half_width = 0.5*bboxes[2]
+        half_height = 0.5*bboxes[3]
+
+        # calc how much bboxes are outside borders ( +small margin ).
+        # value should be negative. If it's positive, then put 0, as no correction
+        w_diff = min( (1 - margin) - (x + half_width), (x - half_width) - margin, 0 )
+        w = bboxes[2] + 2*w_diff
+        if w < min_size:
+            continue
+        h_diff = min( (1 - margin) - (y + half_height), ((y - half_height) - margin), 0)
+        h = bboxes[3] + 2 * h_diff
+        if h < min_size:
+            continue
+        res.append([x, y, w, h, bboxes[4]])
+    return res
+    pass
+
+
+def image_processing(img_ann: ImageLabel) -> [ImageLabel]:
+    results = []
+    labels = correct_bboxes(img_ann.labels)
+    if len(labels) == 0 and len(img_ann.labels) != 0:
+        print('no labels but was!!!')
+    results.append(ImageLabel(
+            image=img_ann.image,
+            labels=img_ann.labels,
+            image_path=os.path.join(processed_images_dir, Path(img_ann.image_path).name),
+            labels_path=os.path.join(processed_labels_dir, Path(img_ann.labels_path).name)
+        )
+    )
+    for i in range(7):
+        try:
+            res = transform(image=img_ann.image, bboxes=labels)
+            path = Path(img_ann.image_path)
+            name = f'{path.stem}_{i + 1}'
+            img = ImageLabel(
+                image=res['image'],
+                labels=res['bboxes'],
+                image_path=os.path.join(processed_images_dir, f'{name}{path.suffix}'),
+                labels_path=os.path.join(processed_labels_dir, f'{name}.txt')
+            )
+            results.append(img)
+        except Exception as e:
+            print(f'Error during transformation: {e}')
+    return results
+
+
+def write_result(img_ann: ImageLabel):
+    cv2.imencode('.jpg', img_ann.image)[1].tofile(img_ann.image_path)
+    print(f'{img_ann.image_path} written')
+
+    with open(img_ann.labels_path, 'w') as f:
+        lines = [f'{ann[4]} {round(ann[0], 5)} {round(ann[1], 5)} {round(ann[2], 5)} {round(ann[3], 5)}\n' for ann in
+                 img_ann.labels]
+        f.writelines(lines)
+        f.close()
+    global total_files_processed
+    print(f'{total_files_processed}. {img_ann.labels_path} written')
+
+
+def read_labels(labels_path) -> [[]]:
+    with open(labels_path, 'r') as f:
+        rows = f.readlines()
+        arr = []
+        for row in rows:
+            str_coordinates = row.split(' ')
+            class_num = str_coordinates.pop(0)
+            coordinates = [float(n.replace(',', '.')) for n in str_coordinates]
+            # noinspection PyTypeChecker
+            coordinates.append(class_num)
+            arr.append(coordinates)
+        return arr
+
+
+def preprocess_annotations():
+    global total_files_processed  # Indicate that we're using the global counter
+    total_files_processed = 0
+
+    os.makedirs(processed_images_dir, exist_ok=True)
+    os.makedirs(processed_labels_dir, exist_ok=True)
+
+    processed_images = set(f.name for f in os.scandir(processed_images_dir))
+    images = []
+    with os.scandir(data_images_dir) as imd:
+        for image_file in imd:
+            if image_file.is_file() and image_file.name not in processed_images:
+                images.append(image_file)
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        executor.map(process_image_file, images)
+
+def process_image_file(image_file): # this function will be executed in thread
+    try:
+        image_path = os.path.join(data_images_dir, image_file.name)
+        labels_path = os.path.join(data_labels_dir, f'{Path(image_path).stem}.txt')
+        image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+
+        img_ann = ImageLabel(
+            image_path=image_path,
+            image=image,
+            labels_path=labels_path,
+            labels=read_labels(labels_path)
+        )
+        try:
+            results = image_processing(img_ann)
+            for res_ann in results:
+                write_result(res_ann)
+        except Exception as e:
+            print(e)
+        global total_files_processed
+        total_files_processed += 1
+    except Exception as e:
+        print(f'Error appeared in thread for {image_file.name}: {e}')
+
+
+def main():
+    while True:
+        preprocess_annotations()
+        print('All processed, waiting for 2 minutes...')
+        time.sleep(120)
+
+
+if __name__ == '__main__':
+    main()
+
+
+please rewrite the whole code to DALI, utilizing GPU.
+I do have 128Gb of RAM, RTX4090 and 32CPU Cores 
+Also note, that for each image I'm making 7 augmented version (each of this versions should be different, cause of random factor of apply one or another augmentation + different random parameters in each augmentation mechanism)
+Make this number 7 configurable in the beginning of the file. Also utilize GPU as match as possible, use batching

requirements.txt

@@ -3,12 +3,14 @@ torch
 torchvision
 torchaudio
 ultralytics
-albumentations~=2.0.4
+albumentations
 
-opencv-python~=4.11.0.86
-matplotlib~=3.10.0
-PyYAML~=6.0.2
-cryptography~=44.0.1
-numpy~=2.1.1
-requests~=2.32.3
+opencv-python
+matplotlib
+PyYAML
+cryptography
+numpy
+requests
 pyyaml
+boto3
+nvidia-dali-cuda120

train.py

@@ -1,4 +1,4 @@
-import io
+import concurrent.futures
 import os
 import random
 import shutil
@@ -31,7 +31,7 @@ test_set = 10
 old_images_percentage = 75
 
 DEFAULT_CLASS_NUM = 80
-
+total_files_copied = 0
 
 def form_dataset(from_date: datetime):
     makedirs(today_dataset, exist_ok=True)
@@ -67,6 +67,25 @@ def form_dataset(from_date: datetime):
 
 
 def copy_annotations(images, folder):
+    global total_files_copied
+    total_files_copied = 0
+
+    def copy_image(image):
+        global total_files_copied
+        total_files_copied += 1
+        label_name = f'{Path(image.path).stem}.txt'
+        label_path = path.join(processed_labels_dir, label_name)
+        if check_label(label_path):
+            shutil.copy(image.path, path.join(destination_images, image.name))
+            shutil.copy(label_path, path.join(destination_labels, label_name))
+        else:
+            shutil.copy(image.path, path.join(corrupted_images_dir, image.name))
+            shutil.copy(label_path, path.join(corrupted_labels_dir, label_name))
+            print(f'Label {label_path} is corrupted! Copy with its image to the corrupted directory ({corrupted_labels_dir})')
+
+        if total_files_copied % 1000 == 0:
+            print(f'{total_files_copied} copied...')
+
     destination_images = path.join(today_dataset, folder, 'images')
     makedirs(destination_images, exist_ok=True)
 
@@ -78,19 +97,10 @@ def copy_annotations(images, folder):
 
     copied = 0
     print(f'Copying annotations to {destination_images} and {destination_labels} folders:')
-    for image in images:
-        label_name = f'{Path(image.path).stem}.txt'
-        label_path = path.join(processed_labels_dir, label_name)
-        if check_label(label_path):
-            shutil.copy(image.path, path.join(destination_images, image.name))
-            shutil.copy(label_path, path.join(destination_labels, label_name))
-        else:
-            shutil.copy(image.path, path.join(corrupted_images_dir, image.name))
-            shutil.copy(label_path, path.join(corrupted_labels_dir, label_name))
-            print(f'Label {label_path} is corrupted! Copy with its image to the corrupted directory ({corrupted_labels_dir})')
-        copied = copied + 1
-        if copied % 1000 == 0:
-            print(f'{copied} copied...')
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        executor.map(copy_image, images)
+
+
     print(f'Copied all {copied} annotations to {destination_images} and {destination_labels} folders')
 
 
@@ -143,11 +153,14 @@ def revert_to_processed_data(date):
 
 def get_latest_model():
     def convert(d: str):
+        if not d.startswith(prefix):
+            return None
         dir_date = datetime.strptime(d.replace(prefix, ''), '%Y-%m-%d')
         dir_model_path = path.join(models_dir, d, 'weights', 'best.pt')
         return {'date': dir_date, 'path': dir_model_path}
 
     dates = [convert(d) for d in next(os.walk(models_dir))[1]]
+    dates = list(filter(lambda x : x is not None, dates))
     sorted_dates = list(sorted(dates, key=lambda x: x['date'] ))
     if len(sorted_dates) == 0:
         return None, None
@@ -223,9 +236,8 @@ def validate(model_path):
 
 
 def upload_model(model_path: str):
-
-    # model = YOLO(model_path)
-    # model.export(format="onnx", imgsz=1280, nms=True, batch=4)
+    model = YOLO(model_path)
+    model.export(format="onnx", imgsz=1280, nms=True, batch=4)
     onnx_model = path.dirname(model_path) + Path(model_path).stem + '.onnx'
 
     with open(onnx_model, 'rb') as f_in:
@@ -250,9 +262,8 @@ def upload_model(model_path: str):
     api.upload_file('azaion.onnx.small', onnx_part_small)
 
 if __name__ == '__main__':
-    # model_path = train_dataset('2024-10-26', from_scratch=True)
-    # validate(path.join('runs', 'detect', 'train7', 'weights', 'best.pt'))
-    # form_data_sample(500)
-    # convert2rknn()
-    model_path = 'azaion.pt'
-    upload_model(model_path)
+    model_path = train_dataset(from_scratch=True)
+    validate(path.join('runs', 'detect', 'train7', 'weights', 'best.pt'))
+    form_data_sample(500)
+    convert2rknn()
+    upload_model('azaion.pt')