Merge branch 'develop'

Author: pythonlessons

CHANGELOG.md

@@ -1,3 +1,14 @@
+## [1.2.5] - 2024-05-04
+### Added
+- Added exception in `mltu.dataProvider.DataProvider` to raise ValueError when dataset is not iterable
+- Added custom training code for YoloV8 object detector: `Tutorials\11_Yolov8\train_yolov8.py`
+- Added custom trained inference code for YoloV8 object detector:`Tutorials\11_Yolov8\test_yolov8.py`
+
+### Changed
+- Fixed `RandomElasticTransform` in `mltu.augmentors` to handle elastic transformation not to exceed image boundaries
+- Modified `YoloPreprocessor` in `mltu.torch.yolo.preprocessors` to output dictionary with np.arrays istead of lists
+
+
 ## [1.2.4] - 2024-03-21
 ### Added
 - Added `RandomElasticTransform` to `mltu.augmentors` to work with `Image` objects

README.md

@@ -26,4 +26,5 @@ Each tutorial has its own requirements.txt file for a specific mltu version. As
 8. [Handwriting words recognition with PyTorch](https://pylessons.com/handwriting-recognition-pytorch), code in ```Tutorials\08_handwriting_recognition_torch``` folder;
 9. [Transformer training with TensorFlow for Translation task](https://pylessons.com/transformers-training), code in ```Tutorials\09_translation_transformer``` folder;
 10. [Speech Recognition in Python | finetune wav2vec2 model for a custom ASR model](https://youtu.be/h6ooEGzjkj0), code in ```Tutorials\10_wav2vec2_torch``` folder;
-11. [YOLOv8: Real-Time Object Detection Simplified](https://youtu.be/vegL__weCxY), code in ```Tutorials\11_Yolov8``` folder;
+11. [YOLOv8: Real-Time Object Detection Simplified](https://youtu.be/vegL__weCxY), code in ```Tutorials\11_Yolov8``` folder;
+12. [YOLOv8: Customizing Object Detector training](https://youtu.be/ysYiV1CbCyY), code in ```Tutorials\11_Yolov8\train_yolov8.py``` folder;

Tutorials/11_Yolov8/README.md

@@ -1,10 +1,12 @@
 # Run Ultralytics YOLOv8 pretrained model
 
-YouTube tutorial link: [YOLOv8: Real-Time Object Detection Simplified](https://youtu.be/vegL__weCxY)
+YouTube tutorial link:
+- [YOLOv8: Real-Time Object Detection Simplified](https://youtu.be/vegL__weCxY);
+- [YOLOv8: Customizing Object Detector training](https://youtu.be/ysYiV1CbCyY);
 
 First, I recommend you to install the required packages in a virtual environment:
 ```bash
-mltu==1.2.3
+mltu==1.2.5
 ultralytics==8.1.28
 torch==2.0.0
 torchvision==0.15.1
@@ -134,5 +136,175 @@ while True:
         break
 
 cap.release()
+cv2.destroyAllWindows()
+```
+
+## Customize YoloV8 Object Detector training:
+```python
+import os
+import time
+import torch
+from mltu.preprocessors import ImageReader
+from mltu.annotations.images import CVImage
+from mltu.transformers import ImageResizer, ImageShowCV2, ImageNormalizer
+from mltu.augmentors import RandomBrightness, RandomRotate, RandomErodeDilate, RandomSharpen, \
+    RandomMirror, RandomFlip, RandomGaussianBlur, RandomSaltAndPepper, RandomDropBlock, RandomMosaic, RandomElasticTransform
+from mltu.torch.model import Model
+from mltu.torch.dataProvider import DataProvider
+from mltu.torch.yolo.annotation import VOCAnnotationReader
+from mltu.torch.yolo.preprocessors import YoloPreprocessor
+from mltu.torch.yolo.loss import v8DetectionLoss
+from mltu.torch.yolo.metrics import YoloMetrics
+from mltu.torch.yolo.optimizer import build_optimizer, AccumulativeOptimizer
+from mltu.torch.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, Model2onnx, WarmupCosineDecay
+
+from ultralytics.nn.tasks import DetectionModel
+from ultralytics.engine.model import Model as BaseModel
+
+# https://www.kaggle.com/datasets/andrewmvd/car-plate-detection
+annotations_path = "Datasets/car-plate-detection/annotations"
+
+# Create a dataset from the annotations, the dataset is a list of lists where each list contains the [image path, annotation path]
+dataset = [[None, os.path.join(annotations_path, f)] for f in os.listdir(annotations_path)]
+
+# Make sure torch can see GPU device, it is not recommended to train with CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+img_size = 416
+labels = {0: "licence"}
+
+# Create a data provider for the dataset
+data_provider = DataProvider(
+    dataset=dataset,
+    skip_validation=True,
+    batch_size=16,
+    data_preprocessors=[
+        VOCAnnotationReader(labels=labels),
+        ImageReader(CVImage),
+        ],
+    transformers=[
+        # ImageShowCV2(),
+        ImageResizer(img_size, img_size),
+        ImageNormalizer(transpose_axis=True),
+    ],
+    batch_postprocessors=[
+        YoloPreprocessor(device, img_size)
+    ],
+    numpy=False,
+)
+
+# split the dataset into train and test
+train_data_provider, val_data_provider = data_provider.split(0.9, shuffle=False)
+
+# Attaach augmentation to the train data provider
+train_data_provider.augmentors = [
+    RandomBrightness(), 
+    RandomErodeDilate(),
+    RandomSharpen(),
+    RandomMirror(),
+    RandomFlip(),
+    RandomElasticTransform(),
+    RandomGaussianBlur(),
+    RandomSaltAndPepper(),
+    RandomRotate(angle=10),
+    RandomDropBlock(),
+    RandomMosaic(),
+]
+
+base_model = BaseModel("yolov8n.pt")
+# Create a YOLO model
+model = DetectionModel('yolov8n.yaml', nc=len(labels))
+
+# Load the weight from base model
+try: model.load_state_dict(base_model.model.state_dict(), strict=False)
+except: pass
+
+model.to(device)
+
+for k, v in model.named_parameters():
+    if any(x in k for x in [".dfl"]):
+        print("freezing", k)
+        v.requires_grad = False
+    elif not v.requires_grad:
+        v.requires_grad = True
+
+lr = 1e-3
+optimizer = build_optimizer(model.model, name="AdamW", lr=lr, weight_decay=0.0, momentum=0.937, decay=0.0005)
+optimizer = AccumulativeOptimizer(optimizer, 16, 64)
+
+# create model object that will handle training and testing of the network
+model = Model(
+    model, 
+    optimizer, 
+    v8DetectionLoss(model), 
+    metrics=[YoloMetrics(nc=len(labels))],
+    log_errors=False,
+    output_path=f"Models/11_Yolov8/{int(time.time())}",
+    clip_grad_norm=10.0,
+    ema=True,
+)
+
+modelCheckpoint = ModelCheckpoint(monitor="val_fitness", mode="max", save_best_only=True, verbose=True)
+tensorBoard = TensorBoard()
+earlyStopping = EarlyStopping(monitor="val_fitness", mode="max", patience=31, verbose=True)
+model2onnx = Model2onnx(input_shape=(1, 3, img_size, img_size), verbose=True, opset_version=14, 
+                    dynamic_axes = {"input": {0: "batch_size", 2: "height", 3: "width"}, 
+                                    "output": {0: "batch_size", 2: "anchors"}},
+                    metadata={"classes": labels})
+warmupCosineDecayBias = WarmupCosineDecay(lr_after_warmup=lr, final_lr=lr, initial_lr=0.1, 
+                                      warmup_steps=len(train_data_provider), warmup_epochs=10, ignore_param_groups=[1, 2]) # lr0
+warmupCosineDecay = WarmupCosineDecay(lr_after_warmup=lr, final_lr=lr/10, initial_lr=1e-7, 
+                                      warmup_steps=len(train_data_provider), warmup_epochs=10, decay_epochs=190, ignore_param_groups=[0]) # lr1 and lr2
+
+# Train the model
+history = model.fit(
+    train_data_provider,
+    test_dataProvider=val_data_provider,
+    epochs=200, 
+    callbacks=[
+        modelCheckpoint, 
+        tensorBoard, 
+        earlyStopping, 
+        model2onnx,
+        warmupCosineDecayBias,
+        warmupCosineDecay
+        ]
+    )
+```
+
+## Test Custom trained YoloV8 Object Detector:
+```python
+import os
+import cv2
+from mltu.annotations.detections import Detections
+from mltu.torch.yolo.detectors.onnx_detector import Detector as OnnxDetector
+
+# https://www.kaggle.com/datasets/andrewmvd/car-plate-detection
+images_path = "Datasets/car-plate-detection/images"
+
+input_width, input_height = 416, 416
+confidence_threshold = 0.5
+iou_threshold = 0.5
+
+detector = OnnxDetector("Models/11_Yolov8/1714135287/model.onnx", input_width, input_height, confidence_threshold, iou_threshold, force_cpu=False)
+
+for image_path in os.listdir(images_path):
+
+    frame = cv2.imread(os.path.join(images_path, image_path))
+
+    # Perform Yolo object detection
+    detections: Detections = detector(frame)
+
+    # Apply the detections to the frame
+    frame = detections.applyToFrame(frame)
+
+    # Print the FPS
+    print(detector.fps)
+
+    # Display the output image
+    cv2.imshow("Object Detection", frame)
+    if cv2.waitKey(0) & 0xFF == ord('q'):
+        break
+
 cv2.destroyAllWindows()
 ```

Tutorials/11_Yolov8/requirements.txt

@@ -1,4 +1,4 @@
-mltu==1.2.3
+mltu==1.2.5
 ultralytics==8.1.28
 torch==2.0.0
 torchvision==0.15.1

Tutorials/11_Yolov8/run_pretrained.py

@@ -1,5 +1,6 @@
 import cv2
 from ultralytics.engine.model import Model as BaseModel
+from mltu.annotations.detections import Detections
 from mltu.torch.yolo.detectors.torch_detector import Detector as TorchDetector
 from mltu.torch.yolo.detectors.onnx_detector import Detector as OnnxDetector
 
@@ -18,7 +19,7 @@
         break
 
     # Perform Yolo object detection
-    detections = detector(frame)
+    detections: Detections = detector(frame)
 
     # Apply the detections to the frame
     frame = detections.applyToFrame(frame)

Tutorials/11_Yolov8/test_yolov8.py

@@ -0,0 +1,33 @@
+import os
+import cv2
+from mltu.annotations.detections import Detections
+from mltu.torch.yolo.detectors.onnx_detector import Detector as OnnxDetector
+
+# https://www.kaggle.com/datasets/andrewmvd/car-plate-detection
+images_path = "Datasets/car-plate-detection/images"
+
+input_width, input_height = 416, 416
+confidence_threshold = 0.5
+iou_threshold = 0.5
+
+detector = OnnxDetector("Models/11_Yolov8/1714135287/model.onnx", input_width, input_height, confidence_threshold, iou_threshold, force_cpu=False)
+
+for image_path in os.listdir(images_path):
+
+    frame = cv2.imread(os.path.join(images_path, image_path))
+
+    # Perform Yolo object detection
+    detections: Detections = detector(frame)
+
+    # Apply the detections to the frame
+    frame = detections.applyToFrame(frame)
+
+    # Print the FPS
+    print(detector.fps)
+
+    # Display the output image
+    cv2.imshow("Object Detection", frame)
+    if cv2.waitKey(0) & 0xFF == ord('q'):
+        break
+
+cv2.destroyAllWindows()

mltu/torch/yolo/train_yolo.py renamed to Tutorials/11_Yolov8/train_yolov8.py

@@ -13,12 +13,12 @@
 from mltu.torch.yolo.loss import v8DetectionLoss
 from mltu.torch.yolo.metrics import YoloMetrics
 from mltu.torch.yolo.optimizer import build_optimizer, AccumulativeOptimizer
-from mltu.torch.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard, Model2onnx, WarmupCosineDecay
+from mltu.torch.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, Model2onnx, WarmupCosineDecay
 
 from ultralytics.nn.tasks import DetectionModel
 from ultralytics.engine.model import Model as BaseModel
 
-
+# https://www.kaggle.com/datasets/andrewmvd/car-plate-detection
 annotations_path = "Datasets/car-plate-detection/annotations"
 
 # Create a dataset from the annotations, the dataset is a list of lists where each list contains the [image path, annotation path]
@@ -72,6 +72,7 @@
 # Create a YOLO model
 model = DetectionModel('yolov8n.yaml', nc=len(labels))
 
+# Load the weight from base model
 try: model.load_state_dict(base_model.model.state_dict(), strict=False)
 except: pass
 
@@ -95,7 +96,7 @@
     v8DetectionLoss(model), 
     metrics=[YoloMetrics(nc=len(labels))],
     log_errors=False,
-    output_path=f"Models/detector/{int(time.time())}",
+    output_path=f"Models/11_Yolov8/{int(time.time())}",
     clip_grad_norm=10.0,
     ema=True,
 )

mltu/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.2.4"
+__version__ = "1.2.5"
 
 from .annotations.images import Image
 from .annotations.images import CVImage

mltu/augmentors.py

@@ -916,6 +916,8 @@ def __call__(self, image: Image, annotation: typing.Any) -> typing.Tuple[Image,
             detections = []
             for detection in annotation:
                 x_min, y_min, x_max, y_max = detection.xyxy_abs
+                x_max = min(x_max, dx.shape[1] - 1)
+                y_max = min(y_max, dy.shape[0] - 1)
                 new_x_min = min(max(0, x_min + dx[y_min, x_min]), image.width - 1)
                 new_y_min = min(max(0, y_min + dy[y_min, x_min]), image.height - 1)
                 new_x_max = min(max(0, x_max + dx[y_max, x_max]), image.width - 1)

mltu/dataProvider.py

@@ -70,6 +70,10 @@ def __init__(
         else:
             self.logger.info("Skipping Dataset validation...")
 
+        # Check if dataset has length
+        if not len(dataset):
+            raise ValueError("Dataset must be iterable")
+
         if limit:
             self.logger.info(f"Limiting dataset to {limit} samples.")
             self._dataset = self._dataset[:limit]

mltu/torch/yolo/preprocessors.py

@@ -1,12 +1,13 @@
 import torch
+import typing
 import numpy as np
 
 class YoloPreprocessor:
-    def __init__(self, device, imgsz=640):
+    def __init__(self, device: torch.device, imgsz: int=640):
         self.device = device
         self.imgsz = imgsz
 
-    def __call__(self, images, annotations):
+    def __call__(self, images, annotations) -> typing.Tuple[np.ndarray, dict]:
         batch = {
             "ori_shape": [],
             "resized_shape": [],
@@ -23,8 +24,8 @@ def __call__(self, images, annotations):
                 batch["bboxes"].append(detection.xywh)
                 batch["batch_idx"].append(i)
 
-        batch["cls"] = torch.tensor(batch["cls"]).to(self.device)
-        batch["bboxes"] = torch.tensor(batch["bboxes"]).to(self.device)
-        batch["batch_idx"] = torch.tensor(batch["batch_idx"]).to(self.device)
+        batch["cls"] = torch.tensor(np.array(batch["cls"])).to(self.device)
+        batch["bboxes"] = torch.tensor(np.array(batch["bboxes"])).to(self.device)
+        batch["batch_idx"] = torch.tensor(np.array(batch["batch_idx"])).to(self.device)
 
         return np.array(images), batch