Feature/sg 747 add full pipeline with preprocessing

[Louis-Dupont]

Includes code from #804, so #804 needs to be merged first

[dagshub]

Join the discussion on DagsHub!

[shaydeci]

Writing down the main concern we have discussed together:

• Hard coding the default _image_processor and _class_names is bad practice, and might lead users who do not use these (which will probably happen - any time they do not train on coco). Lets add setters that will by the pretrained_weights str set these attributes accordingly.
• Please also add a ticket for making it possible to set them from the recipe itself - so that when users complete their training they can use predict right away.

Dont forget to add the implementation for DeciYolo.

[Louis-Dupont]

@shaydeci I also updated the Results classes to work with the changes that were introduced in the previous PR, so you can check it out now

super-gradients/src/super_gradients/training/models/results.py

Lines 1 to 143 in 8e837a8

	from abc import ABC, abstractmethod
	from typing import List, Optional, Tuple
	from dataclasses import dataclass
	from matplotlib import pyplot as plt
	import numpy as np
	from super_gradients.training.utils.detection_utils import DetectionVisualization
	from super_gradients.training.models.predictions import Prediction, DetectionPrediction
	@dataclass
	class Result(ABC):
	"""Results of a given computer vision task (detection, classification, etc.).
	:attr image: Input image
	:attr predictions: Predictions of the model
	:attr class_names: List of the class names to predict
	"""
	image: np.ndarray
	predictions: Prediction
	class_names: List[str]
	@abstractmethod
	def draw(self) -> np.ndarray:
	"""Draw the predictions on the image."""
	pass
	@abstractmethod
	def show(self) -> None:
	"""Display the predictions on the image."""
	pass
	@dataclass
	class Results(ABC):
	"""List of results of a given computer vision task (detection, classification, etc.).
	:attr results: List of results of the run
	"""
	results: List[Result]
	@abstractmethod
	def draw(self) -> List[np.ndarray]:
	"""Draw the predictions on the image."""
	pass
	@abstractmethod
	def show(self) -> None:
	"""Display the predictions on the image."""
	pass
	@dataclass
	class DetectionResult(Result):
	"""Result of a detection task.
	:attr image: Input image
	:attr predictions: Predictions of the model
	:attr class_names: List of the class names to predict
	"""
	image: np.ndarray
	predictions: DetectionPrediction
	class_names: List[str]
	def draw(self, box_thickness: int = 2, show_confidence: bool = True, color_mapping: Optional[List[Tuple[int]]] = None) -> np.ndarray:
	"""Draw the predicted bboxes on the image.
	:param box_thickness: Thickness of bounding boxes.
	:param show_confidence: Whether to show confidence scores on the image.
	:param color_mapping: List of tuples representing the colors for each class.
	Default is None, which generates a default color mapping based on the number of class names.
	:return: Image with predicted bboxes. Note that this does not modify the original image.
	"""
	image_np = self.image.copy()
	color_mapping = color_mapping or DetectionVisualization._generate_color_mapping(len(self.class_names))
	for pred_i in range(len(self.predictions)):
	image_np = DetectionVisualization._draw_box_title(
	color_mapping=color_mapping,
	class_names=self.class_names,
	box_thickness=box_thickness,
	image_np=image_np,
	x1=int(self.predictions.bboxes_xyxy[pred_i, 0]),
	y1=int(self.predictions.bboxes_xyxy[pred_i, 1]),
	x2=int(self.predictions.bboxes_xyxy[pred_i, 2]),
	y2=int(self.predictions.bboxes_xyxy[pred_i, 3]),
	class_id=int(self.predictions.labels[pred_i]),
	pred_conf=self.predictions.confidence[pred_i] if show_confidence else None,
	)
	return image_np
	def show(self, box_thickness: int = 2, show_confidence: bool = True, color_mapping: Optional[List[Tuple[int]]] = None) -> None:
	"""Display the image with predicted bboxes.
	:param box_thickness: Thickness of bounding boxes.
	:param show_confidence: Whether to show confidence scores on the image.
	:param color_mapping: List of tuples representing the colors for each class.
	Default is None, which generates a default color mapping based on the number of class names.
	"""
	image_np = self.draw(box_thickness=box_thickness, show_confidence=show_confidence, color_mapping=color_mapping)
	plt.imshow(image_np, interpolation="nearest")
	plt.axis("off")
	plt.show()
	@dataclass
	class DetectionResults(Results):
	"""Results of a detection task.
	:attr results: List of the predictions results
	"""
	def __init__(self, images: List[np.ndarray], predictions: List[DetectionPrediction], class_names: List[str]):
	self.results: List[DetectionResult] = []
	for image, prediction in zip(images, predictions):
	self.results.append(DetectionResult(image=image, predictions=prediction, class_names=class_names))
	def draw(self, box_thickness: int = 2, show_confidence: bool = True, color_mapping: Optional[List[Tuple[int]]] = None) -> List[np.ndarray]:
	"""Draw the predicted bboxes on the images.
	:param box_thickness: Thickness of bounding boxes.
	:param show_confidence: Whether to show confidence scores on the image.
	:param color_mapping: List of tuples representing the colors for each class.
	Default is None, which generates a default color mapping based on the number of class names.
	:return: List of Images with predicted bboxes for each image. Note that this does not modify the original images.
	"""
	return [prediction.draw(box_thickness=box_thickness, show_confidence=show_confidence, color_mapping=color_mapping) for prediction in self.results]
	def show(self, box_thickness: int = 2, show_confidence: bool = True, color_mapping: Optional[List[Tuple[int]]] = None) -> None:
	"""Display the predicted bboxes on the images.
	:param box_thickness: Thickness of bounding boxes.
	:param show_confidence: Whether to show confidence scores on the image.
	:param color_mapping: List of tuples representing the colors for each class.
	Default is None, which generates a default color mapping based on the number of class names.
	"""
	for prediction in self.results:
	prediction.show(box_thickness=box_thickness, show_confidence=show_confidence, color_mapping=color_mapping)

[shaydeci]

LGTM