Feature/advanced configuration update

README.md

@@ -101,30 +101,35 @@ datadreamer --save_dir <directory> --class_names <objects> --prompts_number <num
 ### Main Parameters
 
 - `--save_dir` (required): Path to the directory for saving generated images and annotations.
-- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: person moon robot.
-- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to 10.
-- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to False.
+- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.
+- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.
+- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.
 
 <a name="additional-parameters"></a>
 
 ### Additional Parameters
 
-- `--task`: Choose between `detection` and `classification`. Default is `detection`.
+- `--task`: Choose between detection and classification. Default is `detection`.
 - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.
 - `--prompt_generator`: Choose between `simple`, `lm` (language model) and `tiny` (tiny LM). Default is `simple`.
 - `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.
 - `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`.
-- `--conf_threshold`: Confidence threshold for annotation. Default is 0.15.
-- `--use_tta`: Toggle test time augmentation for object detection. Default is True.
+- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.
+- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.
+- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `""`.
+- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `hd 8k "highly detailed"`.
+- `--negative_prompt`: List of of negative prompts to guide the generation away from certain features. Default is `cartoon "blue skin" painting scrispture golden illustration "worst quality" "low quality" "normal quality:2" "unrealistic dream" "low resolution" static "sd character" "low quality" "low resolution" greyscale monochrome nose cropped lowres "jpeg artifacts" "deformed iris" "deformed pupils" "bad eyes" "semi-realistic worst quality" "bad lips" "deformed mouth" "deformed face" "deformed fingers" "bad anatomy"`.
+- `--use_tta`: Toggle test time augmentation for object detection. Default is `True`.
 - `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.
-- `--use_image_tester`: Use image tester for image generation. Default is False.
-- `--image_tester_patience`: Patience level for image tester. Default is 1.
+- `--use_image_tester`: Use image tester for image generation. Default is `False`.
+- `--image_tester_patience`: Patience level for image tester. Default is `1`.
 - `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.
+- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.
 - `--batch_size_prompt`: Batch size for prompt generation. Default is 64.
-- `--batch_size_annotation`: Batch size for annotation. Default is 8.
-- `--batch_size_image`: Batch size for image generation. Default is 1.
-- `--device`: Choose between `cuda` and `cpu`. Default is cuda.
-- `--seed`: Set a random seed for image and prompt generation. Default is 42.
+- `--batch_size_annotation`: Batch size for annotation. Default is `8`.
+- `--batch_size_image`: Batch size for image generation. Default is `1`.
+- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.
+- `--seed`: Set a random seed for image and prompt generation. Default is `42`.
 
 <a name="available-models"></a>
 

datadreamer/dataset_annotation/clip_annotator.py

@@ -8,7 +8,7 @@
 from PIL import Image
 from transformers import CLIPModel, CLIPProcessor
 
-from datadreamer.dataset_annotation.image_annotator import BaseAnnotator
+from datadreamer.dataset_annotation.image_annotator import BaseAnnotator, TaskList
 
 
 class CLIPAnnotator(BaseAnnotator):
@@ -19,8 +19,11 @@ class CLIPAnnotator(BaseAnnotator):
         model (CLIPModel): The CLIP model for image-text similarity evaluation.
         processor (CLIPProcessor): The processor for preparing inputs to the CLIP model.
         device (str): The device on which the model will run ('cuda' for GPU, 'cpu' for CPU).
+        size (str): The size of the CLIP model to use ('base' or 'large').
 
     Methods:
+        _init_processor(): Initializes the CLIP processor.
+        _init_model(): Initializes the CLIP model.
         annotate_batch(image, prompts, conf_threshold, use_tta, synonym_dict): Annotates the given image with bounding boxes and labels.
         release(empty_cuda_cache): Releases resources and optionally empties the CUDA cache.
     """
@@ -29,19 +32,41 @@ def __init__(
         self,
         seed: float = 42,
         device: str = "cuda",
+        size: str = "base",
     ) -> None:
         """Initializes the CLIPAnnotator with a specific seed and device.
 
         Args:
             seed (float): Seed for reproducibility. Defaults to 42.
             device (str): The device to run the model on. Defaults to 'cuda'.
         """
-        super().__init__(seed)
-        self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+        super().__init__(seed, task_definition=TaskList.CLASSIFICATION)
+        self.size = size
+        self.model = self._init_model()
+        self.processor = self._init_processor()
         self.device = device
         self.model.to(self.device)
 
+    def _init_processor(self):
+        """Initializes the CLIP processor.
+
+        Returns:
+            CLIPProcessor: The initialized CLIP processor.
+        """
+        if self.size == "large":
+            return CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+        return CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+    def _init_model(self):
+        """Initializes the CLIP model.
+
+        Returns:
+            CLIPModel: The initialized CLIP model.
+        """
+        if self.size == "large":
+            return CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+        return CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+
     def annotate_batch(
         self,
         images: List[PIL.Image.Image],

datadreamer/dataset_annotation/owlv2_annotator.py

@@ -20,6 +20,7 @@ class OWLv2Annotator(BaseAnnotator):
         model (Owlv2ForObjectDetection): The OWLv2 model for object detection.
         processor (Owlv2Processor): The processor for the OWLv2 model.
         device (str): The device on which the model will run ('cuda' for GPU, 'cpu' for CPU).
+        size (str): The size of the OWLv2 model to use ('base' or 'large').
 
     Methods:
         _init_model(): Initializes the OWLv2 model.
@@ -32,6 +33,7 @@ def __init__(
         self,
         seed: float = 42,
         device: str = "cuda",
+        size: str = "base",
     ) -> None:
         """Initializes the OWLv2Annotator with a specific seed and device.
 
@@ -40,6 +42,7 @@ def __init__(
             device (str): The device to run the model on. Defaults to 'cuda'.
         """
         super().__init__(seed)
+        self.size = size
         self.model = self._init_model()
         self.processor = self._init_processor()
         self.device = device
@@ -51,6 +54,10 @@ def _init_model(self):
         Returns:
             Owlv2ForObjectDetection: The initialized OWLv2 model.
         """
+        if self.size == "large":
+            return Owlv2ForObjectDetection.from_pretrained(
+                "google/owlv2-large-patch14-ensemble"
+            )
         return Owlv2ForObjectDetection.from_pretrained(
             "google/owlv2-base-patch16-ensemble"
         )
@@ -61,6 +68,10 @@ def _init_processor(self):
         Returns:
             Owlv2Processor: The initialized processor.
         """
+        if self.size == "large":
+            return Owlv2Processor.from_pretrained(
+                "google/owlv2-large-patch14-ensemble", do_pad=False, do_resize=False
+            )
         return Owlv2Processor.from_pretrained(
             "google/owlv2-base-patch16-ensemble", do_pad=False, do_resize=False
         )
@@ -86,7 +97,8 @@ def _generate_annotations(
         target_sizes = torch.Tensor(images[0].size[::-1]).repeat((n, 1)).to(self.device)
 
         # resize the images to the model's input size
-        images = [images[i].resize((960, 960)) for i in range(n)]
+        img_size = (1008, 1008) if self.size == "large" else (960, 960)
+        images = [images[i].resize(img_size) for i in range(n)]
         inputs = self.processor(
             text=batched_prompts,
             images=images,
@@ -145,6 +157,7 @@ def annotate_batch(
         images: List[PIL.Image.Image],
         prompts: List[str],
         conf_threshold: float = 0.1,
+        iou_threshold: float = 0.2,
         use_tta: bool = False,
         synonym_dict: dict[str, List[str]] | None = None,
     ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
@@ -154,6 +167,7 @@ def annotate_batch(
             images: The images to be annotated.
             prompts: Prompts to guide the annotation.
             conf_threshold (float, optional): Confidence threshold for the annotations. Defaults to 0.1.
+            iou_threshold (float, optional): Intersection over union threshold for non-maximum suppression. Defaults to 0.2.
             use_tta (bool, optional): Flag to apply test-time augmentation. Defaults to False.
             synonym_dict (dict, optional): Dictionary for handling synonyms in labels. Defaults to None.
 
@@ -233,7 +247,9 @@ def annotate_batch(
 
             # output is a list of detections, each item is one tensor with shape (num_boxes, 6), 6 is for [xyxy, conf, cls].
             output = non_max_suppression(
-                all_boxes_cat.unsqueeze(0), conf_thres=conf_threshold, iou_thres=0.2
+                all_boxes_cat.unsqueeze(0),
+                conf_thres=conf_threshold,
+                iou_thres=iou_threshold,
             )
 
             output_boxes = output[0][:, :4]
@@ -268,3 +284,16 @@ def release(self, empty_cuda_cache: bool = False) -> None:
         if empty_cuda_cache:
             with torch.no_grad():
                 torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    import requests
+    from PIL import Image
+
+    url = "https://ultralytics.com/images/bus.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    annotator = OWLv2Annotator(device="cpu", size="large")
+    final_boxes, final_scores, final_labels = annotator.annotate_batch(
+        [im], ["robot", "horse"]
+    )
+    annotator.release()

datadreamer/pipelines/generate_dataset_from_scratch.py

@@ -120,13 +120,78 @@ def parse_args():
         help="Image annotator to use",
     )
 
+    parser.add_argument(
+        "--negative_prompt",
+        type=str,
+        nargs="+",
+        default=[
+            "cartoon",
+            "blue skin",
+            "painting",
+            "scrispture",
+            "golden",
+            "illustration",
+            "worst quality",
+            "low quality",
+            "normal quality:2",
+            "unrealistic dream",
+            "low resolution",
+            "static",
+            "sd character",
+            "low quality",
+            "low resolution",
+            "greyscale",
+            "monochrome",
+            "nose",
+            "cropped",
+            "lowres",
+            "jpeg artifacts",
+            "deformed iris",
+            "deformed pupils",
+            "bad eyes",
+            "semi-realistic worst quality",
+            "bad lips",
+            "deformed mouth",
+            "deformed face",
+            "deformed fingers",
+            "bad anatomy",
+        ],
+        help="List of of negative prompts to guide the generation away from certain features",
+    )
+
+    parser.add_argument(
+        "--prompt_suffix",
+        type=str,
+        nargs="+",
+        default=[
+            "hd",
+            "8k",
+            "highly detailed",
+        ],
+        help="Suffix to add to every image generation prompt, e.g., for adding details like resolution",
+    )
+
+    parser.add_argument(
+        "--prompt_prefix",
+        type=str,
+        default="",
+        help="Prefix to add to every image generation prompt",
+    )
+
     parser.add_argument(
         "--conf_threshold",
         type=float,
         default=0.15,
         help="Confidence threshold for annotation",
     )
 
+    parser.add_argument(
+        "--annotation_iou_threshold",
+        type=float,
+        default=0.2,
+        help="Intersection over Union (IoU) threshold for annotation",
+    )
+
     parser.add_argument(
         "--use_tta",
         default=False,
@@ -156,6 +221,14 @@ def parse_args():
         help="Quantization to use for Mistral language model",
     )
 
+    parser.add_argument(
+        "--annotator_size",
+        type=str,
+        default="base",
+        choices=["base", "large"],
+        help="Size of the annotator model to use",
+    )
+
     parser.add_argument(
         "--batch_size_prompt",
         type=int,
@@ -233,6 +306,10 @@ def check_args(args):
     if not 0 <= args.conf_threshold <= 1:
         raise ValueError("--conf_threshold must be between 0 and 1")
 
+    # Check annotation_iou_threshold
+    if not 0 <= args.annotation_iou_threshold <= 1:
+        raise ValueError("--annotation_iou_threshold must be between 0 and 1")
+
     # Check image_tester_patience
     if args.image_tester_patience < 0:
         raise ValueError("--image_tester_patience must be a non-negative integer")
@@ -242,6 +319,18 @@ def check_args(args):
         if not torch.cuda.is_available():
             raise ValueError("CUDA is not available. Please use --device cpu")
 
+    # Check negative_prompt
+    if not args.negative_prompt or any(
+        not isinstance(name, str) for name in args.negative_prompt
+    ):
+        raise ValueError("--negative_prompt must be a list of strings")
+
+    # Check prompt_suffix
+    if not args.prompt_suffix or any(
+        not isinstance(name, str) for name in args.prompt_suffix
+    ):
+        raise ValueError("--prompt_suffix must be a list of strings")
+
     # Check for LM quantization availability
     if args.lm_quantization != "none" and (
         args.device == "cpu"
@@ -359,6 +448,9 @@ def main():
         # Image generation
         image_generator_class = image_generators[args.image_generator]
         image_generator = image_generator_class(
+            prompt_prefix=args.prompt_prefix,
+            prompt_suffix=", " + ", ".join(args.prompt_suffix),
+            negative_prompt=", ".join(args.negative_prompt),
             seed=args.seed,
             use_clip_image_tester=args.use_image_tester,
             image_tester_patience=args.image_tester_patience,
@@ -402,7 +494,7 @@ def main():
     if args.task == "classification":
         # Classification annotation
         annotator_class = clf_annotators[args.image_annotator]
-        annotator = annotator_class(device=args.device)
+        annotator = annotator_class(device=args.device, size=args.annotator_size)
 
         labels_list = []
         # Split image_paths into batches
@@ -431,7 +523,7 @@ def main():
     else:
         # Annotation
         annotator_class = det_annotators[args.image_annotator]
-        annotator = annotator_class(device=args.device)
+        annotator = annotator_class(device=args.device, size=args.annotator_size)
 
         boxes_list = []
         scores_list = []
@@ -453,6 +545,7 @@ def main():
                 images,
                 args.class_names,
                 conf_threshold=args.conf_threshold,
+                iou_threshold=args.annotation_iou_threshold,
                 use_tta=args.use_tta,
                 synonym_dict=synonym_dict,
             )

datadreamer/prompt_generation/lm_synonym_generator.py

@@ -34,7 +34,7 @@ class LMSynonymGenerator(SynonymGenerator):
 
     def __init__(
         self,
-        synonyms_number: int = 5,
+        synonyms_number: int = 3,
         seed: Optional[float] = 42,
         device: str = "cuda",
     ) -> None:

datadreamer/prompt_generation/synonym_generator.py

@@ -29,7 +29,7 @@ class SynonymGenerator(ABC):
 
     def __init__(
         self,
-        synonyms_number: int = 5,
+        synonyms_number: int = 3,
         seed: Optional[float] = 42,
         device: str = "cuda",
     ) -> None: