diff --git a/README.md b/README.md index 37f9af4..c3b9464 100644 --- a/README.md +++ b/README.md @@ -157,13 +157,13 @@ datadreamer --config ### 🔧 Additional Parameters -- `--task`: Choose between detection and classification. Default is `detection`. +- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`. - `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`. - `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`. - `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3. - `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`. - `--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`. +- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`. - `--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 `""`. @@ -199,6 +199,7 @@ datadreamer --config | | [SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning) | Fast and accurate (1024x1024 images) | | Image Annotation | [OWLv2](https://huggingface.co/google/owlv2-base-patch16-ensemble) | Open-Vocabulary object detector | | | [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) | Zero-shot-image-classification | +| | [SlimSAM](https://huggingface.co/Zigeng/SlimSAM-uniform-50) | Zero-shot-instance-segmentation | @@ -271,6 +272,23 @@ save_dir/ } ``` +3. Instance Segmentation Annotations (instance_segmentation_annotations.json): + +- Each entry corresponds to an image and contains bounding boxes, masks and labels for objects in the image. +- Format: + +```bash +{ + "image_path": { + "boxes": [[x_min, y_min, x_max, y_max], ...], + "masks": [[[x0, y0],[x1, y1],...], [[x0, y0],[x1, y1],...], ....] + "labels": [label_index, ...] + }, + ... + "class_names": ["class1", "class2", ...] +} +``` + ## ⚠️ Limitations diff --git a/datadreamer/dataset_annotation/__init__.py b/datadreamer/dataset_annotation/__init__.py index f4da035..cfdf51a 100644 --- a/datadreamer/dataset_annotation/__init__.py +++ b/datadreamer/dataset_annotation/__init__.py @@ -3,5 +3,12 @@ from .clip_annotator import CLIPAnnotator from .image_annotator import BaseAnnotator, TaskList from .owlv2_annotator import OWLv2Annotator +from .slimsam_annotator import SlimSAMAnnotator -__all__ = ["BaseAnnotator", "TaskList", "OWLv2Annotator", "CLIPAnnotator"] +__all__ = [ + "BaseAnnotator", + "TaskList", + "OWLv2Annotator", + "CLIPAnnotator", + "SlimSAMAnnotator", +] diff --git a/datadreamer/dataset_annotation/owlv2_annotator.py b/datadreamer/dataset_annotation/owlv2_annotator.py index 89d4023..9da41b4 100644 --- a/datadreamer/dataset_annotation/owlv2_annotator.py +++ b/datadreamer/dataset_annotation/owlv2_annotator.py @@ -98,7 +98,7 @@ def _generate_annotations( """ n = len(images) batched_prompts = [prompts] * n - target_sizes = torch.Tensor(images[0].size[::-1]).repeat((n, 1)).to(self.device) + target_sizes = torch.Tensor([img.size[::-1] for img in images]).to(self.device) # resize the images to the model's input size img_size = (1008, 1008) if self.size == "large" else (960, 960) @@ -121,7 +121,8 @@ def _get_annotations( self, pred: Dict[str, torch.Tensor], use_tta: bool, - img_dim: int, + img_width: int, + img_height: int, synonym_dict: Dict[str, List[str]] | None, synonym_dict_rev: Dict[int, int] | None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: @@ -130,7 +131,8 @@ def _get_annotations( Args: pred: The predictions from the model. use_tta (bool): Flag to whether the test-time augmentation was applied. - img_dim (int): The dimension of the image. + img_width (int): The width of the image. + img_height (int): The height of the image. synonym_dict (dict): Dictionary for handling synonyms in labels. synonym_dict_rev (dict): Dictionary for handling synonyms in labels. @@ -143,17 +145,44 @@ def _get_annotations( pred["scores"], pred["labels"], ) - # Flip boxes back if using TTA - if use_tta: - boxes[:, [0, 2]] = img_dim - boxes[:, [2, 0]] if synonym_dict is not None: labels = torch.tensor( [synonym_dict_rev[label.item()] for label in labels], dtype=torch.int64 ) + boxes = self._correct_bboxes_misalignment(boxes, img_width, img_height) + + # Flip boxes back if using TTA + if use_tta: + boxes[:, [0, 2]] = img_width - boxes[:, [2, 0]] + return boxes, scores, labels + def _correct_bboxes_misalignment( + self, input_boxes: torch.Tensor, width: int, height: int + ) -> List[torch.Tensor]: + """This function corrects the bounding boxes misalignment appearing when using + the `transformers==4.45.2`. + + Problem description: With a non-square aspect ratio, the predictions are shifted in the smaller dimension. + Solution: https://discuss.huggingface.co/t/owl-v2-bounding-box-misalignment-problem/66181 + + Args: + input_boxes (torch.Tensor): The bounding boxes to be corrected. + width (int): The width of the image. + height (int): The height of the image. + + Returns: + List[torch.Tensor]: The corrected bounding boxes. + """ + width_ratio = width / height if width < height else 1 + height_ratio = height / width if height < width else 1 + ratios = torch.tensor( + [width_ratio, height_ratio] * 2, device=input_boxes.device + ) + return input_boxes * ratios + def annotate_batch( self, images: List[PIL.Image.Image], @@ -206,31 +235,34 @@ def annotate_batch( final_labels = [] for i, (pred, aug_pred) in enumerate(zip(preds, augmented_preds)): + img_width, img_height = images[i].size boxes, scores, labels = self._get_annotations( pred, False, - images[i].size[0], + img_width, + img_height, synonym_dict, synonym_dict_rev if synonym_dict is not None else None, ) - all_boxes = [boxes.to("cpu")] - all_scores = [scores.to("cpu")] - all_labels = [labels.to("cpu")] + all_boxes = [boxes.cpu()] + all_scores = [scores.cpu()] + all_labels = [labels.cpu()] # Flip boxes back if using TTA if use_tta: aug_boxes, aug_scores, aug_labels = self._get_annotations( aug_pred, True, - images[i].size[0], + img_width, + img_height, synonym_dict, synonym_dict_rev if synonym_dict is not None else None, ) - all_boxes.append(aug_boxes.to("cpu")) - all_scores.append(aug_scores.to("cpu")) - all_labels.append(aug_labels.to("cpu")) + all_boxes.append(aug_boxes.cpu()) + all_scores.append(aug_scores.cpu()) + all_labels.append(aug_labels.cpu()) one_hot_labels = torch.nn.functional.one_hot( torch.cat(all_labels), num_classes=len(prompts) @@ -294,8 +326,8 @@ def release(self, empty_cuda_cache: bool = False) -> None: url = "https://ultralytics.com/images/bus.jpg" im = Image.open(requests.get(url, stream=True).raw) - annotator = OWLv2Annotator(device="cpu", size="large") + annotator = OWLv2Annotator(device="cpu", size="base") final_boxes, final_scores, final_labels = annotator.annotate_batch( - [im], ["robot", "horse"] + [im], ["bus", "person"] ) annotator.release() diff --git a/datadreamer/dataset_annotation/slimsam_annotator.py b/datadreamer/dataset_annotation/slimsam_annotator.py new file mode 100644 index 0000000..7e4e7cd --- /dev/null +++ b/datadreamer/dataset_annotation/slimsam_annotator.py @@ -0,0 +1,153 @@ +from __future__ import annotations + +import logging +from typing import List + +import numpy as np +import PIL +import torch +from transformers import SamModel, SamProcessor + +from datadreamer.dataset_annotation.image_annotator import BaseAnnotator +from datadreamer.dataset_annotation.utils import mask_to_polygon + +logger = logging.getLogger(__name__) + + +class SlimSAMAnnotator(BaseAnnotator): + """A class for image annotation using the SlimSAM model, specializing in instance + segmentation. + + Attributes: + model (SAM): The SAM model for instance segmentation. + processor (SamProcessor): The processor for the SAM model. + device (str): The device on which the model will run ('cuda' for GPU, 'cpu' for CPU). + size (str): The size of the SAM model to use ('base' or 'large'). + + Methods: + _init_model(): Initializes the SAM model. + _init_processor(): Initializes the processor for the SAM 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. + """ + + def __init__( + self, + seed: float = 42, + device: str = "cuda", + size: str = "base", + ) -> None: + """Initializes the SAMAnnotator 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.size = size + self.model = self._init_model() + self.processor = self._init_processor() + self.device = device + self.model.to(self.device) + + def _init_model(self) -> SamModel: + """Initializes the SAM model for object detection. + + Returns: + SamModel: The initialized SAM model. + """ + logger.info(f"Initializing `SlimSAM {self.size} model...") + if self.size == "large": + return SamModel.from_pretrained("Zigeng/SlimSAM-uniform-50") + return SamModel.from_pretrained("Zigeng/SlimSAM-uniform-77") + + def _init_processor(self) -> SamProcessor: + """Initializes the processor for the SAM model. + + Returns: + SamProcessor: The initialized processor. + """ + if self.size == "large": + return SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-50") + return SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-77") + + def annotate_batch( + self, + images: List[PIL.Image.Image], + boxes_batch: List[np.ndarray], + iou_threshold: float = 0.2, + ) -> List[List[List[float]]]: + """Annotates images for the task of instance segmentation using the SlimSAM + model. + + Args: + images: The images to be annotated. + boxes_batch: The bounding boxes of found objects. + iou_threshold (float, optional): Intersection over union threshold for non-maximum suppression. Defaults to 0.2. + + Returns: + List: A list containing the final segment masks represented as a polygon. + """ + final_segments = [] + + n = len(images) + + for i in range(n): + boxes = boxes_batch[i].tolist() + if len(boxes) == 0: + final_segments.append([]) + continue + + inputs = self.processor( + images[i], input_boxes=[boxes], return_tensors="pt" + ).to(self.device) + + with torch.no_grad(): + outputs = self.model(**inputs, return_dict=True) + + masks = self.processor.image_processor.post_process_masks( + outputs.pred_masks.cpu(), + inputs["original_sizes"].cpu(), + inputs["reshaped_input_sizes"].cpu(), + )[0] + + iou_scores = outputs.iou_scores.cpu() + + image_masks = [] + for j in range(len(boxes)): + keep_idx = iou_scores[0, j] >= iou_threshold + filtered_masks = masks[j, keep_idx].cpu().float() + final_masks = filtered_masks.permute(1, 2, 0) + final_masks = final_masks.mean(axis=-1) + final_masks = (final_masks > 0).int() + final_masks = final_masks.numpy().astype(np.uint8) + polygon = mask_to_polygon(final_masks) + if len(polygon) != 0: + image_masks.append(polygon) + + final_segments.append(image_masks) + + return final_segments + + def release(self, empty_cuda_cache: bool = False) -> None: + """Releases the model and optionally empties the CUDA cache. + + Args: + empty_cuda_cache (bool, optional): Whether to empty the CUDA cache. Defaults to False. + """ + self.model = self.model.to("cpu") + 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 = SlimSAMAnnotator(device="cpu", size="large") + final_segments = annotator.annotate_batch([im], [np.array([[3, 229, 559, 650]])]) + print(len(final_segments), len(final_segments[0])) + print(final_segments[0][0][:5]) diff --git a/datadreamer/dataset_annotation/utils.py b/datadreamer/dataset_annotation/utils.py index bfb13b7..dd6b643 100644 --- a/datadreamer/dataset_annotation/utils.py +++ b/datadreamer/dataset_annotation/utils.py @@ -2,6 +2,8 @@ from typing import List +import cv2 +import numpy as np from torchvision import transforms @@ -32,3 +34,27 @@ def apply_tta(image) -> List[transforms.Compose]: augmented_images = [t(image) for t in tta_transforms] return augmented_images + + +def mask_to_polygon(mask: np.ndarray) -> List[List[int]]: + """Converts a binary mask to a polygon. + + Args: + mask: The binary mask to be converted. + + Returns: + List: A list of vertices of the polygon. + """ + # Find contours in the binary mask + contours, _ = cv2.findContours( + mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE + ) + if len(contours) == 0: + return [] + # Find the contour with the largest area + largest_contour = max(contours, key=cv2.contourArea) + + # Extract the vertices of the contour + polygon = largest_contour.reshape(-1, 2).tolist() + + return polygon diff --git a/datadreamer/pipelines/generate_dataset_from_scratch.py b/datadreamer/pipelines/generate_dataset_from_scratch.py index 33811bf..81908a5 100644 --- a/datadreamer/pipelines/generate_dataset_from_scratch.py +++ b/datadreamer/pipelines/generate_dataset_from_scratch.py @@ -16,7 +16,11 @@ from PIL import Image from tqdm import tqdm -from datadreamer.dataset_annotation import CLIPAnnotator, OWLv2Annotator +from datadreamer.dataset_annotation import ( + CLIPAnnotator, + OWLv2Annotator, + SlimSAMAnnotator, +) from datadreamer.image_generation import ( StableDiffusionImageGenerator, StableDiffusionLightningImageGenerator, @@ -54,6 +58,8 @@ det_annotators = {"owlv2": OWLv2Annotator} clf_annotators = {"clip": CLIPAnnotator} +inst_seg_annotators = {"owlv2-slimsam": SlimSAMAnnotator} +inst_seg_detectors = {"owlv2-slimsam": OWLv2Annotator} setup_logging(use_rich=True) @@ -70,7 +76,7 @@ def parse_args(): parser.add_argument( "--task", type=str, - choices=["detection", "classification"], + choices=["detection", "classification", "instance-segmentation"], help="Task to generate data for", ) @@ -116,7 +122,7 @@ def parse_args(): parser.add_argument( "--image_annotator", type=str, - choices=["owlv2", "clip"], + choices=["owlv2", "clip", "owlv2-slimsam"], help="Image annotator to use", ) @@ -357,6 +363,14 @@ def check_args(args): "--image_annotator must be one of the available annotators for classification task" ) + if ( + args.task == "instance-segmentation" + and args.image_annotator not in inst_seg_annotators + ): + raise ValueError( + "--image_annotator must be one of the available annotators for instance segmentation task" + ) + # Check coorect task and dataset_format if args.task == "classification" and args.dataset_format in ["coco", "yolo"]: raise ValueError( @@ -368,6 +382,11 @@ def check_args(args): "--dataset_format must be one of the available dataset formats for detection task: raw, coco, yolo, luxonis-dataset" ) + if args.task == "instance-segmentation" and args.dataset_format in ["cls-single"]: + raise ValueError( + "--dataset_format must be one of the available dataset formats for instance segmentation task: raw, coco, yolo, luxonis-dataset" + ) + # Check split_ratios if ( len(args.split_ratios) != 3 @@ -540,6 +559,7 @@ def read_image_batch(image_batch, batch_num, batch_size): boxes_list = [] scores_list = [] labels_list = [] + segment_list = [] image_paths = [] if args.task == "classification": @@ -583,7 +603,14 @@ def read_image_batch(image_batch, batch_num, batch_size): ) else: # Detection annotation - annotator_class = det_annotators[args.image_annotator] + if args.task == "detection": + annotator_class = det_annotators[args.image_annotator] + else: + annotator_class = inst_seg_detectors[args.image_annotator] + inst_seg_annotator_class = inst_seg_annotators[args.image_annotator] + inst_seg_annotator = inst_seg_annotator_class( + device=args.device, size=args.annotator_size + ) annotator = annotator_class(device=args.device, size=args.annotator_size) for i, image_batch in tqdm( @@ -608,14 +635,31 @@ def read_image_batch(image_batch, batch_num, batch_size): boxes_list.extend(boxes_batch) scores_list.extend(scores_batch) + if args.task == "instance-segmentation": + masks_batch = inst_seg_annotator.annotate_batch( + images=images, + boxes_batch=boxes_batch, + iou_threshold=args.annotation_iou_threshold, + ) + segment_list.extend(masks_batch) + for j, image in enumerate(images): labels = [] # Save bbox visualizations fig, ax = plt.subplots(1) ax.imshow(image) - for box, score, label in zip( - boxes_batch[j], scores_batch[j], local_labels_batch[j] - ): + for k in range(len(boxes_batch[j])): + box = boxes_batch[j][k] + score = scores_batch[j][k] + label = local_labels_batch[j][k] + + if args.task == "instance-segmentation": + if k < len(masks_batch[j]): + mask = masks_batch[j][k] + x_points, y_points = zip(*mask) + + ax.fill(x_points, y_points, label, alpha=0.5) + labels.append(label) x1, y1, x2, y2 = box rect = patches.Rectangle( @@ -658,6 +702,7 @@ def read_image_batch(image_batch, batch_num, batch_size): image_paths=image_paths, labels_list=labels_list, boxes_list=boxes_list, + masks_list=segment_list if len(segment_list) > 0 else None, class_names=args.class_names, save_dir=save_dir, ) @@ -670,6 +715,7 @@ def read_image_batch(image_batch, batch_num, batch_size): "yolo", args.split_ratios, copy_files=False, + is_instance_segmentation=args.task == "instance-segmentation", seed=args.seed, ) # Convert annotations to COCO format @@ -679,6 +725,7 @@ def read_image_batch(image_batch, batch_num, batch_size): args.save_dir, "coco", args.split_ratios, + is_instance_segmentation=args.task == "instance-segmentation", copy_files=False, seed=args.seed, ) @@ -692,6 +739,7 @@ def read_image_batch(image_batch, batch_num, batch_size): args.split_ratios, dataset_plugin=args.dataset_plugin, dataset_name=args.dataset_name, + is_instance_segmentation=args.task == "instance-segmentation", copy_files=False, seed=args.seed, ) diff --git a/datadreamer/utils/coco_converter.py b/datadreamer/utils/coco_converter.py index bcd3546..bb69a78 100644 --- a/datadreamer/utils/coco_converter.py +++ b/datadreamer/utils/coco_converter.py @@ -4,6 +4,7 @@ import os import shutil +import numpy as np from PIL import Image from datadreamer.utils.base_converter import BaseConverter @@ -28,8 +29,9 @@ class COCOConverter(BaseConverter): │ ├── labels.json """ - def __init__(self, seed=42): + def __init__(self, seed=42, is_instance_segmentation: bool = False): super().__init__(seed) + self.is_instance_segmentation = is_instance_segmentation def convert(self, dataset_dir, output_dir, split_ratios, copy_files=True) -> None: """Converts a dataset into a COCO format. @@ -99,19 +101,36 @@ def process_data( "height": image_height, } ) + masks = ( + annotation.get("masks") + if self.is_instance_segmentation + else [None] * len(annotation["boxes"]) + ) + + # Loop through boxes, labels, and masks, appending to annotations + for box, label, mask in zip( + annotation["boxes"], annotation["labels"], masks + ): + bbox = [box[0], box[1], box[2] - box[0], box[3] - box[1]] + segmentation = ( + np.array(mask).reshape(1, -1).tolist() + if mask is not None + else None + ) + area = (box[2] - box[0]) * (box[3] - box[1]) - for box, label in zip(annotation["boxes"], annotation["labels"]): annotations.append( { "id": annotation_id, "image_id": len(images_info), "category_id": label, - "bbox": [box[0], box[1], box[2] - box[0], box[3] - box[1]], - "segmentation": None, # [[box[0], box[1], box[2], box[1], box[2], box[3], box[0], box[3]]], # bbox mask - "area": (box[2] - box[0]) * (box[3] - box[1]), + "bbox": bbox, + "segmentation": segmentation, + "area": area, "iscrowd": 0, } ) + annotation_id += 1 if copy_files: diff --git a/datadreamer/utils/config.py b/datadreamer/utils/config.py index c114321..9d36267 100644 --- a/datadreamer/utils/config.py +++ b/datadreamer/utils/config.py @@ -10,7 +10,7 @@ class Config(LuxonisConfig): save_dir: str = "generated_dataset" class_names: List[str] = ["bear", "bicycle", "bird", "person"] prompts_number: int = 10 - task: Literal["detection", "classification"] = "detection" + task: Literal["detection", "classification", "instance-segmentation"] = "detection" seed: int = 42 device: Literal["cuda", "cpu"] = "cuda" annotate_only: bool = False @@ -39,7 +39,7 @@ class Config(LuxonisConfig): # Profanity filter arguments disable_lm_filter: bool = False # Annotation arguments - image_annotator: Literal["owlv2", "clip"] = "owlv2" + image_annotator: Literal["owlv2", "clip", "owlv2-slimsam"] = "owlv2" conf_threshold: float = 0.15 annotation_iou_threshold: float = 0.2 use_tta: bool = False diff --git a/datadreamer/utils/convert_dataset.py b/datadreamer/utils/convert_dataset.py index 874878b..2e063ed 100644 --- a/datadreamer/utils/convert_dataset.py +++ b/datadreamer/utils/convert_dataset.py @@ -17,6 +17,7 @@ def convert_dataset( split_ratios, dataset_plugin=None, dataset_name=None, + is_instance_segmentation=False, copy_files=True, seed=42, ) -> None: @@ -36,14 +37,19 @@ def convert_dataset( """ if dataset_format == "yolo": - converter = YOLOConverter(seed=seed) + converter = YOLOConverter( + seed=seed, is_instance_segmentation=is_instance_segmentation + ) elif dataset_format == "coco": - converter = COCOConverter(seed=seed) + converter = COCOConverter( + seed=seed, is_instance_segmentation=is_instance_segmentation + ) elif dataset_format == "luxonis-dataset": converter = LuxonisDatasetConverter( dataset_plugin=dataset_plugin, dataset_name=dataset_name, seed=seed, + is_instance_segmentation=is_instance_segmentation, ) elif dataset_format == "cls-single": converter = SingleLabelClsConverter(seed=seed) diff --git a/datadreamer/utils/dataset_utils.py b/datadreamer/utils/dataset_utils.py index 33fe003..a1c5971 100644 --- a/datadreamer/utils/dataset_utils.py +++ b/datadreamer/utils/dataset_utils.py @@ -6,6 +6,7 @@ def save_annotations_to_json( image_paths, labels_list, boxes_list=None, + masks_list=None, class_names=None, save_dir=None, file_name="annotations.json", @@ -16,6 +17,7 @@ def save_annotations_to_json( image_paths (list): List of image paths. labels_list (list): List of labels. boxes_list (list, optional): List of bounding boxes. Defaults to None. + masks_list (list, optional): List of instance segmentation masks. Defaults to None. class_names (list, optional): List of class names. Defaults to None. save_dir (str, optional): Directory to save the JSON file. Defaults to None. file_name (str, optional): Name of the JSON file. Defaults to 'annotations.json'. @@ -38,6 +40,10 @@ def save_annotations_to_json( bboxes = boxes_list[i] annotations[image_name]["boxes"] = bboxes.tolist() + if masks_list is not None: + masks = masks_list[i] + annotations[image_name]["masks"] = masks + annotations["class_names"] = class_names # Save to JSON file diff --git a/datadreamer/utils/luxonis_dataset_converter.py b/datadreamer/utils/luxonis_dataset_converter.py index 9a2e6f9..8462ea1 100644 --- a/datadreamer/utils/luxonis_dataset_converter.py +++ b/datadreamer/utils/luxonis_dataset_converter.py @@ -17,12 +17,22 @@ class LuxonisDatasetConverter(BaseConverter): """Class for converting a dataset to LuxonisDataset format.""" def __init__( - self, dataset_plugin: str = None, dataset_name: str = None, seed: int = 42 + self, + dataset_plugin: str = None, + dataset_name: str = None, + seed: int = 42, + is_instance_segmentation: bool = False, ): super().__init__(seed) + self.is_instance_segmentation = is_instance_segmentation self.dataset_plugin = dataset_plugin self.dataset_name = dataset_name + if self.is_instance_segmentation: + logger.warning( + "Instance segmentation will be treated as semantic segmentation until the support for instance segmentation is added to Luxonis-ml." + ) + def convert( self, dataset_dir: str, @@ -80,6 +90,22 @@ def dataset_generator(): }, } + if "masks" in data[image_path]: # polyline format + masks = data[image_path]["masks"] + for mask, label in zip(masks, labels): + poly = [] + poly += [ + (point[0] / width, point[1] / height) for point in mask + ] + yield { + "file": image_full_path, + "annotation": { + "type": "polyline", + "class": class_names[label], + "points": poly, # masks, + }, + } + if "boxes" in data[image_path]: boxes = data[image_path]["boxes"] for box, label in zip(boxes, labels): diff --git a/datadreamer/utils/yolo_converter.py b/datadreamer/utils/yolo_converter.py index 715e429..5f8fc51 100644 --- a/datadreamer/utils/yolo_converter.py +++ b/datadreamer/utils/yolo_converter.py @@ -4,6 +4,7 @@ import shutil from typing import Dict, List +import numpy as np from PIL import Image from datadreamer.utils import BaseConverter @@ -30,8 +31,9 @@ class YOLOConverter(BaseConverter): │ ├── labels """ - def __init__(self, seed=42): + def __init__(self, seed=42, is_instance_segmentation: bool = False): super().__init__(seed) + self.is_instance_segmentation = is_instance_segmentation def convert( self, @@ -74,6 +76,21 @@ def convert_to_yolo_format( height = (box[3] - box[1]) / image_height return [x_center, y_center, width, height] + def convert_masks_to_yolo_format( + self, masks: List[List[float]], w: int, h: int + ) -> List[float]: + """Converts masks to YOLO format. + + Args: + masks (list of list of float): A list containing the masks. + w (int): The width of the image. + h (int): The height of the image. + + Returns: + list of float: A list containing the masks in YOLO format. + """ + return (np.array(masks) / np.array([w, h])).reshape(-1).tolist() + def process_data( self, data: Dict, @@ -130,11 +147,22 @@ def process_data( label_output_dir, os.path.splitext(image_name)[0] + ".txt" ) with open(label_file, "w") as f: - for box, label in zip(annotation["boxes"], annotation["labels"]): - yolo_box = self.convert_to_yolo_format( - box, image_width, image_height - ) - f.write(f"{label} {' '.join(map(str, yolo_box))}\n") + if self.is_instance_segmentation: + for masks, label in zip( + annotation["masks"], annotation["labels"] + ): + yolo_box = self.convert_masks_to_yolo_format( + masks, image_width, image_height + ) + f.write(f"{label} {' '.join(map(str, yolo_box))}\n") + else: + for box, label in zip( + annotation["boxes"], annotation["labels"] + ): + yolo_box = self.convert_to_yolo_format( + box, image_width, image_height + ) + f.write(f"{label} {' '.join(map(str, yolo_box))}\n") if copy_files: shutil.copy( diff --git a/examples/generate_dataset_and_train_yolo.ipynb b/examples/generate_dataset_and_train_yolo.ipynb index 4f5cc17..9ad1359 100644 --- a/examples/generate_dataset_and_train_yolo.ipynb +++ b/examples/generate_dataset_and_train_yolo.ipynb @@ -78,13 +78,13 @@ "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", - "- `--task`: Choose between detection and classification. Default is `detection`.\n", + "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", - "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification. Default is `owlv2`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n", "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", @@ -96,6 +96,7 @@ "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", diff --git a/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb new file mode 100644 index 0000000..11f9812 --- /dev/null +++ b/examples/generate_instance_segmentation_dataset_and_train_yolo.ipynb @@ -0,0 +1,422 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8ce1517f-7258-406d-9139-9adadb1a1570", + "metadata": { + "id": "8ce1517f-7258-406d-9139-9adadb1a1570" + }, + "source": [ + "\n", + "\n", + "# DataDreamer Tutorial: Generating a dataset for instance segmentation, training a model, and deploying it to the OAK (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5_2ivH03etO", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b5_2ivH03etO", + "outputId": "c92b1e2e-cd3e-4a7d-8be6-776e0dfad5bc" + }, + "outputs": [], + "source": [ + "!pip install -q datadreamer@git+https://github.com/luxonis/datadreamer@dev" + ] + }, + { + "cell_type": "markdown", + "id": "c3704c07", + "metadata": { + "id": "c3704c07" + }, + "source": [ + "## Generate a dataset with your own classes (might take some time to download all models)" + ] + }, + { + "cell_type": "markdown", + "id": "M4v-QieP4tXL", + "metadata": { + "id": "M4v-QieP4tXL" + }, + "source": [ + "Make sure you are using the GPU runtime type (in Google Colab).\n", + "\n", + "~4 min to generate 30 images\n", + "\n", + "~43 secs to annotate them" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ab1e2f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6ab1e2f9", + "outputId": "6f57eb7a-f261-46bc-e574-3631cade8660", + "scrolled": true + }, + "outputs": [], + "source": [ + "!datadreamer --save_dir generated_dataset \\\n", + " --class_names tractor horse bear \\\n", + " --prompts_number 30 \\\n", + " --prompt_generator simple \\\n", + " --num_objects_range 1 1 \\\n", + " --image_generator sdxl-turbo \\\n", + " --task instance-segmentation \\\n", + " --disable_lm_filter \\\n", + " --annotator_size base \\\n", + " --use_tta \\\n", + " --image_annotator owlv2-slimsam \\\n", + " --conf_threshold 0.2 \\\n", + " --seed 42" + ] + }, + { + "cell_type": "markdown", + "id": "7a10755e", + "metadata": { + "id": "7a10755e" + }, + "source": [ + "### Parameters\n", + "- `--save_dir` (required): Path to the directory for saving generated images and annotations.\n", + "- `--class_names` (required): Space-separated list of object names for image generation and annotation. Example: `person moon robot`.\n", + "- `--prompts_number` (optional): Number of prompts to generate for each object. Defaults to `10`.\n", + "- `--annotate_only` (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to `False`.\n", + "- `--task`: Choose between detection, classification and instance segmentation. Default is `detection`.\n", + "- `--dataset_format`: Format of the dataset. Defaults to `raw`. Supported values: `raw`, `yolo`, `coco`, `luxonis-dataset`, `cls-single`.\n", + "- `--split_ratios`: Split ratios for train, validation, and test sets. Defaults to `[0.8, 0.1, 0.1]`.\n", + "- `--num_objects_range`: Range of objects in a prompt. Default is 1 to 3.\n", + "- `--prompt_generator`: Choose between `simple`, `lm` (Mistral-7B), `tiny` (tiny LM), and `qwen2` (Qwen2.5 LM). Default is `qwen2`.\n", + "- `--image_generator`: Choose image generator, e.g., `sdxl`, `sdxl-turbo` or `sdxl-lightning`. Default is `sdxl-turbo`.\n", + "- `--image_annotator`: Specify the image annotator, like `owlv2` for object detection or `clip` for image classification or `owlv2-slimsam` for instance segmentation. Default is `owlv2`.\n", + "- `--conf_threshold`: Confidence threshold for annotation. Default is `0.15`.\n", + "- `--annotation_iou_threshold`: Intersection over Union (IoU) threshold for annotation. Default is `0.2`.\n", + "- `--prompt_prefix`: Prefix to add to every image generation prompt. Default is `\"\"`.\n", + "- `--prompt_suffix`: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is `\", hd, 8k, highly detailed\"`.\n", + "- `--negative_prompt`: 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\"`.\n", + "- `--use_tta`: Toggle test time augmentation for object detection. Default is `False`.\n", + "- `--synonym_generator`: Enhance class names with synonyms. Default is `none`. Other options are `llm`, `wordnet`.\n", + "- `--use_image_tester`: Use image tester for image generation. Default is `False`.\n", + "- `--image_tester_patience`: Patience level for image tester. Default is `1`.\n", + "- `--lm_quantization`: Quantization to use for Mistral language model. Choose between `none` and `4bit`. Default is `none`.\n", + "- `--annotator_size`: Size of the annotator model to use. Choose between `base` and `large`. Default is `base`.\n", + "- `--disable_lm_filter`: Use only a bad word list for profanity filtering. Default is `False`.\n", + "- `--batch_size_prompt`: Batch size for prompt generation. Default is 64.\n", + "- `--batch_size_annotation`: Batch size for annotation. Default is `1`.\n", + "- `--batch_size_image`: Batch size for image generation. Default is `1`.\n", + "- `--device`: Choose between `cuda` and `cpu`. Default is `cuda`.\n", + "- `--seed`: Set a random seed for image and prompt generation. Default is `42`.\n", + "- `--config`: A path to an optional `.yaml` config file specifying the pipeline's arguments.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7add74d9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "7add74d9", + "outputId": "cafd066a-b524-4006-e2d0-cd949d65c567" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from IPython.display import Image\n", + "\n", + "Image(filename=os.path.join(\"generated_dataset/bboxes_visualization\", \"bbox_5.jpg\"))" + ] + }, + { + "cell_type": "markdown", + "id": "64fe2dc9", + "metadata": { + "id": "64fe2dc9" + }, + "source": [ + "## Convert the dataset to YOLO format" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3dd01a6a", + "metadata": { + "id": "3dd01a6a" + }, + "outputs": [], + "source": [ + "from datadreamer.utils.convert_dataset import convert_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9b9bb74d", + "metadata": { + "id": "9b9bb74d" + }, + "outputs": [], + "source": [ + "convert_dataset(\n", + " input_dir=\"generated_dataset\",\n", + " output_dir=\"generated_dataset_yolo\",\n", + " dataset_format=\"yolo\",\n", + " split_ratios=[0.8, 0.1, 0.1],\n", + " copy_files=True,\n", + " is_instance_segmentation=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a167a842", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a167a842", + "outputId": "715988c2-ab27-4ce2-b12c-2fa01188c537" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data.yaml test train\tval\n" + ] + } + ], + "source": [ + "!ls generated_dataset_yolo" + ] + }, + { + "cell_type": "markdown", + "id": "d2d660b0", + "metadata": { + "id": "d2d660b0" + }, + "source": [ + "# Train your model (YOLOv8 as an example)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "982e475e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "982e475e", + "outputId": "1f4cb9f5-1d01-4882-a730-434e5122546f", + "scrolled": true + }, + "outputs": [], + "source": [ + "!pip install -q ultralytics" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "184cf0fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "184cf0fa", + "outputId": "dcc43a26-bc78-4d3d-ddb3-6932a8584df9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating new Ultralytics Settings v0.0.6 file ✅ \n", + "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", + "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6.74M/6.74M [00:00<00:00, 110MB/s]\n" + ] + } + ], + "source": [ + "from ultralytics import YOLO\n", + "\n", + "model = YOLO(\"yolov8n-seg.pt\") # load a pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb4e6754", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "bb4e6754", + "outputId": "66b28d5a-6544-46fa-ee73-3074f141e981", + "scrolled": true + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['WANDB_DISABLED'] = 'true'\n", + "\n", + "results = model.train(data=\"generated_dataset_yolo/data.yaml\", epochs=200)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b05e33", + "metadata": { + "id": "d8b05e33" + }, + "source": [ + "## Show the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b559b1f9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "b559b1f9", + "outputId": "bcb3fae6-27eb-4384-f532-c573ae45c599" + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename=os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "dec0cb11", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dec0cb11", + "outputId": "72cf4330-fa0f-47aa-82c5-242dc6978dcd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ultralytics 8.3.21 🚀 Python-3.10.12 torch-2.5.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "YOLOv8n-seg summary (fused): 195 layers, 3,258,649 parameters, 0 gradients, 12.0 GFLOPs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/generated_dataset_yolo/val/labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00=2.0.0 torchvision>=0.16.0 -transformers>=4.37.0 +transformers>=4.45.2 diffusers>=0.24.0 compel>=2.0.0 tqdm>=4.0.0 @@ -12,6 +12,6 @@ accelerate>=0.25.0 scipy>=1.10.0 bitsandbytes>=0.42.0 nltk>=3.8.1 -luxonis-ml[all]>=0.3.0 +luxonis-ml[all]>=0.4.1 python-box>=7.1.1 -gcsfs>=2023.1.0 +gcsfs>=2023.1.0 \ No newline at end of file diff --git a/tests/core_tests/integration/sample_config.yaml b/tests/core_tests/integration/sample_config.yaml index 0feb485..c8fed56 100644 --- a/tests/core_tests/integration/sample_config.yaml +++ b/tests/core_tests/integration/sample_config.yaml @@ -1,7 +1,7 @@ -class_names: [alien, mars, cat] +class_names: [horse, bear, cat] prompts_number: 1 prompt_generator: simple num_objects_range: [2, 3] image_generator: sdxl-turbo conf_threshold: 0.15 -seed: 43 \ No newline at end of file +seed: 42 \ No newline at end of file diff --git a/tests/core_tests/integration/test_pipeline.py b/tests/core_tests/integration/test_pipeline.py index a6eba19..a49617c 100644 --- a/tests/core_tests/integration/test_pipeline.py +++ b/tests/core_tests/integration/test_pipeline.py @@ -170,3 +170,43 @@ def test_cuda_simple_sdxl_turbo_config_classification_pipeline(): ) # Check the run of the pipeline _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_turbo_config_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-simple-sdxl-turbo-config/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--config ./tests/core_tests/integration/sample_config.yaml " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_turbo_config_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-sdxl-turbo-config/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--config ./tests/core_tests/integration/sample_config.yaml " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) diff --git a/tests/core_tests/unittests/test_annotators.py b/tests/core_tests/unittests/test_annotators.py index 794b898..4e78df2 100644 --- a/tests/core_tests/unittests/test_annotators.py +++ b/tests/core_tests/unittests/test_annotators.py @@ -9,6 +9,7 @@ from datadreamer.dataset_annotation.clip_annotator import CLIPAnnotator from datadreamer.dataset_annotation.owlv2_annotator import OWLv2Annotator +from datadreamer.dataset_annotation.slimsam_annotator import SlimSAMAnnotator # Get the total disk space in GB total_disk_space = psutil.disk_usage("/").total / (1024**3) @@ -65,6 +66,8 @@ def _check_clip_annotator(device: str, size: str = "base"): # Check that the labels are ndarray of integers assert isinstance(labels[0], np.ndarray) and labels[0].dtype == np.int64 + annotator.release(empty_cuda_cache=True if device != "cpu" else False) + @pytest.mark.skipif( not torch.cuda.is_available() or total_disk_space < 16, @@ -87,7 +90,7 @@ def test_cpu_clip_base_annotator(): reason="Test requires GPU and 16GB of HDD", ) def test_cuda_clip_large_annotator(): - _check_clip_annotator("cuda") + _check_clip_annotator("cuda", size="large") @pytest.mark.skipif( @@ -95,4 +98,58 @@ def test_cuda_clip_large_annotator(): reason="Test requires at least 16GB of HDD", ) def test_cpu_clip_large_annotator(): - _check_clip_annotator("cpu") + _check_clip_annotator("cpu", size="large") + + +def _check_slimsam_annotator(device: str, size: str = "base"): + url = "https://ultralytics.com/images/bus.jpg" + im = Image.open(requests.get(url, stream=True).raw) + annotator = SlimSAMAnnotator(device=device, size=size) + masks = annotator.annotate_batch([im], [np.array([[3, 229, 559, 650]])]) + w, h = im.width, im.height + # Check that the masks are lists + assert isinstance(masks, list) and len(masks) == 1 + # Check that the masks are [B, O, N, 2], where + # - B = batch size + # - O = number of objects + # - N = number of points of the mask segment polygon (at least 3 to be polygon) + assert isinstance(masks[0], list) and len(masks[0]) == 1 + assert isinstance(masks[0][0], list) and len(masks[0][0]) >= 3 + for point in masks[0][0]: + # Check that it is a 2D point + assert len(point) == 2 + assert 0 <= point[0] <= w and 0 <= point[1] <= h + + annotator.release(empty_cuda_cache=True if device != "cpu" else False) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", +) +def test_cuda_slimsam_base_annotator(): + _check_slimsam_annotator("cuda") + + +@pytest.mark.skipif( + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", +) +def test_cpu_slimsam_base_annotator(): + _check_slimsam_annotator("cpu") + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_disk_space < 16, + reason="Test requires GPU and 16GB of HDD", +) +def test_cuda_slimsam_large_annotator(): + _check_slimsam_annotator("cuda", size="large") + + +@pytest.mark.skipif( + total_disk_space < 16, + reason="Test requires at least 16GB of HDD", +) +def test_cpu_slimsam_large_annotator(): + _check_slimsam_annotator("cpu", size="large") diff --git a/tests/core_tests/unittests/test_image_generation.py b/tests/core_tests/unittests/test_image_generation.py index 2436f75..6ff15ef 100644 --- a/tests/core_tests/unittests/test_image_generation.py +++ b/tests/core_tests/unittests/test_image_generation.py @@ -111,19 +111,3 @@ def test_cuda_sdxl_turbo_image_generator(): ) def test_cpu_sdxl_turbo_image_generator(): _check_image_generator(StableDiffusionTurboImageGenerator, "cpu") - - -@pytest.mark.skipif( - not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25, - reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD", -) -def test_cuda_sdxl_lightning_image_generator(): - _check_image_generator(StableDiffusionLightningImageGenerator, "cuda") - - -@pytest.mark.skipif( - total_memory < 16 or total_disk_space < 25, - reason="Test requires at least 16GB of RAM and 25GB of HDD", -) -def test_cpu_sdxl_lightning_image_generator(): - _check_image_generator(StableDiffusionLightningImageGenerator, "cpu") diff --git a/tests/heavy_tests/integration/test_pipeline_heavy.py b/tests/heavy_tests/integration/test_pipeline_heavy.py index ad9ec8a..6b7dc3f 100644 --- a/tests/heavy_tests/integration/test_pipeline_heavy.py +++ b/tests/heavy_tests/integration/test_pipeline_heavy.py @@ -57,7 +57,7 @@ def test_cpu_simple_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -79,7 +79,7 @@ def test_cuda_simple_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -101,7 +101,7 @@ def test_cuda_simple_llm_synonym_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -124,7 +124,7 @@ def test_cuda_simple_wordnet_synonym_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -147,7 +147,7 @@ def test_cpu_simple_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -169,7 +169,7 @@ def test_cuda_simple_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -194,7 +194,7 @@ def test_cpu_lm_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -216,7 +216,7 @@ def test_cuda_lm_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -238,7 +238,7 @@ def test_cuda_4bit_lm_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -261,7 +261,7 @@ def test_cpu_lm_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -283,7 +283,7 @@ def test_cuda_lm_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -305,7 +305,7 @@ def test_cuda_4bit_lm_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -331,7 +331,7 @@ def test_cpu_tiny_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -353,7 +353,7 @@ def test_cuda_tiny_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -375,7 +375,7 @@ def test_cpu_tiny_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -397,7 +397,7 @@ def test_cuda_tiny_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -422,7 +422,7 @@ def test_cpu_qwen2_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -444,7 +444,7 @@ def test_cuda_qwen2_sdxl_turbo_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -466,7 +466,7 @@ def test_cpu_qwen2_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -488,7 +488,7 @@ def test_cuda_qwen2_sdxl_detection_pipeline(): # Define the command to run the datadreamer cmd = ( f"datadreamer --save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -501,7 +501,7 @@ def test_cuda_qwen2_sdxl_detection_pipeline(): # ========================================================= -# CLASSIFICATION - SIMPLE LM +# INSTANCE SEGMENTATION - SIMPLE LM # ========================================================= @pytest.mark.skipif( total_memory < 16 or total_disk_space < 35, @@ -514,7 +514,7 @@ def test_cpu_simple_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -538,7 +538,7 @@ def test_cuda_simple_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -562,7 +562,7 @@ def test_cuda_simple_llm_synonym_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -587,7 +587,7 @@ def test_cuda_simple_wordnet_synonym_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--num_objects_range 1 2 " @@ -612,7 +612,7 @@ def test_cpu_simple_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--image_annotator clip " @@ -636,7 +636,7 @@ def test_cuda_simple_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator simple " f"--image_annotator clip " @@ -650,7 +650,7 @@ def test_cuda_simple_sdxl_classification_pipeline(): # ========================================================= -# CLASSIFICATION - LLM +# INSTANCE SEGMENTATION - LLM # ========================================================= @pytest.mark.skipif( total_memory < 32 or total_disk_space < 55, @@ -663,7 +663,7 @@ def test_cpu_lm_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -687,7 +687,7 @@ def test_cuda_lm_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -711,7 +711,7 @@ def test_cuda_4bit_lm_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -736,7 +736,7 @@ def test_cpu_lm_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--image_annotator clip " @@ -760,7 +760,7 @@ def test_cuda_lm_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--image_annotator clip " @@ -784,7 +784,7 @@ def test_cuda_4bit_lm_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator lm " f"--num_objects_range 1 2 " @@ -799,7 +799,7 @@ def test_cuda_4bit_lm_sdxl_classification_pipeline(): # ========================================================= -# CLASSIFICATION - TinyLlama LLM +# INSTANCE SEGMENTATION - TinyLlama LLM # ========================================================= @pytest.mark.skipif( total_memory < 16 or total_disk_space < 35, @@ -812,7 +812,7 @@ def test_cpu_tiny_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--image_annotator clip " @@ -836,7 +836,7 @@ def test_cuda_tiny_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -860,7 +860,7 @@ def test_cpu_tiny_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -884,7 +884,7 @@ def test_cuda_tiny_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator tiny " f"--num_objects_range 1 2 " @@ -911,7 +911,7 @@ def test_cpu_qwen2_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--image_annotator clip " @@ -935,7 +935,7 @@ def test_cuda_qwen2_sdxl_turbo_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -959,7 +959,7 @@ def test_cpu_qwen2_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -983,7 +983,7 @@ def test_cuda_qwen2_sdxl_classification_pipeline(): cmd = ( f"datadreamer --task classification " f"--save_dir {target_folder} " - f"--class_names alien mars cat " + f"--class_names alien bear cat " f"--prompts_number 1 " f"--prompt_generator qwen2 " f"--num_objects_range 1 2 " @@ -994,3 +994,499 @@ def test_cuda_qwen2_sdxl_classification_pipeline(): ) # Check the run of the pipeline _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - SIMPLE LM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-simple-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 55, + reason="Test requires GPU, at least 16GB of RAM and 55GB of HDD", +) +def test_cuda_simple_llm_synonym_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-llm-synonym-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--image_annotator owlv2-slimsam " + f"--use_image_tester " + f"--synonym_generator llm " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_wordnet_synonym_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-wordnet-synonym-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--synonym_generator wordnet " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_simple_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-simple-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--image_annotator owlv2-slimsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_simple_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-simple-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator simple " + f"--image_annotator owlv2-slimsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - LLM +# ========================================================= +@pytest.mark.skipif( + total_memory < 32 or total_disk_space < 55, + reason="Test requires at least 32GB of RAM and 55GB of HDD for running on CPU", +) +def test_cpu_lm_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-lm-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 55, + reason="Test requires at least 16GB of RAM, 55GB of HDD and CUDA support", +) +def test_cuda_lm_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-lm-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 14 or not torch.cuda.is_available() or total_disk_space < 45, + reason="Test requires at least 14GB of RAM, 45GB of HDD and CUDA support", +) +def test_cuda_4bit_lm_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-4bit-lm-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--lm_quantization 4bit " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 32 or total_disk_space < 55, + reason="Test requires at least 32GB of RAM and 55GB of HDD for running on CPU", +) +def test_cpu_lm_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-lm-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--image_annotator owlv2-slimsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or not torch.cuda.is_available() or total_disk_space < 55, + reason="Test requires at least 16GB of RAM, CUDA support and 55GB of HDD", +) +def test_cuda_lm_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-lm-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--image_annotator owlv2-slimsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 14 or not torch.cuda.is_available() or total_disk_space < 45, + reason="Test requires at least 14GB of RAM, CUDA support and 45GB of HDD", +) +def test_cuda_4bit_lm_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-4bit-lm-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator lm " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--lm_quantization 4bit " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - TinyLlama LLM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_tiny_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-tiny-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--image_annotator owlv2-slimsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_tiny_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-tiny-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_tiny_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-tiny-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_tiny_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-tiny-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator tiny " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +# ========================================================= +# INSTANCE SEGMENTATION - Qwen2.5 LLM +# ========================================================= +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_qwen2_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-qwen2-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--image_annotator owlv2-slimsam " + f"--num_objects_range 1 2 " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_qwen2_sdxl_turbo_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-qwen2-sdxl-turbo/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl-turbo " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 35, + reason="Test requires at least 16GB of RAM and 35GB of HDD", +) +def test_cpu_qwen2_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cpu-qwen2-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cpu" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 35, + reason="Test requires GPU, at least 16GB of RAM and 35GB of HDD", +) +def test_cuda_qwen2_sdxl_instance_segmentation_pipeline(): + # Define target folder + target_folder = "data/data-inst-seg-cuda-qwen2-sdxl/" + # Define the command to run the datadreamer + cmd = ( + f"datadreamer --task instance-segmentation " + f"--save_dir {target_folder} " + f"--class_names alien bear cat " + f"--prompts_number 1 " + f"--prompt_generator qwen2 " + f"--num_objects_range 1 2 " + f"--image_annotator owlv2-slimsam " + f"--image_generator sdxl " + f"--use_image_tester " + f"--device cuda" + ) + # Check the run of the pipeline + _check_detection_pipeline(cmd, target_folder) diff --git a/tests/heavy_tests/unittests/test_image_generation_heavy.py b/tests/heavy_tests/unittests/test_image_generation_heavy.py index 30141cc..49721f7 100644 --- a/tests/heavy_tests/unittests/test_image_generation_heavy.py +++ b/tests/heavy_tests/unittests/test_image_generation_heavy.py @@ -66,3 +66,19 @@ def test_cuda_sdxl_image_generator(): ) def test_cpu_sdxl_image_generator(): _check_image_generator(StableDiffusionImageGenerator, "cpu") + + +@pytest.mark.skipif( + not torch.cuda.is_available() or total_memory < 16 or total_disk_space < 25, + reason="Test requires GPU, at least 16GB of RAM and 25GB of HDD", +) +def test_cuda_sdxl_lightning_image_generator(): + _check_image_generator(StableDiffusionLightningImageGenerator, "cuda") + + +@pytest.mark.skipif( + total_memory < 16 or total_disk_space < 25, + reason="Test requires at least 16GB of RAM and 25GB of HDD", +) +def test_cpu_sdxl_lightning_image_generator(): + _check_image_generator(StableDiffusionLightningImageGenerator, "cpu")