[BUFG][Odin]

playground/structs/company_example.py

@@ -19,7 +19,7 @@ va = Agent(llm=llm, ai_name="VA")
 
 # Create a company
 company = Company(
-    org_chart = [[dev, va]],
+    org_chart=[[dev, va]],
     shared_instructions="Do your best",
     ceo=ceo,
 )

pyproject.toml

@@ -73,6 +73,7 @@ psutil = "*"
 ultralytics = "*"
 timm = "*"
 supervision = "*"
+scikit-image = "*"
 
 
 

requirements.txt

@@ -61,4 +61,5 @@ pre-commit==3.2.2
 peft
 psutil
 ultralytics
-supervision
+supervision
+scikit-image

swarms/models/medical_sam.py

@@ -0,0 +1,140 @@
+import os
+from dataclasses import dataclass
+from typing import Tuple
+
+import numpy as np
+import requests
+import torch
+import torch.nn.functional as F
+from skimage import transform
+from torch import Tensor
+
+
+@dataclass
+class MedicalSAM:
+    """
+    MedicalSAM class for performing semantic segmentation on medical images using the SAM model.
+
+    Attributes:
+        model_path (str): The file path to the model weights.
+        device (str): The device to run the model on (default is "cuda:0").
+        model_weights_url (str): The URL to download the model weights from.
+
+    Methods:
+        __post_init__(): Initializes the MedicalSAM object.
+        download_model_weights(model_path: str): Downloads the model weights from the specified URL and saves them to the given file path.
+        preprocess(img): Preprocesses the input image.
+        run(img, box): Runs the semantic segmentation on the input image within the specified bounding box.
+
+    """
+
+    model_path: str
+    device: str = "cuda:0"
+    model_weights_url: str = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
+
+    def __post_init__(self):
+        if not os.path.exists(self.model_path):
+            self.download_model_weights(self.model_path)
+
+        self.model = sam_model_registry["vit_b"](
+            checkpoint=self.model_path
+        )
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+    def download_model_weights(self, model_path: str):
+        """
+        Downloads the model weights from the specified URL and saves them to the given file path.
+
+        Args:
+            model_path (str): The file path where the model weights will be saved.
+
+        Raises:
+            Exception: If the model weights fail to download.
+        """
+        response = requests.get(self.model_weights_url, stream=True)
+        if response.status_code == 200:
+            with open(model_path, "wb") as f:
+                f.write(response.content)
+        else:
+            raise Exception("Failed to download model weights.")
+
+    def preprocess(self, img: np.ndarray) -> Tuple[Tensor, int, int]:
+        """
+        Preprocesses the input image.
+
+        Args:
+            img: The input image.
+
+        Returns:
+            img_tensor: The preprocessed image tensor.
+            H: The original height of the image.
+            W: The original width of the image.
+        """
+        if len(img.shape) == 2:
+            img = np.repeat(img[:, :, None], 3, axis=-1)
+        H, W, _ = img.shape
+        img = transform.resize(
+            img,
+            (1024, 1024),
+            order=3,
+            preserve_range=True,
+            anti_aliasing=True,
+        ).astype(np.uint8)
+        img = img - img.min() / np.clip(
+            img.max() - img.min(), a_min=1e-8, a_max=None
+        )
+        img = torch.tensor(img).float().permute(2, 0, 1).unsqueeze(0)
+        return img, H, W
+
+    @torch.no_grad()
+    def run(self, img: np.ndarray, box: np.ndarray) -> np.ndarray:
+        """
+        Runs the semantic segmentation on the input image within the specified bounding box.
+
+        Args:
+            img: The input image.
+            box: The bounding box coordinates (x1, y1, x2, y2).
+
+        Returns:
+            medsam_seg: The segmented image.
+        """
+        img_tensor, H, W = self.preprocess(img)
+        img_tensor = img_tensor.to(self.device)
+        box_1024 = box / np.array([W, H, W, H]) * 1024
+        img = self.model.image_encoder(img_tensor)
+
+        box_torch = torch.as_tensor(
+            box_1024, dtype=torch.float, device=img_tensor.device
+        )
+
+        if len(box_torch.shape) == 2:
+            box_torch = box_torch[:, None, :]
+
+        sparse_embeddings, dense_embeddings = (
+            self.model.prompt_encoder(
+                points=None,
+                boxes=box_torch,
+                masks=None,
+            )
+        )
+
+        low_res_logits, _ = self.model.mask_decoder(
+            image_embeddings=img,
+            image_pe=self.model.prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=False,
+        )
+
+        low_res_pred = torch.sigmoid(low_res_logits)
+        low_res_pred = F.interpolate(
+            low_res_pred,
+            size=(H, W),
+            mode="bilinear",
+            align_corners=False,
+        )
+        low_res_pred = low_res_pred.squeeze().cpu().numpy()
+        medsam_seg = (low_res_pred > 0.5).astype(np.uint8)
+
+        return medsam_seg

swarms/models/odin.py

@@ -1,47 +1,53 @@
+import os
 import supervision as sv
-from ultraanalytics import YOLO
+from ultralytics import YOLO
 from tqdm import tqdm
 from swarms.models.base_llm import AbstractLLM
-
+from swarms.utils.download_weights_from_url import download_weights_from_url
 
 class Odin(AbstractLLM):
     """
     Odin class represents an object detection and tracking model.
 
-    Args:
-        source_weights_path (str): Path to the weights file for the object detection model.
-        source_video_path (str): Path to the source video file.
-        target_video_path (str): Path to save the output video file.
-        confidence_threshold (float): Confidence threshold for object detection.
-        iou_threshold (float): Intersection over Union (IoU) threshold for object detection.
-
     Attributes:
-        source_weights_path (str): Path to the weights file for the object detection model.
-        source_video_path (str): Path to the source video file.
-        target_video_path (str): Path to save the output video file.
-        confidence_threshold (float): Confidence threshold for object detection.
-        iou_threshold (float): Intersection over Union (IoU) threshold for object detection.
+        source_weights_path (str): The file path to the YOLO model weights.
+        confidence_threshold (float): The confidence threshold for object detection.
+        iou_threshold (float): The intersection over union (IOU) threshold for object detection.
+        
+    Example:
+    >>> odin = Odin(
+    ...     source_weights_path="yolo.weights",
+    ...     confidence_threshold=0.3,
+    ...     iou_threshold=0.7,
+    ... )
+    >>> odin.run(video="input.mp4")
+    
+    
     """
 
     def __init__(
         self,
-        source_weights_path: str = None,
-        target_video_path: str = None,
+        source_weights_path: str = "yolo.weights",
         confidence_threshold: float = 0.3,
         iou_threshold: float = 0.7,
     ):
         super(Odin, self).__init__()
         self.source_weights_path = source_weights_path
-        self.target_video_path = target_video_path
         self.confidence_threshold = confidence_threshold
         self.iou_threshold = iou_threshold
+        
+        if not os.path.exists(self.source_weights_path):
+            download_weights_from_url(
+                url=source_weights_path, save_path=self.source_weights_path
+            )
+    
 
-    def run(self, video_path: str, *args, **kwargs):
+    def run(self, video: str, *args, **kwargs):
         """
         Runs the object detection and tracking algorithm on the specified video.
 
         Args:
-            video_path (str): The path to the input video file.
+            video (str): The path to the input video file.
             *args: Additional positional arguments.
             **kwargs: Additional keyword arguments.
 
@@ -53,14 +59,14 @@ class Odin(AbstractLLM):
         tracker = sv.ByteTrack()
         box_annotator = sv.BoxAnnotator()
         frame_generator = sv.get_video_frames_generator(
-            source_path=self.source_video_path
+            source_path=self.source_video
         )
-        video_info = sv.VideoInfo.from_video_path(
-            video_path=video_path
+        video_info = sv.VideoInfo.from_video(
+            video=video
         )
 
         with sv.VideoSink(
-            target_path=self.target_video_path, video_info=video_info
+            target_path=self.target_video, video_info=video_info
         ) as sink:
             for frame in tqdm(
                 frame_generator, total=video_info.total_frames

swarms/structs/company.py

@@ -5,11 +5,13 @@ from swarms.structs.agent import Agent
 from swarms.utils.logger import logger
 from swarms.structs.conversation import Conversation
 
+
 @dataclass
 class Company:
     """
     Represents a company with a hierarchical organizational structure.
     """
+
     org_chart: List[List[Agent]]
     shared_instructions: str = None
     ceo: Optional[Agent] = None
@@ -171,5 +173,3 @@ class Company:
                 )
                 print(f"{task_description} is being executed")
                 agent.run(task_description)
-
-

swarms/utils/download_weights_from_url.py

@@ -0,0 +1,19 @@
+import requests 
+
+def download_weights_from_url(url: str, save_path: str = "models/weights.pth"):
+    """
+    Downloads model weights from the given URL and saves them to the specified path.
+
+    Args:
+        url (str): The URL from which to download the model weights.
+        save_path (str, optional): The path where the downloaded weights should be saved. 
+            Defaults to "models/weights.pth".
+    """
+    response = requests.get(url, stream=True)
+    response.raise_for_status()
+    
+    with open(save_path, "wb") as f:
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+        
+    print(f"Model weights downloaded and saved to {save_path}")