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tasks/audio.py

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+from fastapi import APIRouter
+from datetime import datetime
+from datasets import load_dataset
+from sklearn.metrics import accuracy_score
+import random
+import os
+
+from .utils.evaluation import AudioEvaluationRequest
+from .utils.emissions import tracker, clean_emissions_data, get_space_info
+
+from dotenv import load_dotenv
+load_dotenv()
+
+router = APIRouter()
+
+DESCRIPTION = "Random Baseline"
+ROUTE = "/audio"
+
+
+
+@router.post(ROUTE, tags=["Audio Task"],
+             description=DESCRIPTION)
+async def evaluate_audio(request: AudioEvaluationRequest):
+    """
+    Evaluate audio classification for rainforest sound detection.
+    
+    Current Model: Random Baseline
+    - Makes random predictions from the label space (0-1)
+    - Used as a baseline for comparison
+    """
+    # Get space info
+    username, space_url = get_space_info()
+
+    # Define the label mapping
+    LABEL_MAPPING = {
+        "chainsaw": 0,
+        "environment": 1
+    }
+    # Load and prepare the dataset
+    # Because the dataset is gated, we need to use the HF_TOKEN environment variable to authenticate
+    dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN"))
+    
+    # Split dataset
+    train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
+    test_dataset = train_test["test"]
+    
+    # Start tracking emissions
+    tracker.start()
+    tracker.start_task("inference")
+    
+    #--------------------------------------------------------------------------------------------
+    # YOUR MODEL INFERENCE CODE HERE
+    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
+    #--------------------------------------------------------------------------------------------   
+    
+    # Make random predictions (placeholder for actual model inference)
+    true_labels = test_dataset["label"]
+    predictions = [random.randint(0, 1) for _ in range(len(true_labels))]
+    
+    #--------------------------------------------------------------------------------------------
+    # YOUR MODEL INFERENCE STOPS HERE
+    #--------------------------------------------------------------------------------------------   
+    
+    # Stop tracking emissions
+    emissions_data = tracker.stop_task()
+    
+    # Calculate accuracy
+    accuracy = accuracy_score(true_labels, predictions)
+    
+    # Prepare results dictionary
+    results = {
+        "username": username,
+        "space_url": space_url,
+        "submission_timestamp": datetime.now().isoformat(),
+        "model_description": DESCRIPTION,
+        "accuracy": float(accuracy),
+        "energy_consumed_wh": emissions_data.energy_consumed * 1000,
+        "emissions_gco2eq": emissions_data.emissions * 1000,
+        "emissions_data": clean_emissions_data(emissions_data),
+        "api_route": ROUTE,
+        "dataset_config": {
+            "dataset_name": request.dataset_name,
+            "test_size": request.test_size,
+            "test_seed": request.test_seed
+        }
+    }
+    
+    return results 

tasks/image.py

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+from fastapi import APIRouter
+from datetime import datetime
+from datasets import load_dataset
+import numpy as np
+from sklearn.metrics import accuracy_score
+import random
+import os
+
+from .utils.evaluation import ImageEvaluationRequest
+from .utils.emissions import tracker, clean_emissions_data, get_space_info
+
+from dotenv import load_dotenv
+load_dotenv()
+
+router = APIRouter()
+
+DESCRIPTION = "Random Baseline"
+ROUTE = "/image"
+
+def parse_boxes(annotation_string):
+    """Parse multiple boxes from a single annotation string.
+    Each box has 5 values: class_id, x_center, y_center, width, height"""
+    values = [float(x) for x in annotation_string.strip().split()]
+    boxes = []
+    # Each box has 5 values
+    for i in range(0, len(values), 5):
+        if i + 5 <= len(values):
+            # Skip class_id (first value) and take the next 4 values
+            box = values[i+1:i+5]
+            boxes.append(box)
+    return boxes
+
+def compute_iou(box1, box2):
+    """Compute Intersection over Union (IoU) between two YOLO format boxes."""
+    # Convert YOLO format (x_center, y_center, width, height) to corners
+    def yolo_to_corners(box):
+        x_center, y_center, width, height = box
+        x1 = x_center - width/2
+        y1 = y_center - height/2
+        x2 = x_center + width/2
+        y2 = y_center + height/2
+        return np.array([x1, y1, x2, y2])
+    
+    box1_corners = yolo_to_corners(box1)
+    box2_corners = yolo_to_corners(box2)
+    
+    # Calculate intersection
+    x1 = max(box1_corners[0], box2_corners[0])
+    y1 = max(box1_corners[1], box2_corners[1])
+    x2 = min(box1_corners[2], box2_corners[2])
+    y2 = min(box1_corners[3], box2_corners[3])
+    
+    intersection = max(0, x2 - x1) * max(0, y2 - y1)
+    
+    # Calculate union
+    box1_area = (box1_corners[2] - box1_corners[0]) * (box1_corners[3] - box1_corners[1])
+    box2_area = (box2_corners[2] - box2_corners[0]) * (box2_corners[3] - box2_corners[1])
+    union = box1_area + box2_area - intersection
+    
+    return intersection / (union + 1e-6)
+
+def compute_max_iou(true_boxes, pred_box):
+    """Compute maximum IoU between a predicted box and all true boxes"""
+    max_iou = 0
+    for true_box in true_boxes:
+        iou = compute_iou(true_box, pred_box)
+        max_iou = max(max_iou, iou)
+    return max_iou
+
+@router.post(ROUTE, tags=["Image Task"],
+             description=DESCRIPTION)
+async def evaluate_image(request: ImageEvaluationRequest):
+    """
+    Evaluate image classification and object detection for forest fire smoke.
+    
+    Current Model: Random Baseline
+    - Makes random predictions for both classification and bounding boxes
+    - Used as a baseline for comparison
+    
+    Metrics:
+    - Classification accuracy: Whether an image contains smoke or not
+    - Object Detection accuracy: IoU (Intersection over Union) for smoke bounding boxes
+    """
+    # Get space info
+    username, space_url = get_space_info()
+    
+    # Load and prepare the dataset
+    dataset = load_dataset(request.dataset_name, token=os.getenv("HF_TOKEN"))
+    
+    # Split dataset
+    train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
+    test_dataset = train_test["test"]
+    
+    # Start tracking emissions
+    tracker.start()
+    tracker.start_task("inference")
+    
+    #--------------------------------------------------------------------------------------------
+    # YOUR MODEL INFERENCE CODE HERE
+    # Update the code below to replace the random baseline with your model inference
+    #--------------------------------------------------------------------------------------------   
+    
+    predictions = []
+    true_labels = []
+    pred_boxes = []
+    true_boxes_list = []  # List of lists, each inner list contains boxes for one image
+    
+    for example in test_dataset:
+        # Parse true annotation (YOLO format: class_id x_center y_center width height)
+        annotation = example.get("annotations", "").strip()
+        has_smoke = len(annotation) > 0
+        true_labels.append(int(has_smoke))
+        
+        # Make random classification prediction
+        pred_has_smoke = random.random() > 0.5
+        predictions.append(int(pred_has_smoke))
+        
+        # If there's a true box, parse it and make random box prediction
+        if has_smoke:
+            # Parse all true boxes from the annotation
+            image_true_boxes = parse_boxes(annotation)
+            true_boxes_list.append(image_true_boxes)
+            
+            # For baseline, make one random box prediction per image
+            # In a real model, you might want to predict multiple boxes
+            random_box = [
+                random.random(),  # x_center
+                random.random(),  # y_center
+                random.random() * 0.5,  # width (max 0.5)
+                random.random() * 0.5   # height (max 0.5)
+            ]
+            pred_boxes.append(random_box)
+    
+    #--------------------------------------------------------------------------------------------
+    # YOUR MODEL INFERENCE STOPS HERE
+    #--------------------------------------------------------------------------------------------   
+    
+    # Stop tracking emissions
+    emissions_data = tracker.stop_task()
+    
+    # Calculate classification accuracy
+    classification_accuracy = accuracy_score(true_labels, predictions)
+    
+    # Calculate mean IoU for object detection (only for images with smoke)
+    # For each image, we compute the max IoU between the predicted box and all true boxes
+    ious = []
+    for true_boxes, pred_box in zip(true_boxes_list, pred_boxes):
+        max_iou = compute_max_iou(true_boxes, pred_box)
+        ious.append(max_iou)
+    
+    mean_iou = float(np.mean(ious)) if ious else 0.0
+    
+    # Prepare results dictionary
+    results = {
+        "username": username,
+        "space_url": space_url,
+        "submission_timestamp": datetime.now().isoformat(),
+        "model_description": DESCRIPTION,
+        "classification_accuracy": float(classification_accuracy),
+        "mean_iou": mean_iou,
+        "energy_consumed_wh": emissions_data.energy_consumed * 1000,
+        "emissions_gco2eq": emissions_data.emissions * 1000,
+        "emissions_data": clean_emissions_data(emissions_data),
+        "api_route": ROUTE,
+        "dataset_config": {
+            "dataset_name": request.dataset_name,
+            "test_size": request.test_size,
+            "test_seed": request.test_seed
+        }
+    }
+    
+    return results