v

my_metricv2.py

@@ -35,6 +35,7 @@ This new module is designed to solve this great ML task and is crafted with a lo
 
 # TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
+
 Calculates how good are predictions given some references, using certain scores
 Args:
     predictions: list of predictions to score. Each predictions
@@ -45,13 +46,26 @@ Returns:
     accuracy: description of the first score,
     another_score: description of the second score,
 Examples:
-    Examples should be written in doctest format, and should illustrate how
-    to use the function.
+    >>> import numpy as np
+    >>> mean_iou = evaluate.load("mean_iou")
+
+    >>> # suppose one has 3 different segmentation maps predicted
+    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
+    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
+
+    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
+    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
+
+    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
+    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
+
+    >>> predicted = [predicted_1, predicted_2, predicted_3]
+    >>> ground_truth = [actual_1, actual_2, actual_3]
+
+    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
+    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
+    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
 
-    >>> my_new_module = evaluate.load("my_new_module")
-    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
-    >>> print(results)
-    {'accuracy': 1.0}
 """
 
 # TODO: Define external resources urls if needed
@@ -96,27 +110,23 @@ class MyMetricv2(evaluate.Metric):
         # TODO: Compute the different scores of the module
 
         # <frame number>, <object id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <confidence>, <x>, <y>, <z>
-        # predictions array de formato []
 
-        # accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
+        return calculate(predictions, references)
 
+def calculate(predictions, references):
+    """Returns the scores"""
+    try: 
+        np_predictions = np.array(predictions)
+        np_references = np.array(references)
+    except:
+        raise ValueError("The predictions and references should be lists of floats in the correct format [frame number, object id, bb_left, bb_top, bb_width, bb_height, confidence]")
+    
+    print(predictions)
+    print(references)
 
-        return {
+    return {
         
             "predictions": predictions,
             "references": references
     
         }
-
-    def add(self, predictions, references):
-        """Returns the scores"""
-        print("add")
-        print(predictions)
-        print(references)
-
-        return {
-            
-                "predictions": predictions,
-                "references": references
-        
-            }