remove strict

Signed-off-by: Ashwin Vaidya <[email protected]>

Author: ashwinvaidya17

model_api/python/model_api/models/classification.py

@@ -262,7 +262,7 @@ def get_hierarchical_predictions(self, logits: np.ndarray):
                     predicted_labels.append(label_str)
                     predicted_scores.append(head_logits[i])
 
-        predictions = zip(predicted_labels, predicted_scores, strict=False)
+        predictions = zip(predicted_labels, predicted_scores)
         return self.labels_resolver.resolve_labels(predictions)
 
     def get_multilabel_predictions(self, logits: np.ndarray):
@@ -275,7 +275,7 @@ def get_multilabel_predictions(self, logits: np.ndarray):
                 scores.append(logits[i])
         labels = [self.labels[i] if self.labels else "" for i in indices]
 
-        return list(zip(indices, labels, scores, strict=False))
+        return list(zip(indices, labels, scores))
 
     def get_multiclass_predictions(self, outputs):
         if self.embedded_topk:
@@ -286,7 +286,7 @@ def get_multiclass_predictions(self, outputs):
             scoresTensor = softmax_numpy(outputs[self.out_layer_names[0]][0])
             indicesTensor = [np.argmax(scoresTensor)]
             labels = [self.labels[i] if self.labels else "" for i in indicesTensor]
-        return list(zip(indicesTensor, labels, scoresTensor, strict=False))
+        return list(zip(indicesTensor, labels, scoresTensor))
 
 
 def addOrFindSoftmaxAndTopkOutputs(inference_adapter, topk, output_raw_scores):

model_api/python/model_api/models/instance_segmentation.py

@@ -185,7 +185,7 @@ def postprocess(self, outputs, meta):
             saliency_maps = [[] for _ in range(len(self.labels))]
         else:
             saliency_maps = []
-        for box, confidence, cls, raw_mask in zip(boxes, scores, labels, masks, strict=False):
+        for box, confidence, cls, raw_mask in zip(boxes, scores, labels, masks):
             x1, y1, x2, y2 = box
             if (x2 - x1) * (y2 - y1) < 1 or (confidence <= self.confidence_threshold and not has_feature_vector_name):
                 continue

model_api/python/model_api/models/sam_models.py

@@ -137,7 +137,7 @@ def preprocess(self, inputs: dict[str, Any]) -> list[dict[str, Any]]:
             ) is None:
                 continue
 
-            for prompt, label in zip(prompts, labels, strict=False):
+            for prompt, label in zip(prompts, labels):
                 if prompt_name == "bboxes":
                     point_coords = self.apply_coords(
                         prompt.reshape(-1, 2, 2),

model_api/python/model_api/models/ssd.py

@@ -122,7 +122,7 @@ def __call__(self, outputs):
         bboxes = outputs[self.bboxes_layer][0]
         scores = outputs[self.scores_layer][0]
         labels = outputs[self.labels_layer][0]
-        return [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes, strict=False)]
+        return [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes)]
 
 
 class BoxesLabelsParser:
@@ -162,9 +162,7 @@ def __call__(self, outputs):
             labels = np.full(len(bboxes), self.default_label, dtype=bboxes.dtype)
         labels = labels.squeeze(0)
 
-        detections = [
-            Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes, strict=False)
-        ]
+        detections = [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes)]
         return detections
 
 

model_api/python/model_api/models/utils.py

@@ -286,7 +286,7 @@ class DetectionWithLandmarks(Detection):
     def __init__(self, xmin, ymin, xmax, ymax, score, id, landmarks_x, landmarks_y):
         super().__init__(xmin, ymin, xmax, ymax, score, id)
         self.landmarks = []
-        for x, y in zip(landmarks_x, landmarks_y, strict=False):
+        for x, y in zip(landmarks_x, landmarks_y):
             self.landmarks.append((x, y))
 
 

model_api/python/model_api/models/yolo.py

@@ -569,7 +569,6 @@ def postprocess(self, outputs, meta):
                     y_maxs[keep_nms],
                     scores[keep_nms],
                     j[keep_nms],
-                    strict=False,
                 ),
             )
         )
@@ -584,7 +583,7 @@ def set_strides_grids(self):
         hsizes = [self.h // stride for stride in strides]
         wsizes = [self.w // stride for stride in strides]
 
-        for hsize, wsize, stride in zip(hsizes, wsizes, strides, strict=False):
+        for hsize, wsize, stride in zip(hsizes, wsizes, strides):
             xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
             grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
             grids.append(grid)
@@ -720,9 +719,7 @@ def _parse_outputs(self, outputs):
         x_maxs = transposed_boxes[3]
         y_maxs = transposed_boxes[2]
 
-        detections = list(
-            starmap(Detection, zip(x_mins, y_mins, x_maxs, y_maxs, out_scores, out_classes, strict=False))
-        )
+        detections = list(starmap(Detection, zip(x_mins, y_mins, x_maxs, y_maxs, out_scores, out_classes)))
 
         return detections