Enable quantization when op may contain tensors with zero-size array

ai_edge_quantizer/calibrator.py

@@ -281,7 +281,13 @@ def _initialize_model_qsvs(
             algorithm_name, op_key
         )
         op_info = qtyping.OpInfo(op, op_key, subgraph_op_id, op_quant_config)
-        op_qsvs = qsv_init_func(op_info, graph_info)
+        # Ignore the input tensors where any dimension of the shape is 0.
+        inputs_to_ignore = [
+            idx
+            for idx in op.inputs
+            if not np.all(graph_info.subgraph_tensors[idx].shape)
+        ]
+        op_qsvs = qsv_init_func(op_info, graph_info, inputs_to_ignore)
         # Step3: initialize tensor qsvs.
         for tensor_name, qsv in op_qsvs.items():
           if tensor_name not in self._model_qsvs:

ai_edge_quantizer/calibrator_test.py

@@ -228,6 +228,18 @@ def test_calibrate_unsupported_ops_success(self):
     )
     self.assertLen(test_calibrator.get_cached_output(), 10)
 
+  def test_calibrate_reshape_with_empty_shape_success(self):
+    test_model_path = os.path.join(
+        TEST_DATA_PREFIX_PATH, "tests/models/reshape_with_empty_shape.tflite"
+    )
+    test_calibrator = calibrator.Calibrator(test_model_path)
+    _add_default_int8xint8_integer_recipe(self._recipe_manager)
+    calib_data = test_utils.create_random_normal_input_data(
+        test_model_path, num_samples=4
+    )
+    test_calibrator.calibrate(calib_data, self._recipe_manager)
+    self.assertNotEmpty(test_calibrator.get_model_qsvs())
+
 
 class CalibratorAlreadyQuantizedModelTest(googletest.TestCase):
 

ai_edge_quantizer/utils/tfl_interpreter_utils.py

@@ -188,9 +188,14 @@ def get_tensor_name_to_content_map(
   """
   tensors = {}
   for tensor_detail in tflite_interpreter.get_tensor_details(subgraph_index):
-    # Don't return temporary, unnamed tensors
+    # Don't return temporary, unnamed tensors.
     if not tensor_detail["name"]:
       continue
+
+    # Don't return tensors where any dimension of the shape is 0.
+    if not np.all(tensor_detail["shape"]):
+      continue
+
     tensors[tensor_detail["name"]] = get_tensor_data(
         tflite_interpreter, tensor_detail, subgraph_index, dequantize
     )