[Frontend][TFLite] fix detection_postprocess's non_max_suppression_at…

…trs["force_suppress"] (apache#12593)

* [Frontend][TFLite]fix detection_postprocess's non_max_suppression_attrs["force_suppress"]

Since tvm only supports operators detection_postprocess use_regular_nms
is false, which will suppress boxes that exceed the threshold regardless
of the class when implementing NMS in tflite, in order for the results
of tvm and tflite to be consistent, we need to set force_suppress to
True.

* [Frontend][TFLite]fix detection_postprocess's non_max_suppression_attrs[force_suppress]

Added a test case that reproduces inconsistent results between tvm and tflite
When the force_suppress is false,it will get a good result if you set the force_suppress as true

python/tvm/relay/frontend/tflite.py

@@ -3355,7 +3355,7 @@ def convert_detection_postprocess(self, op):
         non_max_suppression_attrs = {}
         non_max_suppression_attrs["return_indices"] = False
         non_max_suppression_attrs["iou_threshold"] = custom_options["nms_iou_threshold"]
-        non_max_suppression_attrs["force_suppress"] = False
+        non_max_suppression_attrs["force_suppress"] = True
         non_max_suppression_attrs["top_k"] = anchor_boxes
         non_max_suppression_attrs["max_output_size"] = custom_options["max_detections"]
         non_max_suppression_attrs["invalid_to_bottom"] = False

tests/python/frontend/tflite/test_forward.py

@@ -4311,13 +4311,8 @@ def test_forward_matrix_diag():
 # ----------------
 
 
-def test_detection_postprocess():
-    """Detection PostProcess"""
-    tf_model_file = tf_testing.get_workload_official(
-        "http://download.tensorflow.org/models/object_detection/"
-        "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz",
-        "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/tflite_graph.pb",
-    )
+def _test_detection_postprocess(tf_model_file, box_encodings_size, class_predictions_size):
+    """One iteration of detection postProcess with given model and shapes"""
     converter = tf.lite.TFLiteConverter.from_frozen_graph(
         tf_model_file,
         input_arrays=["raw_outputs/box_encodings", "raw_outputs/class_predictions"],
@@ -4328,16 +4323,16 @@ def test_detection_postprocess():
             "TFLite_Detection_PostProcess:3",
         ],
         input_shapes={
-            "raw_outputs/box_encodings": (1, 1917, 4),
-            "raw_outputs/class_predictions": (1, 1917, 91),
+            "raw_outputs/box_encodings": box_encodings_size,
+            "raw_outputs/class_predictions": class_predictions_size,
         },
     )
     converter.allow_custom_ops = True
     converter.inference_type = tf.lite.constants.FLOAT
     tflite_model = converter.convert()
     np.random.seed(0)
-    box_encodings = np.random.uniform(size=(1, 1917, 4)).astype("float32")
-    class_predictions = np.random.uniform(size=(1, 1917, 91)).astype("float32")
+    box_encodings = np.random.uniform(size=box_encodings_size).astype("float32")
+    class_predictions = np.random.uniform(size=class_predictions_size).astype("float32")
     tflite_output = run_tflite_graph(tflite_model, [box_encodings, class_predictions])
     tvm_output = run_tvm_graph(
         tflite_model,
@@ -4382,6 +4377,26 @@ def test_detection_postprocess():
         )
 
 
+def test_detection_postprocess():
+    """Detection PostProcess"""
+    box_encodings_size = (1, 1917, 4)
+    class_predictions_size = (1, 1917, 91)
+    tf_model_file = tf_testing.get_workload_official(
+        "http://download.tensorflow.org/models/object_detection/"
+        "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz",
+        "ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/tflite_graph.pb",
+    )
+    _test_detection_postprocess(tf_model_file, box_encodings_size, class_predictions_size)
+
+    box_encodings_size = (1, 2034, 4)
+    class_predictions_size = (1, 2034, 91)
+    tf_model_file = download_testdata(
+        "https://github.com/czh978/models_for_tvm_test/raw/main/tflite_graph_with_postprocess.pb",
+        "tflite_graph_with_postprocess.pb",
+    )
+    _test_detection_postprocess(tf_model_file, box_encodings_size, class_predictions_size)
+
+
 #######################################################################
 # Custom Converter
 # ----------------