[FRONTEND][TFLITE] get input tensor information from graph (#7400)

* [FRONTEND][TFLITE] get input tensor information from graph

* remove bare-except

* fix lint

* delete empty line

* comment change

* move some of the tflite frontend code from tvmc to tflite.py

* update shape and dtype when user provided them

* remove unused var. pass user provided shape_dict

* remove duplicate code

python/tvm/driver/tvmc/frontends.py

@@ -198,19 +198,6 @@ def load(self, path, shape_dict=None):
 class TFLiteFrontend(Frontend):
     """ TFLite frontend for TVMC """
 
-    _tflite_m = {
-        0: "float32",
-        1: "float16",
-        2: "int32",
-        3: "uint8",
-        4: "int64",
-        5: "string",
-        6: "bool",
-        7: "int16",
-        8: "complex64",
-        9: "int8",
-    }
-
     @staticmethod
     def name():
         return "tflite"
@@ -241,43 +228,10 @@ def load(self, path, shape_dict=None):
         if version != 3:
             raise TVMCException("input file not tflite version 3")
 
-        logger.debug("tflite_input_type")
-        input_shapes, dtype_dict = TFLiteFrontend._input_type(tflite_model)
-        if shape_dict is not None:
-            input_shapes.update(shape_dict)
-
         logger.debug("parse TFLite model and convert into Relay computation graph")
-        mod, params = relay.frontend.from_tflite(
-            tflite_model, shape_dict=input_shapes, dtype_dict=dtype_dict
-        )
+        mod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict)
         return mod, params
 
-    @staticmethod
-    def _decode_type(n):
-        return TFLiteFrontend._tflite_m[n]
-
-    @staticmethod
-    def _input_type(model):
-        subgraph_count = model.SubgraphsLength()
-        assert subgraph_count > 0
-        shape_dict = {}
-        dtype_dict = {}
-        for subgraph_index in range(subgraph_count):
-            subgraph = model.Subgraphs(subgraph_index)
-            inputs_count = subgraph.InputsLength()
-            assert inputs_count >= 1
-            for input_index in range(inputs_count):
-                input_ = subgraph.Inputs(input_index)
-                assert subgraph.TensorsLength() > input_
-                tensor = subgraph.Tensors(input_)
-                input_shape = tuple(tensor.ShapeAsNumpy())
-                tensor_type = tensor.Type()
-                input_name = tensor.Name().decode("utf8")
-                shape_dict[input_name] = input_shape
-                dtype_dict[input_name] = TFLiteFrontend._decode_type(tensor_type)
-
-        return shape_dict, dtype_dict
-
 
 class PyTorchFrontend(Frontend):
     """ PyTorch frontend for TVMC """

python/tvm/relay/frontend/tflite.py

@@ -3539,7 +3539,45 @@ def get_tensor_name(subgraph, tensor_idx):
     return subgraph.Tensors(tensor_idx).Name().decode("utf-8")
 
 
-def from_tflite(model, shape_dict, dtype_dict):
+def _decode_type(n):
+    _tflite_m = {
+        0: "float32",
+        1: "float16",
+        2: "int32",
+        3: "uint8",
+        4: "int64",
+        5: "string",
+        6: "bool",
+        7: "int16",
+        8: "complex64",
+        9: "int8",
+    }
+    return _tflite_m[n]
+
+
+def _input_type(model):
+    subgraph_count = model.SubgraphsLength()
+    assert subgraph_count > 0
+    shape_dict = {}
+    dtype_dict = {}
+    for subgraph_index in range(subgraph_count):
+        subgraph = model.Subgraphs(subgraph_index)
+        inputs_count = subgraph.InputsLength()
+        assert inputs_count >= 1
+        for input_index in range(inputs_count):
+            input_ = subgraph.Inputs(input_index)
+            assert subgraph.TensorsLength() > input_
+            tensor = subgraph.Tensors(input_)
+            input_shape = tuple(tensor.ShapeAsNumpy())
+            tensor_type = tensor.Type()
+            input_name = tensor.Name().decode("utf8")
+            shape_dict[input_name] = input_shape
+            dtype_dict[input_name] = _decode_type(tensor_type)
+
+    return shape_dict, dtype_dict
+
+
+def from_tflite(model, shape_dict=None, dtype_dict=None):
     """Convert from tflite model into compatible relay Function.
 
     Parameters
@@ -3577,6 +3615,12 @@ def from_tflite(model, shape_dict, dtype_dict):
 
         assert isinstance(model, tflite.Model.Model)
 
+    _shape_dict, _dtype_dict = _input_type(model)
+    if shape_dict is not None:
+        _shape_dict.update(shape_dict)
+    if dtype_dict is not None:
+        _dtype_dict.update(dtype_dict)
+
     # keep the same as tflite
     assert model.SubgraphsLength() == 1, "only support one subgraph (main subgraph)"
     subgraph = model.Subgraphs(0)
@@ -3588,8 +3632,8 @@ def from_tflite(model, shape_dict, dtype_dict):
     exp_tab = ExprTable()
     for model_input in model_inputs:
         model_input_name = get_tensor_name(subgraph, model_input)
-        shape = shape_dict[model_input_name] if model_input_name in shape_dict else None
-        dtype = dtype_dict[model_input_name] if model_input_name in dtype_dict else "float32"
+        shape = _shape_dict[model_input_name] if model_input_name in _shape_dict else None
+        dtype = _dtype_dict[model_input_name] if model_input_name in _dtype_dict else "float32"
         exp_tab.set_expr(model_input_name, _expr.var(model_input_name, shape=shape, dtype=dtype))
 
     # op code in model