Thrsu opened a new issue, #15021:
URL: https://github.com/apache/tvm/issues/15021
I encountered an error when trying to load a TFLite model using the
`relay.frontend.from_tflite` function. The error message is KeyError: 14.
### Actual behavior
```
Traceback (most recent call last):
File "single_test.py", line 42, in <module>
mod, params = relay.frontend.from_tflite(tflite_model, shape_dict)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/tflite.py",
line 4178, in from_tflite
_shape_dict, _dtype_dict = _input_type(model)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/tflite.py",
line 4135, in _input_type
dtype_dict[input_name] = _decode_type(tensor_type)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/tflite.py",
line 4115, in _decode_type
return _tflite_m[n]
KeyError: 14
```
What actually happened
### Environment
- TVM: 0.13.dev0
- Tensorflow: 2.12.0
- TFlite: 2.10.0
- Keras: 2.12.0
### Steps to reproduce
```
from tensorflow import keras
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models
import tflite
import tvm
import tvm.relay as relay
np.random.seed(2023)
input_shape = (1, 2, 5, 5, 2)
input_data = 10 * np.random.random(list(input_shape))
input_data -= 0.5
input_data = input_data.astype("float32")
kwargs={ "data_format": "channels_last", "return_sequences": False,
"filters": 2, "kernel_size": (3, 3), "padding": "same", "dropout": 0.1,
"recurrent_dropout": 0.1, }
layer_cls = keras.layers.ConvLSTM2D
layer = layer_cls(**kwargs)
weights = layer.get_weights()
layer.set_weights(weights)
x = layers.Input(shape=input_shape[1:], dtype="float32")
y = layer(x)
model = models.Model(x, y)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS]
converter._experimental_lower_tensor_list_ops = False
tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
input_name = input_details[0]['name'].split(':')[0]
target = 'llvm'
ctx = tvm.cpu(0)
shape_dict = {input_name: input_shape}
tflite_model = tflite.Model.GetRootAsModel(tflite_model, 0)
mod, params = relay.frontend.from_tflite(tflite_model, shape_dict)
with tvm.transform.PassContext(opt_level=3):
model = relay.build_module.create_executor('vm', mod, ctx, target,
params).evaluate()
tvm_output = model(tvm.nd.array(input_data)).numpy()
```
### Triage
- needs-triage
- frontend:tflite
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