X-CUBE-AI can't analyze model

[dhammerl]

Hello,

I'm trying to convert my keras model for STM32N6. 

CubeMX Version: 6.15.0

X-Cube-ai Version: 10.2.0

The code for my model:

img_input = Input(shape=(10, 141, 141, 3), name='img_input')

# --- CNN branch applied per-frame via TimeDistributed ---
cx = layers.TimeDistributed(layers.Conv2D(16, (3, 3), activation="relu"), name="CNN_Conv1")(img_input)
cx = layers.TimeDistributed(layers.BatchNormalization(), name="CNN_Batch_Norm")(cx)
cx = layers.TimeDistributed(layers.MaxPooling2D(2, 2), name="CNN_Pool1")(cx)

cx = layers.TimeDistributed(layers.Conv2D(32, (3, 3), activation="relu"), name="CNN_Conv2")(cx)
cx = layers.TimeDistributed(layers.MaxPooling2D(2, 2), name="CNN_Pool2")(cx)

cx = layers.TimeDistributed(layers.Conv2D(64, (3, 3), activation='relu'), name="CNN_Conv3")(cx)
cx = layers.TimeDistributed(layers.MaxPooling2D(2, 2), name="CNN_Pool3")(cx)

cx = layers.TimeDistributed(layers.Flatten(), name="CNN_Flatten")(cx)
cx = layers.TimeDistributed(layers.Dense(128, activation='relu'), name="CNN_Dense")(cx)
cx = layers.TimeDistributed(layers.Dropout(0.2), name="CNN_Dropout")(cx)

# --- Feature branch ---
feat_input = Input(shape=(10, 5), name='feat_input')
fx = layers.TimeDistributed(layers.Dense(32, activation='relu'), name="Feature_Dense1")(feat_input)
fx = layers.TimeDistributed(layers.BatchNormalization(), name="Feature_Batch_Norm")(fx)
fx = layers.TimeDistributed(layers.Dense(64, activation='relu'), name="Feature_Dense2")(fx)

# --- Fusion ---
combined = layers.Concatenate(name="Fusion_Concat")([cx, fx])

# LSTM
combined = layers.LSTM(128, name="Fusion_LSTM")(combined)

# Final MLP layers
combined = layers.Dense(64, name="Fusion_Dense", activation="relu")(combined)
combined = layers.Dropout(0.4, name="Fusion_Dropout")(combined)
output = layers.Dense(len(class_names), activation='softmax', name="Fusion_Dense_Softmax_Output")(combined)

model = models.Model(inputs=[img_input, feat_input], outputs=output)

optimizer = optimizers.Adam(learning_rate=1e-4)
model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

Attached are the neutron graphs for the .h5 model and the converted onnx model

Conversion via:

def convert_onnx():
 model = tf.keras.models.load_model("radar_classifier.h5")

 spec = (
 tf.TensorSpec([1, 10, 141, 141, 3], tf.float32, name="img_input"),
 tf.TensorSpec([1, 10, 5], tf.float32, name="feat_input")
 )
 onnx_model, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13)

 onnx.save_model(onnx_model, "radar_classifier.onnx")

onnxh5

But during analyzing i get the following error for the .h5 model (model input type: Keras, with STM32Cube.AI MCU runtime):
NOT IMPLEMENTED: Unsupported wrapped layer type: BatchNormalization
Also, if i skip the BatchNormalization the analyzing works, but my results when training the model are worse, but acceptable. The issue is still, that the model does not get optimized/quantized and i cannot find information why this is.It would net over 14Mib for activations, almost 8Mib for weights and over 490 million MACC. 

During analyzing for the onnx model (model input type: ONNX, with STM32Cube.AI Neural-ART runtime):
INTERNAL ERROR: Found transpose model_CNN_Pool3_max_pooling2d_2_MaxPool__680 before {LSTM__280} with remapping (0, 2, 3, 1)
I am quite new to this topic, any help is appreciated! Thanks!

Best regards

Dominik

[Julian E.]

Hello @dhammerl,

 

You have the list of supported layers on N6 here:

https://stedgeai-dc.st.com/assets/embedded-docs/stneuralart_operator_support.html

 

What we can see for the BatchNormalization is that it is not supported natively but can be executed on software (SW, meaning on the MCU and not NPU) if placed after a CONV.

There are others Normalization layers that are supported, look for "Normalization" on the page of supported layers.

 

The LSTM layer is not supported by the NPU.

It is supported on STM32, so you may want to try using with the MCU only (with STM32Cube.AI MCU runtime).

Note that if you do that, the NPU will not be used...

 

LSTM are a topic we are working on, but I would say that I don't recommend using it for now even for STM32.

 

Bringing your own model is challenging as we don't support everything for now. We are obviously working every day to improve our solution.

I don't know what your use case is, but we have a model zoo with model that work with our ST Edge AI Core (the tool behind CubeAI that converts model). You may want to take a look and see if anything can help you:

GitHub - STMicroelectronics/stm32ai-modelzoo: AI Model Zoo for STM32 devices

(I think there are some LSTM model for STM32, you can look at how they were used)

 

Have a good day,

Julian