tflite interpreter testing

examples/tflite_interpreter_tests.py

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+"""
+Tests to start working with a tflite interpreter.
+"""
+
+import numpy as np
+import tensorflow as tf
+from impsy import mdrnn
+# import keras_mdn_layer as mdn
+# import time
+# import datetime
+from pathlib import Path
+
+MODEL = Path("models") / "musicMDRNN-dim9-layers2-units64-mixtures5-scale10.tflite"
+
+# Load the TFLite model
+interpreter = tf.lite.Interpreter(model_path=str(MODEL))
+interpreter.allocate_tensors()
+# Get the list of signatures
+signatures = interpreter.get_signature_list()
+print("Signatures:\n", signatures)
+
+# Get the signature runner
+runner = interpreter.get_signature_runner()
+hidden_units = 64
+
+states = []
+for i in range(2):
+    states += [
+        np.zeros((1, hidden_units), dtype=np.float32),
+        np.zeros((1, hidden_units), dtype=np.float32),
+    ]
+
+value = np.array(mdrnn.random_sample(out_dim=9), dtype=np.float32)
+print(value)
+# for i in range(num_test_steps):
+#     value = net.generate_touch(value)
+#     proc_touch = mdrnn.proc_generated_touch(value, dimension)
+
+# input_data = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
+
+output = runner(
+    inputs = value,
+    state_h_0 = states[0],
+    state_c_0 = states[1],
+    state_h_1 = states[2],
+    state_c_1 = states[3],
+)
+
+print(output)
+
+
+
+def reset_lstm_states(self):
+
+    assert (
+        len(states) == self.n_rnn_layers * 2
+    ), "length of states list needs to be RNN layers times 2 (h and c for each)"
+    self.lstm_states = states
+
+# # Get input and output tensors
+# input_details = interpreter.get_input_details()
+# output_details = interpreter.get_output_details()
+
+# print("Input details")
+# print(input_details)
+# print("Output details:")
+# print(output_details)
+
+# Prepare input data
+# Replace this with your actual input data
+# input_data = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
+
+# Set the input tensor
+# interpreter.set_tensor(input_details[0]['index'], input_data)
+
+# Run inference
+# interpreter.invoke()
+
+# Get the output tensor
+# output_data = interpreter.get_tensor(output_details[0]['index'])
+
+# print(output_data)