inadob commented on a change in pull request #5394:
URL: https://github.com/apache/incubator-tvm/pull/5394#discussion_r426555314
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -1552,6 +1552,48 @@ def test_forward_squeeze():
_test_squeeze(np.arange(6).reshape((2, 1, 3, 1)), [1, 3])
+#######################################################################
+# Quantize/DeQuantize
+# -------------------
+
+def _test_quantize_dequantize(data):
+ """ One iteration of quantize and dequantize """
+
+ import tensorflow as tf2
+ # Define a dummy model
+ data_in = tf2.keras.layers.Input(shape=data.shape[1:])
+ act_func = tf2.keras.layers.Activation('linear')
+ keras_model = tf2.keras.models.Model(data_in, act_func(data_in))
+
+ # Load the model
+ converter = tf2.lite.TFLiteConverter.from_keras_model(keras_model)
+
+ # To create quantized values with dynamic range of activations, needs
representative dataset
+ def representative_data_gen():
+ for i in range(100):
+ yield [data]
+
+ converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
+ converter.representative_dataset = representative_data_gen
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.uint8
+ converter.inference_output_type = tf.uint8
+
+ # Convert the model to TensorFlow Lite format
+ tflite_model_quant = converter.convert()
+
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ tvm_output = run_tvm_graph(tflite_model_quant, data, 'input_1')
+ tvm.testing.assert_allclose(np.squeeze(tvm_output[0]),
np.squeeze(tflite_output[0]),
+ rtol=1e-5, atol=1e-5)
+
+
+def test_forward_quantize_dequantize():
+ """ Quantize Dequantize """
+ data = np.random.uniform(0, 1, (1, 4, 4, 3)).astype("float32")
+ _test_quantize_dequantize(data)
Review comment:
You can add a version check for TF2
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