我正在通过 keras 在 resnet 模型上进行 QAT,在转换为 tflite 全整数模型时遇到了这个问题。我已经尝试过最新版本的 tf-nightly,但它并没有解决问题。我在 QAT 期间使用量化注释模型进行批量标准化量化
这是我用来转换模型的代码:
converter = tf.lite.TFLiteConverter.from_keras_model(layer)
def representative_dataset_gen():
for _ in range(50):
batch = next(train_generator)
img = np.expand_dims(batch[0][0],0).astype(np.float32)
yield [img]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.experimental_new_converter = True
# converter.target_spec.supported_types = [tf.int8]
converter.inference_input_type = tf.int8 # or tf.uint8
converter.inference_output_type = tf.int8 # or tf.uint8
quantized_tflite_model = converter.convert()
with open("test_try_v2.tflite", 'wb') as f:
f.write(quantized_tflite_model)
如果我通过添加到“target_spec.supported_ops”来绕过这个错误tf.lite.OpsSet.TFLITE_BUILTINS
,我仍然在 edge_tpu 编译器遇到这个 DEQUANTIZE 问题
ERROR: :61 op_context.input->type == kTfLiteUInt8 || op_context.input->type == kTfLiteInt8 || op_context.input->type == kTfLiteInt16 || op_context.input->type == kTfLiteFloat16 was not true.
ERROR: Node number 3 (DEQUANTIZE) failed to prepare.
ERROR: :61 op_context.input->type == kTfLiteUInt8 || op_context.input->type == kTfLiteInt8 || op_context.input->type == kTfLiteInt16 || op_context.input->type == kTfLiteFloat16 was not true.
ERROR: Node number 3 (DEQUANTIZE) failed to prepare.