FrozenGene commented on a change in pull request #4880: [QNN] Add support for per channel weight scale in dense op URL: https://github.com/apache/incubator-tvm/pull/4880#discussion_r379286796
########## File path: python/tvm/relay/frontend/tflite.py ########## @@ -982,13 +982,15 @@ def convert_fully_connected(self, op): weight_value = self.get_tensor_value(weight_tensor) weight_expr = self.exp_tab.new_const(weight_value, dtype=weight_tensor_type_str) + weight_shape = _infer_shape(weight_expr) if input_tensor.qnn_params: out = _qnn.op.dense(in_expr, weight_expr, input_zero_point=input_tensor.qnn_params['zero_point'], kernel_zero_point=weight_tensor.qnn_params['zero_point'], input_scale=input_tensor.qnn_params['scale'], kernel_scale=weight_tensor.qnn_params['scale'], + units=weight_shape[1], Review comment: weight_shape[0] should be the output dimension. The script you see is tf's layout, which is not the same as tflite. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services