diff options
Diffstat (limited to 'ethosu/vela/test/testutil.py')
| -rw-r--r-- | ethosu/vela/test/testutil.py | 26 |
1 files changed, 5 insertions, 21 deletions
diff --git a/ethosu/vela/test/testutil.py b/ethosu/vela/test/testutil.py index acf35fe3..88fc8747 100644 --- a/ethosu/vela/test/testutil.py +++ b/ethosu/vela/test/testutil.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2020-2021 Arm Limited and/or its affiliates <open-source-office@arm.com> +# SPDX-FileCopyrightText: Copyright 2020-2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com> # # SPDX-License-Identifier: Apache-2.0 # @@ -53,21 +53,13 @@ def create_elemwise_op( ofm_quant=default_quant_params(), ): # Creates elementwise operation with constant IFM/IFM2 - if datatype.size_in_bytes() == 1: - np_type = np.uint8 - elif datatype.size_in_bytes() == 2: - np_type = np.int16 - else: - np_type = np.int32 op = Operation(op_type, name) op.add_input_tensor( - create_const_tensor(name + "_ifm", ifm_shape, datatype, np.zeros(ifm_shape), np_type, quantization=ifm_quant) + create_const_tensor(name + "_ifm", ifm_shape, datatype, np.zeros(ifm_shape), quantization=ifm_quant) ) if ifm2_shape is not None: op.add_input_tensor( - create_const_tensor( - name + "_ifm2", ifm2_shape, datatype, np.zeros(ifm2_shape), np_type, quantization=ifm2_quant - ) + create_const_tensor(name + "_ifm2", ifm2_shape, datatype, np.zeros(ifm2_shape), quantization=ifm2_quant) ) ofm = Tensor(ofm_shape, datatype, name + "_ofm") ofm.quantization = ofm_quant @@ -89,25 +81,17 @@ def create_op_with_quant_tensors( op.set_output_tensor(ofm) # Optional weight tensor if weights_shape is not None: - if datatype.size_in_bytes() == 1: - np_type = np.uint8 - elif datatype.size_in_bytes() == 2: - np_type = np.int16 - else: - np_type = np.int32 qp = default_quant_params() if op.type is not Op.FullyConnected: qp.zero_point = np.zeros(weights_shape) - weights = create_const_tensor( - "weights", weights_shape, datatype, np.zeros(weights_shape), np_type, quantization=qp - ) + weights = create_const_tensor("weights", weights_shape, datatype, np.zeros(weights_shape), quantization=qp) op.add_input_tensor(weights) # Optional bias tensor if bias_shape is not None: qp = default_quant_params() if op.type is not Op.FullyConnected: qp.zero_point = np.zeros(bias_shape) - bias = create_const_tensor("bias", bias_shape, DataType.int32, np.zeros(bias_shape), np.int32, quantization=qp) + bias = create_const_tensor("bias", bias_shape, DataType.int32, np.zeros(bias_shape), quantization=qp) op.add_input_tensor(bias) if set_ifm_ofm_shapes: |