diff options
Diffstat (limited to 'ethosu/vela/test/test_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/test/test_graph_optimiser.py | 21 |
1 files changed, 8 insertions, 13 deletions
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py index 152669f7..54dd70f6 100644 --- a/ethosu/vela/test/test_graph_optimiser.py +++ b/ethosu/vela/test/test_graph_optimiser.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2020-2022 Arm Limited and/or its affiliates <open-source-office@arm.com> +# SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> # # SPDX-License-Identifier: Apache-2.0 # @@ -40,9 +40,9 @@ from ethosu.vela.tflite_graph_optimiser import rewrite_fully_connected_input def test_convert_batched_fc(): """Tests shape conversion of batched fully connected""" ifm_shape = [4, 8] - ifm = create_const_tensor("test_in", ifm_shape, np.uint8, np.zeros(ifm_shape)) + ifm = create_const_tensor("test_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) w_shape = [8, 4] - weights = create_const_tensor("weight_in", w_shape, np.uint8, np.zeros(w_shape)) + weights = create_const_tensor("weight_in", w_shape, DataType.uint8, np.zeros(w_shape)) ofm = Tensor(ifm.shape, np.uint8, "test_out") op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) @@ -132,7 +132,8 @@ def create_pad_and_conv2d( qp = testutil.default_quant_params() in0 = Tensor(in_shape, in_dtype, "in") in0.quantization = qp - pad_tensor = create_const_tensor(name="pad", shape=list(np.shape(padding)), values=padding, dtype=pad_dtype) + shape = [] if padding == [] else list(np.shape(padding)) + pad_tensor = create_const_tensor(name="pad", shape=shape, values=padding, dtype=pad_dtype) out = Tensor(out_shape, out_dtype, "out") out.quantization = qp.clone() op = testutil.create_op(Op.Pad, [in0, pad_tensor], out) @@ -543,9 +544,7 @@ def test_quant_static_optimisations(): Tests if the quant value at vela compile time is calculated correctly """ - quant_ifm = create_const_tensor( - "const_quant_ifm", values=np.array(127), value_dtype=np.int8, shape=[], dtype=DataType.int8 - ) + quant_ifm = create_const_tensor("const_quant_ifm", values=np.array(127), shape=[], dtype=DataType.int8) quant_ifm.quantization = testutil.default_quant_params() quant_ifm.quantization.scale_f32 = 0.15748031 quant_ifm.quantization.quant_min = -128 @@ -568,9 +567,7 @@ def test_quant_static_optimisations(): assert op.ofm.values == 127 - quant_ifm = create_const_tensor( - "const_quant_ifm", values=np.array(127), value_dtype=np.int8, shape=[], dtype=DataType.int8 - ) + quant_ifm = create_const_tensor("const_quant_ifm", values=np.array(127), shape=[], dtype=DataType.int8) quant_ifm.quantization = testutil.default_quant_params() quant_ifm.quantization.scale_f32 = 0.15748031 quant_ifm.quantization.quant_min = -128 @@ -600,9 +597,7 @@ def test_optimise_quantize_multiple_values(): when passing multiple values to quantize node """ - quant_ifm = create_const_tensor( - "const_quant_ifm", values=np.array([127, 127]), value_dtype=np.int8, shape=[], dtype=DataType.int8 - ) + quant_ifm = create_const_tensor("const_quant_ifm", values=np.array([127, 127]), shape=[], dtype=DataType.int8) quant_ifm.quantization = testutil.default_quant_params() quant_ifm.quantization.scale_f32 = 0.15748031 quant_ifm.quantization.quant_min = -128 |