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/packages/modules/NeuralNetworks/runtime/test/specs/AIDL_V3/
Dmirror_pad.mod.py30 def test(name, seed, input_dims, orig_paddings, modes=[0, 1], clampReflectPadding=False): argument
37 for i in range(0, len(input_dims)):
40 if paddings[padding_index] == input_dims[i]:
41 paddings[padding_index] = input_dims[i] - 1
43 input_tensor = Input("in", ("TENSOR_FLOAT32", input_dims))
44 padding_tensor = Parameter("padding", ("TENSOR_INT32", [len(input_dims), 2]), paddings)
45 output_dims = [sum(x) for x in zip(input_dims, paddings[0::2], paddings[1::2])]
49 input_data = [random_value(r) for x in range(0, numpy.prod(input_dims))]
51 numpy_input_data = numpy.reshape(input_data, input_dims)
Dmirror_pad_tensorflow.mod.py20 def test(name, input_dims, input_values, paddings, mode, output_dims, output_values): argument
21 t = Input("t", ("TENSOR_FLOAT32", input_dims))
22 paddings = Parameter("paddings", ("TENSOR_INT32", [len(input_dims), 2]), paddings)
/packages/modules/NeuralNetworks/runtime/test/specs/V1_3/
Dfill.mod.py16 def test(name, input_dims, value, output, input_dims_data, output_data): argument
17 model = Model().Operation("FILL", input_dims, value).To(output)
19 input_dims: input_dims_data,
28 input_dims=Input("input0", "TENSOR_INT32", "{1}"),
37 input_dims=Input("input0", "TENSOR_INT32", "{3}"),
46 input_dims=Input("input0", "TENSOR_INT32", "{5}"),
/packages/modules/NeuralNetworks/common/cpu_operations/
DQuantizedLSTM.cpp59 void quantizedLstmStep(const uint8_t* input_data_uint8, const Dims<4>& input_dims, in quantizedLstmStep() argument
72 MatchingFlatSizeSkipDim(input_dims, 0, prev_activ_dims, prevCellState_dims, in quantizedLstmStep()
76 const int input_depth = ArraySize(input_dims, 0); in quantizedLstmStep()
98 Dims<4> const* concat_input_arrays_dims[2] = {&input_dims, &prev_activ_dims}; in quantizedLstmStep()