| /packages/modules/NeuralNetworks/runtime/test/specs/V1_2/ |
| D | reduce_sum.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_SUM", input0, axes, keep_dims).To(output0)
30 keep_dims=False,
41 keep_dims=True,
52 keep_dims=False,
63 keep_dims=True,
|
| D | reduce_prod.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_PROD", input0, axes, keep_dims).To(output0)
30 keep_dims=False,
41 keep_dims=True,
52 keep_dims=False,
63 keep_dims=True,
|
| D | reduce_max.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_MAX", input0, axes, keep_dims).To(output0)
34 keep_dims=False,
45 keep_dims=True,
56 keep_dims=False,
67 keep_dims=True,
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| D | reduce_min.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_MIN", input0, axes, keep_dims).To(output0)
34 keep_dims=False,
45 keep_dims=True,
56 keep_dims=False,
67 keep_dims=True,
|
| D | reduce_all.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_ALL", input0, axes, keep_dims).To(output0)
28 keep_dims=True,
38 keep_dims=False,
48 keep_dims=True,
|
| D | reduce_any.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_ANY", input0, axes, keep_dims).To(output0)
30 keep_dims=True,
40 keep_dims=False,
50 keep_dims=True,
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| /packages/modules/NeuralNetworks/runtime/test/specs/V1_3/ |
| D | reduce_max_quant8_signed.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_MAX", input0, axes, keep_dims).To(output0)
34 keep_dims=False,
45 keep_dims=True,
56 keep_dims=False,
67 keep_dims=True,
|
| D | reduce_min_quant8_signed.mod.py | 17 def test(input0, output0, axes, keep_dims, input_data, output_data): argument
18 model = Model().Operation("REDUCE_MIN", input0, axes, keep_dims).To(output0)
34 keep_dims=False,
45 keep_dims=True,
56 keep_dims=False,
67 keep_dims=True,
|
| /packages/modules/NeuralNetworks/tools/api/ |
| D | types.spec | 2640 * keep_dims is true, the rank of the tensor is reduced by 1 for each entry
2641 * in axis. If keep_dims is true, the reduced dimensions are retained with
2667 * * 2: An {@link %{OperandTypeLinkPfx}INT32} scalar, keep_dims. If positive,
2680 * If all dimensions are reduced and keep_dims is false, the output
5157 * If keep_dims is true, the reduced dimensions are
5170 * * 2: An {@link %{OperandTypeLinkPfx}BOOL} scalar, keep_dims. If true,
5175 * If all dimensions are reduced and keep_dims is false, the output
5185 * If keep_dims is true, the reduced dimensions are
5198 * * 2: An {@link %{OperandTypeLinkPfx}BOOL} scalar, keep_dims. If true,
5203 * If all dimensions are reduced and keep_dims is false, the output
[all …]
|
| /packages/modules/NeuralNetworks/tools/test_generator/ |
| D | README.md | 77 model.Operation("MEAN", i1, [1], 0) # axis = [1], keep_dims = 0
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| /packages/modules/NeuralNetworks/driver/sample_hidl/ |
| D | SampleDriverFloatXNNPACK.cpp | 1152 int keep_dims = getScalarData<int32_t>(operands[ins[2]]); in VisitMeanNode() local
1153 if (keep_dims <= 0) { in VisitMeanNode()
|