| /external/tensorflow/tensorflow/lite/micro/kernels/arc_mli/ |
| D | pooling_slicing_test.cc | 42 float output_min, float output_max, TfLitePadding padding, in TestAveragePoolingQuantized() argument
57 output_min, output_max), in TestAveragePoolingQuantized()
112 float output_min, float output_max, in TestMaxPoolQuantized() argument
128 output_min, output_max), in TestMaxPoolQuantized()
192 const float output_min = -128; in TF_LITE_MICRO_TEST() local
208 output_min, output_max, // output quantization range in TF_LITE_MICRO_TEST()
217 const float output_min = -128; in TF_LITE_MICRO_TEST() local
235 output_min, output_max, // output quantization range in TF_LITE_MICRO_TEST()
245 const float output_min = -128; in TF_LITE_MICRO_TEST() local
266 output_min, output_max, // output quantization range in TF_LITE_MICRO_TEST()
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| D | fully_connected_slicing_test.cc | 44 const float output_min, const float output_max, in TestFullyConnectedQuantized() argument
62 output_min, output_max), in TestFullyConnectedQuantized()
131 const float output_min = -128.0f; in TF_LITE_MICRO_TEST() local
150 bias_scale, expected_output_data, output_dims_data, output_min, in TF_LITE_MICRO_TEST()
160 const float output_min = -128.0f; in TF_LITE_MICRO_TEST() local
186 output_dims_data_local, output_min, output_max, kTfLiteActNone, in TF_LITE_MICRO_TEST()
197 const float output_min = -128.0f; in TF_LITE_MICRO_TEST() local
222 output_dims_data_2, output_min, output_max, kTfLiteActNone, in TF_LITE_MICRO_TEST()
232 const float output_min = -128.0f; in TF_LITE_MICRO_TEST() local
258 expected_output_data_local_2, output_dims_data_local_2, output_min, in TF_LITE_MICRO_TEST()
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | quantized_add_op.cc | 48 float scalar_input_max, float output_min, float output_max, in ScalarAddition() argument
51 scalar_input, scalar_input_min, scalar_input_max, output_min, output_max); in ScalarAddition()
54 full_input[i], full_input_min, full_input_max, output_min, output_max); in ScalarAddition()
66 float output_min, float output_max, qint32* output) { in ScalarAddition() argument
68 scalar_input, scalar_input_min, scalar_input_max, output_min, output_max); in ScalarAddition()
75 FloatToQuantizedUnclamped<qint32>(input_0_float, output_min, output_max); in ScalarAddition()
77 FloatToQuantizedUnclamped<qint32>(input_1_float, output_min, output_max); in ScalarAddition()
123 float output_min, float output_max, qint32* output) { in ScalarAddition() argument
125 scalar_input, scalar_input_min, scalar_input_max, output_min, output_max); in ScalarAddition()
132 FloatToQuantizedUnclamped<qint32>(input_0_float, output_min, output_max); in ScalarAddition()
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| D | quantized_batch_norm_op.cc | 38 Tensor* output, float* output_min, float* output_max) { in ReferenceBatchNorm() argument
49 *output_min = std::numeric_limits<float>::max(); in ReferenceBatchNorm()
78 *output_min = std::min(output_value, *output_min); in ReferenceBatchNorm()
82 FloatToQuantized<T2>(output_value, *output_min, *output_max); in ReferenceBatchNorm()
101 Tensor* output, float* output_min, float* output_max) { in FixedPointBatchNorm() argument
115 *output_min = -(1 << 20); in FixedPointBatchNorm()
139 FloatToQuantized<T2>(scale_value, *output_min, *output_max); in FixedPointBatchNorm()
141 FloatToQuantized<T2>(offset_value, *output_min, *output_max); in FixedPointBatchNorm()
145 FloatToQuantized<T2>(1.0f, *output_min, *output_max); in FixedPointBatchNorm()
151 input_max, *output_min, *output_max); in FixedPointBatchNorm()
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| D | quantized_concat_op.cc | 35 float output_min, float output_max) in RequantizeCopier()
36 : output_min(output_min), in RequantizeCopier()
43 if (input_min == output_min && input_max == output_max) { in Copy()
54 FloatToQuantizedStruct<T> f2q(output_min, output_max); in Copy()
64 float output_min; member
82 float* output_min, float* output_max) { in CalculateInputAndOutputRange() argument
100 *output_min = -largest_value; in CalculateInputAndOutputRange()
103 *output_min = overall_min; in CalculateInputAndOutputRange()
192 float output_min = std::numeric_limits<float>::max(); in Compute() local
196 &input_mins_and_maxes, &output_min, in Compute()
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| D | quantize_op_test.cc | 123 auto output_min = *GetOutput(1); in TEST_P() local
127 EXPECT_EQ(output_min.flat<float>()(slice_idx), 0); in TEST_P()
203 auto output_min = *GetOutput(1); in TEST_P() local
207 EXPECT_EQ(output_min.flat<float>()(slice_idx), -128.0 * (slice_idx + 1)); in TEST_P()
253 auto output_min = *GetOutput(1); in TEST_P() local
257 EXPECT_EQ(output_min.flat<float>()(slice_idx), -128.0 * (slice_idx + 1)); in TEST_P()
415 const float output_min = GetOutput(1)->flat<float>()(0); in TEST_F() local
417 EXPECT_NEAR(0.0f, output_min, 1e-5f); in TEST_F()
437 const float output_min = GetOutput(1)->flat<float>()(0); in TEST_F() local
439 EXPECT_NEAR(0.0f, output_min, 1e-5f); in TEST_F()
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| D | quantized_activation_ops.cc | 51 Tensor* output_min = nullptr; in Compute() local
52 OP_REQUIRES_OK(context, context->allocate_output(1, {}, &output_min)); in Compute()
53 output_min->flat<float>()(0) = min_input; in Compute()
89 Tensor* output_min = nullptr; in Compute() local
90 OP_REQUIRES_OK(context, context->allocate_output(1, {}, &output_min)); in Compute()
91 output_min->flat<float>()(0) = min_input; in Compute()
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| D | quantized_pooling_ops.cc | 99 Tensor* output_min = nullptr; in Compute() local
100 OP_REQUIRES_OK(context, context->allocate_output(1, {}, &output_min)); in Compute()
101 output_min->flat<float>()(0) = min_input; in Compute()
123 Tensor* output_min = nullptr; in Compute() local
124 OP_REQUIRES_OK(context, context->allocate_output(1, {}, &output_min)); in Compute()
125 output_min->flat<float>()(0) = min_input; in Compute()
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| /external/XNNPACK/src/xnnpack/ |
| D | params-init.h | 34 uint8_t output_min, in xnn_init_scalar_qu8_gemm_params() argument
60 (int32_t) (uint32_t) output_min - (int32_t) (uint32_t) output_zero_point; in xnn_init_scalar_qu8_gemm_params()
71 uint8_t output_min, in xnn_init_qu8_gemm_params() argument
114 params.sse2.output_min[i] = output_min; in xnn_init_qu8_gemm_params()
122 params.neon.output_min = output_min; in xnn_init_qu8_gemm_params()
133 (int32_t) (uint32_t) output_min - (int32_t) (uint32_t) output_zero_point; in xnn_init_qu8_gemm_params()
144 int8_t output_min, in xnn_init_scalar_qs8_gemm_params() argument
168 params.scalar.output_min_less_zero_point = (int32_t) output_min - (int32_t) output_zero_point; in xnn_init_scalar_qs8_gemm_params()
177 int8_t output_min, in xnn_init_qs8_gemm_params() argument
215 params.sse2.output_min[i] = (int16_t) output_min; in xnn_init_qs8_gemm_params()
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| /external/XNNPACK/src/operators/ |
| D | binary-elementwise-nd.c | 68 float output_min, in create_binary_elementwise_nd_f16() argument
75 if (isnan(output_min)) { in create_binary_elementwise_nd_f16()
89 …if (fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_min)) >= fp16_ieee_to_fp32_value(fp16… in create_binary_elementwise_nd_f16()
93 fp16_ieee_to_fp32_value(fp16_ieee_from_fp32_value(output_min)), in create_binary_elementwise_nd_f16()
99 fp16_ieee_from_fp32_value(output_min), in create_binary_elementwise_nd_f16()
113 float output_min, in create_binary_elementwise_nd_f32() argument
126 if (isnan(output_min)) { in create_binary_elementwise_nd_f32()
140 if (output_min >= output_max) { in create_binary_elementwise_nd_f32()
143 xnn_operator_type_to_string(operator_type), output_min, output_max); in create_binary_elementwise_nd_f32()
147 const bool linear_activation = (output_max == INFINITY) && (output_min == -output_max); in create_binary_elementwise_nd_f32()
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| D | global-average-pooling-nwc.c | 208 uint8_t output_min, in xnn_create_global_average_pooling_nwc_qu8() argument
227 if (output_min >= output_max) { in xnn_create_global_average_pooling_nwc_qu8()
230 …xnn_operator_type_to_string(xnn_operator_type_global_average_pooling_nwc_qu8), output_min, output_… in xnn_create_global_average_pooling_nwc_qu8()
245 output_zero_point, output_min, output_max); in xnn_create_global_average_pooling_nwc_qu8()
271 int8_t output_min, in xnn_create_global_average_pooling_nwc_qs8() argument
290 if (output_min >= output_max) { in xnn_create_global_average_pooling_nwc_qs8()
293 …xnn_operator_type_to_string(xnn_operator_type_global_average_pooling_nwc_qs8), output_min, output_… in xnn_create_global_average_pooling_nwc_qs8()
308 output_zero_point, output_min, output_max); in xnn_create_global_average_pooling_nwc_qs8()
330 float output_min, in xnn_create_global_average_pooling_nwc_f16() argument
335 if (isnan(output_min)) { in xnn_create_global_average_pooling_nwc_f16()
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| D | global-average-pooling-ncw.c | 22 float output_min, in xnn_create_global_average_pooling_ncw_f32() argument
45 if (isnan(output_min)) { in xnn_create_global_average_pooling_ncw_f32()
59 if (output_min >= output_max) { in xnn_create_global_average_pooling_ncw_f32()
62 …xnn_operator_type_to_string(xnn_operator_type_global_average_pooling_ncw_f32), output_min, output_… in xnn_create_global_average_pooling_ncw_f32()
77 …ooling_op->params.f32_gavgpool = xnn_init_f32_gavgpool_params(nanf(""), output_min, output_max, 0); in xnn_create_global_average_pooling_ncw_f32()
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| /external/XNNPACK/src/subgraph/ |
| D | global-average-pooling-2d.c | 18 float output_min, in xnn_define_global_average_pooling_2d() argument
30 if (isnan(output_min)) { in xnn_define_global_average_pooling_2d()
44 if (output_min >= output_max) { in xnn_define_global_average_pooling_2d()
47 xnn_node_type_to_string(xnn_node_type_global_average_pooling_2d), output_min, output_max); in xnn_define_global_average_pooling_2d()
71 node->activation.output_min = output_min; in xnn_define_global_average_pooling_2d()
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| D | multiply2.c | 18 float output_min, in xnn_define_multiply2() argument
31 if (isnan(output_min)) { in xnn_define_multiply2()
45 if (output_min >= output_max) { in xnn_define_multiply2()
48 xnn_node_type_to_string(xnn_node_type_multiply2), output_min, output_max); in xnn_define_multiply2()
79 node->activation.output_min = output_min; in xnn_define_multiply2()
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| D | divide.c | 18 float output_min, in xnn_define_divide() argument
31 if (isnan(output_min)) { in xnn_define_divide()
45 if (output_min >= output_max) { in xnn_define_divide()
48 xnn_node_type_to_string(xnn_node_type_divide), output_min, output_max); in xnn_define_divide()
79 node->activation.output_min = output_min; in xnn_define_divide()
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| D | subtract.c | 18 float output_min, in xnn_define_subtract() argument
31 if (isnan(output_min)) { in xnn_define_subtract()
45 if (output_min >= output_max) { in xnn_define_subtract()
48 xnn_node_type_to_string(xnn_node_type_subtract), output_min, output_max); in xnn_define_subtract()
79 node->activation.output_min = output_min; in xnn_define_subtract()
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| D | add2.c | 18 float output_min, in xnn_define_add2() argument
31 if (isnan(output_min)) { in xnn_define_add2()
45 if (output_min >= output_max) { in xnn_define_add2()
48 xnn_node_type_to_string(xnn_node_type_add2), output_min, output_max); in xnn_define_add2()
79 node->activation.output_min = output_min; in xnn_define_add2()
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| D | fully-connected.c | 18 float output_min, in xnn_define_fully_connected() argument
32 if (isnan(output_min)) { in xnn_define_fully_connected()
46 if (output_min >= output_max) { in xnn_define_fully_connected()
49 xnn_node_type_to_string(xnn_node_type_fully_connected), output_min, output_max); in xnn_define_fully_connected()
87 node->activation.output_min = output_min; in xnn_define_fully_connected()
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| D | average-pooling-2d.c | 26 float output_min, in xnn_define_average_pooling_2d() argument
62 if (isnan(output_min)) { in xnn_define_average_pooling_2d()
76 if (output_min >= output_max) { in xnn_define_average_pooling_2d()
79 xnn_node_type_to_string(xnn_node_type_average_pooling_2d), output_min, output_max); in xnn_define_average_pooling_2d()
123 node->activation.output_min = output_min; in xnn_define_average_pooling_2d()
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| D | max-pooling-2d.c | 28 float output_min, in xnn_define_max_pooling_2d() argument
70 if (isnan(output_min)) { in xnn_define_max_pooling_2d()
84 if (output_min >= output_max) { in xnn_define_max_pooling_2d()
87 xnn_node_type_to_string(xnn_node_type_max_pooling_2d), output_min, output_max); in xnn_define_max_pooling_2d()
133 node->activation.output_min = output_min; in xnn_define_max_pooling_2d()
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| D | deconvolution-2d.c | 33 float output_min, in xnn_define_deconvolution_2d() argument
89 if (isnan(output_min)) { in xnn_define_deconvolution_2d()
103 if (output_min >= output_max) { in xnn_define_deconvolution_2d()
106 xnn_node_type_to_string(xnn_node_type_deconvolution_2d), output_min, output_max); in xnn_define_deconvolution_2d()
157 node->activation.output_min = output_min; in xnn_define_deconvolution_2d()
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| D | depthwise-convolution-2d.c | 30 float output_min, in xnn_define_depthwise_convolution_2d() argument
79 if (isnan(output_min)) { in xnn_define_depthwise_convolution_2d()
93 if (output_min >= output_max) { in xnn_define_depthwise_convolution_2d()
96 xnn_node_type_to_string(xnn_node_type_depthwise_convolution_2d), output_min, output_max); in xnn_define_depthwise_convolution_2d()
176 node->activation.output_min = output_min; in xnn_define_depthwise_convolution_2d()
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| /external/XNNPACK/include/ |
| D | xnnpack.h | 252 float output_min,
307 float output_min,
360 float output_min,
401 float output_min,
442 float output_min,
492 float output_min, float output_max,
536 float output_min,
623 float output_min,
651 float output_min,
679 float output_min,
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| /external/tensorflow/tensorflow/lite/micro/kernels/ |
| D | hard_swish_test.cc | 60 float input_min, float input_max, float output_min, in TestHardSwishQuantized() argument
68 const float output_scale = ScaleFromMinMax<T>(output_min, output_max); in TestHardSwishQuantized()
69 const int output_zero_point = ZeroPointFromMinMax<T>(output_min, output_max); in TestHardSwishQuantized()
76 std::max(input_max - input_min, output_max - output_min) * (1.5f / 256.f); in TestHardSwishQuantized()
90 std::min(output_max, std::max(output_min, val)); in TestHardSwishQuantized()
129 float input_max, float output_min, in TestHardSwishQuantizedBias() argument
134 const float output_scale = ScaleFromMinMax<T>(output_min, output_max); in TestHardSwishQuantizedBias()
137 const int output_zero_point = ZeroPointFromMinMax<T>(output_min, output_max); in TestHardSwishQuantizedBias()
160 std::min(output_max, std::max(output_min, val)); in TestHardSwishQuantizedBias()
286 float output_min = minmax_pairs[y][0]; in TF_LITE_MICRO_TEST() local
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| /external/tensorflow/tensorflow/lite/delegates/hexagon/builders/tests/ |
| D | activations_test.cc | 195 float output_min, float output_max, in TestQuantizedHardSwish() argument
204 val = std::min(output_max, std::max(output_min, val)); in TestQuantizedHardSwish()
209 /*output=*/{Tensor_Type, {1, 1, 1, size}, output_min, output_max}); in TestQuantizedHardSwish()
224 std::max(input_max - input_min, output_max - output_min) * (1.5f / 256.f); in TestQuantizedHardSwish()
238 float output_min = output_minmax.first; in HardSwishTestImpl() local
242 size, input_min, input_max, output_min, output_max, &random_engine); in HardSwishTestImpl()
260 float output_min = -0.3905796f; in HardSwishBiasTestImpl() local
268 const float output_scale = (output_max - output_min) / quantized_type_range; in HardSwishBiasTestImpl()
292 val = std::min(output_max, std::max(output_min, val)); in HardSwishBiasTestImpl()
298 /*output=*/{Tensor_Type, {1, 1, 1, size}, output_min, output_max}); in HardSwishBiasTestImpl()
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