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Results 1 - 10 of 46 for zero_point_ (0.12 sec)
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tensorflow/compiler/mlir/quantization/common/ir/UniformSupport.h
UniformQuantizedValueConverter(double scale, double zero_point, double clamp_min, double clamp_max, uint32_t storage_bit_width, bool is_signed) : scale_(scale), zero_point_(zero_point), clamp_min_(clamp_min), clamp_max_(clamp_max), scale_double_(scale), zero_point_double_(zero_point), clamp_min_double_(clamp_min),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types_test.cc
/*scale=*/1.0, /*zero_point=*/0); EXPECT_TRUE(quantized_type.getExpressedType().isF32()); } TEST_F(CreateI8F32UniformQuantizedTypeTest, SignedQuantizedTypeSucceeds) { const UniformQuantizedType quantized_type = CreateI8F32UniformQuantizedType(UnknownLoc::get(&ctx_), ctx_, /*scale=*/1.0, /*zero_point=*/0); EXPECT_TRUE(quantized_type.isSigned()); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 28.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.h
UniformQuantizedPerAxisType CreateI8F32UniformQuantizedPerAxisType( Location loc, MLIRContext& context, ArrayRef<double> scales, ArrayRef<int64_t> zero_points, int quantization_dimension, bool narrow_range = false); // Creates a `UniformQuantizedPerAxisType` with the given `scales` and // `zero_points` values. The produced type has f32 as its expressed type and // i32 as its storage type. The available values use the full range of the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.cc
const ArrayRef<int64_t> zero_points, const int quantization_dimension, const bool narrow_range) { return UniformQuantizedPerAxisType::getChecked( loc, /*flags=*/QuantizationFlags::Signed, /*storageType=*/IntegerType::get(&context, /*width=*/8), /*expressedType=*/FloatType::getF32(&context), SmallVector<double>(scales), SmallVector<int64_t>(zero_points), quantization_dimension,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.cc
if (!type) return type; SmallVector<double, 4> scales(mins.size()); SmallVector<int64_t, 4> zero_points(mins.size()); if (auto q_type = dyn_cast<UniformQuantizedType>(type)) { zero_points.push_back(q_type.getZeroPoint()); } else if (auto q_type = dyn_cast<quant::UniformQuantizedPerAxisType>(type)) { zero_points = {q_type.getZeroPoints().begin(), q_type.getZeroPoints().end()}; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 43.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize_composite_functions.cc
int32_zero_points.push_back(zero_points[i]); } scale = rewriter.create<TF::ConstOp>( loc, scale_type, DenseFPElementsAttr::get(scale_type, float_scales)); zero_point = rewriter.create<TF::ConstOp>( loc, zero_point_type, DenseIntElementsAttr::get(zero_point_type, int32_zero_points)); return success(scale && zero_point); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 54.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model_test.cc
EXPECT_THAT(pack_input0->quantization->zero_point[0], Eq(pack_input1->quantization->zero_point[0])); EXPECT_THAT(pack_input1->quantization->zero_point[0], Eq(pack_input2->quantization->zero_point[0])); EXPECT_THAT(pack_input1->quantization->scale[0], FloatEq(pack_output->quantization->scale[0])); EXPECT_THAT(pack_input1->quantization->zero_point[0],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/test_schema.fbs
// QuantizationDetails union should be used. details:QuantizationDetails; // Specifies the dimension of the Tensor's shape that the scales and // zero_points correspond to. For example, a tensor t, with dims=[4, 3, 2, 1] // with quantization params: // scale=[1.0, 2.0, 3.0], zero_point=[1, 2, 3], quantization_dimension=1 // will be quantized across the second dimension of t.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 19 19:46:06 UTC 2021 - 26.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/lstm_quantized.mlir
// CHECK-NEXT: zero_point: [ 0 ] // CHECK-NEXT: }, // CHECK-NEXT: has_rank: true // CHECK-NEXT: }, { // CHECK-NEXT: shape: [ 2048, 528 ], // CHECK-NEXT: type: INT8, // CHECK-NEXT: buffer: 3, // CHECK-NEXT: name: "arg2", // CHECK-NEXT: quantization: { // CHECK-NEXT: scale: [ 0.031926 ], // CHECK-NEXT: zero_point: [ 0 ] // CHECK-NEXT: },
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jul 14 16:41:28 UTC 2022 - 15.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/numerical_utils.cc
// maximum quantized values. QuantizedRange CalculateQuantizedRange(double scale, int32_t zero_point, std::optional<double> rmin, std::optional<double> rmax, int32_t qmin, int32_t qmax) { auto quantize = [scale, zero_point](float f) { return zero_point + static_cast<int32_t>(std::round(f / scale)); };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 17 19:57:04 UTC 2023 - 3.3K bytes - Viewed (0)