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Results 1 - 10 of 31 for zero_point (0.33 sec)
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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/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/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/quantization/common/uniform_quantized_types.h
double scale, int64_t zero_point, bool narrow_range = false); // Creates a `UniformQuantizedType` with the given `scale` and `zero_point` // values. The produced type has f32 as its expressed type and i32 as its
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 int64_t zero_point, const bool narrow_range) { return UniformQuantizedType::getChecked( loc, /*flags=*/QuantizationFlags::Signed, /*storageType=*/IntegerType::get(&context, /*width=*/8), /*expressedType=*/FloatType::getF32(&context), scale, zero_point, /*storageTypeMin=*/llvm::minIntN(8) + (narrow_range ? 1 : 0),
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/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/tests/end2end/quant_stats.pbtxt
# CHECK-NEXT: zero_point: [ 128 ] # CHECK-NEXT: }, # CHECK-NEXT: has_rank: true # CHECK-NEXT: }, { # CHECK-NEXT: shape: [ 4 ], # CHECK-NEXT: type: UINT8, # CHECK-NEXT: buffer: 2, # CHECK-NEXT: name: "input1", # CHECK-NEXT: quantization: { # CHECK-NEXT: scale: [ 0.023529 ], # CHECK-NEXT: zero_point: [ 128 ] # CHECK-NEXT: },
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_algorithm.py
minbound = 0 scale = (quant_max - quant_min) / maxbound zero_point = -quant_min / scale # Limit the range of zero_point and scale in case (quant_max - quant_min) # is unusually small. if abs(zero_point) > 9e9: zero_point = 9e9 if abs(scale) < 1e-9: scale = 1e-9 zero_point = round(zero_point) quantized_hist_mids = np.clip(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 11 19:29:56 UTC 2024 - 14.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.cc
return scale * rate; }; const auto& recalculate_zero_point = [&](int64_t zero_point) -> int64_t { return qmax - std::round((storage_type_max - zero_point) / rate); }; if (auto q_type = dyn_cast<UniformQuantizedType>(type)) { const double scale = recalculate_scale(q_type.getScale()); const double zero_point = recalculate_zero_point(q_type.getZeroPoint());
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/lite/tests/end2end/fake_quant_per_channel_4bit.pbtxt
# CHECK: scale: [ 0.093635 ], # CHECK: zero_point: [ 22 ] # CHECK: } # CHECK: }, { # CHECK: shape: [ 1, 6, 31 ], # CHECK: type: INT8, # CHECK: buffer: 6, # CHECK: name: "output", # CHECK: quantization: { # CHECK: scale: [ 0.093635 ], # CHECK: zero_point: [ 22 ] # CHECK: } # CHECK: } ], # CHECK: inputs: [ 0 ],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.1K bytes - Viewed (0)