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Results 1 - 10 of 24 for SYMMETRIC (0.16 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/tests/insert_custom_aggregation_ops.mlir
// HISTOGRAM-MSE-SYMMETRIC-CHECK-NEXT: "tf.AddV2" // HISTOGRAM-MSE-SYMMETRIC-CHECK-NEXT: return // HISTOGRAM-MSE-SYMMETRIC-CHECK: func @composite_conv2d_with_relu6_fn // HISTOGRAM-MSE-SYMMETRIC-CHECK-NEXT: "tf.Conv2D" // HISTOGRAM-MSE-SYMMETRIC-CHECK-NEXT: "tf.Relu6" // HISTOGRAM-MSE-SYMMETRIC-CHECK-NEXT: return // ----- module { // CHECK-LABEL: func.func @main
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 32.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h
// the value range isn't straddling zero, an empty type is returned. The min/max // are adjusted to be symmetric if `symmetric` flag is set to True. And // `symmetric` can only be set to true when it is signed and narrow_range. Type GetUniformQuantizedTypeForWeight(ElementsAttr attr, bool symmetric, unsigned num_bits, bool is_signed, bool narrow_range,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.cc
// works for both this value and 0.0. if (single_value < 0.0) { mins[0] = single_value; maxs[0] = symmetric ? -single_value : 0.0; } else if (single_value > 0.0) { mins[0] = symmetric ? -single_value : 0.0; maxs[0] = single_value; } else { mins[0] = maxs[0] = single_value; } for (int i = 1; i < dim_size; ++i) {
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/stablehlo/passes/insert_weight_param.cc
Type weight_type; if (IsPerTensor(weight_only_ptq)) { weight_type = dyn_cast<quant::QuantizedType>( quant::GetUniformQuantizedTypeForWeight( attr, /*symmetric=*/true, /*num_bits=*/8, /*is_signed=*/true, /*narrow_range=*/true, /*legacy_float_scale=*/false)); } else { int quantization_dimension = GetQuantizationDimension(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 10.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_quantize_op.cc
PatternRewriter& rewriter, TF::ConstOp op, tensorflow::quantization::QuantizationComponentSpec& weight_spec) { // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset // Currently, support symmetric, per-tensor, signed int8 const bool kIsNarrowRange = true; const bool kIsSigned = true; const int kBitWidth = 8; DenseFPElementsAttr attr;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_driver.cc
// When `disable_per_channel_` is false, per-channel symmetric quantization // parameters are created from the weights when the ops support per-channel // quantization. Otherwise, uses per-tensor asymmetric quantization with // narrow range. // per-axis quantization weight, with symmetric min/max enforced. final_type = GetUniformQuantizedPerAxisTypeForWeight(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 38.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_helper.h
// Computes the effective min/max values of the attribute values. quant::ExtractMinMaxFromAttr(attr, /*dim_size=*/1, /*slice_size=*/1, /*symmetric=*/true, mins, maxs); double scale = maxs[0] / -llvm::minIntN(tensor_property.number_of_bits); quant_type = UniformQuantizedType::getChecked( const_op->getLoc(), quant::QuantizationFlags::Signed,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 28K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize_drq.cc
} if (is_per_channel_quantization) { quant_type = mlir::dyn_cast<quant::QuantizedType>( quant::GetUniformQuantizedPerAxisTypeForWeight( attr, quant_dim, /*symmetric=*/true, bit_width, is_signed, is_narrow_range, is_legacy_float)); } else { quant_type = mlir::dyn_cast<quant::QuantizedType>( quant::GetUniformQuantizedTypeForWeight(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/flatbuffer_operator.cc
flatbuffers::FlatBufferBuilder* builder) { switch (padding) { case mlir::TFL::MirrorPaddingType::REFLECT: return tflite::MirrorPadMode_REFLECT; case mlir::TFL::MirrorPaddingType::SYMMETRIC: return tflite::MirrorPadMode_SYMMETRIC; } llvm_unreachable("invalid mirror_pad_enum in conversion."); } static tflite::TensorType ConvertDerivedTypeAttrForOptionWriter(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 38K bytes - Viewed (0) -
android/guava/src/com/google/common/base/Equivalence.java
* * <ul> * <li>{@code equivalent(x, x)} is true (<i>reflexive</i> property) * <li>{@code equivalent(x, y)} and {@code equivalent(y, x)} each return the same result * (<i>symmetric</i> property) * <li>If {@code equivalent(x, y)} and {@code equivalent(y, z)} are both true, then {@code * equivalent(x, z)} is also true (<i>transitive</i> property) * </ul> *
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Thu May 16 14:34:47 UTC 2024 - 13.8K bytes - Viewed (0)