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Results 1 - 10 of 28 for quant_specs_ (0.25 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize.cc
quant_specs_ = other.quant_specs_; enable_post_training_quantize_ = other.enable_post_training_quantize_; enable_per_channel_quantization_ = !quant_specs_.disable_per_channel; } explicit PrepareQuantizePass(const QuantizationSpecs& quant_specs) : quant_specs_(quant_specs) { enable_post_training_quantize_ = quant_specs.post_training_quantization; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc
explicit PrepareDynamicRangeQuantizePass() { quant_specs_.inference_type = tensorflow::DT_QINT8; quant_specs_.weight_quantization = true; quant_specs_.enable_mlir_dynamic_range_quantizer = true; } // Constructor used by manually creating the pass. explicit PrepareDynamicRangeQuantizePass( const quant::QuantizationSpecs& quant_specs) : quant_specs_(quant_specs) { enable_dynamic_range_per_channel_quantization_ =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize.cc
is_signed, quant_specs_.legacy_float_scale, ctx); if (quant_specs_.post_training_quantization) { patterns_2.add<ConvertLstmStatsToQDQs<LSTMOp>>(ctx, quant_specs_); patterns_2.add<ConvertLstmStatsToQDQs<UnidirectionalSequenceLSTMOp>>( ctx, quant_specs_); patterns_2.add<ConvertSvdfStatsToQDQs>(ctx, quant_specs_); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize_drq.cc
quant_specs_.inference_type = tensorflow::DT_QINT8; } // Constructor used by manually creating the pass. explicit PrepareQuantizeDRQPass(const QuantizationSpecs& quant_specs, OpSet op_set) : quant_specs_(quant_specs), op_set_(op_set) { enable_per_channel_quantization_ = !quant_specs_.disable_per_channel; }
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/quantization/tensorflow/passes/quantize.cc
explicit QuantizePass() { quant_specs_.inference_type = tensorflow::DT_QINT8; } // Constructor used by manually creating the pass. explicit QuantizePass(const QuantizationSpecs& quant_specs, OpSet target_opset) : quant_specs_(quant_specs) { weight_quantization_ = quant_specs.weight_quantization; target_opset_ = target_opset; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 05:52:39 UTC 2024 - 23.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_helper.h
explicit ConvertOpStatsToQDQs(MLIRContext* context, const quant::QuantizationSpecs& quant_specs, PatternBenefit benefit = 1) : OpRewritePattern<SourceOp>(context, benefit), quant_specs_(quant_specs) {} protected: quant::QuantizationSpecs quant_specs_; LogicalResult processInputs( SourceOp op, const operator_property::OpVariant& op_variant,
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/lite/transforms/quantize.cc
quant_specs.inference_type = tensorflow::DT_HALF; } const quant::QuantPassSpec quant_params = { {quant_specs.verify_numeric, error_tolerance_, quant_specs.whole_model_verify, enable_log_if_failed_}, quant_specs}; populateWithGenerated(patterns); if (quant_specs.weight_quantization || quant_specs.use_fake_quant_num_bits || quant_specs.qdq_conversion_mode ==
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc
quant_specs->inference_type = DT_HALF; quant_specs->inference_input_type = DT_HALF; } else { quant_specs->inference_type = DT_QINT8; quant_specs->inference_input_type = DT_QINT8; } } else { // These flags are incompatible with post_training_quantize() as only // QAT models can provide required ranges. quant_specs->disable_infer_tensor_range =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun May 12 12:39:37 UTC 2024 - 17.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model.cc
} quant::QuantizationSpecs quant_specs; quant_specs.inference_type = tflite::TflTypeToTfType(inference_type); quant_specs.post_training_quantization = true; quant_specs.disable_per_channel = disable_per_channel; quant_specs.disable_per_channel_for_dense_layers = disable_per_channel_for_dense_layers; quant_specs.verify_numeric = verify_numeric; quant_specs.whole_model_verify = whole_model_verify;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/saved_model_to_tfl_flatbuffer.cc
pass_config.quant_specs.qdq_conversion_mode = mlir::quant::QDQConversionMode::kQDQStatic; } else if (toco_flags.qdq_conversion_mode() == "DYNAMIC") { pass_config.quant_specs.qdq_conversion_mode = mlir::quant::QDQConversionMode::kQDQDynamic; // Need to set this or else the ops will still use floating point kernels pass_config.quant_specs.inference_type = tensorflow::DT_QINT8;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun May 12 12:39:37 UTC 2024 - 11K bytes - Viewed (0)