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Results 91 - 100 of 178 for dequantize (0.2 sec)
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tensorflow/compiler/mlir/lite/schema/schema.fbs
// set of acceptable options. // LINT.IfChange enum BuiltinOperator : int32 { ADD = 0, AVERAGE_POOL_2D = 1, CONCATENATION = 2, CONV_2D = 3, DEPTHWISE_CONV_2D = 4, DEPTH_TO_SPACE = 5, DEQUANTIZE = 6, EMBEDDING_LOOKUP = 7, FLOOR = 8, FULLY_CONNECTED = 9, HASHTABLE_LOOKUP = 10, L2_NORMALIZATION = 11, L2_POOL_2D = 12, LOCAL_RESPONSE_NORMALIZATION = 13, LOGISTIC = 14,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
RELEASE.md
([CVE-2022-21728](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21728)) * Fixes a heap OOB access in `Dequantize` ([CVE-2022-21726](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21726)) * Fixes an integer overflow in shape inference for `Dequantize` ([CVE-2022-21727](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-21727)) * Fixes a heap OOB access in `FractionalAvgPoolGrad`
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize.cc
// Base struct for quantization. template <QuantizationTrait quantization_trait, typename ConcreteT, typename RootOpT = DequantizeOp> struct TFLQuantizationBase : public quant::QuantizationPattern<ConcreteT, QuantizeOp, DequantizeOp, NumericVerifyOp, RootOpT> { explicit TFLQuantizationBase(MLIRContext* ctx,
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/quantization/lite/quantize_model_test.cc
// The original model reshape->custom->custom->squeeze. ASSERT_THAT(*float_graph->operators(), SizeIs(4)); // The resulting model should be: // reshape->dequantize->custom->custom->quantize->squeeze. ASSERT_THAT(subgraph->operators, SizeIs(6)); const std::vector<BuiltinOperator> op_codes = { BuiltinOperator_RESHAPE, BuiltinOperator_DEQUANTIZE,
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/transforms/prepare_tf.cc
// before converting TF_Conv to TFL_Conv (void)applyPatternsAndFoldGreedily(func, std::move(patterns)); // Remove the wrapper of the tf.FakeQuant* ops and also insert the // tfl.quantize and tfl.dequantize to preserve the quantization parameters. // This is done after the first round of optimization to make sure all the // min/max operands of the tf.FakeQuant* are constants to be matched. The
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
foreach BinaryOp = [TFL_DivOp, TFL_MulOp]<Op> in defm : FuseMulOrDivWithConv2dOrDepthwiseConv2d<BinaryOp>; // This pattern applies when the same quantize/dequantize have been used twice // with the same scale. We want to remove the redundancy. // TODO(fengliuai): move this to the sanity check of pre-quantize pass. def eliminate_dq_q_pairs : Pat< (TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), (replaceWithValue $in),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s // Tests for PopulateFusedGemmStylePatterns are handled in // quantize_composite_functions for module-level evaluation of functions. module attributes {tf_saved_model.semantics} { // CHECK: quantize_simple_xla_call_module(%[[ARG_0:.+]]: tensor<1x4xf32>) func.func private @quantize_simple_xla_call_module(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 01:38:40 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-quantize -verify-each=false | FileCheck %s func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} { %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/quantize.cc
patterns.add<StableHloQuantization, StableHloQuantizationReverse>(&ctx); PopulateCommonQuantizationPatterns(ctx, patterns, enable_per_channel_quantized_weight_); // Quantize all quantizable ops, including ops that are not compute-heavy. PopulateAllQuantizablePatterns(ctx, patterns); if (failed(applyPatternsAndFoldGreedily(module_op, std::move(patterns)))) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 07:08:19 UTC 2024 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/quantization_context.cc
auto &requantize = states_manager_.GetOperandRequantizeState(op, i); if (state.IsEmpty() && requantize.pos == RequantizeState::NO_REQUANTIZE) { input_specs.push_back(original_input_specs[i]); } else if (requantize.pos == RequantizeState::ON_OUTPUT) { input_specs.push_back(TypeAttr::get(requantize.params)); } else {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 08 01:38:03 UTC 2024 - 13.1K bytes - Viewed (0)