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Results 151 - 160 of 202 for conv3d (0.14 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/cc/constant_fold.cc
// specs. absl::flat_hash_set<int> GetQuantizableOperands(Operation* op) { absl::flat_hash_set<int> quantizable_operands; if (isa<TF::DepthwiseConv2dNativeOp, TF::Conv2DOp, TF::Conv3DOp, TF::MatMulOp, TF::BatchMatMulOp>(op)) { quantizable_operands.insert(1); } else if (isa<TF::GatherOp>(op)) { quantizable_operands.insert(0); } else if (auto einsum_op = dyn_cast<TF::EinsumOp>(op)) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/decompose_hybrid_quantization.cc
RewritePatternSet patterns(&getContext()); auto *ctx = &getContext(); auto func = getOperation(); patterns.add<DequantizeConverter<TFL::Conv2DOp>, DequantizeConverter<TFL::Conv3DOp>, DequantizeConverter<TFL::DepthwiseConv2DOp>, DequantizeConverter<TFL::FullyConnectedOp>, DequantizeConverter<TFL::TransposeConvOp>>(ctx);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_uniform_quantized.mlir
// func.func func_name_${key1}_fn (...) { // ...${key2}... // } // ``` // The above template with generate two functions by substituting `key1` and // `key2` with given values. module { for main_op in ["Conv2D", "DepthwiseConv2D", "MatMul"] { parameters[ {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "!tf_type.qint8"},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Aug 29 01:13:58 UTC 2023 - 19.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq.mlir
%1 = "quantfork.stats"(%arg0) {layerStats = dense<[1.27501142, 149.824783]> : tensor<2xf32>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 01 10:21:29 UTC 2023 - 9.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_tfl_passes.cc
// away ops that can't get constant folded after PrepareTF pass. For example, // tf.Conv2D is split into tf.Transpose and tfl.Conv2D. pass_manager->addNestedPass<mlir::func::FuncOp>( mlir::createCanonicalizerPass()); pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass()); // This pass does dead code elimination based on symbol visibility.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 18:45:51 UTC 2024 - 25.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
weight_new_shape, &rewriter); // Replace the fc with conv. // The output would be [1, 1, width, output]. auto conv_output_type = RankedTensorType::get({1, 1, width, output_size}, output_type.getElementType()); auto conv = rewriter.create<TFL::Conv2DOp>( fc_op.getLoc(), conv_output_type, reshaped_input, reshaped_weight,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0) -
platforms/core-runtime/build-operations/src/main/java/org/gradle/internal/operations/BuildOperationType.java
* * The details object should effectively provide the identifying context for the operation. * This is information that is known before the operation is executed. * * The result object should convey the outcome. * This is information that is known after the operation is executed. * * These details and result types need to maintain backwards binary compatibility.
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue Oct 24 14:19:43 UTC 2023 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/quantization.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/hardwares/cpu_hardware.cc
TargetHardwareOpRegistration<CpuHardware, Op> Op##_CpuHardware_hardware( \ Create); // Operation costs on CPU // Currently used for these ops: // tfl.conv_2d / tfl.depthwise_conv_2d / tfl.fully_connected class CpuConvOp : public TargetHardwareOperation { double GetOpCost(mlir::Operation* op) const override { float cost = 0.0; int64_t arithmetic_count;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 06 03:08:33 UTC 2023 - 5.9K bytes - Viewed (0) -
src/cmd/compile/internal/walk/walk.go
n.X = walkExpr(n.X, &init) call := walkExpr(mkcall1(chanfn("chanrecv1", 2, n.X.Type()), nil, &init, n.X, typecheck.NodNil()), &init) return ir.InitExpr(init, call) } func convas(n *ir.AssignStmt, init *ir.Nodes) *ir.AssignStmt { if n.Op() != ir.OAS { base.Fatalf("convas: not OAS %v", n.Op()) } n.SetTypecheck(1) if n.X == nil || n.Y == nil { return n } lt := n.X.Type() rt := n.Y.Type()
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Feb 27 20:56:00 UTC 2024 - 10.4K bytes - Viewed (0)