- Sort Score
- Result 10 results
- Languages All
Results 131 - 140 of 185 for Axis (0.16 sec)
-
tensorflow/compiler/mlir/quantization/tensorflow/passes/preprocess_op.cc
using QuantizationUnit = std::pair<Operation*, int>; using QuantizationUnits = llvm::SetVector<QuantizationUnit>; using ::tensorflow::quantization::OpSet; // Preprocesses ops to allow multi-axis quantization, prior to quantization // passes. Currently, per-channel quantization only supports 1D results. class PreprocessOpPass : public PassWrapper<PreprocessOpPass, OperationPass<ModuleOp>> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.4K bytes - Viewed (0) -
src/runtime/proc_test.go
dk := k1 - k0 if di >= dj && di >= dk && di >= threshold { // divide in two by y axis mi := i0 + di/2 done1 := make(chan struct{}, 1) go matmult(done1, A, B, C, i0, mi, j0, j1, k0, k1, threshold) matmult(nil, A, B, C, mi, i1, j0, j1, k0, k1, threshold) <-done1 } else if dj >= dk && dj >= threshold { // divide in two by x axis mj := j0 + dj/2 done1 := make(chan struct{}, 1)
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed Jun 14 00:03:57 UTC 2023 - 25.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/tf2xla_rewriter_test.cc
%0:3 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<3xi64>) -> (tensor<i64>, tensor<i64>, tensor<i64>) return } })"; XlaComputation GetTestXlaComputation() { XlaBuilder xla_builder("test"); auto param =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:16:07 UTC 2024 - 11.7K bytes - Viewed (0) -
tensorflow/c/eager/c_api_test.cc
TFE_TensorHandle* axis = TestAxisTensorHandle(ctx); TFE_Op* minOp = MinOp(ctx, input, axis); TFE_TensorHandle* retvals[1] = {nullptr}; int num_retvals = 1; TFE_Execute(minOp, &retvals[0], &num_retvals, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteOp(minOp); TFE_DeleteTensorHandle(input); TFE_DeleteTensorHandle(axis); ASSERT_EQ(1, num_retvals);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 03 20:50:20 UTC 2023 - 94.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_patterns.td
// TorchIndexSelect op patterns. //===----------------------------------------------------------------------===// def : Pat<(MHLO_TorchIndexSelectOp $params, $indices, $axis, $batch_dims),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 03 08:58:22 UTC 2024 - 34K bytes - Viewed (0) -
tensorflow/cc/saved_model/testdata/half_plus_two_pbtxt/00000123/saved_model.pbtxt
type: "int" has_minimum: true minimum: 1 } attr { name: "T" type: "type" } attr { name: "axis" type: "int" default_value { i: 0 } } } op { name: "ParseExample" input_arg { name: "serialized"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 26 01:10:27 UTC 2017 - 46.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tools/tflite_op_coverage_spec_getters_gen.cc
bool per_axis) { auto *def_init = llvm::cast<llvm::DefInit>(input_value); auto *val = def_init->getDef()->getValue("tflRuntimeTypePredicate"); // For non-per-axis op, no predicate means accepting AnyTensor. if (!val) return !per_axis; llvm::StringRef supported_types = def_init->getDef()->getValueAsString("tflRuntimeTypeDescription");
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 12.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/passes.h
const QuantizationSpecs& quant_specs, tensorflow::quantization::OpSet op_set); // Creates an instance of the PreprocessOp pass, which will perform op // preprocessing to allow multi-axis quantization, prior to quantization. std::unique_ptr<OperationPass<ModuleOp>> CreatePreprocessOpPass( tensorflow::quantization::OpSet op_set, tensorflow::quantization::QuantizationMethod::PresetMethod
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 12.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_generated.h
struct ConcatenationOptionsBuilder { typedef ConcatenationOptions Table; ::flatbuffers::FlatBufferBuilder &fbb_; ::flatbuffers::uoffset_t start_; void add_axis(int32_t axis) { fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0); } void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 1M bytes - Viewed (0) -
src/compress/bzip2/testdata/Isaac.Newton-Opticks.txt.bz2
_Fig._ 7.] be any refracting Lens, spherically Convex or Concave or Plane on either side, and let CD be its Axis (that is, the Line which cuts both its Surfaces perpendicularly, and passes through the Centres of the Spheres,) and in this Axis produced let F and _f_ be the Foci of the refracted Rays found as above, when the incident Rays on both sides the Lens are parallel to the same Axis; and upon the Diameter F_f_ bisected in E, describe a Circle. Suppose now that any Point Q be the Focus of any...
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Sep 24 18:26:02 UTC 2018 - 129.4K bytes - Viewed (0)