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Results 1 - 10 of 10 for RFFT2D (0.37 sec)
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tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
TF::RFFT2DOp rfft2d = rewriter.create<TF::RFFT2DOp>( rfft_op.getLoc(), rfft2d_out_type, expanded_input.getResult(), expanded_fft_len.getResult()); // Insert the squeeze op. auto squeeze_dim = rewriter.getI64ArrayAttr({-2}); TF::SqueezeOp squeeze = rewriter.create<TF::SqueezeOp>( rfft_op.getLoc(), output_type, rfft2d.getResult(), squeeze_dim);
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/tests/legalize-tf.mlir
} // ----- func.func @rfft2d(%arg0: tensor<10x20x10x30xf32>, %arg1: tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>> { %0 = "tf.RFFT2D"(%arg0, %arg1) : (tensor<10x20x10x30xf32>, tensor<2xi32>) -> tensor<10x20x10x30xcomplex<f32>> func.return %0 : tensor<10x20x10x30xcomplex<f32>> // CHECK-LABEL: rfft2d
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
SCATTER_ND = 122, SELECT_V2 = 123, DENSIFY = 124, SEGMENT_SUM = 125, BATCH_MATMUL = 126, PLACEHOLDER_FOR_GREATER_OP_CODES = 127, CUMSUM = 128, CALL_ONCE = 129, BROADCAST_TO = 130, RFFT2D = 131, CONV_3D = 132, IMAG=133, REAL=134, COMPLEX_ABS=135, HASHTABLE = 136, HASHTABLE_FIND = 137, HASHTABLE_IMPORT = 138, HASHTABLE_SIZE = 139, REDUCE_ALL = 140,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
SCATTER_ND = 122, SELECT_V2 = 123, DENSIFY = 124, SEGMENT_SUM = 125, BATCH_MATMUL = 126, PLACEHOLDER_FOR_GREATER_OP_CODES = 127, CUMSUM = 128, CALL_ONCE = 129, BROADCAST_TO = 130, RFFT2D = 131, CONV_3D = 132, IMAG=133, REAL=134, COMPLEX_ABS=135, HASHTABLE = 136, HASHTABLE_FIND = 137, HASHTABLE_IMPORT = 138, HASHTABLE_SIZE = 139, REDUCE_ALL = 140,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
let hasCanonicalizer = 1; } def TFL_RFFT2dOp : TFL_Op<"rfft2d", [Pure]> { let summary = "2D real-valued fast Fourier transform."; let description = [{ Computes the 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of `input`. Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
// CHECK: %[[CON:.*]] = "tf.ConcatV2"(%[[CST0]], %arg1, %[[CST1]]) : (tensor<1xi32>, tensor<1xi32>, tensor<i32>) -> tensor<2xi32> // CHECK: %[[RFF:.*]] = "tf.RFFT2D"(%[[EXP]], %[[CON]]) : (tensor<10x20x1x30xf32>, tensor<2xi32>) -> tensor<10x20x1x30xcomplex<f64>> // CHECK: %[[SQE:.*]] = "tf.Squeeze"(%[[RFF]]) <{squeeze_dims = [-2]}> : (tensor<10x20x1x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f64>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass.cc
"Polygamma", "PopulationCount", "Qr", "QuantizeAndDequantizeV2", "QuantizeAndDequantizeV3", "QuantizeAndDequantizeV4", "RFFT", "RFFT2D", "RFFT3D", "RGBToHSV", "RandomShuffle", "RandomStandardNormal", "RandomUniform", "RandomUniformInt", "ReadVariableOp", "ReadVariableXlaSplitND",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 85.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
} def TF_RFFT2DOp : TF_Op<"RFFT2D", [Pure]> { let summary = "2D real-valued fast Fourier transform."; let description = [{ Computes the 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of `input`. Since the DFT of a real signal is Hermitian-symmetric, `RFFT2D` only returns the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
RELEASE.md
* Added dynamic range quantization support for the BatchMatMul op. * Both symmetric and asymmetric quantized input tensor are supported. * Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently only supports float32 input. * Add 5D support to `SLICE` op. * TFLite Supports SingatureDef: * TFLiteConverter exports models with SignatureDef
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/schema/schema_generated.h
"SCATTER_ND", "SELECT_V2", "DENSIFY", "SEGMENT_SUM", "BATCH_MATMUL", "PLACEHOLDER_FOR_GREATER_OP_CODES", "CUMSUM", "CALL_ONCE", "BROADCAST_TO", "RFFT2D", "CONV_3D", "IMAG", "REAL", "COMPLEX_ABS", "HASHTABLE", "HASHTABLE_FIND", "HASHTABLE_IMPORT", "HASHTABLE_SIZE", "REDUCE_ALL",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 1M bytes - Viewed (0)