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Results 71 - 80 of 83 for output_shapes (0.16 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/passes/duplicate_shape_determining_constants.cc
CompileTimeConstantOperand<TF::SegmentSumV2Op, 2>, // $num_segments CompileTimeConstantOperand<TF::SliceOp, 1, 2>, // $begin, $size CompileTimeConstantOperand<TF::SparseToDenseOp, 1>, // $output_shape CompileTimeConstantOperand<TF::SplitOp, 0>, // $split_dim // $size_splits, $split_dim CompileTimeConstantOperand<TF::SplitVOp, 1, 2>,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 05:52:39 UTC 2024 - 17.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
Value none = rewriter.create<TFL::NoValueOp>( op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr()); Value output_shape = CreateCastToInt32(tf_op.getInputSizes(), op->getLoc(), rewriter); rewriter.replaceOpWithNewOp<TFL::Conv3DTransposeOp>( op, tf_op.getType(), output_shape, tf_op.getFilter(), tf_op.getOutBackprop(), /*bias=*/none, dilation_depth_factor, dilation_height_factor,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/uniform_quantized_stablehlo_to_tfl_pass.cc
.getScale(), /*filter_scales=*/filter_quantized_type.getScales()); const ArrayRef<int64_t> output_shape = op->getResult(0).getType().cast<TensorType>().getShape(); const SmallVector<int64_t, 1> bias_shape = { output_shape[output_shape.size() - 1]}; // `tfl.fully_connected`'s `GetChannelDimIndex` is 0. const auto bias_quantized_type =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
// Shape of the final output. (Except for dimension folding in the // single diagonal case.) Shape output_shape; for (int i = 0; i < num_dims - 2; i++) { output_shape.push_back(input_type.getDimSize(i)); } output_shape.push_back(num_diags); output_shape.push_back(max_diag_len); // A slice is the shape of what GatherOp copies per lookup. So the last
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/translate/export_graphdef.cc
(*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr; (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr; } } TF_RETURN_IF_ERROR( SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr())); DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype)); AttrValue type_attr; type_attr.set_type(dtype);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 35.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v2/tf_executor_to_graph.cc
(*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr; (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr; } } TF_RETURN_IF_ERROR( SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr())); DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype)); AttrValue type_attr; type_attr.set_type(dtype);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 23:04:51 UTC 2024 - 35.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/lower_tf.mlir
// CHECK-DAG: [[OUTPUT_SHAPE:%.+]] = "tf.ConcatV2"([[OUTPUT_BATCH]], [[OUTER_SHAPE_0]], [[OUTER_SHAPE_1]], [[PADDED_SHAPE_SPLITS]]#3, [[ZERO_I64]]) // CHECK-DAG: [[RESHAPED:%.+]] = "tf.Reshape"([[PADDED]], [[RESHAPED_SHAPE]]) // CHECK-DAG: [[PERMUTED:%.+]] = "tf.Transpose"([[RESHAPED]], [[PERMUTATION]]) // CHECK-DAG: [[RESULT:%.+]] = "tf.Reshape"([[PERMUTED]], [[OUTPUT_SHAPE]]) // CHECK-DAG: return [[RESULT]]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 92K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.cc
// the shape inference pass is run early in the pass pipeline, shape inference // during import is not necessary. config.enable_shape_inference = false; // Some graphs may require _output_shapes (an unregistered attribute) // to override shapes. It is unfortunately not always set correctly so only // do it optionally. config.unconditionally_use_set_output_shapes =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 17:24:39 UTC 2024 - 45.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/constant-fold.mlir
func.func @testUnimplementedOp() -> (tensor<i32>, tensor<i32>) { %0 = arith.constant dense<1> : tensor<i32> %1 = arith.constant dense<2> : tensor<i32> %2 = "tf.Maximum"(%0, %1) {_output_shapes = ["tfshape$"]} : (tensor<i32>, tensor<i32>) -> tensor<i32> %3 = "tf.Minimum"(%0, %1) {random_attr = "hello"} : (tensor<i32>, tensor<i32>) -> tensor<i32> func.return %2, %3: tensor<i32>, tensor<i32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 31 23:22:24 UTC 2024 - 36.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.td
let summary = "Transpose convolution operator"; let description = [{ Performs transpose convolution operation on input. }]; let arguments = (ins TFL_I32Tensor:$output_shape, TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$weights, TFL_TensorOf<[F32, QI8, QUI8, QI16]>:$input, TFL_TensorOfOrNone<[F32, QI32, I64]>:$bias, TFL_PaddingAttr:$padding,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 186K bytes - Viewed (0)