- Sort Score
- Result 10 results
- Languages All
Results 1 - 10 of 21 for _output_shapes (0.21 sec)
-
tensorflow/compiler/mlir/tensorflow/translate/export_tf_dialect_op.cc
end++; if (begin != end) { mlir::TF::ResultShapeRange output_shapes = { mlir::TF::ResultShapeIterator(begin), mlir::TF::ResultShapeIterator(end)}; SetShapeAttribute("_output_shapes", output_shapes, attributes); } } return absl::OkStatus(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 11.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v1/compile_mlir_util.h
// HLO-level inputs are supplied, and HLO-level outputs are produced. // xla_params is the HLO-level inputs and returns is the HLO-level outputs. // If unconditionally_use_output_shapes is true then the unregistered // attribute _output_shapes is always used to set the output shapes of the ops. ABSL_DEPRECATED( "Use v1/compile_tf_graph.h::CompileTensorflowGraphToHlo instead.") Status BuildHloFromGraph( const Graph& graph, xla::XlaBuilder& builder,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 17:24:39 UTC 2024 - 10.4K 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/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/lite/utils/lstm_utils_test.cc
SmallVector<int64_t, 2> output_shape{1, mlir::ShapedType::kDynamic}; EXPECT_EQ(mlir::cast<RankedTensorType>(output_types[0]).getShape().size(), output_shape.size()); for (int i = 0; i < output_shape.size(); i++) { EXPECT_EQ(mlir::cast<RankedTensorType>(output_types[0]).getDimSize(i), output_shape[i]); } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/convert_tensor_test.cc
PartialTensorShape output_shape = ConvertTypeToTensorShape(mlir::UnrankedTensorType::get(b.getF32Type())); EXPECT_TRUE(output_shape.IsIdenticalTo(PartialTensorShape())); } TEST(ConvertTypeToTensorTypeTest, NonFullyDefinedRankedTensorType) { mlir::MLIRContext context; RegisterDialects(context); mlir::Builder b(&context); PartialTensorShape output_shape = ConvertTypeToTensorShape(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils.cc
return failure(); // Build the lstm op. SmallVector<int64_t, 3> output_shape; if (time_majored) { output_shape = {time, batch, n_output}; } else { output_shape = {batch, time, n_output}; } auto result_type = mlir::RankedTensorType::get( output_shape, mlir::cast<RankedTensorType>(final_inputs.getType()).getElementType());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 36.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/post_quantize.cc
SmallVector<int64_t, 4> output_shape; for (int i = 0; i < num_dimensions; ++i) { perm.push_back(perm_tensor.getValues<IntegerAttr>()[i].getInt()); output_shape.push_back(input_shape[perm[i]]); // Check that the derived output shape matches the static shape. assert(!output_type.hasStaticShape() || output_type.getShape()[i] == output_shape[i]); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 17.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/utils.h
inline DenseElementsAttr GetShape(Value output_val, bool truncate = false) { auto output_shape = output_val.getType().dyn_cast<ShapedType>().getShape(); SmallVector<int32_t> shape; shape.reserve(output_shape.size()); bool needs_truncation = true; for (size_t dim_idx = 0; dim_idx < output_shape.size(); ++dim_idx) { int64_t dim = output_shape[dim_idx]; if (truncate && needs_truncation && dim == 1) { continue;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 11.6K bytes - Viewed (0)