- Sort Score
- Result 10 results
- Languages All
Results 21 - 30 of 111 for _output_shapes (0.38 sec)
-
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/cc/saved_model/testdata/chunked_saved_model/chunked_model/saved_model.pbtxt
} } attr { key: "_output_shapes" value { list { shape { } } } } } node_def { name: "num_shards" op: "Const" attr { key: "_output_shapes" value { list { shape {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 21:43:11 UTC 2023 - 531.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
%cst_0 = arith.constant dense<1> : tensor<3xi32> %0 = "tf.Squeeze"(%arg0) {T = f32, _output_shapes = ["tfshape$dim { size: 4 } dim { size: 64 } dim { size: 64 }"], device = "", squeeze_dims = []} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>
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/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
) ] ) ) self.assertTrue( self._contains_op( output_graphdef, 'Const', '_output_shapes', per_channel_size_attr, ) ) elif target_opset == quant_opts_pb2.UNIFORM_QUANTIZED: self.assertTrue(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/translate/import_model.cc
if (node.IsWhileNode()) { auto* output_shapes = node.attrs().Find("output_shapes"); auto* element_types = node.attrs().Find("T"); if (output_shapes && !output_shapes->list().shape().empty()) { const auto& output_shape = output_shapes->list().shape(idx); const auto& element_type = element_types->list().type(idx); return ConvertToMlirTensorType(output_shape, element_type, &builder); } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 183.2K 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/tf2xla/tests/legalize-tf.mlir
// CHECK-DAG: [[LINSPACE:%.*]] = chlo.broadcast_add [[MUL]], [[START]] {broadcast_dimensions = array<i64>} // CHECK: return [[LINSPACE]] %0 = "tf.Const"() {_output_shapes = ["tfshape$"], device = "", dtype = i32, value = dense<4> : tensor<i32>} : () -> tensor<i32> %1 = "tf.LinSpace"(%arg0, %arg1, %0) : (tensor<f32>, tensor<f32>, tensor<i32>) -> tensor<4xf32> func.return %1 : tensor<4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 335.5K 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)