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Results 21 - 30 of 88 for input_shapes_ (0.29 sec)
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tensorflow/compiler/mlir/lite/tests/fuse-tftext.mlir
func.func private @whitespace_tokenizer_rank1(%arg0: tensor<1x!tf_type.string> {tf._user_specified_name = "input"}) -> (tensor<?x!tf_type.string>, tensor<?xi64>) attributes {tf._input_shapes = [#tf_type.shape<1>], tf._implements = #tf_type.func<@"tftext:WhitespaceTokenizer", {}>, tf.signature.is_stateful} { %0 = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi64>} : () -> tensor<2xi64>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 460.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.h
// InferShapeForFunction. FailureOr<bool> InferModuleShape(ModuleOp module, int64_t max_iterations = 10, ArrayRef<TypeID> ops_to_skip = {}, ArrayRef<ArrayRef<int64_t>> input_shapes = {}); // Given a tensorflow NodeShape string, returns a vector of argument shapes // that can be used with InferShapeForFunction. // TF NodeShape uses `,` to separate dimensions, and `:` to separate arguments.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/convert_type.h
// Converts an TensorFlow shape to the one used in MLIR. void ConvertToMlirShape(const TensorShape& input_shape, llvm::SmallVectorImpl<int64_t>* shape); // Converts an TensorFlow shape proto to the one used in MLIR. Status ConvertToMlirShape(const TensorShapeProto& input_shape, llvm::SmallVectorImpl<int64_t>* shape);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 2.2K bytes - Viewed (0) -
tensorflow/compiler/jit/tests/device_compiler_test_helper.cc
{{"dtype", DT_FLOAT}, {"shape", input_shape}}); *graph.add_node() = MakeNode("b", "Placeholder", {}, {{"dtype", DT_FLOAT}, {"shape", input_shape}}); *graph.add_node() = MakeNode("c", "Placeholder", {}, {{"dtype", DT_FLOAT}, {"shape", input_shape}}); *graph.add_node() = MakeNode("m", "TestFn", {"a", "b", "c"}, {}); return graph; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Feb 09 08:24:16 UTC 2024 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/sink_in_invariant_ops.mlir
// CHECK-LABEL: func private @batched_function // CHECK: arg1 func.func private @batched_function(%arg0: tensor<1x3xf32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32> attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} { // CHECK: [[handle:%.*]] = "tf.VarHandleOp"() // CHECK: "tf.ReadVariableOp"([[handle]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 21K bytes - Viewed (0) -
tensorflow/cc/gradients/array_grad_test.cc
TensorShape updates_shape({4}); TensorShape input_shape({8}); auto input = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(input_shape)); auto updates = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(updates_shape)); auto indices = Const(scope_, {{4}, {3}, {1}, {7}}); auto y = ScatterNdNonAliasingAdd(scope_, input, indices, updates); RunTest({input, updates}, {input_shape, updates_shape}, {y}, {input_shape}); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 10 23:33:32 UTC 2023 - 19.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
target_opset: quant_opts_pb2.OpSet, ): lhs_batch_size, rhs_batch_size = batch_sizes input_shape = (*lhs_batch_size, 1, 1024) filter_shape = (*rhs_batch_size, 1024, 3) static_input_shape = [dim if dim is not None else 2 for dim in input_shape] model = self._create_matmul_model( input_shape, filter_shape, self._input_saved_model_path, has_bias,
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/quantization/tensorflow/utils/tf_to_xla_attribute_utils.cc
Value &padding, int num_dims) { ShapedType input_shape = mlir::cast<ShapedType>(input.getType()); SmallVector<int64_t> spatial_dims(num_dims - 2); absl::c_iota(spatial_dims, 1); bool has_dynamic_spatial_dim = absl::c_any_of( spatial_dims, [&input_shape](int64_t dim) { return input_shape.isDynamicDim(dim); }); if (conv_padding.strref() == "SAME" && has_dynamic_spatial_dim) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 17:58:54 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/cc/gradients/array_grad.cc
Shape::Attrs shape_attrs; shape_attrs.out_type_ = op.input_type(1); auto input_shape = Shape(scope, op.input(0), shape_attrs); // We interleave multiples and input_shape to get split_shape, // reshape grad to split_shape, and reduce along all even // dimensions (the tiled dimensions) to get the result // with shape input_shape. For example // input_shape = [20, 30, 40] // multiples = [2, 3, 4]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 10 23:33:32 UTC 2023 - 31.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.cc
StringAttr conv_padding, ArrayAttr explicit_paddings) { auto input_shape = mlir::cast<ShapedType>(input.getType()); auto filter_shape = mlir::cast<ShapedType>(filter.getType()); if (!input_shape.hasRank() || input_shape.getRank() != 4 || !filter_shape.hasRank() || filter_shape.getRank() != 4) { emitError(loc, "input and filter are expected to be 4D tensors");
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 47.1K bytes - Viewed (0)