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Results 61 - 70 of 213 for se_shape (0.2 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/utils/tf_to_xla_attribute_utils_test.cc
ShapedType packed_shape_type = mlir::dyn_cast<ShapedType>(packed_value.getType()); llvm::SmallVector<int64_t> packed_shape(packed_shape_type.getShape().begin(), packed_shape_type.getShape().end()); EXPECT_THAT(packed_shape, testing::ElementsAreArray(expected_packed_shape)); llvm::SmallVector<int8_t> packed_value_vector( packed_value_attr.getValues<int8_t>());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 3.5K bytes - Viewed (0) -
tensorflow/cc/gradients/image_grad_test.cc
template <typename T> void MakeOp(const OpType op_type, const Tensor& x_data, const Input& y_shape, const bool align_corners, const bool half_pixel_centers, Output* x, Output* y) { *x = Const<T>(scope_, x_data); switch (op_type) { case RESIZE_NEAREST: *y = ResizeNearestNeighbor( scope_, *x, y_shape, ResizeNearestNeighbor::AlignCorners(align_corners)); return;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 15 04:08:05 UTC 2019 - 12.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.mlir
%dq_pool = "quantfork.dcast"(%q_pool) : (tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>) -> tensor<*xf32> %reshape = "tf.Reshape"(%dq_pool, %cst) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32> %q_reshape = "quantfork.qcast"(%reshape) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/flatbuffer_import.cc
} else if (op_name == "tfl.reshape" && op_state.operands.size() == 1) { // Special case for reshape: the second op is optional in the old // converter and kernel, so we create the second operand, which is // required by the new converter, from the reshape op's option. auto new_shape = op.builtin_options.AsReshapeOptions()->new_shape; auto shape_type = tensorflow::GetTypeFromTFTensorShape(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 66.8K bytes - Viewed (0) -
tensorflow/compiler/jit/partially_decluster_pass_test.cc
Output reshape_input = ops::Placeholder(s.WithOpName("reshape_input"), DT_FLOAT, ops::Placeholder::Attrs{}); Output reshape = ops::Reshape(s.WithOpName("reshape"), reshape_input, shape); AddToCluster({shape.node(), reshape.node()}, "cluster_0"); auto graph = std::make_unique<Graph>(OpRegistry::Global()); TF_ASSERT_OK(s.ToGraph(graph.get())); TF_ASSERT_OK(PartiallyDecluster(&graph));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jun 10 12:32:39 UTC 2022 - 23K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/legacy_reshape.json
// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32> // CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32> { "version": 3, "operator_codes": [ { "builtin_code": "RESHAPE" } ], "subgraphs": [ { "tensors": [ { "shape": [1, 4], "name": "input",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 986 bytes - Viewed (0) -
tensorflow/c/experimental/saved_model/public/tensor_spec.h
#define TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_PUBLIC_TENSOR_SPEC_H_ #include <stddef.h> #include "tensorflow/c/c_api_macros.h" #include "tensorflow/c/tf_datatype.h" #include "tensorflow/c/tf_shape.h" #ifdef __cplusplus extern "C" { #endif // __cplusplus // An opaque type corresponding to TensorSpec typedef struct TF_TensorSpec TF_TensorSpec; // Returns the dtype associated with the TensorSpec.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 30 17:58:21 UTC 2020 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
// CHECK-DAG: %[[VAL_4:.*]] = "tfl.pseudo_const"(){{.*}}dense<[2, 1]> : tensor<2xi32> // CHECK: %[[VAL_5:.*]] = "tfl.reshape"(%[[VAL_0]], %[[VAL_2]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32> // CHECK: %[[VAL_6:.*]] = "tfl.reshape"(%[[VAL_1]], %[[VAL_2]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
// `input`. In other words, the shape of the `Reshape` op are not // changed after the transformation. (IsTailOfShape $rhs, $input), (HasRankAtMost<4> $input), (HasRankAtMost<4> $lhs), (HasRankAtMost<4> $rhs), (SameElementType $input, $rhs)]>; // Move binary op before reshape: // binary(reshape(lhs), reshape(rhs)) => reshape(binary(lhs, rhs))
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/jit/increase_dynamism_for_auto_jit_pass.h
// // Slice(op, begin, size <must be constant>) => // Slice(op, begin, actual_size(op.shape(), size, begin)); // _XlaCompileTimeConstantInputs={2} // // where // // actual_size(op_shape, size, begin)[i] = // size[i] == -1 ? (op_shape[i] - size[i]) // : size[i] // // This pass, combined with jit/partially_decluster_pass, reduces the number of
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Oct 26 21:01:34 UTC 2018 - 2.2K bytes - Viewed (0)