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src/path/filepath/path_test.go
{`c:/`, `c:/`, ``}, {`c:/foo`, `c:/`, `foo`}, {`c:/foo/bar`, `c:/foo/`, `bar`}, {`//host/share`, `//host/share`, ``}, {`//host/share/`, `//host/share/`, ``}, {`//host/share/foo`, `//host/share/`, `foo`}, {`\\host\share`, `\\host\share`, ``}, {`\\host\share\`, `\\host\share\`, ``}, {`\\host\share\foo`, `\\host\share\`, `foo`}, } func TestSplit(t *testing.T) { var splittests []SplitTest
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Mar 22 16:38:19 UTC 2024 - 47.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test_base.py
tensor_spec.TensorSpec( shape=shape, dtype=dtypes.float32, name='input_tensor' ) ), ) return model # Prepares sample einsum input data shapes. # This function returns: # 1. Shape for input 1 # 2. Shape for input 2 # 3. Shape for bias # 4. Signature for input 1 (Could contain None dimension)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 18.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/unfuse_batch_norm_pass.cc
} // Gets the shape of operand, assuming it is a dynamic shape with static rank. Value getShapeValue(Location loc, Value operand, PatternRewriter &rewriter) { RankedTensorType resultType = mlir::dyn_cast<RankedTensorType>(operand.getType()); return rewriter.create<shape::ShapeOfOp>( loc, RankedTensorType::get(/*shape=*/{resultType.getRank()},
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.2K bytes - Viewed (0) -
pkg/scheduler/apis/config/validation/validation_pluginargs_test.go
&field.Error{ Type: field.ErrorTypeInvalid, Field: "shape[0].utilization", }, &field.Error{ Type: field.ErrorTypeInvalid, Field: "shape[1].score", }, &field.Error{ Type: field.ErrorTypeInvalid, Field: "shape[2].score", }, &field.Error{ Type: field.ErrorTypeInvalid, Field: "shape[3].utilization", }, }), }, }
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Wed Apr 24 18:25:29 UTC 2024 - 27.3K bytes - Viewed (0) -
src/os/path_windows_test.go
// UNC Absolute {`\\srv\share\long`, `\\?\UNC\srv\share\long`}, {`//srv/share/long`, `\\?\UNC\srv\share\long`}, {`/\srv/share/long`, `\\?\UNC\srv\share\long`}, {`\\srv\share\long\`, `\\?\UNC\srv\share\long\`}, {`\\srv\share\bar\.\long`, `\\?\UNC\srv\share\bar\long`}, {`\\srv\share\bar\..\long`, `\\?\UNC\srv\share\long`}, {`\\srv\share\bar\..\..\long`, `\\?\UNC\srv\share\long`}, // share name is not removed by ".."
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Apr 23 16:37:32 UTC 2024 - 8K 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/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
weight_row = array_ops.ones( shape=array_ops.shape(input_vocabs_placeholder), dtype=dtypes.float32 ) # shape: (?, 2) weight = array_ops.transpose_v2( array_ops_stack.stack([weight_row, weight_row]) ) # shape: (2, 2) output_tensor = math_ops.matmul(matmul_input, weight) return input_vocabs_placeholder, lookup_vals, output_tensor
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 08:51:46 UTC 2024 - 51.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/einsum.cc
auto value_type = mlir::cast<RankedTensorType>(value.getType()); auto shape = value_type.getShape(); SmallVector<int64_t, 4> transposed_shape(shape.begin(), shape.end()); for (int i = 0, end = shape.size(); i < end; ++i) { transposed_shape[i] = shape[permutation[i]]; } auto transposed_type = RankedTensorType::get(transposed_shape, value_type.getElementType());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 33.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.cc
} // Converts the tensor shape proto into an MLIR shape attribute. absl::StatusOr<mlir::Attribute> ConvertTensorShapeProto( const TensorShapeProto& shape, mlir::MLIRContext* context) { if (shape.unknown_rank()) return mlir::TF::ShapeAttr::get(context, std::nullopt); llvm::SmallVector<int64_t, 4> dims; dims.reserve(shape.dim().size()); for (const auto& dim : shape.dim()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 20.5K bytes - Viewed (0) -
tensorflow/compiler/jit/pjrt_device_context.cc
cpu_tensor->shape(), cpu_tensor->dtype(), std::nullopt); TF_ASSIGN_OR_RETURN(xla::Shape shape, shape_determination_fns.shape_representation_fn( cpu_tensor->shape(), cpu_tensor->dtype(), /*fast_mem=*/false, layout_preference)); const xla::Layout* device_layout = &(shape.layout()); // The device id should match the local_hardware_id in
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 13 08:49:31 UTC 2024 - 11.6K bytes - Viewed (0)