Functions#
- tensorrt.plugin.cdiv(first: Union[int, tensorrt.plugin._tensor.ShapeExpr], second: Union[int, tensorrt.plugin._tensor.ShapeExpr]) tensorrt.plugin._tensor.ShapeExpr #
Computes symbolic ceiling division of first by second
- tensorrt.plugin.max(first: Union[int, tensorrt.plugin._tensor.ShapeExpr], second: Union[int, tensorrt.plugin._tensor.ShapeExpr]) tensorrt.plugin._tensor.ShapeExpr #
Computes the maximum of first and second
- tensorrt.plugin.min(first: Union[int, tensorrt.plugin._tensor.ShapeExpr], second: Union[int, tensorrt.plugin._tensor.ShapeExpr]) tensorrt.plugin._tensor.ShapeExpr #
Computes the minimum of first and second
- tensorrt.plugin.size_tensor(opt: tensorrt.plugin._tensor.ShapeExpr, upper_bound: tensorrt.plugin._tensor.ShapeExpr) tensorrt.plugin._tensor.SizeTensorDesc #
Constructs a size tensor with the specified autotune shape expression opt and upper_bound
- Parameters
- Returns
A tensor descriptor for a size tensor with the specified autotune extent and upper-bound
- Return type
- tensorrt.plugin.from_shape_expr(shape_expr: Union[Tuple[Union[tensorrt.plugin._tensor.ShapeExpr, int]], tensorrt.plugin._tensor.ShapeExprs], dtype: tensorrt.tensorrt.DataType) tensorrt.plugin._tensor.TensorDesc #
Constructs a tensor descriptor with the specified shape expression and data type
- Parameters
shape_expr (Union[Tuple[Union[ShapeExpr, int]], ShapeExprs]) – Expressions or constants denoting the shape of the tensor
dtype (trt.DataType) – Data type of the tensor
- Returns
Tensor descriptor with the specified shape expression and data type
- Return type