def field(cls, members, shape=None, name="<Struct>", offset=None, needs_grad=False, layout=Layout.AOS): if shape is None and offset is not None: raise TaichiSyntaxError( "shape cannot be None when offset is being set") field_dict = {} for key, dtype in members.items(): field_name = name + '.' + key if isinstance(dtype, CompoundType): field_dict[key] = dtype.field(shape=None, name=field_name, offset=offset, needs_grad=needs_grad) else: field_dict[key] = impl.field(dtype, shape=None, name=field_name, offset=offset, needs_grad=needs_grad) if shape is not None: if isinstance(shape, numbers.Number): shape = (shape, ) if isinstance(offset, numbers.Number): offset = (offset, ) if offset is not None and len(shape) != len(offset): raise TaichiSyntaxError( f'The dimensionality of shape and offset must be the same ({len(shape)} != {len(offset)})' ) dim = len(shape) if layout == Layout.SOA: for e in field_dict.values(): ti.root.dense(impl.index_nd(dim), shape).place(e, offset=offset) if needs_grad: for e in field_dict.values(): ti.root.dense(impl.index_nd(dim), shape).place(e.grad, offset=offset) else: ti.root.dense(impl.index_nd(dim), shape).place(*tuple(field_dict.values()), offset=offset) if needs_grad: grads = tuple(e.grad for e in field_dict.values()) ti.root.dense(impl.index_nd(dim), shape).place(*grads, offset=offset) return StructField(field_dict, name=name)
def allocate_dual(x, dual_root): """Allocate dual field for forward mode autodiff """ dtype = x.dtype shape = x.shape dim = len(shape) x_dual = impl.field(dtype) x._set_grad(x_dual, reverse_mode=False) x._get_field_members()[0].ptr.set_dual(x_dual._get_field_members()[0].ptr) dual_root.dense(impl.index_nd(dim), shape).place(x_dual)
def field(cls, members, shape=None, name="<Struct>", offset=None, needs_grad=False, layout=Layout.AOS): """Creates a :class:`~taichi.StructField` with each element has this struct as its type. Args: members (dict): a dict, each item is like `name: type`. shape (Tuple[int]): width and height of the field. offset (Tuple[int]): offset of the indices of the created field. For example if `offset=(-10, -10)` the indices of the field will start at `(-10, -10)`, not `(0, 0)`. needs_grad (bool): enabling gradient field or not. layout: AOS or SOA. Example: >>> vec3 = ti.types.vector(3, ti.f32) >>> sphere = {"center": vec3, "radius": float} >>> F = ti.Struct.field(sphere, shape=(3, 3)) >>> F {'center': array([[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]], dtype=float32), 'radius': array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], dtype=float32)} """ if shape is None and offset is not None: raise TaichiSyntaxError( "shape cannot be None when offset is being set") field_dict = {} for key, dtype in members.items(): field_name = name + '.' + key if isinstance(dtype, CompoundType): field_dict[key] = dtype.field(shape=None, name=field_name, offset=offset, needs_grad=needs_grad) else: field_dict[key] = impl.field(dtype, shape=None, name=field_name, offset=offset, needs_grad=needs_grad) if shape is not None: if isinstance(shape, numbers.Number): shape = (shape, ) if isinstance(offset, numbers.Number): offset = (offset, ) if offset is not None and len(shape) != len(offset): raise TaichiSyntaxError( f'The dimensionality of shape and offset must be the same ({len(shape)} != {len(offset)})' ) dim = len(shape) if layout == Layout.SOA: for e in field_dict.values(): impl.root.dense(impl.index_nd(dim), shape).place(e, offset=offset) if needs_grad: for e in field_dict.values(): impl.root.dense(impl.index_nd(dim), shape).place(e.grad, offset=offset) else: impl.root.dense(impl.index_nd(dim), shape).place(*tuple(field_dict.values()), offset=offset) if needs_grad: grads = tuple(e.grad for e in field_dict.values()) impl.root.dense(impl.index_nd(dim), shape).place(*grads, offset=offset) return StructField(field_dict, name=name)
def field(cls, n, m, dtype, shape=None, name="", offset=None, needs_grad=False, layout=None): # TODO(archibate): deprecate layout '''ti.Matrix.field''' self = cls.empty(n, m) self.entries = [] self.n = n self.m = m self.dt = dtype if isinstance(dtype, (list, tuple, np.ndarray)): # set different dtype for each element in Matrix # see #2135 if m == 1: assert len(np.shape(dtype)) == 1 and len( dtype ) == n, f'Please set correct dtype list for Vector. The shape of dtype list should be ({n}, ) instead of {np.shape(dtype)}' for i in range(n): self.entries.append(impl.field(dtype[i], name=name)) else: assert len(np.shape(dtype)) == 2 and len(dtype) == n and len( dtype[0] ) == m, f'Please set correct dtype list for Matrix. The shape of dtype list should be ({n}, {m}) instead of {np.shape(dtype)}' for i in range(n): for j in range(m): self.entries.append(impl.field(dtype[i][j], name=name)) else: for _ in range(n * m): self.entries.append(impl.field(dtype, name=name)) self.grad = self.make_grad() if layout is not None: assert shape is not None, 'layout is useless without shape' if shape is None: assert offset is None, "shape cannot be None when offset is being set" if shape is not None: if isinstance(shape, numbers.Number): shape = (shape, ) if isinstance(offset, numbers.Number): offset = (offset, ) if offset is not None: assert len(shape) == len( offset ), f'The dimensionality of shape and offset must be the same ({len(shape)} != {len(offset)})' if layout is None: layout = ti.AOS dim = len(shape) if layout.soa: for i, e in enumerate(self.entries): ti.root.dense(impl.index_nd(dim), shape).place(e, offset=offset) if needs_grad: for i, e in enumerate(self.entries): ti.root.dense(impl.index_nd(dim), shape).place(e.grad, offset=offset) else: var_list = [] for i, e in enumerate(self.entries): var_list.append(e) ti.root.dense(impl.index_nd(dim), shape).place(*tuple(var_list), offset=offset) grad_var_list = [] if needs_grad: for i, e in enumerate(self.entries): grad_var_list.append(e.grad) ti.root.dense(impl.index_nd(dim), shape).place(*tuple(grad_var_list), offset=offset) return self
def field(cls, n, m, dtype, shape=None, name="", offset=None, needs_grad=False, layout=None): # TODO(archibate): deprecate layout """Construct a data container to hold all elements of the Matrix. Args: n (int): The desired number of rows of the Matrix. m (int): The desired number of columns of the Matrix. dtype (DataType, optional): The desired data type of the Matrix. shape (Union[int, tuple of int], optional): The desired shape of the Matrix. name (string, optional): The custom name of the field. offset (Union[int, tuple of int], optional): The coordinate offset of all elements in a field. needs_grad (bool, optional): Whether the Matrix need gradients. layout (:class:`~taichi.lang.impl.Layout`, optional): The field layout, i.e., Array Of Structure(AOS) or Structure Of Array(SOA). Returns: :class:`~taichi.lang.matrix.Matrix`: A :class:`~taichi.lang.matrix.Matrix` instance serves as the data container. """ self = cls.empty(n, m) self.entries = [] self.n = n self.m = m self.dt = dtype if isinstance(dtype, (list, tuple, np.ndarray)): # set different dtype for each element in Matrix # see #2135 if m == 1: assert len(np.shape(dtype)) == 1 and len( dtype ) == n, f'Please set correct dtype list for Vector. The shape of dtype list should be ({n}, ) instead of {np.shape(dtype)}' for i in range(n): self.entries.append(impl.field(dtype[i], name=name)) else: assert len(np.shape(dtype)) == 2 and len(dtype) == n and len( dtype[0] ) == m, f'Please set correct dtype list for Matrix. The shape of dtype list should be ({n}, {m}) instead of {np.shape(dtype)}' for i in range(n): for j in range(m): self.entries.append(impl.field(dtype[i][j], name=name)) else: for _ in range(n * m): self.entries.append(impl.field(dtype, name=name)) self.grad = self.make_grad() if layout is not None: assert shape is not None, 'layout is useless without shape' if shape is None: assert offset is None, "shape cannot be None when offset is being set" if shape is not None: if isinstance(shape, numbers.Number): shape = (shape, ) if isinstance(offset, numbers.Number): offset = (offset, ) if offset is not None: assert len(shape) == len( offset ), f'The dimensionality of shape and offset must be the same ({len(shape)} != {len(offset)})' if layout is None: layout = ti.AOS dim = len(shape) if layout.soa: for i, e in enumerate(self.entries): ti.root.dense(impl.index_nd(dim), shape).place(e, offset=offset) if needs_grad: for i, e in enumerate(self.entries): ti.root.dense(impl.index_nd(dim), shape).place(e.grad, offset=offset) else: var_list = [] for i, e in enumerate(self.entries): var_list.append(e) ti.root.dense(impl.index_nd(dim), shape).place(*tuple(var_list), offset=offset) grad_var_list = [] if needs_grad: for i, e in enumerate(self.entries): grad_var_list.append(e.grad) ti.root.dense(impl.index_nd(dim), shape).place(*tuple(grad_var_list), offset=offset) return self
def field(cls, n, m, dtype, shape=None, name="", offset=None, needs_grad=False, layout=Layout.AOS): """Construct a data container to hold all elements of the Matrix. Args: n (int): The desired number of rows of the Matrix. m (int): The desired number of columns of the Matrix. dtype (DataType, optional): The desired data type of the Matrix. shape (Union[int, tuple of int], optional): The desired shape of the Matrix. name (string, optional): The custom name of the field. offset (Union[int, tuple of int], optional): The coordinate offset of all elements in a field. needs_grad (bool, optional): Whether the Matrix need gradients. layout (Layout, optional): The field layout, i.e., Array Of Structure (AOS) or Structure Of Array (SOA). Returns: :class:`~taichi.lang.matrix.Matrix`: A :class:`~taichi.lang.matrix.Matrix` instance serves as the data container. """ entries = [] if isinstance(dtype, (list, tuple, np.ndarray)): # set different dtype for each element in Matrix # see #2135 if m == 1: assert len(np.shape(dtype)) == 1 and len( dtype ) == n, f'Please set correct dtype list for Vector. The shape of dtype list should be ({n}, ) instead of {np.shape(dtype)}' for i in range(n): entries.append( impl.create_field_member(dtype[i], name=name)) else: assert len(np.shape(dtype)) == 2 and len(dtype) == n and len( dtype[0] ) == m, f'Please set correct dtype list for Matrix. The shape of dtype list should be ({n}, {m}) instead of {np.shape(dtype)}' for i in range(n): for j in range(m): entries.append( impl.create_field_member(dtype[i][j], name=name)) else: for _ in range(n * m): entries.append(impl.create_field_member(dtype, name=name)) entries, entries_grad = zip(*entries) entries, entries_grad = MatrixField(entries, n, m), MatrixField( entries_grad, n, m) entries.set_grad(entries_grad) if shape is None: assert offset is None, "shape cannot be None when offset is being set" if shape is not None: if isinstance(shape, numbers.Number): shape = (shape, ) if isinstance(offset, numbers.Number): offset = (offset, ) if offset is not None: assert len(shape) == len( offset ), f'The dimensionality of shape and offset must be the same ({len(shape)} != {len(offset)})' dim = len(shape) if layout == Layout.SOA: for e in entries.get_field_members(): ti.root.dense(impl.index_nd(dim), shape).place(ScalarField(e), offset=offset) if needs_grad: for e in entries_grad.get_field_members(): ti.root.dense(impl.index_nd(dim), shape).place(ScalarField(e), offset=offset) else: ti.root.dense(impl.index_nd(dim), shape).place(entries, offset=offset) if needs_grad: ti.root.dense(impl.index_nd(dim), shape).place(entries_grad, offset=offset) return entries