From 53c8fa3fb07c2e425e7d81bc85efbf67cf4735d4 Mon Sep 17 00:00:00 2001 From: Arian Jamasb Date: Fri, 1 Sep 2023 17:55:18 +0200 Subject: [PATCH 001/106] transfer less data to gpu --- proteinworkshop/models/base.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index 7a1490e0..d0ed5453 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -272,7 +272,10 @@ def configure_losses( for k, v in loss_dict.items() } - def on_after_batch_transfer( + def transfer_batch_to_device(self, batch: Union[Batch, ProteinBatch], dataloader_idx: int) -> Union[Batch, ProteinBatch]: + return batch.to(self.device, list(self.encoder.required_batch_attributes)) + + def on_before_batch_transfer( self, batch: Union[Batch, ProteinBatch], dataloader_idx: int ) -> Union[Batch, ProteinBatch]: """ From f0b43bcf7189e5576701bfd1de66f42e2ba2421d Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sat, 16 Sep 2023 05:53:29 -0700 Subject: [PATCH 002/106] add rigid uitls --- .../graph_encoders/components/rigid_utils.py | 1378 +++++++++++++++++ 1 file changed, 1378 insertions(+) create mode 100644 proteinworkshop/models/graph_encoders/components/rigid_utils.py diff --git a/proteinworkshop/models/graph_encoders/components/rigid_utils.py b/proteinworkshop/models/graph_encoders/components/rigid_utils.py new file mode 100644 index 00000000..e45a65e0 --- /dev/null +++ b/proteinworkshop/models/graph_encoders/components/rigid_utils.py @@ -0,0 +1,1378 @@ +from typing import Any, Callable, Optional, Sequence, Tuple + +import numpy as np +import torch + + +def get_rigid_from_three_points(x1, x2, x3, eps=1e-8): + # CA -> C vector + v1 = x3 - x2 + + # Robust normalization + denom = np.sqrt(np.sum(np.square(v1), axis=-1) + eps) + e1 = np.stack( + [v1[..., i, :] / denom[..., i] for i in range(v1.shape[1])], axis=1 + ) + + # CA -> N vector + v2 = x1 - x2 + + # Project v2 in direction of e1 and eliminate that contribution from v2 to get a vector orthogonal to e1 + proj = np.stack( + [ + e1[:, i, :] * np.sum(e1 * v2, axis=-1)[:, i] + for i in range(e1.shape[1]) + ], + axis=1, + ) + u2 = v2 - proj + + # Robust normalization + denom = np.sqrt(np.sum(np.square(u2), axis=-1) + eps) + e2 = np.stack( + [u2[..., i, :] / denom[..., i] for i in range(u2.shape[1])], axis=1 + ) + + # Get third axis + e3 = np.cross(e1, e2, axis=-1) + + # Make rotation mat + R = np.stack([e1, e2, e3], axis=-1) + + # Get translation (CA coords) + t = x2 + + # Construct rigid tuple + rigid = (R, t) + + return rigid + + +_quat_elements = ["a", "b", "c", "d"] +_qtr_keys = [l1 + l2 for l1 in _quat_elements for l2 in _quat_elements] +_qtr_ind_dict = {key: ind for ind, key in enumerate(_qtr_keys)} + + +def identity_rot_mats( + batch_dims: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + rots = torch.eye( + 3, dtype=dtype, device=device, requires_grad=requires_grad + ) + rots = rots.view(*((1,) * len(batch_dims)), 3, 3) + rots = rots.expand(*batch_dims, -1, -1) + + return rots + + +def identity_quats( + batch_dims: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + quat = torch.zeros( + (*batch_dims, 4), + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + + with torch.no_grad(): + quat[..., 0] = 1 + + return quat + + +def _to_mat(pairs): + mat = np.zeros((4, 4)) + for pair in pairs: + key, value = pair + ind = _qtr_ind_dict[key] + mat[ind // 4][ind % 4] = value + + return mat + + +_QTR_MAT = np.zeros((4, 4, 3, 3)) +_QTR_MAT[..., 0, 0] = _to_mat([("aa", 1), ("bb", 1), ("cc", -1), ("dd", -1)]) +_QTR_MAT[..., 0, 1] = _to_mat([("bc", 2), ("ad", -2)]) +_QTR_MAT[..., 0, 2] = _to_mat([("bd", 2), ("ac", 2)]) +_QTR_MAT[..., 1, 0] = _to_mat([("bc", 2), ("ad", 2)]) +_QTR_MAT[..., 1, 1] = _to_mat([("aa", 1), ("bb", -1), ("cc", 1), ("dd", -1)]) +_QTR_MAT[..., 1, 2] = _to_mat([("cd", 2), ("ab", -2)]) +_QTR_MAT[..., 2, 0] = _to_mat([("bd", 2), ("ac", -2)]) +_QTR_MAT[..., 2, 1] = _to_mat([("cd", 2), ("ab", 2)]) +_QTR_MAT[..., 2, 2] = _to_mat([("aa", 1), ("bb", -1), ("cc", -1), ("dd", 1)]) + + +def quat_to_rot(quat: torch.Tensor) -> torch.Tensor: + """ + Converts a quaternion to a rotation matrix. + Args: + quat: [*, 4] quaternions + Returns: + [*, 3, 3] rotation matrices + """ + # [*, 4, 4] + quat = quat[..., None] * quat[..., None, :] + + # [4, 4, 3, 3] + mat = quat.new_tensor(_QTR_MAT, requires_grad=False) + + # [*, 4, 4, 3, 3] + shaped_qtr_mat = mat.view((1,) * len(quat.shape[:-2]) + mat.shape) + quat = quat[..., None, None] * shaped_qtr_mat + + # [*, 3, 3] + return torch.sum(quat, dim=(-3, -4)) + + +def rot_to_quat( + rot: torch.Tensor, +): + if rot.shape[-2:] != (3, 3): + raise ValueError("Input rotation is incorrectly shaped") + + rot = [[rot[..., i, j] for j in range(3)] for i in range(3)] + [[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]] = rot + + k = [ + [ + xx + yy + zz, + zy - yz, + xz - zx, + yx - xy, + ], + [ + zy - yz, + xx - yy - zz, + xy + yx, + xz + zx, + ], + [ + xz - zx, + xy + yx, + yy - xx - zz, + yz + zy, + ], + [ + yx - xy, + xz + zx, + yz + zy, + zz - xx - yy, + ], + ] + + k = (1.0 / 3.0) * torch.stack([torch.stack(t, dim=-1) for t in k], dim=-2) + + _, vectors = torch.linalg.eigh(k) + return vectors[..., -1] + + +_QUAT_MULTIPLY = np.zeros((4, 4, 4)) +_QUAT_MULTIPLY[:, :, 0] = [ + [1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0], + [0, 0, 0, -1], +] + +_QUAT_MULTIPLY[:, :, 1] = [ + [0, 1, 0, 0], + [1, 0, 0, 0], + [0, 0, 0, 1], + [0, 0, -1, 0], +] + +_QUAT_MULTIPLY[:, :, 2] = [ + [0, 0, 1, 0], + [0, 0, 0, -1], + [1, 0, 0, 0], + [0, 1, 0, 0], +] + +_QUAT_MULTIPLY[:, :, 3] = [ + [0, 0, 0, 1], + [0, 0, 1, 0], + [0, -1, 0, 0], + [1, 0, 0, 0], +] + +_QUAT_MULTIPLY_BY_VEC = _QUAT_MULTIPLY[:, 1:, :] + + +def quat_multiply_by_vec(quat, vec): + """Multiply a quaternion by a pure-vector quaternion.""" + mat = quat.new_tensor(_QUAT_MULTIPLY_BY_VEC) + reshaped_mat = mat.view((1,) * len(quat.shape[:-1]) + mat.shape) + return torch.sum( + reshaped_mat * quat[..., :, None, None] * vec[..., None, :, None], + dim=(-3, -2), + ) + + +def rot_matmul(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + Performs matrix multiplication of two rotation matrix tensors. Written + out by hand to avoid AMP downcasting. + Args: + a: [*, 3, 3] left multiplicand + b: [*, 3, 3] right multiplicand + Returns: + The product ab + """ + + def row_mul(i): + return torch.stack( + [ + a[..., i, 0] * b[..., 0, 0] + + a[..., i, 1] * b[..., 1, 0] + + a[..., i, 2] * b[..., 2, 0], + a[..., i, 0] * b[..., 0, 1] + + a[..., i, 1] * b[..., 1, 1] + + a[..., i, 2] * b[..., 2, 1], + a[..., i, 0] * b[..., 0, 2] + + a[..., i, 1] * b[..., 1, 2] + + a[..., i, 2] * b[..., 2, 2], + ], + dim=-1, + ) + + return torch.stack( + [ + row_mul(0), + row_mul(1), + row_mul(2), + ], + dim=-2, + ) + + +def quat_multiply(quat1, quat2): + """Multiply a quaternion by another quaternion.""" + mat = quat1.new_tensor(_QUAT_MULTIPLY) + reshaped_mat = mat.view((1,) * len(quat1.shape[:-1]) + mat.shape) + return torch.sum( + reshaped_mat * quat1[..., :, None, None] * quat2[..., None, :, None], + dim=(-3, -2), + ) + + +def rot_vec_mul(r: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + """ + Applies a rotation to a vector. Written out by hand to avoid transfer + to avoid AMP downcasting. + Args: + r: [*, 3, 3] rotation matrices + t: [*, 3] coordinate tensors + Returns: + [*, 3] rotated coordinates + """ + x, y, z = torch.unbind(t, dim=-1) + return torch.stack( + [ + r[..., 0, 0] * x + r[..., 0, 1] * y + r[..., 0, 2] * z, + r[..., 1, 0] * x + r[..., 1, 1] * y + r[..., 1, 2] * z, + r[..., 2, 0] * x + r[..., 2, 1] * y + r[..., 2, 2] * z, + ], + dim=-1, + ) + + +def invert_rot_mat(rot_mat: torch.Tensor): + return rot_mat.transpose(-1, -2) + + +def invert_quat(quat: torch.Tensor): + quat_prime = quat.clone() + quat_prime[..., 1:] *= -1 + inv = quat_prime / torch.sum(quat**2, dim=-1, keepdim=True) + return inv + + +def identity_trans( + batch_dims: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, +) -> torch.Tensor: + trans = torch.zeros( + (*batch_dims, 3), + dtype=dtype, + device=device, + requires_grad=requires_grad, + ) + return trans + + +class Rotation: + """ + A 3D rotation. Depending on how the object is initialized, the + rotation is represented by either a rotation matrix or a + quaternion, though both formats are made available by helper functions. + To simplify gradient computation, the underlying format of the + rotation cannot be changed in-place. Like Rigid, the class is designed + to mimic the behavior of a torch Tensor, almost as if each Rotation + object were a tensor of rotations, in one format or another. + """ + + def __init__( + self, + rot_mats: Optional[torch.Tensor] = None, + quats: Optional[torch.Tensor] = None, + normalize_quats: bool = True, + ): + """ + Args: + rot_mats: + A [*, 3, 3] rotation matrix tensor. Mutually exclusive with + quats + quats: + A [*, 4] quaternion. Mutually exclusive with rot_mats. If + normalize_quats is not True, must be a unit quaternion + normalize_quats: + If quats is specified, whether to normalize quats + """ + if (rot_mats is None and quats is None) or ( + rot_mats is not None and quats is not None + ): + raise ValueError("Exactly one input argument must be specified") + + if (rot_mats is not None and rot_mats.shape[-2:] != (3, 3)) or ( + quats is not None and quats.shape[-1] != 4 + ): + raise ValueError( + "Incorrectly shaped rotation matrix or quaternion" + ) + + # Force full-precision + if quats is not None: + quats = quats.to(dtype=torch.float32) + if rot_mats is not None: + rot_mats = rot_mats.to(dtype=torch.float32) + + if quats is not None and normalize_quats: + quats = quats / torch.linalg.norm(quats, dim=-1, keepdim=True) + + self._rot_mats = rot_mats + self._quats = quats + + @staticmethod + def identity( + shape, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, + fmt: str = "quat", + ): + """ + Returns an identity Rotation. + Args: + shape: + The "shape" of the resulting Rotation object. See documentation + for the shape property + dtype: + The torch dtype for the rotation + device: + The torch device for the new rotation + requires_grad: + Whether the underlying tensors in the new rotation object + should require gradient computation + fmt: + One of "quat" or "rot_mat". Determines the underlying format + of the new object's rotation + Returns: + A new identity rotation + """ + if fmt == "rot_mat": + rot_mats = identity_rot_mats( + shape, + dtype, + device, + requires_grad, + ) + return Rotation(rot_mats=rot_mats, quats=None) + elif fmt == "quat": + quats = identity_quats(shape, dtype, device, requires_grad) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError(f"Invalid format: f{fmt}") + + # Magic methods + + def __getitem__(self, index: Any): + """ + Allows torch-style indexing over the virtual shape of the rotation + object. See documentation for the shape property. + Args: + index: + A torch index. E.g. (1, 3, 2), or (slice(None,)) + Returns: + The indexed rotation + """ + if type(index) != tuple: + index = (index,) + + if self._rot_mats is not None: + rot_mats = self._rot_mats[index + (slice(None), slice(None))] + return Rotation(rot_mats=rot_mats) + elif self._quats is not None: + quats = self._quats[index + (slice(None),)] + return Rotation(quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def __mul__( + self, + right: torch.Tensor, + ): + """ + Pointwise left multiplication of the rotation with a tensor. Can be + used to e.g. mask the Rotation. + Args: + right: + The tensor multiplicand + Returns: + The product + """ + if not (isinstance(right, torch.Tensor)): + raise TypeError("The other multiplicand must be a Tensor") + + if self._rot_mats is not None: + rot_mats = self._rot_mats * right[..., None, None] + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = self._quats * right[..., None] + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def __rmul__( + self, + left: torch.Tensor, + ): + """ + Reverse pointwise multiplication of the rotation with a tensor. + Args: + left: + The left multiplicand + Returns: + The product + """ + return self.__mul__(left) + + # Properties + + @property + def shape(self) -> torch.Size: + """ + Returns the virtual shape of the rotation object. This shape is + defined as the batch dimensions of the underlying rotation matrix + or quaternion. If the Rotation was initialized with a [10, 3, 3] + rotation matrix tensor, for example, the resulting shape would be + [10]. + + Returns: + The virtual shape of the rotation object + """ + s = None + if self._quats is not None: + s = self._quats.shape[:-1] + else: + s = self._rot_mats.shape[:-2] + + return s + + @property + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the underlying rotation. + Returns: + The dtype of the underlying rotation + """ + if self._rot_mats is not None: + return self._rot_mats.dtype + elif self._quats is not None: + return self._quats.dtype + else: + raise ValueError("Both rotations are None") + + @property + def device(self) -> torch.device: + """ + The device of the underlying rotation + Returns: + The device of the underlying rotation + """ + if self._rot_mats is not None: + return self._rot_mats.device + elif self._quats is not None: + return self._quats.device + else: + raise ValueError("Both rotations are None") + + @property + def requires_grad(self) -> bool: + """ + Returns the requires_grad property of the underlying rotation + Returns: + The requires_grad property of the underlying tensor + """ + if self._rot_mats is not None: + return self._rot_mats.requires_grad + elif self._quats is not None: + return self._quats.requires_grad + else: + raise ValueError("Both rotations are None") + + def get_rot_mats(self) -> torch.Tensor: + """ + Returns the underlying rotation as a rotation matrix tensor. + Returns: + The rotation as a rotation matrix tensor + """ + rot_mats = self._rot_mats + if rot_mats is None: + if self._quats is None: + raise ValueError("Both rotations are None") + else: + rot_mats = quat_to_rot(self._quats) + + return rot_mats + + def get_quats(self) -> torch.Tensor: + """ + Returns the underlying rotation as a quaternion tensor. + Depending on whether the Rotation was initialized with a + quaternion, this function may call torch.linalg.eigh. + Returns: + The rotation as a quaternion tensor. + """ + quats = self._quats + if quats is None: + if self._rot_mats is None: + raise ValueError("Both rotations are None") + else: + quats = rot_to_quat(self._rot_mats) + + return quats + + def get_cur_rot(self) -> torch.Tensor: + """ + Return the underlying rotation in its current form + Returns: + The stored rotation + """ + if self._rot_mats is not None: + return self._rot_mats + elif self._quats is not None: + return self._quats + else: + raise ValueError("Both rotations are None") + + # Rotation functions + + def compose_q_update_vec( + self, q_update_vec: torch.Tensor, normalize_quats: bool = True + ): + """ + Returns a new quaternion Rotation after updating the current + object's underlying rotation with a quaternion update, formatted + as a [*, 3] tensor whose final three columns represent x, y, z such + that (1, x, y, z) is the desired (not necessarily unit) quaternion + update. + Args: + q_update_vec: + A [*, 3] quaternion update tensor + normalize_quats: + Whether to normalize the output quaternion + Returns: + An updated Rotation + """ + quats = self.get_quats() + new_quats = quats + quat_multiply_by_vec(quats, q_update_vec) + return Rotation( + rot_mats=None, + quats=new_quats, + normalize_quats=normalize_quats, + ) + + def compose_r(self, r): + """ + Compose the rotation matrices of the current Rotation object with + those of another. + Args: + r: + An update rotation object + Returns: + An updated rotation object + """ + r1 = self.get_rot_mats() + r2 = r.get_rot_mats() + new_rot_mats = rot_matmul(r1, r2) + return Rotation(rot_mats=new_rot_mats, quats=None) + + def compose_q(self, r, normalize_quats: bool = True): + """ + Compose the quaternions of the current Rotation object with those + of another. + Depending on whether either Rotation was initialized with + quaternions, this function may call torch.linalg.eigh. + Args: + r: + An update rotation object + Returns: + An updated rotation object + """ + q1 = self.get_quats() + q2 = r.get_quats() + new_quats = quat_multiply(q1, q2) + return Rotation( + rot_mats=None, quats=new_quats, normalize_quats=normalize_quats + ) + + def apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Apply the current Rotation as a rotation matrix to a set of 3D + coordinates. + Args: + pts: + A [*, 3] set of points + Returns: + [*, 3] rotated points + """ + rot_mats = self.get_rot_mats() + return rot_vec_mul(rot_mats, pts) + + def invert_apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + The inverse of the apply() method. + Args: + pts: + A [*, 3] set of points + Returns: + [*, 3] inverse-rotated points + """ + rot_mats = self.get_rot_mats() + inv_rot_mats = invert_rot_mat(rot_mats) + return rot_vec_mul(inv_rot_mats, pts) + + def invert(self): + """ + Returns the inverse of the current Rotation. + Returns: + The inverse of the current Rotation + """ + if self._rot_mats is not None: + return Rotation( + rot_mats=invert_rot_mat(self._rot_mats), quats=None + ) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=invert_quat(self._quats), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + # "Tensor" stuff + + def unsqueeze( + self, + dim: int, + ): + """ + Analogous to torch.unsqueeze. The dimension is relative to the + shape of the Rotation object. + + Args: + dim: A positive or negative dimension index. + Returns: + The unsqueezed Rotation. + """ + if dim >= len(self.shape): + raise ValueError("Invalid dimension") + + if self._rot_mats is not None: + rot_mats = self._rot_mats.unsqueeze(dim if dim >= 0 else dim - 2) + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = self._quats.unsqueeze(dim if dim >= 0 else dim - 1) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + @staticmethod + def cat( + rs: Sequence[Any], + dim: int, + ): + """ + Concatenates rotations along one of the batch dimensions. Analogous + to torch.cat(). + Note that the output of this operation is always a rotation matrix, + regardless of the format of input rotations. + Args: + rs: + A list of rotation objects + dim: + The dimension along which the rotations should be + concatenated + Returns: + A concatenated Rotation object in rotation matrix format + """ + rot_mats = [r.get_rot_mats() for r in rs] + rot_mats = torch.cat(rot_mats, dim=dim if dim >= 0 else dim - 2) + + return Rotation(rot_mats=rot_mats, quats=None) + + def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]): + """ + Apply a Tensor -> Tensor function to underlying rotation tensors, + mapping over the rotation dimension(s). Can be used e.g. to sum out + a one-hot batch dimension. + Args: + fn: + A Tensor -> Tensor function to be mapped over the Rotation + Returns: + The transformed Rotation object + """ + if self._rot_mats is not None: + rot_mats = self._rot_mats.view(self._rot_mats.shape[:-2] + (9,)) + rot_mats = torch.stack( + list(map(fn, torch.unbind(rot_mats, dim=-1))), dim=-1 + ) + rot_mats = rot_mats.view(rot_mats.shape[:-1] + (3, 3)) + return Rotation(rot_mats=rot_mats, quats=None) + elif self._quats is not None: + quats = torch.stack( + list(map(fn, torch.unbind(self._quats, dim=-1))), dim=-1 + ) + return Rotation(rot_mats=None, quats=quats, normalize_quats=False) + else: + raise ValueError("Both rotations are None") + + def cuda(self): + """ + Analogous to the cuda() method of torch Tensors + Returns: + A copy of the Rotation in CUDA memory + """ + if self._rot_mats is not None: + return Rotation(rot_mats=self._rot_mats.cuda(), quats=None) + elif self._quats is not None: + return Rotation( + rot_mats=None, quats=self._quats.cuda(), normalize_quats=False + ) + else: + raise ValueError("Both rotations are None") + + def to(self, device: Optional[torch.device], dtype: Optional[torch.dtype]): + """ + Analogous to the to() method of torch Tensors + Args: + device: + A torch device + dtype: + A torch dtype + Returns: + A copy of the Rotation using the new device and dtype + """ + if self._rot_mats is not None: + return Rotation( + rot_mats=self._rot_mats.to(device=device, dtype=dtype), + quats=None, + ) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=self._quats.to(device=device, dtype=dtype), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + def detach(self): + """ + Returns a copy of the Rotation whose underlying Tensor has been + detached from its torch graph. + Returns: + A copy of the Rotation whose underlying Tensor has been detached + from its torch graph + """ + if self._rot_mats is not None: + return Rotation(rot_mats=self._rot_mats.detach(), quats=None) + elif self._quats is not None: + return Rotation( + rot_mats=None, + quats=self._quats.detach(), + normalize_quats=False, + ) + else: + raise ValueError("Both rotations are None") + + +class Rigid: + """ + A class representing a rigid transformation. Little more than a wrapper + around two objects: a Rotation object and a [*, 3] translation + Designed to behave approximately like a single torch tensor with the + shape of the shared batch dimensions of its component parts. + """ + + def __init__( + self, + rots: Optional[Rotation], + trans: Optional[torch.Tensor], + ): + """ + Args: + rots: A [*, 3, 3] rotation tensor + trans: A corresponding [*, 3] translation tensor + """ + # (we need device, dtype, etc. from at least one input) + + batch_dims, dtype, device, requires_grad = None, None, None, None + if trans is not None: + batch_dims = trans.shape[:-1] + dtype = trans.dtype + device = trans.device + requires_grad = trans.requires_grad + elif rots is not None: + batch_dims = rots.shape + dtype = rots.dtype + device = rots.device + requires_grad = rots.requires_grad + else: + raise ValueError("At least one input argument must be specified") + + if rots is None: + rots = Rotation.identity( + batch_dims, + dtype, + device, + requires_grad, + ) + elif trans is None: + trans = identity_trans( + batch_dims, + dtype, + device, + requires_grad, + ) + + if (rots.shape != trans.shape[:-1]) or (rots.device != trans.device): + raise ValueError("Rots and trans incompatible") + + # Force full precision. Happens to the rotations automatically. + trans = trans.to(dtype=torch.float32) + + self._rots = rots + self._trans = trans + + @staticmethod + def identity( + shape: Tuple[int], + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + requires_grad: bool = True, + fmt: str = "quat", + ): + """ + Constructs an identity transformation. + Args: + shape: + The desired shape + dtype: + The dtype of both internal tensors + device: + The device of both internal tensors + requires_grad: + Whether grad should be enabled for the internal tensors + Returns: + The identity transformation + """ + return Rigid( + Rotation.identity(shape, dtype, device, requires_grad, fmt=fmt), + identity_trans(shape, dtype, device, requires_grad), + ) + + def __getitem__( + self, + index: Any, + ): + """ + Indexes the affine transformation with PyTorch-style indices. + The index is applied to the shared dimensions of both the rotation + and the translation. + E.g.:: + r = Rotation(rot_mats=torch.rand(10, 10, 3, 3), quats=None) + t = Rigid(r, torch.rand(10, 10, 3)) + indexed = t[3, 4:6] + assert(indexed.shape == (2,)) + assert(indexed.get_rots().shape == (2,)) + assert(indexed.get_trans().shape == (2, 3)) + Args: + index: A standard torch tensor index. E.g. 8, (10, None, 3), + or (3, slice(0, 1, None)) + Returns: + The indexed tensor + """ + if type(index) != tuple: + index = (index,) + + return Rigid( + self._rots[index], + self._trans[index + (slice(None),)], + ) + + def __mul__( + self, + right: torch.Tensor, + ): + """ + Pointwise left multiplication of the transformation with a tensor. + Can be used to e.g. mask the Rigid. + Args: + right: + The tensor multiplicand + Returns: + The product + """ + if not (isinstance(right, torch.Tensor)): + raise TypeError("The other multiplicand must be a Tensor") + + new_rots = self._rots * right + new_trans = self._trans * right[..., None] + + return Rigid(new_rots, new_trans) + + def __rmul__( + self, + left: torch.Tensor, + ): + """ + Reverse pointwise multiplication of the transformation with a + tensor. + Args: + left: + The left multiplicand + Returns: + The product + """ + return self.__mul__(left) + + @property + def shape(self) -> torch.Size: + """ + Returns the shape of the shared dimensions of the rotation and + the translation. + + Returns: + The shape of the transformation + """ + s = self._trans.shape[:-1] + return s + + @property + def device(self) -> torch.device: + """ + Returns the device on which the Rigid's tensors are located. + Returns: + The device on which the Rigid's tensors are located + """ + return self._trans.device + + def get_rots(self) -> Rotation: + """ + Getter for the rotation. + Returns: + The rotation object + """ + return self._rots + + def get_trans(self) -> torch.Tensor: + """ + Getter for the translation. + Returns: + The stored translation + """ + return self._trans + + def compose_q_update_vec( + self, + q_update_vec: torch.Tensor, + ): + """ + Composes the transformation with a quaternion update vector of + shape [*, 6], where the final 6 columns represent the x, y, and + z values of a quaternion of form (1, x, y, z) followed by a 3D + translation. + Args: + q_vec: The quaternion update vector. + Returns: + The composed transformation. + """ + q_vec, t_vec = q_update_vec[..., :3], q_update_vec[..., 3:] + new_rots = self._rots.compose_q_update_vec(q_vec) + + trans_update = self._rots.apply(t_vec) + new_translation = self._trans + trans_update + + return Rigid(new_rots, new_translation) + + def compose( + self, + r, + ): + """ + Composes the current rigid object with another. + Args: + r: + Another Rigid object + Returns: + The composition of the two transformations + """ + new_rot = self._rots.compose_r(r._rots) + new_trans = self._rots.apply(r._trans) + self._trans + return Rigid(new_rot, new_trans) + + def apply( + self, + pts: torch.Tensor, + ) -> torch.Tensor: + """ + Applies the transformation to a coordinate tensor. + Args: + pts: A [*, 3] coordinate tensor. + Returns: + The transformed points. + """ + rotated = self._rots.apply(pts) + return rotated + self._trans + + def invert_apply(self, pts: torch.Tensor) -> torch.Tensor: + """ + Applies the inverse of the transformation to a coordinate tensor. + Args: + pts: A [*, 3] coordinate tensor + Returns: + The transformed points. + """ + pts = pts - self._trans + return self._rots.invert_apply(pts) + + def invert(self): + """ + Inverts the transformation. + Returns: + The inverse transformation. + """ + rot_inv = self._rots.invert() + trn_inv = rot_inv.apply(self._trans) + + return Rigid(rot_inv, -1 * trn_inv) + + def map_tensor_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]): + """ + Apply a Tensor -> Tensor function to underlying translation and + rotation tensors, mapping over the translation/rotation dimensions + respectively. + Args: + fn: + A Tensor -> Tensor function to be mapped over the Rigid + Returns: + The transformed Rigid object + """ + new_rots = self._rots.map_tensor_fn(fn) + new_trans = torch.stack( + list(map(fn, torch.unbind(self._trans, dim=-1))), dim=-1 + ) + + return Rigid(new_rots, new_trans) + + def to_tensor_4x4(self) -> torch.Tensor: + """ + Converts a transformation to a homogenous transformation tensor. + Returns: + A [*, 4, 4] homogenous transformation tensor + """ + tensor = self._trans.new_zeros((*self.shape, 4, 4)) + tensor[..., :3, :3] = self._rots.get_rot_mats() + tensor[..., :3, 3] = self._trans + tensor[..., 3, 3] = 1 + return tensor + + @staticmethod + def from_tensor_4x4(t: torch.Tensor): + """ + Constructs a transformation from a homogenous transformation + tensor. + Args: + t: [*, 4, 4] homogenous transformation tensor + Returns: + T object with shape [*] + """ + if t.shape[-2:] != (4, 4): + raise ValueError("Incorrectly shaped input tensor") + + rots = Rotation(rot_mats=t[..., :3, :3], quats=None) + trans = t[..., :3, 3] + + return Rigid(rots, trans) + + def to_tensor_7(self) -> torch.Tensor: + """ + Converts a transformation to a tensor with 7 final columns, four + for the quaternion followed by three for the translation. + Returns: + A [*, 7] tensor representation of the transformation + """ + tensor = self._trans.new_zeros((*self.shape, 7)) + tensor[..., :4] = self._rots.get_quats() + tensor[..., 4:] = self._trans + + return tensor + + @staticmethod + def from_tensor_7( + t: torch.Tensor, + normalize_quats: bool = False, + ): + if t.shape[-1] != 7: + raise ValueError("Incorrectly shaped input tensor") + + quats, trans = t[..., :4], t[..., 4:] + + rots = Rotation( + rot_mats=None, quats=quats, normalize_quats=normalize_quats + ) + + return Rigid(rots, trans) + + @staticmethod + def from_3_points( + p_neg_x_axis: torch.Tensor, + origin: torch.Tensor, + p_xy_plane: torch.Tensor, + eps: float = 1e-8, + fixed=True, + ): + """ + Implements algorithm 21. Constructs transformations from sets of 3 + points using the Gram-Schmidt algorithm. + Args: + p_neg_x_axis: [*, 3] coordinates + origin: [*, 3] coordinates used as frame origins + p_xy_plane: [*, 3] coordinates + eps: Small epsilon value + Returns: + A transformation object of shape [*] + """ + + # THIS FUNCTION IS WRONG! IT DOES NOT CORRECTLY IMPLEMENT ALGORITHM 21. + # THE MISTAKE COMES FROM CREATING e0 FROM origin AND p_neg_x_axis AND + # FROM CREATING e1 FROM p_xy_plane AND origin. e0 SHOULD USE origin AND + # p_xy_plane AND e1 SHOULD USE origin and p_neg_x_axis. + + p_neg_x_axis = torch.unbind(p_neg_x_axis, dim=-1) + origin = torch.unbind(origin, dim=-1) + p_xy_plane = torch.unbind(p_xy_plane, dim=-1) + + if fixed: + e0 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)] + e1 = [c1 - c2 for c1, c2 in zip(p_neg_x_axis, origin)] + else: + e0 = [c1 - c2 for c1, c2 in zip(origin, p_neg_x_axis)] + e1 = [c1 - c2 for c1, c2 in zip(p_xy_plane, origin)] + + denom = torch.sqrt(sum((c * c for c in e0)) + eps) + e0 = [c / denom for c in e0] + dot = sum((c1 * c2 for c1, c2 in zip(e0, e1))) + e1 = [c2 - c1 * dot for c1, c2 in zip(e0, e1)] + denom = torch.sqrt(sum((c * c for c in e1)) + eps) + e1 = [c / denom for c in e1] + e2 = [ + e0[1] * e1[2] - e0[2] * e1[1], + e0[2] * e1[0] - e0[0] * e1[2], + e0[0] * e1[1] - e0[1] * e1[0], + ] + + rots = torch.stack([c for tup in zip(e0, e1, e2) for c in tup], dim=-1) + rots = rots.reshape(rots.shape[:-1] + (3, 3)) + + rot_obj = Rotation(rot_mats=rots, quats=None) + + return Rigid(rot_obj, torch.stack(origin, dim=-1)) + + def unsqueeze( + self, + dim: int, + ): + """ + Analogous to torch.unsqueeze. The dimension is relative to the + shared dimensions of the rotation/translation. + + Args: + dim: A positive or negative dimension index. + Returns: + The unsqueezed transformation. + """ + if dim >= len(self.shape): + raise ValueError("Invalid dimension") + rots = self._rots.unsqueeze(dim) + trans = self._trans.unsqueeze(dim if dim >= 0 else dim - 1) + + return Rigid(rots, trans) + + @staticmethod + def cat( + ts: Sequence[Any], + dim: int, + ): + """ + Concatenates transformations along a new dimension. + Args: + ts: + A list of T objects + dim: + The dimension along which the transformations should be + concatenated + Returns: + A concatenated transformation object + """ + rots = Rotation.cat([t._rots for t in ts], dim) + trans = torch.cat( + [t._trans for t in ts], dim=dim if dim >= 0 else dim - 1 + ) + + return Rigid(rots, trans) + + def apply_rot_fn(self, fn: Callable[[Rotation], Rotation]): + """ + Applies a Rotation -> Rotation function to the stored rotation + object. + Args: + fn: A function of type Rotation -> Rotation + Returns: + A transformation object with a transformed rotation. + """ + return Rigid(fn(self._rots), self._trans) + + def apply_trans_fn(self, fn: Callable[[torch.Tensor], torch.Tensor]): + """ + Applies a Tensor -> Tensor function to the stored translation. + Args: + fn: + A function of type Tensor -> Tensor to be applied to the + translation + Returns: + A transformation object with a transformed translation. + """ + return Rigid(self._rots, fn(self._trans)) + + def scale_translation(self, trans_scale_factor: float): + """ + Scales the translation by a constant factor. + Args: + trans_scale_factor: + The constant factor + Returns: + A transformation object with a scaled translation. + """ + fn = lambda t: t * trans_scale_factor + return self.apply_trans_fn(fn) + + def stop_rot_gradient(self): + """ + Detaches the underlying rotation object + Returns: + A transformation object with detached rotations + """ + fn = lambda r: r.detach() + return self.apply_rot_fn(fn) + + @staticmethod + def make_transform_from_reference(n_xyz, ca_xyz, c_xyz, eps=1e-20): + """ + Returns a transformation object from reference coordinates. + + Note that this method does not take care of symmetries. If you + provide the atom positions in the non-standard way, the N atom will + end up not at [-0.527250, 1.359329, 0.0] but instead at + [-0.527250, -1.359329, 0.0]. You need to take care of such cases in + your code. + + Args: + n_xyz: A [*, 3] tensor of nitrogen xyz coordinates. + ca_xyz: A [*, 3] tensor of carbon alpha xyz coordinates. + c_xyz: A [*, 3] tensor of carbon xyz coordinates. + Returns: + A transformation object. After applying the translation and + rotation to the reference backbone, the coordinates will + approximately equal to the input coordinates. + """ + translation = -1 * ca_xyz + n_xyz = n_xyz + translation + c_xyz = c_xyz + translation + + c_x, c_y, c_z = [c_xyz[..., i] for i in range(3)] + norm = torch.sqrt(eps + c_x**2 + c_y**2) + sin_c1 = -c_y / norm + cos_c1 = c_x / norm + + c1_rots = sin_c1.new_zeros((*sin_c1.shape, 3, 3)) + c1_rots[..., 0, 0] = cos_c1 + c1_rots[..., 0, 1] = -1 * sin_c1 + c1_rots[..., 1, 0] = sin_c1 + c1_rots[..., 1, 1] = cos_c1 + c1_rots[..., 2, 2] = 1 + + norm = torch.sqrt(eps + c_x**2 + c_y**2 + c_z**2) + sin_c2 = c_z / norm + cos_c2 = torch.sqrt(c_x**2 + c_y**2) / norm + + c2_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3)) + c2_rots[..., 0, 0] = cos_c2 + c2_rots[..., 0, 2] = sin_c2 + c2_rots[..., 1, 1] = 1 + c2_rots[..., 2, 0] = -1 * sin_c2 + c2_rots[..., 2, 2] = cos_c2 + + c_rots = rot_matmul(c2_rots, c1_rots) + n_xyz = rot_vec_mul(c_rots, n_xyz) + + _, n_y, n_z = [n_xyz[..., i] for i in range(3)] + norm = torch.sqrt(eps + n_y**2 + n_z**2) + sin_n = -n_z / norm + cos_n = n_y / norm + + n_rots = sin_c2.new_zeros((*sin_c2.shape, 3, 3)) + n_rots[..., 0, 0] = 1 + n_rots[..., 1, 1] = cos_n + n_rots[..., 1, 2] = -1 * sin_n + n_rots[..., 2, 1] = sin_n + n_rots[..., 2, 2] = cos_n + + rots = rot_matmul(n_rots, c_rots) + + rots = rots.transpose(-1, -2) + translation = -1 * translation + + rot_obj = Rotation(rot_mats=rots, quats=None) + + return Rigid(rot_obj, translation) + + def cuda(self): + """ + Moves the transformation object to GPU memory + + Returns: + A version of the transformation on GPU + """ + return Rigid(self._rots.cuda(), self._trans.cuda()) From c97391d22020b03ae8fb507423e77cb634cee9f4 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 25 Sep 2023 11:14:23 -0700 Subject: [PATCH 003/106] add graph model hparams --- proteinworkshop/config/hparams/egnn_ca_angles.yaml | 3 +++ proteinworkshop/config/hparams/egnn_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/egnn_ca_bb.yaml | 3 +++ proteinworkshop/config/hparams/egnn_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/egnn_ca_seq.yaml | 3 +++ proteinworkshop/config/hparams/gcpnet_ca_angles.yaml | 3 +++ proteinworkshop/config/hparams/gcpnet_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/gcpnet_ca_bb.yaml | 0 proteinworkshop/config/hparams/gcpnet_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/gcpnet_ca_seq.yaml | 3 +++ proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml | 3 +++ proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml | 3 +++ proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml | 3 +++ proteinworkshop/config/hparams/mace_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/mace_ca_bb.yaml | 3 +++ proteinworkshop/config/hparams/mace_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/mace_ca_seq.yaml | 3 +++ proteinworkshop/config/hparams/schnet_ca_angles.yaml | 3 +++ proteinworkshop/config/hparams/schnet_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/schnet_ca_bb.yaml | 3 +++ proteinworkshop/config/hparams/schnet_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/schnet_ca_seq.yaml | 3 +++ proteinworkshop/config/hparams/tfn_ca_angles.yaml | 3 +++ proteinworkshop/config/hparams/tfn_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/tfn_ca_bb.yaml | 3 +++ proteinworkshop/config/hparams/tfn_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/tfn_ca_seq.yaml | 3 +++ 29 files changed, 84 insertions(+) create mode 100644 proteinworkshop/config/hparams/egnn_ca_angles.yaml create mode 100644 proteinworkshop/config/hparams/egnn_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/egnn_ca_bb.yaml create mode 100644 proteinworkshop/config/hparams/egnn_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/egnn_ca_seq.yaml create mode 100644 proteinworkshop/config/hparams/gcpnet_ca_angles.yaml create mode 100644 proteinworkshop/config/hparams/gcpnet_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/gcpnet_ca_bb.yaml create mode 100644 proteinworkshop/config/hparams/gcpnet_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/gcpnet_ca_seq.yaml create mode 100644 proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml create mode 100644 proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml create mode 100644 proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml create mode 100644 proteinworkshop/config/hparams/mace_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/mace_ca_bb.yaml create mode 100644 proteinworkshop/config/hparams/mace_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/mace_ca_seq.yaml create mode 100644 proteinworkshop/config/hparams/schnet_ca_angles.yaml create mode 100644 proteinworkshop/config/hparams/schnet_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/schnet_ca_bb.yaml create mode 100644 proteinworkshop/config/hparams/schnet_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/schnet_ca_seq.yaml create mode 100644 proteinworkshop/config/hparams/tfn_ca_angles.yaml create mode 100644 proteinworkshop/config/hparams/tfn_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/tfn_ca_bb.yaml create mode 100644 proteinworkshop/config/hparams/tfn_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/tfn_ca_seq.yaml diff --git a/proteinworkshop/config/hparams/egnn_ca_angles.yaml b/proteinworkshop/config/hparams/egnn_ca_angles.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_base.yaml b/proteinworkshop/config/hparams/egnn_ca_base.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_bb.yaml b/proteinworkshop/config/hparams/egnn_ca_bb.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_sc.yaml b/proteinworkshop/config/hparams/egnn_ca_sc.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/egnn_ca_seq.yaml b/proteinworkshop/config/hparams/egnn_ca_seq.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/egnn_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_angles.yaml b/proteinworkshop/config/hparams/gcpnet_ca_angles.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_base.yaml b/proteinworkshop/config/hparams/gcpnet_ca_base.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml b/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml new file mode 100644 index 00000000..e69de29b diff --git a/proteinworkshop/config/hparams/gcpnet_ca_sc.yaml b/proteinworkshop/config/hparams/gcpnet_ca_sc.yaml new file mode 100644 index 00000000..90f43dca --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gcpnet_ca_seq.yaml b/proteinworkshop/config/hparams/gcpnet_ca_seq.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/gcpnet_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml new file mode 100644 index 00000000..6620943f --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml new file mode 100644 index 00000000..35fe8156 --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.1 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml b/proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/gear_net_edge_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_base.yaml b/proteinworkshop/config/hparams/mace_ca_base.yaml new file mode 100644 index 00000000..8a4aff61 --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0010 + decoder_dropout: 0.0 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_bb.yaml b/proteinworkshop/config/hparams/mace_ca_bb.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_sc.yaml b/proteinworkshop/config/hparams/mace_ca_sc.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/mace_ca_seq.yaml b/proteinworkshop/config/hparams/mace_ca_seq.yaml new file mode 100644 index 00000000..90f43dca --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_angles.yaml b/proteinworkshop/config/hparams/schnet_ca_angles.yaml new file mode 100644 index 00000000..4fa85834 --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.1 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_base.yaml b/proteinworkshop/config/hparams/schnet_ca_base.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_bb.yaml b/proteinworkshop/config/hparams/schnet_ca_bb.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_sc.yaml b/proteinworkshop/config/hparams/schnet_ca_sc.yaml new file mode 100644 index 00000000..e28bc240 --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/schnet_ca_seq.yaml b/proteinworkshop/config/hparams/schnet_ca_seq.yaml new file mode 100644 index 00000000..90f43dca --- /dev/null +++ b/proteinworkshop/config/hparams/schnet_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_angles.yaml b/proteinworkshop/config/hparams/tfn_ca_angles.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_base.yaml b/proteinworkshop/config/hparams/tfn_ca_base.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_bb.yaml b/proteinworkshop/config/hparams/tfn_ca_bb.yaml new file mode 100644 index 00000000..039828cb --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.5 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_sc.yaml b/proteinworkshop/config/hparams/tfn_ca_sc.yaml new file mode 100644 index 00000000..f466e78b --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.3 \ No newline at end of file diff --git a/proteinworkshop/config/hparams/tfn_ca_seq.yaml b/proteinworkshop/config/hparams/tfn_ca_seq.yaml new file mode 100644 index 00000000..e3a108c4 --- /dev/null +++ b/proteinworkshop/config/hparams/tfn_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0001 + decoder_dropout: 0.3 \ No newline at end of file From a5d0eaa996bd9dfa1bd5ffc413ed2656126a837e Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 28 Sep 2023 14:17:38 -0700 Subject: [PATCH 004/106] add hparams --- proteinworkshop/config/hparams/gcpnet_ca_bb.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml b/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml index e69de29b..e28bc240 100644 --- a/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml +++ b/proteinworkshop/config/hparams/gcpnet_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0003 + decoder_dropout: 0.5 \ No newline at end of file From fd434462f20443c139e1714d02571abfa7aa6806 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:06:37 -0700 Subject: [PATCH 005/106] add support for loading hparam override --- proteinworkshop/config/train.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/proteinworkshop/config/train.yaml b/proteinworkshop/config/train.yaml index 37025489..84979a23 100644 --- a/proteinworkshop/config/train.yaml +++ b/proteinworkshop/config/train.yaml @@ -24,6 +24,7 @@ defaults: - logger: csv # Also supported: tensorboard, wandb # debugging config (enable through command line, e.g. `python train.py debug=default) - debug: null + - optional hparams: ${encoder}_${features} - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. task_name: "train" From c2c8b014326cda10c8e6410e88a89a62c21fd799 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:07:49 -0700 Subject: [PATCH 006/106] add optional hparam override to finetune run config --- proteinworkshop/config/finetune.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/proteinworkshop/config/finetune.yaml b/proteinworkshop/config/finetune.yaml index bd6fb6d5..fafca6b4 100644 --- a/proteinworkshop/config/finetune.yaml +++ b/proteinworkshop/config/finetune.yaml @@ -25,6 +25,7 @@ defaults: - finetune: default # Specifies finetuning config. See: proteinworkshop/config/finetune/ # debugging config (enable through command line, e.g. `python train.py debug=default) - debug: null + - optional hparams: ${encoder}_${features} - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. task_name: "finetune" From c2dcbeed5aa1fca81d25578306bf2dd570350a41 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:09:06 -0700 Subject: [PATCH 007/106] set in memory=True as default for cath and fold classification datasets --- proteinworkshop/config/dataset/cath.yaml | 1 + proteinworkshop/config/dataset/fold_family.yaml | 1 + proteinworkshop/config/dataset/fold_fold.yaml | 1 + proteinworkshop/config/dataset/fold_superfamily.yaml | 1 + 4 files changed, 4 insertions(+) diff --git a/proteinworkshop/config/dataset/cath.yaml b/proteinworkshop/config/dataset/cath.yaml index f625ae12..c6a66b9c 100644 --- a/proteinworkshop/config/dataset/cath.yaml +++ b/proteinworkshop/config/dataset/cath.yaml @@ -9,4 +9,5 @@ datamodule: dataset_fraction: 1.0 # Fraction of the dataset to use transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite the dataset if it already exists + in_memory: True # Whether to load the entire dataset into memory num_classes: 23 # Number of classes diff --git a/proteinworkshop/config/dataset/fold_family.yaml b/proteinworkshop/config/dataset/fold_family.yaml index 1060774b..6a10a515 100644 --- a/proteinworkshop/config/dataset/fold_family.yaml +++ b/proteinworkshop/config/dataset/fold_family.yaml @@ -9,4 +9,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True # Whether to load the entire dataset into memory num_classes: 1195 # Number of classes diff --git a/proteinworkshop/config/dataset/fold_fold.yaml b/proteinworkshop/config/dataset/fold_fold.yaml index 8d0afe79..c7d62620 100644 --- a/proteinworkshop/config/dataset/fold_fold.yaml +++ b/proteinworkshop/config/dataset/fold_fold.yaml @@ -9,4 +9,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True # Whether to load the entire dataset into memory num_classes: 1195 # Number of classes diff --git a/proteinworkshop/config/dataset/fold_superfamily.yaml b/proteinworkshop/config/dataset/fold_superfamily.yaml index e53d2b00..b92f92b4 100644 --- a/proteinworkshop/config/dataset/fold_superfamily.yaml +++ b/proteinworkshop/config/dataset/fold_superfamily.yaml @@ -9,4 +9,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True # Whether to load the entire dataset into memory num_classes: 1195 # Number of classes From d041a3e26fff96fcbee031acd6a2cc2abfece852 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:11:40 -0700 Subject: [PATCH 008/106] add torch geometric compile --- proteinworkshop/train.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/proteinworkshop/train.py b/proteinworkshop/train.py index 6532c4a7..74aeaebf 100644 --- a/proteinworkshop/train.py +++ b/proteinworkshop/train.py @@ -12,6 +12,7 @@ import lovely_tensors as lt import torch import torch.nn as nn +import torch_geometric from graphein.protein.tensor.dataloader import ProteinDataLoader from lightning.pytorch.callbacks import Callback from lightning.pytorch.loggers import Logger @@ -121,6 +122,7 @@ def train_model( == "flash.core.optimizers.LinearWarmupCosineAnnealingLR" and cfg.scheduler.interval == "step" ): + datamodule.setup() # type: ignore num_steps = _num_training_steps( datamodule.train_dataloader(), trainer ) @@ -173,7 +175,7 @@ def train_model( if cfg.get("compile"): log.info("Compiling model!") - model = torch.compile(model) # type: ignore + model = torch_geometric.compile(model, dynamic=True) if cfg.get("task_name") == "train": log.info("Starting training!") From d83c62e613c4c34921d694d396e614c65c4b37b3 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:16:14 -0700 Subject: [PATCH 009/106] fix scenario where only seq_pos_enc is a node feature --- proteinworkshop/features/factory.py | 69 ++++++++++++++--------------- 1 file changed, 34 insertions(+), 35 deletions(-) diff --git a/proteinworkshop/features/factory.py b/proteinworkshop/features/factory.py index 6a31884d..0a97306c 100644 --- a/proteinworkshop/features/factory.py +++ b/proteinworkshop/features/factory.py @@ -1,4 +1,4 @@ -from typing import List, Union +from typing import List, Union, Literal import torch import torch.nn as nn @@ -27,44 +27,44 @@ VectorNodeFeature, ) +StructureRepresentation = Literal["ca", "ca_bb", "full_atom"] + class ProteinFeaturiser(nn.Module): """ - Configurable protein featuriser. + Initialise a protein featuriser. + + :param representation: Representation to use for the protein. + One of ``"ca", "ca_bb", "full_atom"``. + :type representation: StructureRepresentation + :param scalar_node_features: List of scalar-values node features to + compute. Options: ``"amino_acid_one_hot", + "sequence_positional_encoding", "alpha", "kappa", "dihedrals" + "sidechain_torsions"``. + :type scalar_node_features: List[ScalarNodeFeature] + :param vector_node_features: List of vector-valued node features to + compute. # TODO types + :type vector_node_features: List[VectorNodeFeature] + :param edge_types: List of edge types to compute. + Options: # TODO types + :type edge_types: List[str] + :param scalar_edge_features: List of scalar-valued edge features to + compute. # TODO types + :type scalar_edge_features: List[ScalarEdgeFeature] + :param vector_edge_features: List of vector-valued edge features to + compute. # TODO types + :type vector_edge_features: List[VectorEdgeFeature] """ def __init__( self, - representation: str, + representation: StructureRepresentation, scalar_node_features: List[ScalarNodeFeature], vector_node_features: List[VectorNodeFeature], edge_types: List[str], scalar_edge_features: List[ScalarEdgeFeature], vector_edge_features: List[VectorEdgeFeature], ): - """Initialise a protein featuriser. - - :param representation: Representation to use for the protein. - One of ``"ca", "ca_bb", "full_atom"``. # TODO types - :type representation: str - :param scalar_node_features: List of scalar-values node features to - compute. Options: ``"amino_acid_one_hot", - "sequence_positional_encoding", "alpha", "kappa", "dihedrals" - "sidechain_torsions"``. # TODO types - :type scalar_node_features: List[ScalarNodeFeature] - :param vector_node_features: List of vector-valued node features to - compute. # TODO types - :type vector_node_features: List[VectorNodeFeature] - :param edge_types: List of edge types to compute. - Options: # TODO types - :type edge_types: List[str] - :param scalar_edge_features: List of scalar-valued edge features to - compute. # TODO types - :type scalar_edge_features: List[ScalarEdgeFeature] - :param vector_edge_features: List of vector-valued edge features to - compute. # TODO types - :type vector_edge_features: List[VectorEdgeFeature] - """ super(ProteinFeaturiser, self).__init__() self.representation = representation self.scalar_node_features = scalar_node_features @@ -95,14 +95,14 @@ def forward( batch.x = self.positional_encoding(slices) # This is necessary to concat node features with the positional encoding concat_nf = True - - scalar_features = compute_scalar_node_features( - batch, self.scalar_node_features - ) - if concat_nf: - batch.x = torch.cat([batch.x, scalar_features], dim=-1) - else: - batch.x = scalar_features + if self.scalar_node_features != ["sequence_positional_encoding"]: + scalar_features = compute_scalar_node_features( + batch, self.scalar_node_features + ) + if concat_nf: + batch.x = torch.cat([batch.x, scalar_features], dim=-1) + else: + batch.x = scalar_features batch.x = torch.nan_to_num( batch.x, nan=0.0, posinf=0.0, neginf=0.0 ) @@ -148,7 +148,6 @@ def __repr__(self) -> str: if __name__ == "__main__": import hydra import omegaconf - from loguru import logger from proteinworkshop import constants From eedc9e511bdbe8e7c3c80766834dcf39e507f721 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:17:52 -0700 Subject: [PATCH 010/106] refactor logging to use a single logging call --- proteinworkshop/models/base.py | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index d0ed5453..9f99a021 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -272,10 +272,7 @@ def configure_losses( for k, v in loss_dict.items() } - def transfer_batch_to_device(self, batch: Union[Batch, ProteinBatch], dataloader_idx: int) -> Union[Batch, ProteinBatch]: - return batch.to(self.device, list(self.encoder.required_batch_attributes)) - - def on_before_batch_transfer( + def on_after_batch_transfer( self, batch: Union[Batch, ProteinBatch], dataloader_idx: int ) -> Union[Batch, ProteinBatch]: """ @@ -368,9 +365,8 @@ def log_metrics( :param batch: Batch of data :type batch: Batch """ - # Log loss - for k, v in loss.items(): - self.log(f"{stage}/loss/{k}", v, prog_bar=True, logger=True) + # Log losses + log_dict = {f"{stage}/loss/{k}": v for k, v in loss.items()} # Log metrics for m in self.metric_names: @@ -391,14 +387,11 @@ def log_metrics( val = metric(pred, target) except RuntimeError: val = metric(pred, target.unsqueeze(-1)) - self.log( - f"{stage}/{output}/{m}", - val, - prog_bar=True, - logger=True, - ) + log_dict[f"{stage}/{output}/{m}"] = val + except (ValueError, RuntimeError): continue + self.log_dict(log_dict) class BenchMarkModel(BaseModel): From 005db2634173f8e985b4872a8b13d67a7f38dedd Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 14:53:41 -0700 Subject: [PATCH 011/106] remove duplicated entry --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6c1a5c27..4828bb25 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -109,7 +109,6 @@ "vu": "\\mathbf{u}", "vv": "\\mathbf{v}", "vw": "\\mathbf{w}", - "vx": "\\mathbf{x}", "vy": "\\mathbf{y}", "vz": "\\mathbf{z}", } From f18477318bc58526c25a9924bc2c979acb591938 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 23 Oct 2023 15:29:43 -0700 Subject: [PATCH 012/106] minor linting --- proteinworkshop/datasets/atom3d_datamodule.py | 2 +- proteinworkshop/datasets/base.py | 109 +++++++++--------- proteinworkshop/datasets/cath.py | 49 ++++---- proteinworkshop/datasets/cc_pdb.py | 81 ++++++------- .../datasets/components/res_dataset.py | 2 +- proteinworkshop/datasets/deep_sea_proteins.py | 56 ++++----- proteinworkshop/datasets/ec_reaction.py | 1 - .../datasets/fold_classification.py | 1 - proteinworkshop/datasets/go.py | 1 - proteinworkshop/datasets/pdb_dataset.py | 3 - proteinworkshop/datasets/sequence.py | 7 +- proteinworkshop/features/edges.py | 1 - proteinworkshop/metrics/auprc.py | 14 +-- .../graph_encoders/components/radial.py | 2 +- .../graph_encoders/components/rigid_utils.py | 36 +++--- .../graph_encoders/components/wrappers.py | 2 +- proteinworkshop/models/utils.py | 8 +- proteinworkshop/scripts/download_foldcomp.py | 1 - tests/test_package_install.py | 4 +- 19 files changed, 185 insertions(+), 195 deletions(-) diff --git a/proteinworkshop/datasets/atom3d_datamodule.py b/proteinworkshop/datasets/atom3d_datamodule.py index 71133dee..ccfa1dc1 100644 --- a/proteinworkshop/datasets/atom3d_datamodule.py +++ b/proteinworkshop/datasets/atom3d_datamodule.py @@ -71,7 +71,7 @@ def get_test_data_path( # default to testing PPI methods with DB5 "PPI": f"PPI/splits/{ppi_split}/data/{test_phase}" if use_dips_for_testing - else f"PPI/raw/DB5/data/", + else "PPI/raw/DB5/data/", "RES": f"RES/splits/split-by-cath-topology/data/{test_phase}", "MSP": f"MSP/splits/split-by-sequence-identity-30/data/{test_phase}", } diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index bd61dba2..d55802c7 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -210,6 +210,60 @@ def get_class_weights(self) -> torch.Tensor: class ProteinDataset(Dataset): + """Dataset for loading protein structures. + + :param pdb_codes: List of PDB codes to load. This can also be a list + of identifiers to specific to your filenames if you have + pre-downloaded structures. + :type pdb_codes: List[str] + :param root: Path to root directory, defaults to ``None``. + :type root: Optional[str], optional + :param pdb_dir: Path to directory containing raw PDB files, + defaults to ``None``. + :type pdb_dir: Optional[str], optional + :param processed_dir: Directory to store processed data, defaults to + ``None``. + :type processed_dir: Optional[str], optional + :param pdb_paths: If specified, the dataset will load structures from + these paths instead of downloading them from the RCSB PDB or using + the identifies in ``pdb_codes``. This is useful if you have already + downloaded structures and want to use them. defaults to ``None`` + :type pdb_paths: Optional[List[str]], optional + :param chains: List of chains to load for each PDB code, + defaults to ``None``. + :type chains: Optional[List[str]], optional + :param graph_labels: List of tensors to set as graph labels for each + examples. If not specified, no graph labels will be set. + defaults to ``None``. + :type graph_labels: Optional[List[torch.Tensor]], optional + :param node_labels: List of tensors to set as node labels for each + examples. If not specified, no node labels will be set. + defaults to ``None``. + :type node_labels: Optional[List[torch.Tensor]], optional + :param transform: List of transforms to apply to each example, + defaults to ``None``. + :type transform: Optional[List[Callable]], optional + :param pre_transform: Transform to apply to each example before + processing, defaults to ``None``. + :type pre_transform: Optional[Callable], optional + :param pre_filter: Filter to apply to each example before processing, + defaults to ``None``. + :type pre_filter: Optional[Callable], optional + :param log: Whether to log. If ``True``, logs will be printed to + stdout, defaults to ``True``. + :type log: bool, optional + :param overwrite: Whether to overwrite existing files, defaults to + ``False``. + :type overwrite: bool, optional + :param format: Format to save structures in, defaults to "pdb". + :type format: Literal[mmtf, pdb, optional + :param in_memory: Whether to load data into memory, defaults to False. + :type in_memory: bool, optional + :param store_het: Whether to store heteroatoms in the graph, + defaults to ``False``. + :type store_het: bool, optional + """ + def __init__( self, pdb_codes: List[str], @@ -230,59 +284,6 @@ def __init__( store_het: bool = False, out_names: Optional[List[str]] = None, ): - """Dataset for loading protein structures. - - :param pdb_codes: List of PDB codes to load. This can also be a list - of identifiers to specific to your filenames if you have - pre-downloaded structures. - :type pdb_codes: List[str] - :param root: Path to root directory, defaults to ``None``. - :type root: Optional[str], optional - :param pdb_dir: Path to directory containing raw PDB files, - defaults to ``None``. - :type pdb_dir: Optional[str], optional - :param processed_dir: Directory to store processed data, defaults to - ``None``. - :type processed_dir: Optional[str], optional - :param pdb_paths: If specified, the dataset will load structures from - these paths instead of downloading them from the RCSB PDB or using - the identifies in ``pdb_codes``. This is useful if you have already - downloaded structures and want to use them. defaults to ``None`` - :type pdb_paths: Optional[List[str]], optional - :param chains: List of chains to load for each PDB code, - defaults to ``None``. - :type chains: Optional[List[str]], optional - :param graph_labels: List of tensors to set as graph labels for each - examples. If not specified, no graph labels will be set. - defaults to ``None``. - :type graph_labels: Optional[List[torch.Tensor]], optional - :param node_labels: List of tensors to set as node labels for each - examples. If not specified, no node labels will be set. - defaults to ``None``. - :type node_labels: Optional[List[torch.Tensor]], optional - :param transform: List of transforms to apply to each example, - defaults to ``None``. - :type transform: Optional[List[Callable]], optional - :param pre_transform: Transform to apply to each example before - processing, defaults to ``None``. - :type pre_transform: Optional[Callable], optional - :param pre_filter: Filter to apply to each example before processing, - defaults to ``None``. - :type pre_filter: Optional[Callable], optional - :param log: Whether to log. If ``True``, logs will be printed to - stdout, defaults to ``True``. - :type log: bool, optional - :param overwrite: Whether to overwrite existing files, defaults to - ``False``. - :type overwrite: bool, optional - :param format: Format to save structures in, defaults to "pdb". - :type format: Literal[mmtf, pdb, optional - :param in_memory: Whether to load data into memory, defaults to False. - :type in_memory: bool, optional - :param store_het: Whether to store heteroatoms in the graph, - defaults to ``False``. - :type store_het: bool, optional - """ self.pdb_codes = [pdb.lower() for pdb in pdb_codes] self.pdb_dir = pdb_dir self.pdb_paths = pdb_paths @@ -302,7 +303,7 @@ def __init__( for p in self.processed_file_names ): logger.info( - f"All structures already processed and overwrite=False. Skipping download." + "All structures already processed and overwrite=False. Skipping download." ) self._skip_download = True else: diff --git a/proteinworkshop/datasets/cath.py b/proteinworkshop/datasets/cath.py index d6277650..79cc969c 100644 --- a/proteinworkshop/datasets/cath.py +++ b/proteinworkshop/datasets/cath.py @@ -14,6 +14,30 @@ class CATHDataModule(ProteinDataModule): + """Data module for CATH dataset. + + :param path: Path to store data. + :type path: str + :param batch_size: Batch size for dataloaders. + :type batch_size: int + :param format: Format to load PDB files in. + :type format: Literal["mmtf", "pdb"] + :param pdb_dir: Path to directory containing PDB files. + :type pdb_dir: str + :param pin_memory: Whether to pin memory for dataloaders. + :type pin_memory: bool + :param in_memory: Whether to load the entire dataset into memory. + :type in_memory: bool + :param num_workers: Number of workers for dataloaders. + :type num_workers: int + :param dataset_fraction: Fraction of dataset to use. + :type dataset_fraction: float + :param transforms: List of transforms to apply to dataset. + :type transforms: Optional[List[Callable]] + :param overwrite: Whether to overwrite existing data. + Defaults to ``False``. + :type overwrite: bool + """ def __init__( self, path: str, @@ -27,30 +51,6 @@ def __init__( transforms: Optional[Iterable[Callable]] = None, overwrite: bool = False, ) -> None: - """Data module for CATH dataset. - - :param path: Path to store data. - :type path: str - :param batch_size: Batch size for dataloaders. - :type batch_size: int - :param format: Format to load PDB files in. - :type format: Literal["mmtf", "pdb"] - :param pdb_dir: Path to directory containing PDB files. - :type pdb_dir: str - :param pin_memory: Whether to pin memory for dataloaders. - :type pin_memory: bool - :param in_memory: Whether to load the entire dataset into memory. - :type in_memory: bool - :param num_workers: Number of workers for dataloaders. - :type num_workers: int - :param dataset_fraction: Fraction of dataset to use. - :type dataset_fraction: float - :param transforms: List of transforms to apply to dataset. - :type transforms: Optional[List[Callable]] - :param overwrite: Whether to overwrite existing data. - Defaults to ``False``. - :type overwrite: bool - """ super().__init__() self.data_dir = Path(path) @@ -270,7 +270,6 @@ def test_dataloader(self) -> ProteinDataLoader: import pathlib import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/cc_pdb.py b/proteinworkshop/datasets/cc_pdb.py index 9ffd4776..f1cea7df 100644 --- a/proteinworkshop/datasets/cc_pdb.py +++ b/proteinworkshop/datasets/cc_pdb.py @@ -17,6 +17,46 @@ class CCPDBDataModule(ProteinDataModule): + """Data module for CCPDB datasets. + + :param path: Path to store data. + :type path: str + :param pdb_dir: Path to directory containing structure files. + :type pdb_dir: str + :param name: Name of dataset to use. + :type name: CCPDB_DATASET_NAMES + :param batch_size: Batch size for dataloaders. + :type batch_size: int + :param num_workers: Number of workers for dataloaders. + :type num_workers: int + :param pin_memory: Whether to pin memory for dataloaders. + :type pin_memory: bool + :param in_memory: Whether to load dataset into memory, defaults to + ``False`` + :type in_memory: bool, optional + :param format: Format of the structure files, defaults to ``"mmtf"``. + :type format: Literal[mmtf, pdb], optional + :param obsolete_strategy: How to deal with obsolete PDBs, + defaults to "drop" + :type obsolete_strategy: str, optional + :param split_strategy: How to split the data, + defaults to ``"random"`` + :type split_strategy: Literal["random", 'stratified"], optional + :param val_fraction: Fraction of the dataset to use for validation, + defaults to ``0.1`` + :type val_fraction: float, optional + :param test_fraction: Fraction of the dataset to use for testing, + defaults to ``0.1``. + :type test_fraction: float, optional + :param transforms: List of transforms to apply to each example, + defaults to ``None``. + :type transforms: Optional[List[Callable]], optional + :param overwrite: Whether to overwrite existing data, defaults to + ``False`` + :type overwrite: bool, optional + :raises ValueError: If train, val, and test fractions do not sum to 1. + """ + def __init__( self, path: str, @@ -35,45 +75,6 @@ def __init__( transforms: Optional[List[Callable]] = None, overwrite: bool = False, ): - """Data module for CCPDB datasets. - - :param path: Path to store data. - :type path: str - :param pdb_dir: Path to directory containing structure files. - :type pdb_dir: str - :param name: Name of dataset to use. - :type name: CCPDB_DATASET_NAMES - :param batch_size: Batch size for dataloaders. - :type batch_size: int - :param num_workers: Number of workers for dataloaders. - :type num_workers: int - :param pin_memory: Whether to pin memory for dataloaders. - :type pin_memory: bool - :param in_memory: Whether to load dataset into memory, defaults to - ``False`` - :type in_memory: bool, optional - :param format: Format of the structure files, defaults to ``"mmtf"``. - :type format: Literal[mmtf, pdb], optional - :param obsolete_strategy: How to deal with obsolete PDBs, - defaults to "drop" - :type obsolete_strategy: str, optional - :param split_strategy: How to split the data, - defaults to ``"random"`` - :type split_strategy: Literal["random", 'stratified"], optional - :param val_fraction: Fraction of the dataset to use for validation, - defaults to ``0.1`` - :type val_fraction: float, optional - :param test_fraction: Fraction of the dataset to use for testing, - defaults to ``0.1``. - :type test_fraction: float, optional - :param transforms: List of transforms to apply to each example, - defaults to ``None``. - :type transforms: Optional[List[Callable]], optional - :param overwrite: Whether to overwrite existing data, defaults to - ``False`` - :type overwrite: bool, optional - :raises ValueError: If train, val, and test fractions do not sum to 1. - """ super().__init__() self.root = pathlib.Path(path) if not os.path.exists(self.root): @@ -264,7 +265,7 @@ def test_dataloader(self) -> ProteinDataLoader: num_workers = 4 pin_memory = True - dataset = CCPDBDataset( + dataset = CCPDBDataModule( path, pdb_dir, name, batch_size, num_workers, pin_memory ) dataset.download() diff --git a/proteinworkshop/datasets/components/res_dataset.py b/proteinworkshop/datasets/components/res_dataset.py index 91daf682..638dbaf7 100644 --- a/proteinworkshop/datasets/components/res_dataset.py +++ b/proteinworkshop/datasets/components/res_dataset.py @@ -8,7 +8,7 @@ from proteinworkshop.datasets.components.atom3d_dataset import BaseTransform -_amino_acids = lambda x: { +_amino_acids = lambda x: { # noqa: E731 "ALA": 0, "ARG": 1, "ASN": 2, diff --git a/proteinworkshop/datasets/deep_sea_proteins.py b/proteinworkshop/datasets/deep_sea_proteins.py index 365b604d..d0cfef51 100644 --- a/proteinworkshop/datasets/deep_sea_proteins.py +++ b/proteinworkshop/datasets/deep_sea_proteins.py @@ -17,6 +17,34 @@ class DeepSeaProteinsDataModule(ProteinDataModule): + """Data module for Deep Sea Proteins dataset. + + :param path: Path to store data. + :type path: os.PathLike + :param pdb_dir: Path to directory containing PDB files. + :type pdb_dir: os.PathLike + :param validation_fold: Name of validation fold to use. + :type validation_fold: int + :param batch_size: Batch size for dataloaders. + :type batch_size: int + :param in_memory: Whether to load the entire dataset into memory, defaults to False + :type in_memory: bool, optional + :param pin_memory: Whether to pin dataloader memory, defaults to True + :type pin_memory: bool, optional + :param num_workers: Number of dataloader workers, defaults to 16 + :type num_workers: int, optional + :param obsolete_strategy: Strategy to deal with obsolete PDbs, + defaults to "drop" + :type obsolete_strategy: str, optional + :param format: Format of the structure files, defaults to "mmtf" + :type format: Literal[mmtf, pdb], optional + :param transforms: Transforms to apply, defaults to None + :type transforms: Optional[Iterable[Callable]], optional + :param overwrite: Whether to overwrite existing data, defaults to + ``False`` + :type overwrite: bool, optional + """ + def __init__( self, path: os.PathLike, @@ -31,33 +59,6 @@ def __init__( transforms: Optional[Iterable[Callable]] = None, overwrite: bool = False, ): - """Data module for Deep Sea Proteins dataset. - - :param path: Path to store data. - :type path: os.PathLike - :param pdb_dir: Path to directory containing PDB files. - :type pdb_dir: os.PathLike - :param validation_fold: Name of validation fold to use. - :type validation_fold: int - :param batch_size: Batch size for dataloaders. - :type batch_size: int - :param in_memory: Whether to load the entire dataset into memory, defaults to False - :type in_memory: bool, optional - :param pin_memory: Whether to pin dataloader memory, defaults to True - :type pin_memory: bool, optional - :param num_workers: Number of dataloader workers, defaults to 16 - :type num_workers: int, optional - :param obsolete_strategy: Strategy to deal with obsolete PDbs, - defaults to "drop" - :type obsolete_strategy: str, optional - :param format: Format of the structure files, defaults to "mmtf" - :type format: Literal[mmtf, pdb], optional - :param transforms: Transforms to apply, defaults to None - :type transforms: Optional[Iterable[Callable]], optional - :param overwrite: Whether to overwrite existing data, defaults to - ``False`` - :type overwrite: bool, optional - """ super().__init__() self.data_dir = pathlib.Path(path) if not os.path.exists(self.data_dir): @@ -256,7 +257,6 @@ def test_dataloader(self) -> ProteinDataLoader: if __name__ == "__main__": import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/ec_reaction.py b/proteinworkshop/datasets/ec_reaction.py index cbb8694b..bac16578 100644 --- a/proteinworkshop/datasets/ec_reaction.py +++ b/proteinworkshop/datasets/ec_reaction.py @@ -206,7 +206,6 @@ def parse_dataset(self, split: str) -> pd.DataFrame: import pathlib import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/fold_classification.py b/proteinworkshop/datasets/fold_classification.py index efbac18f..35e62465 100644 --- a/proteinworkshop/datasets/fold_classification.py +++ b/proteinworkshop/datasets/fold_classification.py @@ -264,7 +264,6 @@ def exclude_pdbs(self): if __name__ == "__main__": import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/go.py b/proteinworkshop/datasets/go.py index f1a40fd3..c75dc6fb 100644 --- a/proteinworkshop/datasets/go.py +++ b/proteinworkshop/datasets/go.py @@ -268,7 +268,6 @@ def parse_dataset(self, split: str) -> pd.DataFrame: import pathlib import hydra - import omegaconf from proteinworkshop import constants diff --git a/proteinworkshop/datasets/pdb_dataset.py b/proteinworkshop/datasets/pdb_dataset.py index 847a01f1..c29cea41 100644 --- a/proteinworkshop/datasets/pdb_dataset.py +++ b/proteinworkshop/datasets/pdb_dataset.py @@ -225,9 +225,6 @@ def test_dataset(self) -> Dataset: if __name__ == "__main__": import pathlib - import hydra - import omegaconf - from proteinworkshop import constants cfg = omegaconf.OmegaConf.load( diff --git a/proteinworkshop/datasets/sequence.py b/proteinworkshop/datasets/sequence.py index e4f88107..860bb9fb 100644 --- a/proteinworkshop/datasets/sequence.py +++ b/proteinworkshop/datasets/sequence.py @@ -14,7 +14,7 @@ try: import esm -except: +except ImportError: logger.warning( "ESM not installed. If you are using a sequence dataset this will be required to fold structures. See: https://github.com/facebookresearch/esm#quickstart" ) @@ -24,6 +24,9 @@ class SequenceDataset(Dataset): + """Dataset class for working with Sequence Datasets. Provides utilities + for batch folding and embedding with ESM(Fold).""" + def __init__( self, fasta_file: Optional[str] = None, @@ -39,8 +42,6 @@ def __init__( use_embeddings: bool = True, use_structure: bool = True, ): - """Dataset class for working with Sequence Datasets. Provides utilities - for batch folding and embedding with ESM(Fold).""" self.root = root if root is not None else os.getcwd() if not os.path.exists(self.root): os.makedirs(self.root) diff --git a/proteinworkshop/features/edges.py b/proteinworkshop/features/edges.py index 48861c44..d6015a79 100644 --- a/proteinworkshop/features/edges.py +++ b/proteinworkshop/features/edges.py @@ -2,7 +2,6 @@ import functools from typing import List, Literal, Optional, Tuple, Union -import beartype import graphein.protein.tensor.edges as gp import torch from beartype import beartype diff --git a/proteinworkshop/metrics/auprc.py b/proteinworkshop/metrics/auprc.py index 32698069..e6290700 100644 --- a/proteinworkshop/metrics/auprc.py +++ b/proteinworkshop/metrics/auprc.py @@ -1,20 +1,18 @@ """Implementation of the AUPRC metric in ``torchmetrics``.""" -from typing import Any import torch from torchmetrics import Metric class AUPRC(Metric): - """Class for AUPRC metric.""" + """Class for AUPRC metric. - def __init__(self, compute_on_cpu: bool = True) -> None: - """Initialises the AUPRC metric. - - :param compute_on_cpu: Whether to compute the metric on CPU, + :param compute_on_cpu: Whether to compute the metric on CPU, defaults to ``True``. - :type compute_on_cpu: bool, optional - """ + :type compute_on_cpu: bool, optional + """ + + def __init__(self, compute_on_cpu: bool = True) -> None: super().__init__() self.add_state("preds", default=[], dist_reduce_fx="cat") self.add_state("targets", default=[], dist_reduce_fx="cat") diff --git a/proteinworkshop/models/graph_encoders/components/radial.py b/proteinworkshop/models/graph_encoders/components/radial.py index 8337b08b..2a4b6a50 100644 --- a/proteinworkshop/models/graph_encoders/components/radial.py +++ b/proteinworkshop/models/graph_encoders/components/radial.py @@ -98,7 +98,7 @@ def __repr__(self) -> str: @jaxtyped @beartype def compute_rbf( - distances: Float[torch.Tensor, "num_edges"], + distances: Float[torch.Tensor, " num_edges"], min_distance: float = 0.0, max_distance: float = 10.0, num_rbf: int = 8, diff --git a/proteinworkshop/models/graph_encoders/components/rigid_utils.py b/proteinworkshop/models/graph_encoders/components/rigid_utils.py index e45a65e0..26580dbf 100644 --- a/proteinworkshop/models/graph_encoders/components/rigid_utils.py +++ b/proteinworkshop/models/graph_encoders/components/rigid_utils.py @@ -318,6 +318,17 @@ class Rotation: rotation cannot be changed in-place. Like Rigid, the class is designed to mimic the behavior of a torch Tensor, almost as if each Rotation object were a tensor of rotations, in one format or another. + + :param rot_mats: A ``[*, 3, 3]`` rotation matrix tensor. Mutually exclusive + with quats. Defaults to ``None``. + :type rot_mats: Optional[torch.Tensor] + :param quats: A [*, 4] quaternion. Mutually exclusive with rot_mats. If + normalize_quats is not ``True``, must be a unit quaternion. Defaults to + ``None``. + :type quats: Optional[torch.Tensor] + :param normalize_quats: If quats is specified, whether to normalize quats. + Defaults to ``True``. + :type normalize_quats: bool, optional """ def __init__( @@ -326,17 +337,6 @@ def __init__( quats: Optional[torch.Tensor] = None, normalize_quats: bool = True, ): - """ - Args: - rot_mats: - A [*, 3, 3] rotation matrix tensor. Mutually exclusive with - quats - quats: - A [*, 4] quaternion. Mutually exclusive with rot_mats. If - normalize_quats is not True, must be a unit quaternion - normalize_quats: - If quats is specified, whether to normalize quats - """ if (rot_mats is None and quats is None) or ( rot_mats is not None and quats is not None ): @@ -823,6 +823,11 @@ class Rigid: around two objects: a Rotation object and a [*, 3] translation Designed to behave approximately like a single torch tensor with the shape of the shared batch dimensions of its component parts. + + :param rots: A [*, 3, 3] rotation tensor + :type rots: Optional[Rotation] + :param trans: A corresponding [*, 3] translation tensor + :type trans: Optional[torch.Tensor] """ def __init__( @@ -830,11 +835,6 @@ def __init__( rots: Optional[Rotation], trans: Optional[torch.Tensor], ): - """ - Args: - rots: A [*, 3, 3] rotation tensor - trans: A corresponding [*, 3] translation tensor - """ # (we need device, dtype, etc. from at least one input) batch_dims, dtype, device, requires_grad = None, None, None, None @@ -1285,7 +1285,7 @@ def scale_translation(self, trans_scale_factor: float): Returns: A transformation object with a scaled translation. """ - fn = lambda t: t * trans_scale_factor + fn = lambda t: t * trans_scale_factor # noqa: E731 return self.apply_trans_fn(fn) def stop_rot_gradient(self): @@ -1294,7 +1294,7 @@ def stop_rot_gradient(self): Returns: A transformation object with detached rotations """ - fn = lambda r: r.detach() + fn = lambda r: r.detach() # noqa: E731 return self.apply_rot_fn(fn) @staticmethod diff --git a/proteinworkshop/models/graph_encoders/components/wrappers.py b/proteinworkshop/models/graph_encoders/components/wrappers.py index d12a1acb..704c33e3 100644 --- a/proteinworkshop/models/graph_encoders/components/wrappers.py +++ b/proteinworkshop/models/graph_encoders/components/wrappers.py @@ -82,7 +82,7 @@ def clone(self): @jaxtyped @beartype - def mask(self, node_mask: Bool[torch.Tensor, "n_nodes"]): + def mask(self, node_mask: Bool[torch.Tensor, " n_nodes"]): return ScalarVector( self.scalar * node_mask[:, None], self.vector * node_mask[:, None, None], diff --git a/proteinworkshop/models/utils.py b/proteinworkshop/models/utils.py index 88bb2d45..89cc6673 100644 --- a/proteinworkshop/models/utils.py +++ b/proteinworkshop/models/utils.py @@ -169,7 +169,7 @@ def get_loss( raise ValueError(f"Incorrect Loss provided: {name}") -def flatten_list(l: List[List]) -> List: +def flatten_list(l: List[List]) -> List: # noqa: E741 return [item for sublist in l for item in sublist] @@ -179,7 +179,7 @@ def centralize( batch: Union[Batch, ProteinBatch], key: str, batch_index: torch.Tensor, - node_mask: Optional[Bool[torch.Tensor, "n_nodes"]] = None, + node_mask: Optional[Bool[torch.Tensor, " n_nodes"]] = None, ) -> Tuple[ torch.Tensor, torch.Tensor ]: # note: cannot make assumptions on output shape @@ -217,7 +217,7 @@ def decentralize( key: str, batch_index: torch.Tensor, entities_centroid: torch.Tensor, - node_mask: Optional[Bool[torch.Tensor, "n_nodes"]] = None, + node_mask: Optional[Bool[torch.Tensor, " n_nodes"]] = None, ) -> torch.Tensor: # note: cannot make assumptions on output shape if node_mask is not None: masked_values = torch.ones_like(batch[key]) * torch.inf @@ -236,7 +236,7 @@ def localize( pos: Float[torch.Tensor, "batch_num_nodes 3"], edge_index: Int64[torch.Tensor, "2 batch_num_edges"], norm_pos_diff: bool = True, - node_mask: Optional[Bool[torch.Tensor, "n_nodes"]] = None, + node_mask: Optional[Bool[torch.Tensor, " n_nodes"]] = None, ) -> Float[torch.Tensor, "batch_num_edges 3 3"]: row, col = edge_index[0], edge_index[1] diff --git a/proteinworkshop/scripts/download_foldcomp.py b/proteinworkshop/scripts/download_foldcomp.py index f35ae196..e62a030e 100644 --- a/proteinworkshop/scripts/download_foldcomp.py +++ b/proteinworkshop/scripts/download_foldcomp.py @@ -1,7 +1,6 @@ import argparse import os import pathlib -import shutil from typing import Optional import foldcomp diff --git a/tests/test_package_install.py b/tests/test_package_install.py index 74cd4f20..1a08ffce 100644 --- a/tests/test_package_install.py +++ b/tests/test_package_install.py @@ -2,10 +2,8 @@ import sys -import pytest - # Import package, test suite, and other packages as needed -import proteinworkshop +import proteinworkshop # noqa: F401 def test_proteinworkshop_imported(): From 90835eaeb7268352e5008fe5fc704817a1e36e5c Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 13:45:17 -0700 Subject: [PATCH 013/106] formatting --- proteinworkshop/datasets/base.py | 2 +- proteinworkshop/datasets/cath.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index d55802c7..97b45ea1 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -256,7 +256,7 @@ class ProteinDataset(Dataset): ``False``. :type overwrite: bool, optional :param format: Format to save structures in, defaults to "pdb". - :type format: Literal[mmtf, pdb, optional + :type format: Literal[mmtf, pdb], optional :param in_memory: Whether to load data into memory, defaults to False. :type in_memory: bool, optional :param store_het: Whether to store heteroatoms in the graph, diff --git a/proteinworkshop/datasets/cath.py b/proteinworkshop/datasets/cath.py index 79cc969c..312721e7 100644 --- a/proteinworkshop/datasets/cath.py +++ b/proteinworkshop/datasets/cath.py @@ -38,6 +38,7 @@ class CATHDataModule(ProteinDataModule): Defaults to ``False``. :type overwrite: bool """ + def __init__( self, path: str, From 282c6c3d06e39277ff00b4f3829c3f354fadfd5e Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 13:47:55 -0700 Subject: [PATCH 014/106] add pre-trained inverse folding config --- .../config/sweeps/pt_inverse_folding.yaml | 26 ++++++++++++++----- 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/proteinworkshop/config/sweeps/pt_inverse_folding.yaml b/proteinworkshop/config/sweeps/pt_inverse_folding.yaml index b98e0e9d..6db50753 100644 --- a/proteinworkshop/config/sweeps/pt_inverse_folding.yaml +++ b/proteinworkshop/config/sweeps/pt_inverse_folding.yaml @@ -1,6 +1,6 @@ program: proteinworkshop/finetune.py method: grid -name: pre_trained_inverse_folding +name: pre_trained_inverse_folding_scaling metric: # Does not matter, as we are using sweep to run the experiment. goal: minimize name: val/loss/total @@ -13,10 +13,10 @@ parameters: values: [cath] encoder: - values: [schnet, dimenet_plus_plus, egnn, gcpnet, gear_net_edge] + values: [schnet, gcpnet, gear_net_edge, tfn, mace, egnn] optimiser.optimizer.lr: - values: [0.0001] + values: [0.0001, 0.0003, 0.00001, 0.001] features: values: [ca_angles, ca_bb] @@ -28,7 +28,7 @@ parameters: value: False trainer.max_epochs: - value: 150 + value: 250 # We don't want to freeze the encoder finetune.encoder.freeze: @@ -40,18 +40,32 @@ parameters: inverse_folding, ] + +_pt_epoch: + values: ["epoch_0", "epoch_1", "epoch_2", epoch_3", "epoch_4", "epoch_5", "epoch_6", "epoch_7", "epoch_8", "epoch_9"] + + decoder.residue_type.dropout: + value: ${hparams.hparams.decoder_dropout} + # Uses checkpoint_root/pretrain_task/encoder/features/last.ckpt ckpt_path: values: [ # Note: One needed to manually update this path to point to the one's corresponding `last.ckpt` pre-trained checkpoint - #"$USER/ProteinWorkshop/checkpoints/${_pre_train_task}/${hydra:runtime.choices.encoder}/${hydra:runtime.choices.features}/last.ckpt", - ??? + "/home/jamasba/scratch/pworkshop/ckpts/${_pre_train_task}/${hydra:runtime.choices.encoder}/${hydra:runtime.choices.features}/${_pt_epoch}.ckpt", ] +test: value: True + trainer: + value: gpu + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_epoch_${_pt_epoch}" + command: - ${env} - HYDRA_FULL_ERROR=1 From eec1308b83a7f18ed1866555db323fc33f45d75d Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 13:48:34 -0700 Subject: [PATCH 015/106] add EGNN pretraining config --- .../config/sweeps/pre_train_egnn.yaml | 55 +++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 proteinworkshop/config/sweeps/pre_train_egnn.yaml diff --git a/proteinworkshop/config/sweeps/pre_train_egnn.yaml b/proteinworkshop/config/sweeps/pre_train_egnn.yaml new file mode 100644 index 00000000..3594dbb1 --- /dev/null +++ b/proteinworkshop/config/sweeps/pre_train_egnn.yaml @@ -0,0 +1,55 @@ +program: proteinworkshop/train.py +method: grid +name: pretrain_egnn +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: + [ + inverse_folding, + sequence_denoising, + #plddt_prediction, + structure_denoising, + torsional_denoising, + ] + + dataset: + value: afdb_rep_v4 + + dataset.datamodule.num_workers: + value: 16 + + encoder: + values: [egnn] + + optimiser.optimizer.lr: + values: [0.0001] + + features: + values: [ca_angles, ca_bb] + + scheduler: + value: linear_warmup_cosine_decay + + callbacks.model_checkpoint.every_n_epochs: + value: 1 + + extras.enforce_tags: + value: False + + trainer: + value: ddp + + +trainer.max_epochs: + value: 10 + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} From 02abcd8e41d73ff538c97ff546c5d567bf9df90e Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 13:49:49 -0700 Subject: [PATCH 016/106] add find graph encoder hparams --- .../config/sweeps/find_hparams.yaml | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 proteinworkshop/config/sweeps/find_hparams.yaml diff --git a/proteinworkshop/config/sweeps/find_hparams.yaml b/proteinworkshop/config/sweeps/find_hparams.yaml new file mode 100644 index 00000000..940bd3cd --- /dev/null +++ b/proteinworkshop/config/sweeps/find_hparams.yaml @@ -0,0 +1,54 @@ +program: proteinworkshop/train.py +method: grid +name: baseline_hyperparameter_search +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: [multiclass_graph_classification] + + dataset: + values: [fold_family] + + encoder: + values: [schnet, gear_net_edge, egnn, gcpnet] #, tfn] + + optimiser.optimizer.lr: + values: [0.00001, 0.0001, 0.0003, 0.001] + + decoder.graph_label.dropout: + values: [0.0, 0.1, 0.3, 0.5] + + features: + values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] + + scheduler: + value: plateau + + extras.enforce_tags: + value: False + + #+aux_task: + # values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + + trainer.max_epochs: + value: 300 + + test: + value: True + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_lr_${optimiser.optimizer.lr}_d_${decoder.graph_label.dropout}" + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} From 184737948b19efde1d82a22eb66c74db8d08abfd Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 13:51:00 -0700 Subject: [PATCH 017/106] add baseline inverse folding config --- .../sweeps/baseline_inverse_folding.yaml | 33 ++++++++++++++----- 1 file changed, 24 insertions(+), 9 deletions(-) diff --git a/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml b/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml index 45a67481..574e09ef 100644 --- a/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml +++ b/proteinworkshop/config/sweeps/baseline_inverse_folding.yaml @@ -7,19 +7,16 @@ metric: # Does not matter, as we are using sweep to run the experiment. parameters: task: - values: [inverse_folding] + value: inverse_folding dataset: - values: [cath] + value: cath encoder: - values: [schnet, dimenet_plus_plus, egnn, gcpnet, gear_net_edge] - - optimiser.optimizer.lr: - values: [0.0001] + values: [schnet, egnn, gcpnet, gear_net_edge, tfn, mace] features: - values: [ca_base, ca_seq, ca_angles, ca_bb] + values: [ca_seq, ca_angles, ca_bb] scheduler: value: plateau @@ -28,14 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+51,9 @@ lightning = "^2.0.7" python-dotenv = "^1.0.0" wget = "^3.2" opt-einsum = "^3.3.0" -torch = ">=2.0.0, !=2.0.1" +torch = "^2.1.0" pyrootutils = "^1.0.4" -torch-geometric = "^2.3.1" +torch-geometric = "^2.4.0" hydra-colorlog = "^1.2.0" foldcomp = "^0.0.5" @@ -62,6 +62,9 @@ foldcomp = "^0.0.5" black = "^23.7.0" isort = "^5.12.0" poethepoet = "^0.22.0" +ruff = "^0.1.1" +pydocstyle = "^6.3.0" +pre-commit = "^3.5.0" [tool.poetry.group.test.dependencies] @@ -106,6 +109,14 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" # Code-formatting and code-style config +[tool.ruff] +# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default. +select = ["E", "F", "I"] # <-- Note: I suggest including the "I" for additional checks! +ignore = ["F722"] # Ignore Jaxtyping forward references: https://github.com/google/jaxtyping/blob/v0.2.12/FAQ.md + +# Assuming you're developing for Python 3.10 +target-version = "py310" + [tool.black] exclude = ''' /( From 034268a965265863fe5d9d9a5de0451a7e984727 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 13:51:54 -0700 Subject: [PATCH 019/106] add pre-commit config --- .pre-commit-config.yaml | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..109beb97 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files +- repo: https://github.com/ambv/black + rev: 23.9.1 + hooks: + - id: black +- repo: https://github.com/jsh9/pydoclint + # pydoclint version. + rev: 0.3.3 + hooks: + - id: pydoclint + args: + - "--config=pyproject.toml" +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.1.1 + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] From cdfcdb85845a9309db6aaa060f3838d1e7af28fe Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 14:07:38 -0700 Subject: [PATCH 020/106] Add porject support for py311 --- poetry.lock | 12 ++++++++---- pyproject.toml | 2 +- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/poetry.lock b/poetry.lock index aa21ea94..07cf1723 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1355,7 +1355,10 @@ files = [ [package.dependencies] cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""} -greenlet = {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""} +greenlet = [ + {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""}, + {version = ">=3.0rc3", markers = "platform_python_implementation == \"CPython\" and python_version >= \"3.11\""}, +] "zope.event" = "*" "zope.interface" = "*" @@ -3010,7 +3013,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.20.3", markers = "python_version < \"3.10\""}, - {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, + {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, ] python-dateutil = ">=2.8.1" pytz = ">=2020.1" @@ -5421,5 +5425,5 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" -python-versions = ">= 3.9, < 3.11" -content-hash = "8c731ed68d4d72668ecc3d2ddce545c73602ad666fc41e232dc5aaf26be9f389" +python-versions = ">= 3.9, < 3.12" +content-hash = "cfd35954ea772103f138cccca7fef671d2af804a380b02b195215bfd9d52faee" diff --git a/pyproject.toml b/pyproject.toml index 6186d3b9..27b7831f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ packages = [ "Documentation" = "https://proteins.sh" [tool.poetry.dependencies] -python = ">= 3.9, < 3.11" +python = ">= 3.9, < 3.12" graphein = "^1.7.3" loguru = "^0.7.0" fair-esm = "^2.0.0" From ff44a348ca7c9e8c2425bac56be6cb06b84d250c Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 25 Oct 2023 14:20:52 -0700 Subject: [PATCH 021/106] Add cpu index url for torch for CI --- poetry.lock | 2 +- pyproject.toml | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 07cf1723..d38b8747 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5426,4 +5426,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = ">= 3.9, < 3.12" -content-hash = "cfd35954ea772103f138cccca7fef671d2af804a380b02b195215bfd9d52faee" +content-hash = "3fe084059322a873de1a10a43c224c58773f27767c0c8fb25408c6d1f5c6ee91" diff --git a/pyproject.toml b/pyproject.toml index 27b7831f..cd2fb5fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,6 +89,10 @@ watermark = "^2.4.3" sphinx-codeautolink = 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[package.extras] +dynamo = ["jinja2"] opt-einsum = ["opt-einsum (>=3.3)"] +[package.source] +type = "legacy" +url = "https://download.pytorch.org/whl/cpu" +reference = "torch" + [[package]] name = "torch-geometric" version = "2.4.0" @@ -5426,4 +5420,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = ">= 3.9, < 3.12" -content-hash = "3fe084059322a873de1a10a43c224c58773f27767c0c8fb25408c6d1f5c6ee91" +content-hash = "04c7ac234a4936d6ff89fcec3cbef37e67f15edf60c74ad1e3b2f1981af719c5" diff --git a/pyproject.toml b/pyproject.toml index cd2fb5fa..0cc9ba12 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,7 +51,7 @@ lightning = "^2.0.7" python-dotenv = "^1.0.0" wget = "^3.2" opt-einsum = "^3.3.0" -torch = "^2.1.0" +torch = {version="^2.1.0", source="torch"} pyrootutils = "^1.0.4" torch-geometric = "^2.4.0" hydra-colorlog = "^1.2.0" From e42cf36eac2091123017769f894bb7d600db1a56 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 26 Oct 2023 02:49:12 -0700 Subject: [PATCH 023/106] rollback to max python 3.10 due to lack of torchdrug support --- .github/workflows/code-tests.yaml | 2 +- poetry.lock | 12 ++++-------- pyproject.toml | 2 +- 3 files changed, 6 insertions(+), 10 deletions(-) diff --git a/.github/workflows/code-tests.yaml b/.github/workflows/code-tests.yaml index 05587a85..92767204 100644 --- a/.github/workflows/code-tests.yaml +++ b/.github/workflows/code-tests.yaml @@ -19,7 +19,7 @@ jobs: strategy: matrix: platform: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.9, "3.10", 3.11] + python-version: [3.9, "3.10"] runs-on: ubuntu-latest steps: diff --git a/poetry.lock b/poetry.lock index 11cfae1e..b9ce463d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1355,10 +1355,7 @@ files = [ [package.dependencies] cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""} -greenlet = [ - {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""}, - {version = ">=3.0rc3", markers = "platform_python_implementation == \"CPython\" and python_version >= \"3.11\""}, -] +greenlet = {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""} "zope.event" = "*" "zope.interface" = "*" @@ -3013,8 +3010,7 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.20.3", markers = "python_version < \"3.10\""}, - {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, - {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, ] python-dateutil = ">=2.8.1" pytz = ">=2020.1" @@ -5419,5 +5415,5 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" -python-versions = ">= 3.9, < 3.12" -content-hash = "04c7ac234a4936d6ff89fcec3cbef37e67f15edf60c74ad1e3b2f1981af719c5" +python-versions = ">= 3.9, < 3.11" +content-hash = "12235e0cb221be5ef120e306f3baa53a7881c69d071ddbacc36df945d5ebb846" diff --git a/pyproject.toml b/pyproject.toml index 0cc9ba12..df7ee9fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ packages = [ "Documentation" = "https://proteins.sh" [tool.poetry.dependencies] -python = ">= 3.9, < 3.12" +python = ">= 3.9, < 3.11" graphein = "^1.7.3" loguru = "^0.7.0" fair-esm = "^2.0.0" From ac9f5b767220ebfda21b222a1f9b794370ef597f Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 26 Oct 2023 07:13:02 -0700 Subject: [PATCH 024/106] bump graphein to 1.7.4 for PyG 2.4+ support (and backwards compatibility --- poetry.lock | 18 ++++++++++-------- pyproject.toml | 2 +- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/poetry.lock b/poetry.lock index b9ce463d..106469f5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -188,20 +188,21 @@ files = [ [[package]] name = "asttokens" -version = "2.4.0" +version = "2.4.1" description = "Annotate AST trees with source code positions" optional = false python-versions = "*" files = [ - {file = "asttokens-2.4.0-py2.py3-none-any.whl", hash = "sha256:cf8fc9e61a86461aa9fb161a14a0841a03c405fa829ac6b202670b3495d2ce69"}, - {file = "asttokens-2.4.0.tar.gz", hash = "sha256:2e0171b991b2c959acc6c49318049236844a5da1d65ba2672c4880c1c894834e"}, + {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"}, + {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"}, ] [package.dependencies] six = ">=1.12.0" [package.extras] -test = ["astroid", "pytest"] +astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"] +test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] [[package]] name = "async-timeout" @@ -1399,13 +1400,13 @@ test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre [[package]] name = "graphein" -version = "1.7.3" +version = "1.7.4" description = "Protein & Interactomic Graph Construction for Machine Learning" optional = false python-versions = ">=3.7" files = [ - {file = "graphein-1.7.3-py3-none-any.whl", hash = "sha256:64650c746f4d2523d25dac33dca0ed17adeeb31ecc87699e948f02514f814ec1"}, - {file = "graphein-1.7.3.tar.gz", hash = "sha256:e2618020c8da3046df31fee6942959a36b8147cc104d49a86d2516f6cb407462"}, + {file = "graphein-1.7.4-py3-none-any.whl", hash = "sha256:b0e345dda75099a47892adae745dcce8c66294ad45102e7c1c9353bd184c3d2e"}, + {file = "graphein-1.7.4.tar.gz", hash = "sha256:4cc0f76becc4990c95dee55ebe945186465adfe9542a43798ac90120fdd22282"}, ] [package.dependencies] @@ -1415,6 +1416,7 @@ bioservices = ">=1.10.0" deepdiff = "*" jaxtyping = "*" loguru = "*" +looseversion = "*" matplotlib = ">=3.4.3" multipledispatch = "*" networkx = "*" @@ -5416,4 +5418,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = ">= 3.9, < 3.11" -content-hash = "12235e0cb221be5ef120e306f3baa53a7881c69d071ddbacc36df945d5ebb846" +content-hash = "2c3184dc9980bac43556fd6e3bae038d0472db1d65682461a7de26fef6e8bc7b" diff --git a/pyproject.toml b/pyproject.toml index df7ee9fd..a747da02 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ packages = [ [tool.poetry.dependencies] python = ">= 3.9, < 3.11" -graphein = "^1.7.3" +graphein = "^1.7.4" loguru = "^0.7.0" fair-esm = "^2.0.0" hydra-core = "^1.3.2" From 736bf63cf5a2f02ab5e377f2c3d60aac92f60319 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 26 Oct 2023 15:09:39 -0700 Subject: [PATCH 025/106] add warning log --- proteinworkshop/datasets/base.py | 50 ++++++++++++++++++++------------ 1 file changed, 32 insertions(+), 18 deletions(-) diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index 97b45ea1..2a6e9263 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -1,3 +1,4 @@ +"""Base classes for protein structure datamodules and datasets.""" import os import pathlib from abc import ABC, abstractmethod @@ -57,6 +58,12 @@ def pair_data(a: Data, b: Data) -> Data: class ProteinDataModule(L.LightningDataModule, ABC): + """Base class for Protein datamodules. + + .. seealso:: + L.LightningDataModule + """ + prepare_data_per_node = ( True # class default for lighting 2.0 compatability ) @@ -64,7 +71,7 @@ class ProteinDataModule(L.LightningDataModule, ABC): @abstractmethod def download(self): """ - Implements downloading of raw data. + Implement downloading of raw data. Typically this will be an index file of structure identifiers (for datasets derived from the PDB) but @@ -85,8 +92,7 @@ def setup(self, stage: Optional[str] = None): @property @lru_cache def obsolete_pdbs(self) -> Dict[str, str]: - """This method returns a mapping of obsolete PDB codes - to their updated replacements. + """Returns a mapping of obsolete PDB codes to their updated replacement. :return: Mapping of obsolete PDB codes to their updated replacements. :rtype: Dict[str, str] @@ -95,7 +101,7 @@ def obsolete_pdbs(self) -> Dict[str, str]: @beartype def compose_transforms(self, transforms: Iterable[Callable]) -> T.Compose: - """Composes an iterable of Transforms into a single transform. + """Compose an iterable of Transforms into a single transform. :param transforms: An iterable of transforms. :type transforms: Iterable[Callable] @@ -113,7 +119,7 @@ def compose_transforms(self, transforms: Iterable[Callable]) -> T.Compose: @abstractmethod def parse_dataset(self, split: str) -> pd.DataFrame: """ - This methods implements the parsing of the raw dataset to a dataframe. + Implement the parsing of the raw dataset to a dataframe. Override this method to implement custom parsing of raw data. @@ -139,12 +145,13 @@ def parse_labels(self) -> Any: @abstractmethod def exclude_pdbs(self): + """Return a list of PDBs/IDs to exclude from the dataset.""" ... @abstractmethod def train_dataset(self) -> Dataset: """ - Implements the construction of the training dataset. + Implement the construction of the training dataset. :return: The training dataset. :rtype: Dataset @@ -154,7 +161,7 @@ def train_dataset(self) -> Dataset: @abstractmethod def val_dataset(self) -> Dataset: """ - Implements the construction of the validation dataset. + Implement the construction of the validation dataset. :return: The validation dataset. :rtype: Dataset @@ -164,7 +171,7 @@ def val_dataset(self) -> Dataset: @abstractmethod def test_dataset(self) -> Dataset: """ - Implements the construction of the test dataset. + Implement the construction of the test dataset. :return: The test dataset. :rtype: Dataset @@ -174,7 +181,7 @@ def test_dataset(self) -> Dataset: @abstractmethod def train_dataloader(self) -> ProteinDataLoader: """ - Implements the construction of the training dataloader. + Implement the construction of the training dataloader. :return: The training dataloader. :rtype: ProteinDataLoader @@ -183,7 +190,7 @@ def train_dataloader(self) -> ProteinDataLoader: @abstractmethod def val_dataloader(self) -> ProteinDataLoader: - """Implements the construction of the validation dataloader. + """Implement the construction of the validation dataloader. :return: The validation dataloader. :rtype: ProteinDataLoader @@ -192,7 +199,7 @@ def val_dataloader(self) -> ProteinDataLoader: @abstractmethod def test_dataloader(self) -> ProteinDataLoader: - """Implements the construction of the test dataloader. + """Implement the construction of the test dataloader. :return: The test dataloader. :rtype: ProteinDataLoader @@ -200,6 +207,7 @@ def test_dataloader(self) -> ProteinDataLoader: ... def get_class_weights(self) -> torch.Tensor: + """Return tensor of class weights.""" labels: Dict[str, torch.Tensor] = self.parse_labels() labels = list(labels.values()) # type: ignore labels = np.array(labels) # type: ignore @@ -256,7 +264,7 @@ class ProteinDataset(Dataset): ``False``. :type overwrite: bool, optional :param format: Format to save structures in, defaults to "pdb". - :type format: Literal[mmtf, pdb], optional + :type format: Literal[mmtf, pdb, ent], optional :param in_memory: Whether to load data into memory, defaults to False. :type in_memory: bool, optional :param store_het: Whether to store heteroatoms in the graph, @@ -279,7 +287,7 @@ def __init__( pre_filter: Optional[Callable] = None, log: bool = True, overwrite: bool = False, - format: Literal["mmtf", "pdb"] = "pdb", + format: Literal["mmtf", "pdb", "ent"] = "pdb", in_memory: bool = False, store_het: bool = False, out_names: Optional[List[str]] = None, @@ -320,8 +328,7 @@ def __init__( def download(self): """ - Downloads structure files not already present in the raw directory - (``raw_dir``). + Download structure files not present in the raw directory (``raw_dir``). Structures are downloaded from the RCSB PDB using the Graphein multiprocessed downloader. @@ -332,6 +339,11 @@ def download(self): Downloaded files are stored in ``self.raw_dir``. """ + if self.format == "ent": # Skip downloads from ASTRAL + logger.warning( + "Downloads in .ent format are assumed to be from ASTRAL. These data should have already been downloaded" + ) + return if self._skip_download: logger.info( "All structures already processed and overwrite=False. Skipping download." @@ -366,6 +378,7 @@ def download(self): ) def len(self) -> int: + """Return length of the dataset.""" return len(self.pdb_codes) @property @@ -401,6 +414,8 @@ def processed_file_names(self) -> Union[str, List[str], Tuple]: :return: List of processed file names. :rtype: Union[str, List[str], Tuple] """ + if self.overwrite: + return ["this_forces_a_processing_cycle"] if self.out_names is not None: return [f"{name}.pt" for name in self.out_names] if self.chains is not None: @@ -412,8 +427,7 @@ def processed_file_names(self) -> Union[str, List[str], Tuple]: return [f"{pdb}.pt" for pdb in self.pdb_codes] def process(self): - """Processes the raw data into PyTorch Geometric Data objects using - Graphein. + """Process raw data into PyTorch Geometric Data objects with Graphein. Processed data are stored in ``self.processed_dir`` as ``.pt`` files. """ @@ -486,7 +500,7 @@ def process(self): def get(self, idx: int) -> Data: """ - Returns PyTorch Geometric Data object for a given index. + Return PyTorch Geometric Data object for a given index. :param idx: Index to retrieve. :type idx: int From d3536d7214a30903afbbe0cba797f65f503de042 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 26 Oct 2023 15:32:31 -0700 Subject: [PATCH 026/106] add list to track processed files in case of overwrite --- proteinworkshop/datasets/base.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index 2a6e9263..851a15c8 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -305,6 +305,8 @@ def __init__( self.store_het = store_het self.out_names = out_names + self._processed_files = [] + # Determine whether to download raw structures if not self.overwrite and all( os.path.exists(Path(self.root) / "processed" / p) @@ -414,6 +416,8 @@ def processed_file_names(self) -> Union[str, List[str], Tuple]: :return: List of processed file names. :rtype: Union[str, List[str], Tuple] """ + if self._processed_files: + return self._processed_files if self.overwrite: return ["this_forces_a_processing_cycle"] if self.out_names is not None: @@ -496,6 +500,7 @@ def process(self): graph.node_y = self.node_labels[i] # type: ignore torch.save(graph, Path(self.processed_dir) / fname) + self._processed_files.append(fname) logger.info("Completed processing.") def get(self, idx: int) -> Data: From 4ee8b90db400700518ca8a42f6aaa5c95f9fc027 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 27 Oct 2023 12:31:10 -0700 Subject: [PATCH 027/106] addmodel attribution script --- proteinworkshop/config/explain.yaml | 47 ++++++++ proteinworkshop/explain.py | 176 ++++++++++++++++++++++++++++ 2 files changed, 223 insertions(+) create mode 100644 proteinworkshop/config/explain.yaml create mode 100644 proteinworkshop/explain.py diff --git a/proteinworkshop/config/explain.yaml b/proteinworkshop/config/explain.yaml new file mode 100644 index 00000000..027562a6 --- /dev/null +++ b/proteinworkshop/config/explain.yaml @@ -0,0 +1,47 @@ +# @package _global_ + +# === 1. Set config parameters === +name: "" # default name for the experiment, "" means logger (eg. wandb) will generate a unique name +seed: 52 # seed for random number generators in pytorch, numpy and python.random +num_workers: 16 # number of subprocesses to use for data loading. + +# === 2. Specify defaults here. Defaults will be overwritten by equivalently named options in this file === +defaults: + - env: default + - dataset: fold_fold + - features: ca + - encoder: egnn + - decoder: default + - transforms: none + - callbacks: default + - trainer: gpu + - extras: default + - hydra: default + - metrics: default + - task: inverse_folding + - logger: wandb + - finetune: full_model + # debugging config (enable through command line, e.g. `python train.py debug=default) + - debug: null + #- sweeper: wandb + #- task: classification + #- graphein: default + # optional hardware config for machine/user specific settings + # (optional since it doesn't need to exist and is excluded from version control) + #- optional hardware: cpu + #- optional output_decoder: ${task.output}_${decoder} + - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. + +task_name: "visualise" + +compile: True + +explain: + n_steps: 50 + output: "graph_label" # Name of the model output to attribute + split: ["train", "val", "test"] + + +# simply provide checkpoint path and image filepath to embed dataset and plot embeddings +ckpt_path: /home/jamasba/scratch/workshop_ckpts/runs/2023-09-18_23-46-54/checkpoints/epoch_038.ckpt +output_dir: ./explanations diff --git a/proteinworkshop/explain.py b/proteinworkshop/explain.py new file mode 100644 index 00000000..ad0d7882 --- /dev/null +++ b/proteinworkshop/explain.py @@ -0,0 +1,176 @@ +import collections +import functools +import os +import pathlib +import sys +from typing import List + +import hydra +import lightning as L +import omegaconf +import torch +from captum.attr import IntegratedGradients +from graphein.protein.tensor.io import to_dataframe +from loguru import logger as log +from torch_geometric.utils import unbatch +from tqdm import tqdm + +from proteinworkshop import ( + constants, + register_custom_omegaconf_resolvers, + utils, +) +from proteinworkshop.configs import config +from proteinworkshop.models.base import BenchMarkModel + + +def explain(cfg: omegaconf.DictConfig): + assert cfg.ckpt_path, "No checkpoint path provided." + assert cfg.output_dir, "No plot name provided." + + cfg = config.validate_config(cfg) + + L.seed_everything(cfg.seed) + + log.info("Instantiating datamodule:... ") + datamodule: L.LightningDataModule = hydra.utils.instantiate( + cfg.dataset.datamodule + ) + + log.info("Instantiating model:... ") + model: L.LightningModule = BenchMarkModel(cfg) + + # Initialize lazy layers for parameter counts + # This is also required for the model to be able to load weights + # Otherwise lazy layers will have their parameters reset + # https://pytorch.org/docs/stable/generated/torch.nn.modules.lazy.LazyModuleMixin.html#torch.nn.modules.lazy.LazyModuleMixin + log.info("Initializing lazy layers...") + with torch.no_grad(): + datamodule.setup() # type: ignore + batch = next(iter(datamodule.val_dataloader())) + log.info(f"Unfeaturized batch: {batch}") + batch = model.featurise(batch) + log.info(f"Featurized batch: {batch}") + out = model.forward(batch) + log.info(f"Model output: {out}") + del batch, out + + # Load weights + # We only want to load weights + log.info(f"Loading weights from checkpoint {cfg.ckpt_path}...") + state_dict = torch.load(cfg.ckpt_path)["state_dict"] + + if cfg.finetune.encoder.load_weights: + encoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("encoder"): + encoder_weights[k.replace("encoder.", "")] = v + # log.info(f"Loading encoder weights: {encoder_weights}") + err = model.encoder.load_state_dict(encoder_weights, strict=False) + log.warning(f"Error loading encoder weights: {err}") + + if cfg.finetune.decoder.load_weights: + decoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("decoder"): + decoder_weights[k.replace("decoder.", "")] = v + # log.info(f"Loading decoder weights: {decoder_weights}") + err = model.decoder.load_state_dict(decoder_weights, strict=False) + log.warning(f"Error loading decoder weights: {err}") + else: + model.decoder = None + + OUTPUT = cfg.explain.output + output_dir = pathlib.Path(cfg.output_dir) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Wrap forward function and pass to captum + def _forward_wrapper(model, node_feats, batch, output: str): + batch.edge_index = batch.edge_index[:2, :] + batch.x = node_feats + return model.forward(batch)[output] + + fwd = functools.partial(_forward_wrapper, model.cuda(), output=OUTPUT) + ig = IntegratedGradients(fwd) + + # Iterate over batches and perform attribution + dataloaders = {} + if "train" in cfg.explain.split: + dataloaders["train"] = datamodule.train_dataloader() + if "val" in cfg.explain.split: + dataloaders["val"] = datamodule.val_dataloader() + if "test" in cfg.explain.split: + dataloaders["test"] = datamodule.test_dataloader() + + for split, dataloader in dataloaders.items(): + log.info(f"Performing attribution for split: {split}") + + for batch in tqdm(dataloader): + batch = batch.cuda() + batch = model.featurise(batch) + node_features = batch.x + attribution = ig.attribute( + node_features, + baselines=torch.ones_like(batch.x), + additional_forward_args=batch, + target=model.get_labels(batch)[OUTPUT].long(), + internal_batch_size=cfg.dataset.datamodule.batch_size, + n_steps=cfg.explain.n_steps, + ) + attribution = attribution.sum(-1) + + # Unbatch and write each protein to disk + batch = batch.cpu() + batch_items = batch.to_data_list() + attribution_scores = unbatch(attribution, batch.batch) + for elem, score in tqdm(zip(batch_items, attribution_scores)): + # Scale score between 0-100 + score = score - score.min() + score = score / score.max() + score = score * 100 + + df = to_dataframe( + x=elem.coords, + residue_types=elem.residues, + chains=elem.chains, + insertions=None, + b_factors=score.cpu(), # Write attribution score in B factor column + occupancy=None, + charge=None, + alt_loc=None, + segment_id=None, + biopandas=True, + ) + output_path = output_dir / f"{elem.id}.pdb" + df.to_pdb(str(output_path)) + + +# Load hydra config from yaml files and command line arguments. +@hydra.main( + version_base="1.3", + config_path=str(constants.HYDRA_CONFIG_PATH), + config_name="explain", +) +def _main(cfg: omegaconf.DictConfig) -> None: + """Load and validate the hydra config.""" + utils.extras(cfg) + cfg = config.validate_config(cfg) + explain(cfg) + + +def _script_main(args: List[str]) -> None: + """ + Provides an entry point for the script dispatcher. + + Sets the sys.argv to the provided args and calls the main train function. + """ + sys.argv = args + register_custom_omegaconf_resolvers() + _main() + + +if __name__ == "__main__": + # pylint: disable=no-value-for-parameter + register_custom_omegaconf_resolvers() + _main() # type: ignore From d24bcf67b6d26088ab3a8a47009b7d41c8787e0a Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 27 Oct 2023 13:25:31 -0700 Subject: [PATCH 028/106] update graphein version to 1.7.5+ and add captum dependency --- poetry.lock | 78 ++++++++++++++++++++++++++++++++------------------ pyproject.toml | 3 +- 2 files changed, 52 insertions(+), 29 deletions(-) diff --git a/poetry.lock b/poetry.lock index 106469f5..800760b0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -471,6 +471,28 @@ webencodings = "*" [package.extras] css = ["tinycss2 (>=1.1.0,<1.3)"] +[[package]] +name = "captum" +version = "0.6.0" +description = "Model interpretability for PyTorch" +optional = false +python-versions = ">=3.6" +files = [ + {file = "captum-0.6.0-py3-none-any.whl", hash = "sha256:6b157d6c625cc365c5f9630f1380fbb283194001dd77c0f7c07c827bdbe58661"}, + {file = "captum-0.6.0.tar.gz", hash = "sha256:01bb374622d46b56acd590462757bf2087ed3721dcebf4922db0fa77548d6524"}, +] + +[package.dependencies] +matplotlib = "*" +numpy = "*" +torch = ">=1.6" + +[package.extras] +dev = ["annoy", "black (==22.3.0)", "flake8", "flask", "flask-compress", "ipython", "ipywidgets", "jupyter", "mypy (>=0.760)", "parameterized", "pytest", "pytest-cov", "scikit-learn", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-katex", "ufmt", "usort (==1.0.2)"] +insights = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter"] +test = ["parameterized", "pytest", "pytest-cov"] +tutorials = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter", "torchtext", "torchvision"] 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"cf7151035faa5bfb8d169ab7bb100bda85eee82b9aac23239fe59161d024fef1" diff --git a/pyproject.toml b/pyproject.toml index a747da02..8519ec99 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ packages = [ [tool.poetry.dependencies] python = ">= 3.9, < 3.11" -graphein = "^1.7.4" +graphein = "^1.7.5" loguru = "^0.7.0" fair-esm = "^2.0.0" hydra-core = "^1.3.2" @@ -56,6 +56,7 @@ pyrootutils = "^1.0.4" torch-geometric = "^2.4.0" hydra-colorlog = "^1.2.0" foldcomp = "^0.0.5" +captum = "^0.6.0" [tool.poetry.group.dev.dependencies] From 03271da613b553ed0dccf05418a589cc163031f2 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 27 Oct 2023 23:58:04 -0700 Subject: [PATCH 029/106] add some more docstrings, clean up --- README.md | 15 +++++++++++++++ proteinworkshop/config/explain.yaml | 17 ++++++----------- proteinworkshop/explain.py | 29 +++++++++++++++++++++++------ 3 files changed, 44 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index fe10f712..bc230846 100644 --- a/README.md +++ b/README.md @@ -241,6 +241,21 @@ wandb agent mywandbgroup/proteinworkshop/2gwtt7oy --count 8 python proteinworkshop/embed.py dataset=cath encoder=egnn ckpt_path=PATH/TO/CHECKPOINT ``` +### Performing attribution of a pre-trained model + +We provide a utility in `proteinworkshop/explain.py` for performing attribution of a pre-trained model using integrated gradients. + +This will + +To run it: + +```bash +python proteinworkshop/explain.py ckpt_path=PATH/TO/CHECKPOINT output_dir=ATTRIBUTION/DIRECTORY +``` + +See the `explain` section of `proteinworkshop/config/explain.yaml` for additional parameters. + + ### Verifying a config ```bash diff --git a/proteinworkshop/config/explain.yaml b/proteinworkshop/config/explain.yaml index 027562a6..5921a4ea 100644 --- a/proteinworkshop/config/explain.yaml +++ b/proteinworkshop/config/explain.yaml @@ -23,25 +23,20 @@ defaults: - finetune: full_model # debugging config (enable through command line, e.g. `python train.py debug=default) - debug: null - #- sweeper: wandb - #- task: classification - #- graphein: default # optional hardware config for machine/user specific settings # (optional since it doesn't need to exist and is excluded from version control) #- optional hardware: cpu - #- optional output_decoder: ${task.output}_${decoder} - _self_ # see: https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order/. Adding _self_ at bottom means values in this file override defaults. task_name: "visualise" compile: True -explain: - n_steps: 50 - output: "graph_label" # Name of the model output to attribute - split: ["train", "val", "test"] - - -# simply provide checkpoint path and image filepath to embed dataset and plot embeddings +# simply provide checkpoint path and output directory to embed dataset and write attributed PDB files ckpt_path: /home/jamasba/scratch/workshop_ckpts/runs/2023-09-18_23-46-54/checkpoints/epoch_038.ckpt output_dir: ./explanations + +explain: + n_steps: 50 # Number of steps to perform Integrated Gradients for + output: "graph_label" # Name of the model output to attribute + split: ["train", "val", "test"] # Dataloaders to perform attribution for diff --git a/proteinworkshop/explain.py b/proteinworkshop/explain.py index ad0d7882..522ed11a 100644 --- a/proteinworkshop/explain.py +++ b/proteinworkshop/explain.py @@ -3,15 +3,17 @@ import os import pathlib import sys -from typing import List +from typing import List, Union import hydra import lightning as L import omegaconf import torch from captum.attr import IntegratedGradients +from graphein.protein.tensor.data import ProteinBatch from graphein.protein.tensor.io import to_dataframe from loguru import logger as log +from torch_geometric.data import Batch from torch_geometric.utils import unbatch from tqdm import tqdm @@ -26,7 +28,9 @@ def explain(cfg: omegaconf.DictConfig): assert cfg.ckpt_path, "No checkpoint path provided." - assert cfg.output_dir, "No plot name provided." + assert ( + cfg.output_dir + ), "No output directory for attributed PDB files provided." cfg = config.validate_config(cfg) @@ -86,15 +90,27 @@ def explain(cfg: omegaconf.DictConfig): os.makedirs(output_dir) # Wrap forward function and pass to captum - def _forward_wrapper(model, node_feats, batch, output: str): - batch.edge_index = batch.edge_index[:2, :] - batch.x = node_feats + def _forward_wrapper( + model: L.LightningModule, + node_feats: torch.Tensor, + batch: Union[Batch, ProteinBatch], + output: str, + ): + """Wrapper function around forward pass. + + Sets node features to the provided node features and returns the + model output for the specified output. + + The node feature update is necessary to set the interpolated features + from IntegratedGradients. + """ + batch.edge_index = batch.edge_index[:2, :] # Handle GearNet edge case + batch.x = node_feats # Update node features return model.forward(batch)[output] fwd = functools.partial(_forward_wrapper, model.cuda(), output=OUTPUT) ig = IntegratedGradients(fwd) - # Iterate over batches and perform attribution dataloaders = {} if "train" in cfg.explain.split: dataloaders["train"] = datamodule.train_dataloader() @@ -103,6 +119,7 @@ def _forward_wrapper(model, node_feats, batch, output: str): if "test" in cfg.explain.split: dataloaders["test"] = datamodule.test_dataloader() + # Iterate over batches and perform attribution for split, dataloader in dataloaders.items(): log.info(f"Performing attribution for split: {split}") From 684b3e54a7547fb93835a62a1271a5383ded101f Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sat, 28 Oct 2023 00:01:30 -0700 Subject: [PATCH 030/106] add attribution to cli --- proteinworkshop/scripts/cli.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/proteinworkshop/scripts/cli.py b/proteinworkshop/scripts/cli.py index 79480477..8a9ac4a7 100644 --- a/proteinworkshop/scripts/cli.py +++ b/proteinworkshop/scripts/cli.py @@ -71,6 +71,17 @@ def main(): help="Additional arguments passed to the finetune script.", ) + # ... explain sub-command + explain_parser = subparsers.add_parser( + "explain", + help="explain a model. See proteinworkshop/explain.py for more details.", + ) + explain_parser.add_argument( + "explain_args", + nargs="*", + help="Additional arguments passed to the explain script.", + ) + # Parse arguments args = parser.parse_args() @@ -114,9 +125,14 @@ def main(): finetune_main(args.finetune_args) + elif args.command == "explain": + from proteinworkshop.explain import _script_main as explain_main + + explain_main(args.explain_args) + else: _valid = "\n\t".join( - ["\n\tinstall", "\tdownload", "\ttrain", "\tfinetune"] + ["\n\tinstall", "\tdownload", "\ttrain", "\tfinetune", "\texplain"] ) if args.command is None: raise ValueError(f"Missing command. Valid commands are: {_valid}") From 089aff7907b8e4f95dfbfa1fd2dd2b56de9f9bc9 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sat, 28 Oct 2023 00:03:18 -0700 Subject: [PATCH 031/106] update gitignore --- .gitignore | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 1576616d..bef72f57 100644 --- a/.gitignore +++ b/.gitignore @@ -146,4 +146,7 @@ proteinworkshop/data/pdb/ logs/ wandb/ .DS_Store -.env \ No newline at end of file +.env + +# Explanations +explanations/ From b7ffda0329b61fbd2f1d58493e7ebfa89919b9fb Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 3 Nov 2023 02:29:14 -0700 Subject: [PATCH 032/106] add DDP support #44 --- proteinworkshop/config/trainer/ddp.yaml | 8 +++----- pyproject.toml | 1 + 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/proteinworkshop/config/trainer/ddp.yaml b/proteinworkshop/config/trainer/ddp.yaml index 4e5238e3..3626f923 100644 --- a/proteinworkshop/config/trainer/ddp.yaml +++ b/proteinworkshop/config/trainer/ddp.yaml @@ -1,11 +1,9 @@ defaults: - default.yaml -# use "ddp_spawn" instead of "ddp", -# it's slower but normal "ddp" currently doesn't work ideally with hydra -# https://github.com/facebookresearch/hydra/issues/2070 -# https://pytorch-lightning.readthedocs.io/en/latest/accelerators/gpu_intermediate.html#distributed-data-parallel-spawn -strategy: ddp_spawn +# NOTE: https://github.com/facebookresearch/hydra/issues/2070 +# for improved Hydra + Lightning + DDP support is not merged yet +strategy: ddp_find_unused_parameters_true accelerator: gpu devices: 4 diff --git a/pyproject.toml b/pyproject.toml index 8519ec99..f8eebfa5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -102,6 +102,7 @@ install-torch-scatter = "pip install torch_scatter -f https://data.pyg.org/whl/t install-torch-spline-conv = "pip install torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cpu.html" install-openfold = "pip install git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769 && pip install git+https://github.com/aqlaboratory/openfold.git@4b41059694619831a7db195b7e0988fc4ff3a307" install-torchdrug = "pip install torchdrug" +install-flash = "pip install lightning-flash --no-dependencies" [tool.poetry.scripts] train_workshop = "proteinworkshop.train:_main" From 30679e3cd9a07dd638355a66c2e67522abf56139 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 3 Nov 2023 02:29:45 -0700 Subject: [PATCH 033/106] update readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bc230846..257958da 100644 --- a/README.md +++ b/README.md @@ -527,8 +527,8 @@ We use `poetry` to manage the project's underlying dependencies and to push upda To keep with the code style for the `proteinworkshop` repository, using the following lines, please format your commits before opening a pull request: ```bash # assuming you are located in the `ProteinWorkshop` top-level directory -isort . -autoflake -r --in-place --remove-unused-variables --remove-all-unused-imports --ignore-init-module-imports . +isort . +autoflake -r --in-place --remove-unused-variables --remove-all-unused-imports --ignore-init-module-imports . black --config=pyproject.toml . ``` From 65892661c3fb2fd9766563c6706a97a35c1436b0 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 3 Nov 2023 03:00:32 -0700 Subject: [PATCH 034/106] update readme --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cd815e20..e7489f38 100644 --- a/README.md +++ b/README.md @@ -256,9 +256,9 @@ See the `visualise` section of `proteinworkshop/config/visualise.yaml` for addit We provide a utility in `proteinworkshop/explain.py` for performing attribution of a pre-trained model using integrated gradients. -This will +This will write PDB files for all the structures in a dataset for a supervised task with residue-level attributions in the b_factor column. To visualise the attributions, we recommend using the [Protein Viewer VSCode extension](https://marketplace.visualstudio.com/items?itemName=ArianJamasb.protein-viewer) and changing the 3D representation to colour by `Uncertainty/Disorder`. -To run it: +To run the attribution: ```bash python proteinworkshop/explain.py ckpt_path=PATH/TO/CHECKPOINT output_dir=ATTRIBUTION/DIRECTORY @@ -376,8 +376,11 @@ Pre-training corpuses (with the exception of `pdb`, `cath`, and `astral`) are pr | `esmatlas` | [ESMAtlas](https://esmatlas.com/) predictions (full) | [Kim et al.](https://academic.oup.com/bioinformatics/article/39/4/btad153/7085592) | | 1 Tb | [GPL-3.0](https://github.com/steineggerlab/foldcomp/blob/master/LICENSE.txt) / [CC-BY 4.0](https://esmatlas.com/about) | `esmatlas_v2023_02`| [ESMAtlas](https://esmatlas.com/) predictions (v2023_02 release) | [Kim et al.](https://academic.oup.com/bioinformatics/article/39/4/btad153/7085592) | | 137 Gb| [GPL-3.0](https://github.com/steineggerlab/foldcomp/blob/master/LICENSE.txt) / [CC-BY 4.0](https://esmatlas.com/about) | `highquality_clust30`| [ESMAtlas](https://esmatlas.com/) High Quality predictions | [Kim et al.](https://academic.oup.com/bioinformatics/article/39/4/btad153/7085592) | 37M Chains | 114 Gb | [GPL-3.0](https://github.com/steineggerlab/foldcomp/blob/master/LICENSE.txt) / [CC-BY 4.0](https://esmatlas.com/about) +| `igfold_paired_oas` | IGFold Predictions for [Paired OAS](https://journals.aai.org/jimmunol/article/201/8/2502/107069/Observed-Antibody-Space-A-Resource-for-Data-Mining) | [Ruffolo et al.](https://www.nature.com/articles/s41467-023-38063-x) | 104,994 paired Ab chains | | [CC-BY 4.0](https://www.nature.com/articles/s41467-023-38063-x#rightslink) +| `igfold_jaffe` | IGFold predictions for [Jaffe2022](https://www.nature.com/articles/s41586-022-05371-z) data | [Ruffolo et al.](https://www.nature.com/articles/s41467-023-38063-x) | 1,340,180 paired Ab chains | | [CC-BY 4.0](https://www.nature.com/articles/s41467-023-38063-x#rightslink) | `pdb`| Experimental structures deposited in the [RCSB Protein Data Bank](https://www.rcsb.org/) | [wwPDB consortium](https://academic.oup.com/nar/article/47/D1/D520/5144142) | ~800k Chains |23 Gb | [CC0 1.0](https://www.rcsb.org/news/feature/611e8d97ef055f03d1f222c6) | +
Additionally, we provide several species-specific compilations (mostly reference species) From 594a5968fa548406b9e37f84abc4843a66f58f2e Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 3 Nov 2023 03:11:01 -0700 Subject: [PATCH 035/106] ignore igfold dataset in overwrite test --- tests/dataset/test_datasets.py | 42 +++++++++++++++++++--------------- 1 file changed, 24 insertions(+), 18 deletions(-) diff --git a/tests/dataset/test_datasets.py b/tests/dataset/test_datasets.py index 607eea80..b8b8fb47 100644 --- a/tests/dataset/test_datasets.py +++ b/tests/dataset/test_datasets.py @@ -49,27 +49,33 @@ def test_instantiate_datasets(tmp_path): def test_datasets_have_overwrite_attr(tmp_path): - for t in os.listdir(DATASET_CONFIG_DIR): - config_path = DATASET_CONFIG_DIR / t - cfg = omegaconf.OmegaConf.load(config_path) + for t in os.listdir(DATASET_CONFIG_DIR): + config_path = DATASET_CONFIG_DIR / t + cfg = omegaconf.OmegaConf.load(config_path) - if "data_dir" in cfg.datamodule: - cfg.datamodule.data_dir = tmp_path + if "data_dir" in cfg.datamodule: + cfg.datamodule.data_dir = tmp_path - if "path" in cfg.datamodule: - cfg.datamodule.path = tmp_path + if "path" in cfg.datamodule: + cfg.datamodule.path = tmp_path - if "pdb_dir" in cfg.datamodule: - cfg.datamodule.pdb_dir = tmp_path + if "pdb_dir" in cfg.datamodule: + cfg.datamodule.pdb_dir = tmp_path - if "transforms" in cfg.datamodule: - cfg.datamodule.transforms = None + if "transforms" in cfg.datamodule: + cfg.datamodule.transforms = None - if "transform" in cfg.datamodule: - cfg.datamodule.transform = None + if "transform" in cfg.datamodule: + cfg.datamodule.transform = None - if cfg.datamodule._target_ in {"graphein.ml.datasets.foldcomp_dataset.FoldCompLightningDataModule", "proteinworkshop.datasets.atom3d_datamodule.ATOM3DDataModule"}: - continue - else: - dm = instantiate(cfg.datamodule) - assert hasattr(dm, "overwrite"), f"Datamodules {dm} has no overwrite attribute" + if cfg.datamodule._target_ in { + "graphein.ml.datasets.foldcomp_dataset.FoldCompLightningDataModule", + "proteinworkshop.datasets.atom3d_datamodule.ATOM3DDataModule", + "proteinworkshop.datasets.igfold.IgFoldDataModule", + }: + continue + else: + dm = instantiate(cfg.datamodule) + assert hasattr( + dm, "overwrite" + ), f"Datamodules {dm} has no overwrite attribute" From 2007c4b58c110226ddb6ac6264196a34dd8f2084 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 3 Nov 2023 03:17:03 -0700 Subject: [PATCH 036/106] add IGFold prediction datasets --- .../config/dataset/igfold_jaffe.yaml | 12 + .../config/dataset/igfold_paired_oas.yaml | 12 + proteinworkshop/datasets/igfold.py | 304 ++++++++++++++++++ 3 files changed, 328 insertions(+) create mode 100644 proteinworkshop/config/dataset/igfold_jaffe.yaml create mode 100644 proteinworkshop/config/dataset/igfold_paired_oas.yaml create mode 100644 proteinworkshop/datasets/igfold.py diff --git a/proteinworkshop/config/dataset/igfold_jaffe.yaml b/proteinworkshop/config/dataset/igfold_jaffe.yaml new file mode 100644 index 00000000..697b3219 --- /dev/null +++ b/proteinworkshop/config/dataset/igfold_jaffe.yaml @@ -0,0 +1,12 @@ +datamodule: + _target_: proteinworkshop.datasets.igfold.IgFoldDataModule + path: ${env.paths.data}/igfold_jaffe/ # Directory where the dataset is stored + dataset_name: "paired_oas" + format: "pdb" # Format of the raw PDB/MMTF files + train_val_test: [0.8, 0.1, 0.1] + batch_size: 32 # Batch size for dataloader + dataset_fraction: 1.0 # Fraction of the dataset to use + pin_memory: True # Pin memory for dataloader + num_workers: 8 # Number of workers for dataloader + transforms: ${transforms} +num_classes: 2 diff --git a/proteinworkshop/config/dataset/igfold_paired_oas.yaml b/proteinworkshop/config/dataset/igfold_paired_oas.yaml new file mode 100644 index 00000000..2c8e3247 --- /dev/null +++ b/proteinworkshop/config/dataset/igfold_paired_oas.yaml @@ -0,0 +1,12 @@ +datamodule: + _target_: proteinworkshop.datasets.igfold.IgFoldDataModule + path: ${env.paths.data}/igfold_paired_oas/ # Directory where the dataset is stored + dataset_name: "paired_oas" + format: "pdb" # Format of the raw PDB/MMTF files + train_val_test: [0.8, 0.1, 0.1] + batch_size: 32 # Batch size for dataloader + dataset_fraction: 1.0 # Fraction of the dataset to use + pin_memory: True # Pin memory for dataloader + num_workers: 8 # Number of workers for dataloader + transforms: ${transforms} +num_classes: 2 diff --git a/proteinworkshop/datasets/igfold.py b/proteinworkshop/datasets/igfold.py new file mode 100644 index 00000000..7c830030 --- /dev/null +++ b/proteinworkshop/datasets/igfold.py @@ -0,0 +1,304 @@ +"""DataModule for IgFold antibody prediction corpuses.""" +import glob +import os +import pathlib +import random +import tarfile +from typing import Callable, List, Literal, Optional + +import omegaconf +import wget +from graphein.protein.tensor.dataloader import ProteinDataLoader +from loguru import logger +from sklearn.model_selection import train_test_split + +from proteinworkshop.datasets.base import ProteinDataModule, ProteinDataset + +IgFoldDatasetNames = Literal["paired_oas", "jaffe"] + + +class IgFoldDataModule(ProteinDataModule): + """ + Datamodule for IgFold predictions for pOAS and Jaffe Antibody Datasets. + + :param path: Path to store data + :type path: os.PathLike + :param dataset_name: Name of dataset to use (``"paired_oas"`` or + ``"jaffe"``). + :type dataset_name: IgFoldDatasetNames + :param batch_size: Batch size for dataloader. + :type batch_size: int + :param train_val_test: Split sizes for dataset partitions. + :type train_val_test: List[float] + :param format: Format of the structure files. + :type format: Literal["pdb", "mmtf"] + :param in_memory: Whether to load the entire dataset into memory. + :type in_memory: bool + :param pin_memory: Whether to pin memory for the dataloader. + :type pin_memory: bool + :param num_workers: Number of processes to use for the dataloader. + :type num_workers: int + :param dataset_fraction: Fraction of the dataset to use. Defaults to + ``1.0``. + :type dataset_fraction: float + :param transforms: List of transforms to apply to the data. + :type transforms: Optional[List[Callable]] + """ + + def __init__( + self, + path: os.PathLike, + dataset_name: IgFoldDatasetNames, + batch_size: int, + train_val_test: List[float] = [0.8, 0.1, 0.1], + format: Literal["pdb"] = "pdb", + in_memory: bool = False, + pin_memory: bool = True, + num_workers: int = 16, + dataset_fraction: float = 1.0, + transforms: Optional[List[Callable]] = None, + ): + if dataset_name == "paired_oas": + self.DATASET_URL = "https://data.graylab.jhu.edu/OAS_paired.tar.gz" + self.TAR_FILENAME = "OAS_paired.tar.gz" + elif dataset_name == "jaffe": + self.DATASET_URL = "https://data.graylab.jhu.edu/Jaffe2022.tar.gz" + self.TAR_FILENAME = "Jaffe2022.tar.gz" + else: + raise ValueError( + f"Dataset {dataset_name} not supported. Must be one of 'jaffe' / 'paired_oas" + ) + self.UNCOMPRESSED_DIR_NAME = "predictions_flat" + # Dataset args + assert sum(train_val_test) == 1, "Split sizes are invalid" + self.train_val_test = train_val_test + self.in_memory = in_memory + self.format = format + self.dataset_fraction = dataset_fraction + + # Paths + self.data_dir: pathlib.Path = pathlib.Path(path) + if not os.path.exists(self.data_dir): + os.makedirs(self.data_dir, exist_ok=True) + self.structure_dir = self.data_dir / "structures" + + # Dataloader args + self.batch_size = batch_size + self.pin_memory = pin_memory + self.num_workers = num_workers + + if transforms is not None: + self.transform = self.compose_transforms( + omegaconf.OmegaConf.to_container( + transforms, resolve=True + ) # type: ignore + ) + else: + self.transform = None + + def setup(self, stage: Optional[str] = None): + """Perform datamodule setup tasks (downloads structure data). + + :param stage: Model training/val/test stage. Defaults to ``None`` + :type stage: Optional[str] + """ + self.download() + + def download(self): + """Download structure data to ``self.data_dir / self.TAR_FILENAME``.""" + if not os.path.exists(self.data_dir / self.TAR_FILENAME): + logger.info(f"Downloading IgFold structures to {self.data_dir}") + wget.download( + self.DATASET_URL, out=str(self.data_dir / self.TAR_FILENAME) + ) + else: + logger.info( + f"IgFold structures found in {self.data_dir}" + ) + + if not os.path.exists(self.structure_dir): + logger.info(f"Extracting tarfile to {self.structure_dir}") + tar = tarfile.open(str(self.data_dir / self.TAR_FILENAME)) + tar.extractall(str(self.data_dir)) + tar.close() + os.rename( + self.data_dir / self.UNCOMPRESSED_DIR_NAME, self.structure_dir + ) + + def parse_dataset( + self, split: Literal["train", "val", "test"] + ) -> List[str]: + """ + Parse dataset for a given ``split``. + + :param split: Name of split + :type split: Literal["train", "val", "test"] + :returns: List of filenames for a given ``split``. + :rtype: List[str] + """ + # If we've already split, return the split data + if hasattr(self, f"{split}_pdbs"): + return getattr(self, f"{split}_pdbs") + + structure_files = glob.glob( + str(self.structure_dir / f"*.{self.format}"), recursive=True + ) + logger.info( + f"Found {len(structure_files)} pdbs in: {self.structure_dir}" + ) + + # Selection subset + structure_files = random.sample( + structure_files, int(self.dataset_fraction * len(structure_files)) + ) + + structure_files = [f.split("/")[-1] for f in structure_files] + structure_files = [f.replace(".pdb", "") for f in structure_files] + + train_size, val_size, test_size = self.train_val_test + + # Split dataset + logger.info( + f"Splitting {len(structure_files)} structures into {train_size}, {val_size}, {test_size} split" + ) + train, val = train_test_split( + structure_files, test_size=val_size + test_size + ) + val, test = train_test_split( + val, test_size=test_size / (val_size + test_size) + ) + logger.info( + f"Split sizes: {len(train)} train, {len(val)} val, {len(test)} test" + ) + + self.train_pdbs = train + self.val_pdbs = val + self.test_pdbs = test + + return getattr(self, f"{split}_pdbs") + + def parse_labels(self): + """Parse labels. + + This is a no-op for this dataset as it has no labels. + """ + pass + + def exclude_pdbs(self): + """ + Identify PDBs to exclude. + + This is a no-op for this dataset as there are no PDBs to exclude.. + """ + pass + + def _get_dataset( + self, split: Literal["train", "val", "test"] + ) -> ProteinDataset: + """ + Retrieve dataset for a given split. + + :param split: Name of split to retrieve. + :type split: Literal["train", "val", "test"] + :returns: ProteinDataset for a given ``split``. + :rtype: ProteinDataset + """ + pdb_codes = self.parse_dataset(split) + logger.info(f"Initialising Graphein dataset for {split}...") + + return ProteinDataset( + pdb_codes=pdb_codes, + root=str(self.data_dir), + pdb_dir=str(self.structure_dir), + overwrite=False, + transform=self.transform, + format=self.format, + in_memory=self.in_memory, + ) + + def train_dataset(self) -> ProteinDataset: + """Return the train dataset. + + :returns: Train dataset + :rtype: ProteinDataset + """ + return self._get_dataset("train") + + def val_dataset(self) -> ProteinDataset: + """Return the val dataset. + + :returns: Val dataset + :rtype: ProteinDataset + """ + return self._get_dataset("val") + + def test_dataset(self) -> ProteinDataset: + """Return the test dataset. + + :returns: Test dataset + :rtype: ProteinDataset + """ + return self._get_dataset("test") + + def train_dataloader(self) -> ProteinDataLoader: + """Return the train dataloader. + + :returns: Train dataloader + :rtype: ProteinDataLoader + """ + return ProteinDataLoader( + self.train_dataset(), + batch_size=self.batch_size, + shuffle=True, + pin_memory=self.pin_memory, + num_workers=self.num_workers, + ) + + def val_dataloader(self) -> ProteinDataLoader: + """Return the val dataloader. + + :returns: val dataloader + :rtype: ProteinDataLoader + """ + return ProteinDataLoader( + self.val_dataset(), + batch_size=self.batch_size, + shuffle=False, + pin_memory=self.pin_memory, + num_workers=self.num_workers, + ) + + def test_dataloader(self) -> ProteinDataLoader: + """Return the test dataloader. + + :returns: Test dataloader + :rtype: ProteinDataLoader + """ + return ProteinDataLoader( + self.test_dataset(), + batch_size=self.batch_size, + shuffle=False, + pin_memory=self.pin_memory, + num_workers=self.num_workers, + ) + + +if __name__ == "__main__": + import hydra + + from proteinworkshop import constants + + cfg = omegaconf.OmegaConf.load( + constants.HYDRA_CONFIG_PATH / "dataset" / "igfold_paired_oas.yaml" + ) + cfg.datamodule.path = str( + pathlib.Path(constants.DATA_PATH) / "igfold_paired_oas" + ) + cfg.datamodule.transforms = None + + datamodule = hydra.utils.instantiate(cfg.datamodule) + datamodule.download() + dl = datamodule.test_dataloader() + for i in dl: + print(i) + break From 63ccd148ded8e49335143eec70936f598c7a6172 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 3 Nov 2023 03:17:50 -0700 Subject: [PATCH 037/106] Add igfold datamodule --- proteinworkshop/datasets/igfold.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/proteinworkshop/datasets/igfold.py b/proteinworkshop/datasets/igfold.py index 7c830030..aafde620 100644 --- a/proteinworkshop/datasets/igfold.py +++ b/proteinworkshop/datasets/igfold.py @@ -112,9 +112,7 @@ def download(self): self.DATASET_URL, out=str(self.data_dir / self.TAR_FILENAME) ) else: - logger.info( - f"IgFold structures found in {self.data_dir}" - ) + logger.info(f"IgFold structures found in {self.data_dir}") if not os.path.exists(self.structure_dir): logger.info(f"Extracting tarfile to {self.structure_dir}") From 75dc816ffab1a816941a4869f0c7381dcd97393a Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 7 Nov 2023 14:39:25 -0800 Subject: [PATCH 038/106] fix binary graph classification config --- .../task/binary_graph_classification.yaml | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 proteinworkshop/config/task/binary_graph_classification.yaml diff --git a/proteinworkshop/config/task/binary_graph_classification.yaml b/proteinworkshop/config/task/binary_graph_classification.yaml new file mode 100644 index 00000000..0525f0ba --- /dev/null +++ b/proteinworkshop/config/task/binary_graph_classification.yaml @@ -0,0 +1,34 @@ +# @package _global_ + +defaults: + - _self_ + - override /metrics: + - accuracy + - f1_score + - f1_max + - auprc + - rocauc + - override /decoder: + - graph_label + +callbacks: + early_stopping: + monitor: val/graph_label/accuracy + mode: "max" + model_checkpoint: + monitor: val/graph_label/accuracy + mode: "max" + +task: + task: "classification" + classification_type: "binary" + metric_average: "micro" + + losses: + graph_label: bce + label_smoothing: 0.0 + + output: + - "graph_label" + supervise_on: + - "graph_label" From 9771abd63387c3ae7b4a738c5da496226f1001f2 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 12:38:39 -0800 Subject: [PATCH 039/106] add cdconv model --- README.md | 1 + docs/source/configs/model.rst | 13 + .../source/modules/proteinworkshop.models.rst | 6 + .../downstream_component.html | 1 + .../pretrain_component.html | 1 + proteinworkshop/config/encoder/cdconv.yaml | 11 + proteinworkshop/datasets/base.py | 35 +- proteinworkshop/models/base.py | 49 +-- .../models/graph_encoders/cdconv.py | 141 +++++++ .../models/graph_encoders/layers/cdconv.py | 368 ++++++++++++++++++ proteinworkshop/tasks/remove_missing_ca.py | 3 + 11 files changed, 572 insertions(+), 57 deletions(-) create mode 100644 proteinworkshop/config/encoder/cdconv.yaml create mode 100644 proteinworkshop/models/graph_encoders/cdconv.py create mode 100644 proteinworkshop/models/graph_encoders/layers/cdconv.py diff --git a/README.md b/README.md index e7489f38..ae47d9c9 100644 --- a/README.md +++ b/README.md @@ -324,6 +324,7 @@ Read [the docs](https://www.proteins.sh) for a full list of modules available in | `GearNet`| [Zhang et al.](https://arxiv.org/abs/2203.06125) | ✓ | `DimeNet++` | [Gasteiger et al.](https://arxiv.org/abs/2011.14115) | ✗ | `SchNet` | [Schütt et al.](https://arxiv.org/abs/1706.08566) | ✗ +| `CDConv` | [Fan et al.](https://openreview.net/forum?id=P5Z-Zl9XJ7) | ✓ ### Equivariant Graph Encoders diff --git a/docs/source/configs/model.rst b/docs/source/configs/model.rst index a7323632..b47ebb93 100644 --- a/docs/source/configs/model.rst +++ b/docs/source/configs/model.rst @@ -113,6 +113,19 @@ where :math:`\mathrm{FC(\cdot)}` denotes a linear transformation upon the messag :caption: config/encoder/gear_net_edge.yaml + +:py:class`CDConv ` (``cdconv``) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +CDConv is an SE(3) invariant architecture that uses independent learnable weights for sequential displacement, whilst directly encoding geometric displacements. + +As a result of the downsampling procedures, this architecture is only suitable for graph-level prediction tasks. + +.. literalinclude:: ../../../proteinworkshop/config/encoder/cdconv.yaml + :language: yaml + :caption: config/encoder/cdconv.yaml + + Vector-Equivariant Encoders ============================= diff --git a/docs/source/modules/proteinworkshop.models.rst b/docs/source/modules/proteinworkshop.models.rst index 0d57c20e..07f898b9 100644 --- a/docs/source/modules/proteinworkshop.models.rst +++ b/docs/source/modules/proteinworkshop.models.rst @@ -65,6 +65,12 @@ GearNet (``gear_net``, ``gear_net_edge``) :undoc-members: :show-inheritance: +CDConv (``cdconv``) +^^^^^^^^^^^^^^^^^^^^^ +.. automodule:: proteinworkshop.models.graph_encoders.cdconv + :members: + :undoc-members: + :show-inheritance: Vector-Equivariant Encoders ============================= diff --git a/docs/source/quickstart_component/downstream_component.html b/docs/source/quickstart_component/downstream_component.html index eb7fb504..b72f64ec 100644 --- a/docs/source/quickstart_component/downstream_component.html +++ b/docs/source/quickstart_component/downstream_component.html @@ -87,6 +87,7 @@ var modelList = [ ['schnet', "SchNet"], ['dimenetpp', 'DimeNet'], + ['cdconv', "CDConv"] ['egnn', 'EGNN'], ['gvp', "GVP"], ['gcpnet', 'GCPNet'], diff --git a/docs/source/quickstart_component/pretrain_component.html b/docs/source/quickstart_component/pretrain_component.html index f5b6aa04..8807ad99 100644 --- a/docs/source/quickstart_component/pretrain_component.html +++ b/docs/source/quickstart_component/pretrain_component.html @@ -80,6 +80,7 @@ var modelList = [ ['schnet', "SchNet"], ['dimenetpp', 'DimeNet'], + ['cdconv', "CDConv"] ['egnn', 'EGNN'], ['gvp', "GVP"], ['gcpnet', 'GCPNet'], diff --git a/proteinworkshop/config/encoder/cdconv.yaml b/proteinworkshop/config/encoder/cdconv.yaml new file mode 100644 index 00000000..0a638e4a --- /dev/null +++ b/proteinworkshop/config/encoder/cdconv.yaml @@ -0,0 +1,11 @@ +_target_: "proteinworkshop.models.graph_encoders.cdconv.CDConvModel" +geometric_radius: 4.0 +sequential_kernel_size: 5 +kernel_channels: [24] +channels: [256, 512, 1024, 2048] +base_width: 64.0 +embedding_dim: 16 +batch_norm: True +dropout: 0.2 +bias: False +readout: "sum" \ No newline at end of file diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index 7b352ba7..b84754fb 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -359,9 +359,7 @@ def download(self): pdb for pdb in self.pdb_codes if not ( - os.path.exists( - Path(self.raw_dir) / f"{pdb}.{self.format}" - ) + os.path.exists(Path(self.raw_dir) / f"{pdb}.{self.format}") or os.path.exists( Path(self.raw_dir) / f"{pdb}.{self.format}.gz" ) @@ -371,13 +369,9 @@ def download(self): to_download = list(set(to_download)) logger.info(f"Downloading {len(to_download)} structures") file_format = ( - self.format[:-3] - if self.format.endswith(".gz") - else self.format - ) - download_pdb_multiprocessing( - to_download, self.raw_dir, format=file_format + self.format[:-3] if self.format.endswith(".gz") else self.format ) + download_pdb_multiprocessing(to_download, self.raw_dir, format=file_format) def len(self) -> int: """Return length of the dataset.""" @@ -424,8 +418,7 @@ def processed_file_names(self) -> Union[str, List[str], Tuple]: return [f"{name}.pt" for name in self.out_names] if self.chains is not None: return [ - f"{pdb}_{chain}.pt" - for pdb, chain in zip(self.pdb_codes, self.chains) + f"{pdb}_{chain}.pt" for pdb, chain in zip(self.pdb_codes, self.chains) ] else: return [f"{pdb}.pt" for pdb in self.pdb_codes] @@ -448,9 +441,7 @@ def process(self): index_pdb_tuples = [ (i, pdb) for i, pdb in enumerate(self.pdb_codes) - if not os.path.exists( - Path(self.processed_dir) / f"{pdb}.pt" - ) + if not os.path.exists(Path(self.processed_dir) / f"{pdb}.pt") ] logger.info(f"Processing {len(index_pdb_tuples)} unprocessed structures") else: @@ -459,7 +450,10 @@ def process(self): raw_dir = Path(self.raw_dir) for index_pdb_tuple in tqdm(index_pdb_tuples): try: - i, pdb = index_pdb_tuple # NOTE: here, we unpack the tuple to get each PDB's original index in `self.pdb_codes` + ( + i, + pdb, + ) = index_pdb_tuple # NOTE: here, we unpack the tuple to get each PDB's original index in `self.pdb_codes` path = raw_dir / f"{pdb}.{self.format}" if path.exists(): path = str(path) @@ -478,18 +472,14 @@ def process(self): store_het=self.store_het, ) except Exception as e: - logger.error( - f"Error processing {pdb} {self.chains[i]}: {e}" - ) # type: ignore + logger.error(f"Error processing {pdb} {self.chains[i]}: {e}") # type: ignore raise e if self.out_names is not None: fname = self.out_names[i] + ".pt" else: fname = ( - f"{pdb}.pt" - if self.chains is None - else f"{pdb}_{self.chains[i]}.pt" + f"{pdb}.pt" if self.chains is None else f"{pdb}_{self.chains[i]}.pt" ) graph.id = fname.split(".")[0] @@ -528,4 +518,7 @@ def _batch_format(self, x: Data) -> Data: # Set this to ensure proper batching behaviour x.x = torch.zeros(x.coords.shape[0]) # type: ignore x.amino_acid_one_hot = amino_acid_one_hot(x) + x.seq_pos = torch.arange(x.coords.shape[0]).unsqueeze( + -1 + ) # Add sequence position return x diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index 9f99a021..04905029 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -37,9 +37,7 @@ def forward(self, batch: Batch) -> torch.Tensor: ... @abc.abstractmethod - def training_step( - self, batch: Batch, batch_idx: torch.Tensor - ) -> torch.Tensor: + def training_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: """Implement training step. :param batch: Mini-batch of data. @@ -52,9 +50,7 @@ def training_step( ... @abc.abstractmethod - def validation_step( - self, batch: Batch, batch_idx: torch.Tensor - ) -> torch.Tensor: + def validation_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: """Implement validation step. :param batch: Mini-batch of data. @@ -112,18 +108,14 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: for output in self.config.task.supervise_on: if output == "node_label": labels["node_label"] = batch.node_y - if isinstance( - self.losses["node_label"], torch.nn.BCEWithLogitsLoss - ): + if isinstance(self.losses["node_label"], torch.nn.BCEWithLogitsLoss): labels["node_label"] = F.one_hot( labels["node_label"], num_classes=self.config.dataset.num_classes, ).float() elif output == "graph_label": labels["graph_label"] = batch.graph_y - if isinstance( - self.losses["graph_label"], torch.nn.BCEWithLogitsLoss - ): + if isinstance(self.losses["graph_label"], torch.nn.BCEWithLogitsLoss): labels["graph_label"] = F.one_hot( labels["graph_label"], num_classes=self.config.dataset.num_classes, @@ -173,9 +165,7 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: return Label(labels) @beartype - def compute_loss( - self, y_hat: ModelOutput, y: Label - ) -> Dict[str, torch.Tensor]: + def compute_loss(self, y_hat: ModelOutput, y: Label) -> Dict[str, torch.Tensor]: """ Compute loss by iterating over all outputs. @@ -212,13 +202,9 @@ def configure_optimizers(self): # sourcery skip: extract-method if self.config.get("scheduler"): logger.info("Instantiating scheduler...") - scheduler = hydra.utils.instantiate( - self.config.scheduler, optimiser - ) + scheduler = hydra.utils.instantiate(self.config.scheduler, optimiser) scheduler = OmegaConf.to_container(scheduler) - scheduler["scheduler"] = scheduler["scheduler"]( - optimizer=optimiser - ) + scheduler["scheduler"] = scheduler["scheduler"](optimizer=optimiser) optimiser_config = { "optimizer": optimiser, "lr_scheduler": scheduler, @@ -249,9 +235,7 @@ def _build_output_decoders(self) -> nn.ModuleDict: decoders[output_head] = hydra.utils.instantiate(cfg) return decoders - def configure_losses( - self, loss_dict: Dict[str, str] - ) -> Dict[str, Callable]: + def configure_losses(self, loss_dict: Dict[str, str]) -> Dict[str, Callable]: """ Configures losses from config. Returns a dictionary of losses mapping each output name to its respective loss function. @@ -346,9 +330,7 @@ def configure_metrics(self): setattr(self, "metric_names", metric_names) @beartype - def log_metrics( - self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch - ): + def log_metrics(self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch): """ Logs metrics to logger. @@ -428,9 +410,7 @@ def __init__(self, cfg: DictConfig) -> None: logger.info(self.featuriser) logger.info("Instantiating task transform...") - self.task_transform = hydra.utils.instantiate( - cfg.get("task.transform") - ) + self.task_transform = hydra.utils.instantiate(cfg.get("task.transform")) logger.info(self.task_transform) self.save_hyperparameters() @@ -452,6 +432,7 @@ def _create_example_batch(self) -> ProteinBatch: ] for p in proteins: setattr(p, "x", torch.zeros(p.coords.shape[0])) + setattr(p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1)) batch = ProteinBatch.from_data_list(proteins) return self.featurise(batch) @@ -490,16 +471,12 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: emb_type = self.decoder[ output_head ].input # node_embedding or graph_embedding - output[output_head] = self.decoder[output_head]( - output[emb_type] - ) + output[output_head] = self.decoder[output_head](output[emb_type]) return self.compute_output(output, batch) @beartype - def transform_encoder_output( - self, output: EncoderOutput, batch - ) -> EncoderOutput: + def transform_encoder_output(self, output: EncoderOutput, batch) -> EncoderOutput: """ Modifies graph encoder output. diff --git a/proteinworkshop/models/graph_encoders/cdconv.py b/proteinworkshop/models/graph_encoders/cdconv.py new file mode 100644 index 00000000..ef0fdea9 --- /dev/null +++ b/proteinworkshop/models/graph_encoders/cdconv.py @@ -0,0 +1,141 @@ +""" +CDConv implementation adapted from the MIT-licensed source: +https://github.com/hehefan/Continuous-Discrete-Convolution/tree/main +""" + +from typing import List, Set, Union + +import omegaconf +import torch +import torch.nn as nn +import torch.nn.functional as F +from graphein.protein.tensor.data import ProteinBatch +from torch_geometric.data import Batch + +from proteinworkshop.models.graph_encoders.layers.cdconv import AvgPooling, BasicBlock +from proteinworkshop.models.utils import get_aggregation +from proteinworkshop.types import EncoderOutput + + +class CDConvModel(nn.Module): + def __init__( + self, + geometric_radius: float, + sequential_kernel_size: float, + kernel_channels: List[int], + channels: List[int], + base_width: float = 16.0, + embedding_dim: int = 16, + batch_norm: bool = True, + dropout: float = 0.2, + bias: bool = False, + readout: str = "sum", + ) -> nn.Module: + super().__init__() + + geometric_radii = [geometric_radius * i for i in range(1, len(channels) + 1)] + assert ( + len(geometric_radii) == len(channels) + ), "Model: 'geometric_radii' and 'channels' should have the same number of elements!" + + self.embedding = torch.nn.LazyLinear(embedding_dim) + + self.local_mean_pool = AvgPooling() + + if isinstance(kernel_channels, omegaconf.ListConfig): + kernel_channels = list(kernel_channels) + + layers = [] + in_channels = embedding_dim + for i, radius in enumerate(geometric_radii): + layers.append( + BasicBlock( + r=radius, + l=sequential_kernel_size, + kernel_channels=kernel_channels, + in_channels=in_channels, + out_channels=channels[i], + base_width=base_width, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + ) + ) + layers.append( + BasicBlock( + r=radius, + l=sequential_kernel_size, + kernel_channels=kernel_channels, + in_channels=channels[i], + out_channels=channels[i], + base_width=base_width, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + ) + ) + in_channels = channels[i] + + self.layers = nn.Sequential(*layers) + self.readout = get_aggregation(readout) + + @property + def required_batch_attributes(self) -> Set[str]: + return {"x", "pos", "seq_pos", "batch"} + + @staticmethod + def orientation(pos): + u = F.normalize(pos[1:, :] - pos[:-1, :], p=2, dim=1) + u1 = u[1:, :] + u2 = u[:-1, :] + b = F.normalize(u2 - u1, p=2, dim=1) + n = F.normalize(torch.cross(u2, u1), p=2, dim=1) + o = F.normalize(torch.cross(b, n), p=2, dim=1) + ori = torch.stack([b, n, o], dim=1) + return torch.cat( + [torch.unsqueeze(ori[0], 0), ori, torch.unsqueeze(ori[-1], 0)], axis=0 + ) + + def forward(self, data: Union[ProteinBatch, Batch]) -> EncoderOutput: + x, pos, seq, batch = ( + self.embedding(data.x), + data.pos, + data.seq_pos, + data.batch, + ) + + ori = self.orientation(pos) + + for i, layer in enumerate(self.layers): + x = layer(x, pos, seq, ori, batch) + if i == len(self.layers) - 1: + x = self.readout(x, batch) + elif i % 2 == 1: + x, pos, seq, ori, batch = self.local_mean_pool(x, pos, seq, ori, batch) + + return EncoderOutput({"graph_embedding": x, "node_embedding": None}) + + +if __name__ == "__main__": + import hydra + + from proteinworkshop import constants + + cfg = omegaconf.OmegaConf.load( + constants.SRC_PATH / "config" / "encoder" / "cdconv.yaml" + ) + enc = hydra.utils.instantiate(cfg) + print(enc) + + from graphein.protein.tensor.data import get_random_protein + + protein = get_random_protein() + protein.seq_pos = torch.arange(protein.coords.shape[0]) + + batch = ProteinBatch().from_data_list([protein] * 4) + batch.x = torch.randn((batch.coords.shape[0], 16)) + batch.pos = batch.coords[:, 1, :] + print(batch) + print(enc.orientation(batch.pos).shape) + + # enc(batch) diff --git a/proteinworkshop/models/graph_encoders/layers/cdconv.py b/proteinworkshop/models/graph_encoders/layers/cdconv.py new file mode 100644 index 00000000..d957407b --- /dev/null +++ b/proteinworkshop/models/graph_encoders/layers/cdconv.py @@ -0,0 +1,368 @@ +""" +CDConv implementation adapted from the MIT-licensed source: +https://github.com/hehefan/Continuous-Discrete-Convolution/tree/main +""" + +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch_sparse as sparse +from torch import Tensor +from torch_geometric.nn import radius +from torch_geometric.nn.conv import MessagePassing +from torch_geometric.typing import OptTensor +from torch_geometric.utils import add_self_loops, remove_self_loops +from torch_scatter import scatter_max, scatter_mean + + +class Linear(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + batch_norm: bool = True, + dropout: float = 0.0, + bias: bool = False, + leakyrelu_negative_slope: float = 0.1, + momentum: float = 0.2, + ) -> nn.Module: + super(Linear, self).__init__() + + module = [] + if batch_norm: + module.append(nn.BatchNorm1d(in_channels, momentum=momentum)) + module.append(nn.LeakyReLU(leakyrelu_negative_slope)) + module.append(nn.Dropout(dropout)) + module.append(nn.Linear(in_channels, out_channels, bias=bias)) + self.module = nn.Sequential(*module) + + def forward(self, x): + return self.module(x) + + +class MLP(nn.Module): + def __init__( + self, + in_channels: int, + mid_channels: int, + out_channels: int, + batch_norm: bool, + dropout: float = 0.0, + bias: bool = True, + leakyrelu_negative_slope: float = 0.2, + momentum: float = 0.2, + ) -> nn.Module: + super(MLP, self).__init__() + + module = [] + if batch_norm: + module.append(nn.BatchNorm1d(in_channels, momentum=momentum)) + module.append(nn.LeakyReLU(leakyrelu_negative_slope)) + module.append(nn.Dropout(dropout)) + if mid_channels is None: + module.append(nn.Linear(in_channels, out_channels, bias=bias)) + else: + module.append(nn.Linear(in_channels, mid_channels, bias=bias)) + if batch_norm: + if mid_channels is None: + module.append(nn.BatchNorm1d(out_channels, momentum=momentum)) + else: + module.append(nn.BatchNorm1d(mid_channels, momentum=momentum)) + module.append(nn.LeakyReLU(leakyrelu_negative_slope)) + if mid_channels is None: + module.append(nn.Dropout(dropout)) + else: + module.append(nn.Linear(mid_channels, out_channels, bias=bias)) + + self.module = nn.Sequential(*module) + + def forward(self, input): + return self.module(input) + + +class BasicBlock(nn.Module): + def __init__( + self, + r: float, + l: float, + kernel_channels: list[int], + in_channels: int, + out_channels: int, + base_width: float = 16.0, + batch_norm: bool = True, + dropout: float = 0.0, + bias: bool = False, + leakyrelu_negative_slope: float = 0.1, + momentum: float = 0.2, + ) -> nn.Module: + super(BasicBlock, self).__init__() + + if in_channels != out_channels: + self.identity = Linear( + in_channels=in_channels, + out_channels=out_channels, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + leakyrelu_negative_slope=leakyrelu_negative_slope, + momentum=momentum, + ) + else: + self.identity = nn.Sequential() + + width = int(out_channels * (base_width / 64.0)) + self.input = MLP( + in_channels=in_channels, + mid_channels=None, + out_channels=width, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + leakyrelu_negative_slope=leakyrelu_negative_slope, + momentum=momentum, + ) + self.conv = CDConv( + r=r, + l=l, + kernel_channels=kernel_channels, + in_channels=width, + out_channels=width, + ) + self.output = Linear( + in_channels=width, + out_channels=out_channels, + batch_norm=batch_norm, + dropout=dropout, + bias=bias, + leakyrelu_negative_slope=leakyrelu_negative_slope, + momentum=momentum, + ) + + def forward(self, x, pos, seq, ori, batch): + identity = self.identity(x) + x = self.input(x) + x = self.conv(x, pos, seq, ori, batch) + out = self.output(x) + identity + return out + + +class MaxPooling(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, pos, seq, ori, batch): + idx = torch.div(seq.squeeze(1), 2, rounding_mode="floor") + idx = torch.cat([idx, idx[-1].view((1,))]) + + idx = (idx[0:-1] != idx[1:]).to(torch.float32) + idx = torch.cumsum(idx, dim=0) - idx + idx = idx.to(torch.int64) + x = scatter_max(src=x, index=idx, dim=0)[0] + pos = scatter_mean(src=pos, index=idx, dim=0) + seq = scatter_max( + src=torch.div(seq, 2, rounding_mode="floor"), index=idx, dim=0 + )[0] + ori = scatter_mean(src=ori, index=idx, dim=0) + ori = torch.nn.functional.normalize(ori, 2, -1) + batch = scatter_max(src=batch, index=idx, dim=0)[0] + + return x, pos, seq, ori, batch + + +class AvgPooling(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, pos, seq, ori, batch): + idx = torch.div(seq.squeeze(1), 2, rounding_mode="floor") + idx = torch.cat([idx, idx[-1].view((1,))]) + + idx = (idx[0:-1] != idx[1:]).to(torch.float32) + idx = torch.cumsum(idx, dim=0) - idx + idx = idx.to(torch.int64) + x = scatter_mean(src=x, index=idx, dim=0) + pos = scatter_mean(src=pos, index=idx, dim=0) + seq = scatter_max( + src=torch.div(seq, 2, rounding_mode="floor"), index=idx, dim=0 + )[0] + ori = scatter_mean(src=ori, index=idx, dim=0) + ori = torch.nn.functional.normalize(ori, 2, -1) + batch = scatter_max(src=batch, index=idx, dim=0)[0] + + return x, pos, seq, ori, batch + + +def kaiming_uniform(tensor, size): + fan = 1 + for i in range(1, len(size)): + fan *= size[i] + gain = math.sqrt(2.0 / (1 + math.sqrt(5) ** 2)) + std = gain / math.sqrt(fan) + bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation + with torch.no_grad(): + return tensor.uniform_(-bound, bound) + + +class WeightNet(nn.Module): + def __init__(self, l: int, kernel_channels: list[int]): + super(WeightNet, self).__init__() + + self.l = l + self.kernel_channels = kernel_channels + + self.Ws = nn.ParameterList() + self.bs = nn.ParameterList() + + for i, channels in enumerate(kernel_channels): + if i == 0: + self.Ws.append(torch.nn.Parameter(torch.empty(l, 3 + 3 + 1, channels))) + self.bs.append(torch.nn.Parameter(torch.empty(l, channels))) + else: + self.Ws.append( + torch.nn.Parameter(torch.empty(l, kernel_channels[i - 1], channels)) + ) + self.bs.append(torch.nn.Parameter(torch.empty(l, channels))) + + self.relu = nn.LeakyReLU(0.2) + + def reset_parameters(self): + for i, channels in enumerate(self.kernel_channels): + if i == 0: + kaiming_uniform(self.Ws[0].data, size=[self.l, 3 + 3 + 1, channels]) + else: + kaiming_uniform( + self.Ws[i].data, + size=[self.l, self.kernel_channels[i - 1], channels], + ) + self.bs[i].data.fill_(0.0) + + def forward(self, input, idx): + for i in range(len(self.kernel_channels)): + W = torch.index_select(self.Ws[i], 0, idx) + b = torch.index_select(self.bs[i], 0, idx) + if i == 0: + weight = self.relu(torch.bmm(input.unsqueeze(1), W).squeeze(1) + b) + else: + weight = self.relu(torch.bmm(weight.unsqueeze(1), W).squeeze(1) + b) + + return weight + + +class CDConv(MessagePassing): + def __init__( + self, + r: float, + l: float, + kernel_channels: list[int], + in_channels: int, + out_channels: int, + add_self_loops: bool = True, + **kwargs, + ): + kwargs.setdefault("aggr", "sum") + super().__init__(**kwargs) + self.r = r + self.l = l + self.kernel_channels = kernel_channels + self.in_channels = in_channels + self.out_channels = out_channels + + self.WeightNet = WeightNet(l, kernel_channels) + self.W = torch.nn.Parameter( + torch.empty(kernel_channels[-1] * in_channels, out_channels) + ) + + self.add_self_loops = add_self_loops + + self.reset_parameters() + + def reset_parameters(self): + self.WeightNet.reset_parameters() + kaiming_uniform( + self.W.data, + size=[self.kernel_channels * self.in_channels, self.out_channels], + ) + + def forward( + self, x: OptTensor, pos: Tensor, seq: Tensor, ori: Tensor, batch: Tensor + ) -> Tensor: + row, col = radius(pos, pos, self.r, batch, batch, max_num_neighbors=9999) + edge_index = torch.stack([col, row], dim=0) + + if self.add_self_loops: + if isinstance(edge_index, Tensor): + edge_index, _ = remove_self_loops(edge_index) + edge_index, _ = add_self_loops( + edge_index, num_nodes=min(pos.size(0), pos.size(0)) + ) + + elif isinstance(edge_index, sparse.SparseTensor): + edge_index = sparse.set_diag(edge_index) + + out = self.propagate( + edge_index, + x=(x, None), + pos=(pos, pos), + seq=(seq, seq), + ori=(ori.reshape((-1, 9)), ori.reshape((-1, 9))), + size=None, + ) + out = torch.matmul(out, self.W) + + return out + + def message( + self, + x_j: Optional[Tensor], + pos_i: Tensor, + pos_j: Tensor, + seq_i: Tensor, + seq_j: Tensor, + ori_i: Tensor, + ori_j: Tensor, + ) -> Tensor: + # orientation + pos = pos_j - pos_i + distance = torch.norm(input=pos, p=2, dim=-1, keepdim=True) + pos /= distance + 1e-9 + + pos = torch.matmul(ori_i.reshape((-1, 3, 3)), pos.unsqueeze(2)).squeeze(2) + ori = torch.sum( + input=ori_i.reshape((-1, 3, 3)) * ori_j.reshape((-1, 3, 3)), + dim=2, + keepdim=False, + ) + + # + normed_distance = distance / self.r + + seq = seq_j - seq_i + s = self.l // 2 + seq = torch.clamp(input=seq, min=-s, max=s) + seq_idx = (seq + s).squeeze(1).to(torch.int64) + normed_length = torch.abs(seq) / s + + # generated kernel weight: PointConv or PSTNet + delta = torch.cat([pos, ori, distance], dim=1) + kernel_weight = self.WeightNet(delta, seq_idx) + + # smooth: IEConv II + smooth = 0.5 - torch.tanh(normed_distance * normed_length * 16.0 - 14.0) * 0.5 + + # convolution + msg = torch.matmul((kernel_weight * smooth).unsqueeze(2), x_j.unsqueeze(1)) + + msg = msg.reshape((-1, msg.size(1) * msg.size(2))) + + return msg + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(r={self.r}, " + f"l={self.l}," + f"kernel_channels={self.kernel_channels}," + f"in_channels={self.in_channels}," + f"out_channels={self.out_channels})" + ) diff --git a/proteinworkshop/tasks/remove_missing_ca.py b/proteinworkshop/tasks/remove_missing_ca.py index 27129947..1307b893 100644 --- a/proteinworkshop/tasks/remove_missing_ca.py +++ b/proteinworkshop/tasks/remove_missing_ca.py @@ -47,6 +47,9 @@ def __call__(self, data): if hasattr(data, "amino_acid_one_hot"): data.amino_acid_one_hot = data.amino_acid_one_hot[mask] + + if hasattr(data, "seq_pos"): + data.seq_pos = data.seq_pos[mask] return data From 0acaaa583029ab5e9128b4b448accd79f5672ed1 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 12:40:18 -0800 Subject: [PATCH 040/106] fix test fixture --- tests/conftest.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 49588c79..a6583e72 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -8,7 +8,6 @@ from hydra import compose, initialize from hydra.core.global_hydra import GlobalHydra from omegaconf import DictConfig, open_dict -from proteinworkshop import constants from proteinworkshop.datasets.utils import create_example_batch @@ -20,7 +19,9 @@ def cfg_train_global() -> DictConfig: :return: A DictConfig object containing a default Hydra configuration for training. """ - with initialize(version_base="1.3", config_path="../proteinworkshop/config/"): + with initialize( + version_base="1.3", config_path="../proteinworkshop/config/" + ): cfg = compose( config_name="train.yaml", return_hydra_config=True, @@ -42,6 +43,7 @@ def cfg_train_global() -> DictConfig: cfg.dataset.datamodule.pin_memory = False cfg.extras.print_config = False cfg.extras.enforce_tags = False + cfg.hydra.job.num = 0 return cfg @@ -53,7 +55,9 @@ def cfg_finetune_global() -> DictConfig: :return: A DictConfig containing a default Hydra configuration for evaluation. """ - with initialize(version_base="1.3", config_path="../proteinworkshop/config/"): + with initialize( + version_base="1.3", config_path="../proteinworkshop/config/" + ): cfg = compose( config_name="finetune.yaml", return_hydra_config=True, @@ -74,6 +78,7 @@ def cfg_finetune_global() -> DictConfig: cfg.extras.print_config = False cfg.extras.enforce_tags = False cfg.logger = None + cfg.hydra.job.num = 0 return cfg From e07cca2b8e81369f37ae785d3a7e1602f4a9d9a5 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 12:40:45 -0800 Subject: [PATCH 041/106] update docs --- docs/source/configs/model.rst | 2 +- docs/source/modules/proteinworkshop.models.rst | 4 ++-- docs/source/quickstart_component/downstream_component.html | 2 +- docs/source/quickstart_component/pretrain_component.html | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/configs/model.rst b/docs/source/configs/model.rst index b47ebb93..46a4560d 100644 --- a/docs/source/configs/model.rst +++ b/docs/source/configs/model.rst @@ -213,7 +213,7 @@ where the weights :math:`\vw` of the tensor product are computed via a learnt ra MACE (Batatia et al., 2022) is a higher order E(3) or SE(3) equivariant GNN originally developed for molecular dynamics simulations. MACE provides an efficient approach to computing high body order equivariant features in the Tensor Field Network framework via Atomic Cluster Expansion: -They first aggregate neighbourhood features analogous to the node update equation for TFN above (the :math:`A` functions in Batatia et al. (2022) (eq.9)) and then take :math:`k-1` repeated self-tensor products of these neighbourhood features. +They first aggregate neighbourhood features analogous to the node update equation for TFN above (the :math:`A` functions in Batatia et al. (2022) (eq.9)) and then take :math:`k-1` repeated self-tensor products of these neighbourhood features. In our formalism, this corresponds to: .. math:: diff --git a/docs/source/modules/proteinworkshop.models.rst b/docs/source/modules/proteinworkshop.models.rst index 07f898b9..563b213b 100644 --- a/docs/source/modules/proteinworkshop.models.rst +++ b/docs/source/modules/proteinworkshop.models.rst @@ -5,7 +5,7 @@ To switch between different encoder architectures, simply change the ``encoder`` .. code-block:: bash workshop train encoder= dataset=cath task=inverse_folding trainer=cpu - # or + # or python proteinworkshop/train.py encoder= dataset=cath task=inverse_folding trainer=cpu # or trainer=gpu Where ```` is given by bracketed name in the listing below. For example, the encoder name for SchNet is ``schnet``. @@ -124,4 +124,4 @@ Decoders .. automodule:: proteinworkshop.models.decoders.mlp_decoder :members: :undoc-members: - :show-inheritance: \ No newline at end of file + :show-inheritance: diff --git a/docs/source/quickstart_component/downstream_component.html b/docs/source/quickstart_component/downstream_component.html index b72f64ec..f2f7720c 100644 --- a/docs/source/quickstart_component/downstream_component.html +++ b/docs/source/quickstart_component/downstream_component.html @@ -184,4 +184,4 @@ $("#datasets").children().get(6).click(); $("#features").children().get(2).click(); $("#auxtask").children().get(0).click(); - \ No newline at end of file + diff --git a/docs/source/quickstart_component/pretrain_component.html b/docs/source/quickstart_component/pretrain_component.html index 8807ad99..a6ce8107 100644 --- a/docs/source/quickstart_component/pretrain_component.html +++ b/docs/source/quickstart_component/pretrain_component.html @@ -152,4 +152,4 @@ $("#features").children().get(2).click(); $("#auxtask").children().get(0).click(); - \ No newline at end of file + From 56a9c5ef3e8cdbbd86938b8b3f5cf20a03bba42b Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 12:44:01 -0800 Subject: [PATCH 042/106] linting --- .../models/graph_encoders/cdconv.py | 20 +++++--- .../models/graph_encoders/layers/cdconv.py | 48 ++++++++++++++----- 2 files changed, 51 insertions(+), 17 deletions(-) diff --git a/proteinworkshop/models/graph_encoders/cdconv.py b/proteinworkshop/models/graph_encoders/cdconv.py index ef0fdea9..d99bfeeb 100644 --- a/proteinworkshop/models/graph_encoders/cdconv.py +++ b/proteinworkshop/models/graph_encoders/cdconv.py @@ -12,7 +12,10 @@ from graphein.protein.tensor.data import ProteinBatch from torch_geometric.data import Batch -from proteinworkshop.models.graph_encoders.layers.cdconv import AvgPooling, BasicBlock +from proteinworkshop.models.graph_encoders.layers.cdconv import ( + AvgPooling, + BasicBlock, +) from proteinworkshop.models.utils import get_aggregation from proteinworkshop.types import EncoderOutput @@ -33,9 +36,11 @@ def __init__( ) -> nn.Module: super().__init__() - geometric_radii = [geometric_radius * i for i in range(1, len(channels) + 1)] - assert ( - len(geometric_radii) == len(channels) + geometric_radii = [ + geometric_radius * i for i in range(1, len(channels) + 1) + ] + assert len(geometric_radii) == len( + channels ), "Model: 'geometric_radii' and 'channels' should have the same number of elements!" self.embedding = torch.nn.LazyLinear(embedding_dim) @@ -93,7 +98,8 @@ def orientation(pos): o = F.normalize(torch.cross(b, n), p=2, dim=1) ori = torch.stack([b, n, o], dim=1) return torch.cat( - [torch.unsqueeze(ori[0], 0), ori, torch.unsqueeze(ori[-1], 0)], axis=0 + [torch.unsqueeze(ori[0], 0), ori, torch.unsqueeze(ori[-1], 0)], + axis=0, ) def forward(self, data: Union[ProteinBatch, Batch]) -> EncoderOutput: @@ -111,7 +117,9 @@ def forward(self, data: Union[ProteinBatch, Batch]) -> EncoderOutput: if i == len(self.layers) - 1: x = self.readout(x, batch) elif i % 2 == 1: - x, pos, seq, ori, batch = self.local_mean_pool(x, pos, seq, ori, batch) + x, pos, seq, ori, batch = self.local_mean_pool( + x, pos, seq, ori, batch + ) return EncoderOutput({"graph_embedding": x, "node_embedding": None}) diff --git a/proteinworkshop/models/graph_encoders/layers/cdconv.py b/proteinworkshop/models/graph_encoders/layers/cdconv.py index d957407b..a36c4f9a 100644 --- a/proteinworkshop/models/graph_encoders/layers/cdconv.py +++ b/proteinworkshop/models/graph_encoders/layers/cdconv.py @@ -200,7 +200,9 @@ def kaiming_uniform(tensor, size): fan *= size[i] gain = math.sqrt(2.0 / (1 + math.sqrt(5) ** 2)) std = gain / math.sqrt(fan) - bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation + bound = ( + math.sqrt(3.0) * std + ) # Calculate uniform bounds from standard deviation with torch.no_grad(): return tensor.uniform_(-bound, bound) @@ -217,11 +219,15 @@ def __init__(self, l: int, kernel_channels: list[int]): for i, channels in enumerate(kernel_channels): if i == 0: - self.Ws.append(torch.nn.Parameter(torch.empty(l, 3 + 3 + 1, channels))) + self.Ws.append( + torch.nn.Parameter(torch.empty(l, 3 + 3 + 1, channels)) + ) self.bs.append(torch.nn.Parameter(torch.empty(l, channels))) else: self.Ws.append( - torch.nn.Parameter(torch.empty(l, kernel_channels[i - 1], channels)) + torch.nn.Parameter( + torch.empty(l, kernel_channels[i - 1], channels) + ) ) self.bs.append(torch.nn.Parameter(torch.empty(l, channels))) @@ -230,7 +236,9 @@ def __init__(self, l: int, kernel_channels: list[int]): def reset_parameters(self): for i, channels in enumerate(self.kernel_channels): if i == 0: - kaiming_uniform(self.Ws[0].data, size=[self.l, 3 + 3 + 1, channels]) + kaiming_uniform( + self.Ws[0].data, size=[self.l, 3 + 3 + 1, channels] + ) else: kaiming_uniform( self.Ws[i].data, @@ -243,9 +251,13 @@ def forward(self, input, idx): W = torch.index_select(self.Ws[i], 0, idx) b = torch.index_select(self.bs[i], 0, idx) if i == 0: - weight = self.relu(torch.bmm(input.unsqueeze(1), W).squeeze(1) + b) + weight = self.relu( + torch.bmm(input.unsqueeze(1), W).squeeze(1) + b + ) else: - weight = self.relu(torch.bmm(weight.unsqueeze(1), W).squeeze(1) + b) + weight = self.relu( + torch.bmm(weight.unsqueeze(1), W).squeeze(1) + b + ) return weight @@ -286,9 +298,16 @@ def reset_parameters(self): ) def forward( - self, x: OptTensor, pos: Tensor, seq: Tensor, ori: Tensor, batch: Tensor + self, + x: OptTensor, + pos: Tensor, + seq: Tensor, + ori: Tensor, + batch: Tensor, ) -> Tensor: - row, col = radius(pos, pos, self.r, batch, batch, max_num_neighbors=9999) + row, col = radius( + pos, pos, self.r, batch, batch, max_num_neighbors=9999 + ) edge_index = torch.stack([col, row], dim=0) if self.add_self_loops: @@ -328,7 +347,9 @@ def message( distance = torch.norm(input=pos, p=2, dim=-1, keepdim=True) pos /= distance + 1e-9 - pos = torch.matmul(ori_i.reshape((-1, 3, 3)), pos.unsqueeze(2)).squeeze(2) + pos = torch.matmul( + ori_i.reshape((-1, 3, 3)), pos.unsqueeze(2) + ).squeeze(2) ori = torch.sum( input=ori_i.reshape((-1, 3, 3)) * ori_j.reshape((-1, 3, 3)), dim=2, @@ -349,10 +370,15 @@ def message( kernel_weight = self.WeightNet(delta, seq_idx) # smooth: IEConv II - smooth = 0.5 - torch.tanh(normed_distance * normed_length * 16.0 - 14.0) * 0.5 + smooth = ( + 0.5 + - torch.tanh(normed_distance * normed_length * 16.0 - 14.0) * 0.5 + ) # convolution - msg = torch.matmul((kernel_weight * smooth).unsqueeze(2), x_j.unsqueeze(1)) + msg = torch.matmul( + (kernel_weight * smooth).unsqueeze(2), x_j.unsqueeze(1) + ) msg = msg.reshape((-1, msg.size(1) * msg.size(2))) From 5549a34d86236476b7744aed5034a7d82f094494 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 12:44:57 -0800 Subject: [PATCH 043/106] linting --- proteinworkshop/models/base.py | 52 +++++++++++++++++++++++++--------- 1 file changed, 39 insertions(+), 13 deletions(-) diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index 04905029..a0056278 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -37,7 +37,9 @@ def forward(self, batch: Batch) -> torch.Tensor: ... @abc.abstractmethod - def training_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: + def training_step( + self, batch: Batch, batch_idx: torch.Tensor + ) -> torch.Tensor: """Implement training step. :param batch: Mini-batch of data. @@ -50,7 +52,9 @@ def training_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: ... @abc.abstractmethod - def validation_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: + def validation_step( + self, batch: Batch, batch_idx: torch.Tensor + ) -> torch.Tensor: """Implement validation step. :param batch: Mini-batch of data. @@ -108,14 +112,18 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: for output in self.config.task.supervise_on: if output == "node_label": labels["node_label"] = batch.node_y - if isinstance(self.losses["node_label"], torch.nn.BCEWithLogitsLoss): + if isinstance( + self.losses["node_label"], torch.nn.BCEWithLogitsLoss + ): labels["node_label"] = F.one_hot( labels["node_label"], num_classes=self.config.dataset.num_classes, ).float() elif output == "graph_label": labels["graph_label"] = batch.graph_y - if isinstance(self.losses["graph_label"], torch.nn.BCEWithLogitsLoss): + if isinstance( + self.losses["graph_label"], torch.nn.BCEWithLogitsLoss + ): labels["graph_label"] = F.one_hot( labels["graph_label"], num_classes=self.config.dataset.num_classes, @@ -165,7 +173,9 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: return Label(labels) @beartype - def compute_loss(self, y_hat: ModelOutput, y: Label) -> Dict[str, torch.Tensor]: + def compute_loss( + self, y_hat: ModelOutput, y: Label + ) -> Dict[str, torch.Tensor]: """ Compute loss by iterating over all outputs. @@ -202,9 +212,13 @@ def configure_optimizers(self): # sourcery skip: extract-method if self.config.get("scheduler"): logger.info("Instantiating scheduler...") - scheduler = hydra.utils.instantiate(self.config.scheduler, optimiser) + scheduler = hydra.utils.instantiate( + self.config.scheduler, optimiser + ) scheduler = OmegaConf.to_container(scheduler) - scheduler["scheduler"] = scheduler["scheduler"](optimizer=optimiser) + scheduler["scheduler"] = scheduler["scheduler"]( + optimizer=optimiser + ) optimiser_config = { "optimizer": optimiser, "lr_scheduler": scheduler, @@ -235,7 +249,9 @@ def _build_output_decoders(self) -> nn.ModuleDict: decoders[output_head] = hydra.utils.instantiate(cfg) return decoders - def configure_losses(self, loss_dict: Dict[str, str]) -> Dict[str, Callable]: + def configure_losses( + self, loss_dict: Dict[str, str] + ) -> Dict[str, Callable]: """ Configures losses from config. Returns a dictionary of losses mapping each output name to its respective loss function. @@ -330,7 +346,9 @@ def configure_metrics(self): setattr(self, "metric_names", metric_names) @beartype - def log_metrics(self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch): + def log_metrics( + self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch + ): """ Logs metrics to logger. @@ -410,7 +428,9 @@ def __init__(self, cfg: DictConfig) -> None: logger.info(self.featuriser) logger.info("Instantiating task transform...") - self.task_transform = hydra.utils.instantiate(cfg.get("task.transform")) + self.task_transform = hydra.utils.instantiate( + cfg.get("task.transform") + ) logger.info(self.task_transform) self.save_hyperparameters() @@ -432,7 +452,9 @@ def _create_example_batch(self) -> ProteinBatch: ] for p in proteins: setattr(p, "x", torch.zeros(p.coords.shape[0])) - setattr(p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1)) + setattr( + p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1) + ) batch = ProteinBatch.from_data_list(proteins) return self.featurise(batch) @@ -471,12 +493,16 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: emb_type = self.decoder[ output_head ].input # node_embedding or graph_embedding - output[output_head] = self.decoder[output_head](output[emb_type]) + output[output_head] = self.decoder[output_head]( + output[emb_type] + ) return self.compute_output(output, batch) @beartype - def transform_encoder_output(self, output: EncoderOutput, batch) -> EncoderOutput: + def transform_encoder_output( + self, output: EncoderOutput, batch + ) -> EncoderOutput: """ Modifies graph encoder output. From d9c4ca9921e59be815896864968c351ceae30589 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 15:37:59 -0800 Subject: [PATCH 044/106] add full model config for finetuning --- proteinworkshop/config/finetune/full_model.yaml | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 proteinworkshop/config/finetune/full_model.yaml diff --git a/proteinworkshop/config/finetune/full_model.yaml b/proteinworkshop/config/finetune/full_model.yaml new file mode 100644 index 00000000..a8814112 --- /dev/null +++ b/proteinworkshop/config/finetune/full_model.yaml @@ -0,0 +1,8 @@ +# Controls the default configuration for the finetuning process. +encoder: + load_weights: True # Whether or not to load the weights of the encoder from the pretrained model. + freeze: False # Whether or not to freeze the weights of the encoder. + +decoder: + load_weights: True # Whether or not to load the weights of the decoder(s) from the pretrained model. + freeze: False # Whether or not to freeze the weights of the decoder(s). From fc7521cf6e37bee240e2df7d87ac99eafa6546a1 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 15:39:00 -0800 Subject: [PATCH 045/106] linting --- proteinworkshop/datasets/base.py | 31 +++++++++++++++++++++++-------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index b84754fb..2101ba75 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -359,7 +359,9 @@ def download(self): pdb for pdb in self.pdb_codes if not ( - os.path.exists(Path(self.raw_dir) / f"{pdb}.{self.format}") + os.path.exists( + Path(self.raw_dir) / f"{pdb}.{self.format}" + ) or os.path.exists( Path(self.raw_dir) / f"{pdb}.{self.format}.gz" ) @@ -369,9 +371,13 @@ def download(self): to_download = list(set(to_download)) logger.info(f"Downloading {len(to_download)} structures") file_format = ( - self.format[:-3] if self.format.endswith(".gz") else self.format + self.format[:-3] + if self.format.endswith(".gz") + else self.format + ) + download_pdb_multiprocessing( + to_download, self.raw_dir, format=file_format ) - download_pdb_multiprocessing(to_download, self.raw_dir, format=file_format) def len(self) -> int: """Return length of the dataset.""" @@ -418,7 +424,8 @@ def processed_file_names(self) -> Union[str, List[str], Tuple]: return [f"{name}.pt" for name in self.out_names] if self.chains is not None: return [ - f"{pdb}_{chain}.pt" for pdb, chain in zip(self.pdb_codes, self.chains) + f"{pdb}_{chain}.pt" + for pdb, chain in zip(self.pdb_codes, self.chains) ] else: return [f"{pdb}.pt" for pdb in self.pdb_codes] @@ -441,11 +448,17 @@ def process(self): index_pdb_tuples = [ (i, pdb) for i, pdb in enumerate(self.pdb_codes) - if not os.path.exists(Path(self.processed_dir) / f"{pdb}.pt") + if not os.path.exists( + Path(self.processed_dir) / f"{pdb}.pt" + ) ] - logger.info(f"Processing {len(index_pdb_tuples)} unprocessed structures") + logger.info( + f"Processing {len(index_pdb_tuples)} unprocessed structures" + ) else: - index_pdb_tuples = [(i, pdb) for i, pdb in enumerate(self.pdb_codes)] + index_pdb_tuples = [ + (i, pdb) for i, pdb in enumerate(self.pdb_codes) + ] raw_dir = Path(self.raw_dir) for index_pdb_tuple in tqdm(index_pdb_tuples): @@ -479,7 +492,9 @@ def process(self): fname = self.out_names[i] + ".pt" else: fname = ( - f"{pdb}.pt" if self.chains is None else f"{pdb}_{self.chains[i]}.pt" + f"{pdb}.pt" + if self.chains is None + else f"{pdb}_{self.chains[i]}.pt" ) graph.id = fname.split(".")[0] From 5315f7e85e9872cde7fc7f5595faee969302e5c0 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 12 Nov 2023 17:05:32 -0800 Subject: [PATCH 046/106] fix device request logging --- proteinworkshop/configs/config.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/proteinworkshop/configs/config.py b/proteinworkshop/configs/config.py index 716f2e42..99e4767c 100644 --- a/proteinworkshop/configs/config.py +++ b/proteinworkshop/configs/config.py @@ -245,18 +245,20 @@ def validate_cuda(cfg: DictConfig) -> DictConfig: # if cfg.trainer.devices <= 1: # cfg.trainer.strategy = None - logger.debug(f"Requested GPUs: {cfg.get('trainer.devices')}.") - if isinstance(cfg.get("trainer.devices"), int): + logger.debug(f"Requested GPUs: {cfg.trainer.get('devices')}.") + if isinstance(cfg.trainer.get("devices"), int): cfg.trainer.devices = min( torch.cuda.device_count(), cfg.trainer.devices ) logger.debug(f"GPU count set to: {cfg.trainer.devices}") requesting_multiple_device_indices = ( - isinstance(cfg.get("trainer.devices"), list) and len(cfg.get("trainer.devices")) > 1 + isinstance(cfg.trainer.get("devices"), list) + and len(cfg.trainer.get("devices")) > 1 ) requesting_multiple_devices = ( - isinstance(cfg.get("trainer.devices"), int) and cfg.get("trainer.devices") > 1 + isinstance(cfg.trainer.get("devices"), int) + and cfg.trainer.get("devices") > 1 ) if ( requesting_multiple_device_indices or requesting_multiple_devices From 6ba482bc0573aa67c4a667b6a168aaad3b97c2a8 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 13 Nov 2023 04:51:48 -0800 Subject: [PATCH 047/106] add multihot label encoder --- .../transforms/multihot_label_encoding.yaml | 3 +++ .../tasks/multihot_label_encoding.py | 24 +++++++++++++++++++ 2 files changed, 27 insertions(+) create mode 100644 proteinworkshop/config/transforms/multihot_label_encoding.yaml create mode 100644 proteinworkshop/tasks/multihot_label_encoding.py diff --git a/proteinworkshop/config/transforms/multihot_label_encoding.yaml b/proteinworkshop/config/transforms/multihot_label_encoding.yaml new file mode 100644 index 00000000..aa04ee84 --- /dev/null +++ b/proteinworkshop/config/transforms/multihot_label_encoding.yaml @@ -0,0 +1,3 @@ +multihot_label_encoding: + _target_: proteinworkshop.tasks.multihot_label_encoding.MultiHotLabelEncoding + num_classes : ${dataset.num_classes} diff --git a/proteinworkshop/tasks/multihot_label_encoding.py b/proteinworkshop/tasks/multihot_label_encoding.py new file mode 100644 index 00000000..113ad967 --- /dev/null +++ b/proteinworkshop/tasks/multihot_label_encoding.py @@ -0,0 +1,24 @@ +from typing import Union + +import torch +from graphein.protein.tensor.data import Protein +from torch_geometric import transforms as T +from torch_geometric.data import Data + + +class MultiHotLabelEncoding(T.BaseTransform): + """ + Transform to multihot encode labels for multilabel classification. + + :param num_classes: Number of classes to encode. + :type num_classes: int + """ + + def __init__(self, num_classes: int) -> None: + self.num_classes = num_classes + + def __call__(self, data: Union[Protein, Data]) -> Union[Protein, Data]: + labels = torch.zeros((1, self.num_classes)) + labels[:, data.graph_y] = 1 + data.graph_y = labels + return data From 33caa0508f7e9a403aa4e9024ef25993724e0e2e Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:04:40 -0800 Subject: [PATCH 048/106] speed up positional encoding computation --- proteinworkshop/features/factory.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/proteinworkshop/features/factory.py b/proteinworkshop/features/factory.py index 0a97306c..63ae4300 100644 --- a/proteinworkshop/features/factory.py +++ b/proteinworkshop/features/factory.py @@ -1,4 +1,4 @@ -from typing import List, Union, Literal +from typing import List, Literal, Union import torch import torch.nn as nn @@ -8,7 +8,6 @@ from loguru import logger from torch_geometric.data import Batch from torch_geometric.nn.encoding import PositionalEncoding -from torch_geometric.utils import unbatch from proteinworkshop.features.edge_features import ( compute_scalar_edge_features, @@ -85,14 +84,7 @@ def forward( if self.scalar_node_features: concat_nf = False if hasattr(self, "positional_encoding"): - lin_range = torch.arange( - 0, batch.coords.shape[0], device=batch.coords.device - ) - idx = batch._slice_dict["coords"] - slices = unbatch(lin_range, batch.batch) - slices = [s - idx[i] for i, s in enumerate(slices)] - slices = torch.cat(slices, dim=0) - batch.x = self.positional_encoding(slices) + batch.x = self.positional_encoding(batch.seq_pos) # This is necessary to concat node features with the positional encoding concat_nf = True if self.scalar_node_features != ["sequence_positional_encoding"]: From 6f275161fc4e3a6612e15c64b2eed23b9f9fe3a7 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:05:36 -0800 Subject: [PATCH 049/106] fix device logging --- proteinworkshop/configs/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/configs/config.py b/proteinworkshop/configs/config.py index 99e4767c..1f60c8fc 100644 --- a/proteinworkshop/configs/config.py +++ b/proteinworkshop/configs/config.py @@ -244,7 +244,7 @@ def validate_cuda(cfg: DictConfig) -> DictConfig: # Make sure cuda config is correct # if cfg.trainer.devices <= 1: # cfg.trainer.strategy = None - + logger.debug(f"CUDA available: {torch.cuda.is_available()}") logger.debug(f"Requested GPUs: {cfg.trainer.get('devices')}.") if isinstance(cfg.trainer.get("devices"), int): cfg.trainer.devices = min( From ca6635977abf77aaafb732d5d992fecb38e5b907 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:07:03 -0800 Subject: [PATCH 050/106] add num_classes to GO datasets --- proteinworkshop/config/dataset/go-bp.yaml | 2 +- proteinworkshop/config/dataset/go-cc.yaml | 2 +- proteinworkshop/config/dataset/go-mf.yaml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/proteinworkshop/config/dataset/go-bp.yaml b/proteinworkshop/config/dataset/go-bp.yaml index 447960df..91db6159 100644 --- a/proteinworkshop/config/dataset/go-bp.yaml +++ b/proteinworkshop/config/dataset/go-bp.yaml @@ -11,4 +11,4 @@ datamodule: split: "BP" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files -num_classes: 2 # Number of classes +num_classes: 1943 # Number of classes diff --git a/proteinworkshop/config/dataset/go-cc.yaml b/proteinworkshop/config/dataset/go-cc.yaml index d60e99ae..0bbaaf69 100644 --- a/proteinworkshop/config/dataset/go-cc.yaml +++ b/proteinworkshop/config/dataset/go-cc.yaml @@ -11,4 +11,4 @@ datamodule: split: "CC" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files -num_classes: 2 # Number of classes +num_classes: 320 # Number of classes diff --git a/proteinworkshop/config/dataset/go-mf.yaml b/proteinworkshop/config/dataset/go-mf.yaml index 34b19835..f4f4c2e4 100644 --- a/proteinworkshop/config/dataset/go-mf.yaml +++ b/proteinworkshop/config/dataset/go-mf.yaml @@ -11,4 +11,4 @@ datamodule: split: "MF" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files -num_classes: 2 # Number of classes +num_classes: 489 # Number of classes From 103b613c83c55ec6ecf374c967ccf1eefe45e642 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:07:37 -0800 Subject: [PATCH 051/106] lint cdconv config --- proteinworkshop/config/encoder/cdconv.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/config/encoder/cdconv.yaml b/proteinworkshop/config/encoder/cdconv.yaml index 0a638e4a..00eb21e4 100644 --- a/proteinworkshop/config/encoder/cdconv.yaml +++ b/proteinworkshop/config/encoder/cdconv.yaml @@ -8,4 +8,4 @@ embedding_dim: 16 batch_norm: True dropout: 0.2 bias: False -readout: "sum" \ No newline at end of file +readout: "sum" From 1e6090d3fda982c3184f3dacfac9164eb8fe9482 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:08:38 -0800 Subject: [PATCH 052/106] add multilabel classification task configs --- .../task/binary_graph_classification.yaml | 4 +- .../task/multilabel_graph_classification.yaml | 45 +++++++++++++++++++ .../task/multilabel_node_classification.yaml | 42 +++++++++++++++++ 3 files changed, 89 insertions(+), 2 deletions(-) create mode 100644 proteinworkshop/config/task/multilabel_graph_classification.yaml create mode 100644 proteinworkshop/config/task/multilabel_node_classification.yaml diff --git a/proteinworkshop/config/task/binary_graph_classification.yaml b/proteinworkshop/config/task/binary_graph_classification.yaml index 0525f0ba..3cda0b53 100644 --- a/proteinworkshop/config/task/binary_graph_classification.yaml +++ b/proteinworkshop/config/task/binary_graph_classification.yaml @@ -13,10 +13,10 @@ defaults: callbacks: early_stopping: - monitor: val/graph_label/accuracy + monitor: val/graph_label/auprc mode: "max" model_checkpoint: - monitor: val/graph_label/accuracy + monitor: val/graph_label/auprc mode: "max" task: diff --git a/proteinworkshop/config/task/multilabel_graph_classification.yaml b/proteinworkshop/config/task/multilabel_graph_classification.yaml new file mode 100644 index 00000000..7f47d180 --- /dev/null +++ b/proteinworkshop/config/task/multilabel_graph_classification.yaml @@ -0,0 +1,45 @@ +# @package _global_ + +defaults: + - override /metrics: + - accuracy + - f1_score + - f1_max + - rocauc + - auprc + - override /decoder: + - graph_label + - override /transforms: + - remove_missing_ca + - multihot_label_encoding + +metrics: + accuracy: + num_labels: ${dataset.num_classes} + f1_score: + num_labels: ${dataset.num_classes} + rocauc: + num_labels: ${dataset.num_classes} + + +callbacks: + early_stopping: + monitor: val/graph_label/accuracy + mode: "max" + model_checkpoint: + monitor: val/graph_label/accuracy + mode: "max" + +task: + task: "classification" + classification_type: "multilabel" + metric_average: "micro" + + losses: + graph_label: bce + label_smoothing: 0.0 + + output: + - "graph_label" + supervise_on: + - "graph_label" diff --git a/proteinworkshop/config/task/multilabel_node_classification.yaml b/proteinworkshop/config/task/multilabel_node_classification.yaml new file mode 100644 index 00000000..75872cad --- /dev/null +++ b/proteinworkshop/config/task/multilabel_node_classification.yaml @@ -0,0 +1,42 @@ +# @package _global_ + +defaults: + - override /metrics: + - accuracy + - f1_score + - f1_max + - auprc + - rocauc + - override /decoder: + - node_label + +callbacks: + early_stopping: + monitor: val/node_label/accuracy + mode: "max" + model_checkpoint: + monitor: val/node_label/accuracy + mode: "max" + +metrics: + accuracy: + num_labels: ${dataset.num_classes} + f1_score: + num_labels: ${dataset.num_classes} + rocauc: + num_labels: ${dataset.num_classes} + + +task: + task: "classification" + classification_type: "multilabel" + metric_average: "micro" + + losses: + node_label: bce + label_smoothing: 0.0 + + output: + - "node_label" + supervise_on: + - "node_label" From 9270e44ba449d85d8c6ce67c45aeb75c8c11a096 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:09:31 -0800 Subject: [PATCH 053/106] refactor f1_max for multilabel classif. --- proteinworkshop/metrics/f1_max.py | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/proteinworkshop/metrics/f1_max.py b/proteinworkshop/metrics/f1_max.py index f7f4f665..01c78150 100644 --- a/proteinworkshop/metrics/f1_max.py +++ b/proteinworkshop/metrics/f1_max.py @@ -2,8 +2,11 @@ from typing import Any import torch +from torch import Tensor import torch.nn.functional as F from torchmetrics import Metric +import beartype +from jaxtyping import Float class F1Max(Metric): @@ -38,9 +41,16 @@ def compute(self) -> Any: :return: F1Max metric value :rtype: Any """ - return self.f1_max(torch.cat(self.preds), torch.cat(self.targets)) + if self.preds[0].ndim == 2: + # return self.f1_max(torch.stack(self.preds), torch.stack(self.targets)) + return self.f1_max(torch.cat(self.preds), torch.cat(self.targets)) + return self.f1_max(torch.cat(self.preds, dim=0), torch.cat(self.targets, dim=0)) - def f1_max(self, pred, target): + def f1_max( + self, + pred: Float[Tensor, "batch classes"], + target: Float[Tensor, "batch classes"], + ): """ F1 score with the optimal threshold. This function first enumerates all possible thresholds for deciding @@ -52,9 +62,11 @@ def f1_max(self, pred, target): target (Tensor): binary targets of shape :math:`(B, N)` """ pred = torch.softmax(pred, dim=1) - target = F.one_hot(target.long(), num_classes=pred.shape[1]).float() + + if target.ndim == 1: + target = F.one_hot(target.long(), num_classes=pred.shape[1]).float() order = pred.argsort(descending=True, dim=1) - target = target.gather(1, order) + target = target.gather(1, order).int() precision = target.cumsum(1) / torch.ones_like(target).cumsum(1) recall = target.cumsum(1) / (target.sum(1, keepdim=True) + 1e-10) is_start = torch.zeros_like(target).bool() @@ -82,10 +94,5 @@ def f1_max(self, pred, target): is_start, torch.zeros_like(recall), recall[all_order - 1] ) all_recall = all_recall.cumsum(0) / pred.shape[0] - all_f1 = ( - 2 - * all_precision - * all_recall - / (all_precision + all_recall + 1e-10) - ) + all_f1 = 2 * all_precision * all_recall / (all_precision + all_recall + 1e-10) return all_f1.max() From 4be174a996f55c3dcb294095fc6c82c48d4f9342 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:11:16 -0800 Subject: [PATCH 054/106] add auprc to classification metrics, linting --- proteinworkshop/models/base.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index a0056278..b21fb26c 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -121,8 +121,11 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: ).float() elif output == "graph_label": labels["graph_label"] = batch.graph_y - if isinstance( - self.losses["graph_label"], torch.nn.BCEWithLogitsLoss + if ( + isinstance( + self.losses["graph_label"], torch.nn.BCEWithLogitsLoss + ) + and batch.graph_y.ndim == 1 ): labels["graph_label"] = F.one_hot( labels["graph_label"], @@ -304,6 +307,7 @@ def configure_metrics(self): "auprc", "accuracy", "f1_max", + "rocauc", } REGRESSION_METRICS: Set[str] = {"mse", "mae", "r2", "rmse"} CONTINUOUS_OUTPUTS: Set[str] = { @@ -321,6 +325,13 @@ def configure_metrics(self): for stage in {"train", "val", "test"}: metric = hydra.utils.instantiate(metric_conf) if output == "residue_type": + if metric in { + "auprc", + "f1_score", + "f1_max", + "roc_auc", + }: + continue metric.num_classes = 23 metric.task = "multiclass" @@ -391,7 +402,7 @@ def log_metrics( except (ValueError, RuntimeError): continue - self.log_dict(log_dict) + self.log_dict(log_dict, prog_bar=True) class BenchMarkModel(BaseModel): From b32afdac34f8252230feb4b5f3aa971cb2561b93 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 15 Nov 2023 16:11:53 -0800 Subject: [PATCH 055/106] linting --- proteinworkshop/metrics/f1_max.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/proteinworkshop/metrics/f1_max.py b/proteinworkshop/metrics/f1_max.py index 01c78150..d9f318ce 100644 --- a/proteinworkshop/metrics/f1_max.py +++ b/proteinworkshop/metrics/f1_max.py @@ -2,11 +2,10 @@ from typing import Any import torch -from torch import Tensor import torch.nn.functional as F -from torchmetrics import Metric -import beartype from jaxtyping import Float +from torch import Tensor +from torchmetrics import Metric class F1Max(Metric): @@ -44,7 +43,9 @@ def compute(self) -> Any: if self.preds[0].ndim == 2: # return self.f1_max(torch.stack(self.preds), torch.stack(self.targets)) return self.f1_max(torch.cat(self.preds), torch.cat(self.targets)) - return self.f1_max(torch.cat(self.preds, dim=0), torch.cat(self.targets, dim=0)) + return self.f1_max( + torch.cat(self.preds, dim=0), torch.cat(self.targets, dim=0) + ) def f1_max( self, @@ -64,7 +65,9 @@ def f1_max( pred = torch.softmax(pred, dim=1) if target.ndim == 1: - target = F.one_hot(target.long(), num_classes=pred.shape[1]).float() + target = F.one_hot( + target.long(), num_classes=pred.shape[1] + ).float() order = pred.argsort(descending=True, dim=1) target = target.gather(1, order).int() precision = target.cumsum(1) / torch.ones_like(target).cumsum(1) @@ -94,5 +97,10 @@ def f1_max( is_start, torch.zeros_like(recall), recall[all_order - 1] ) all_recall = all_recall.cumsum(0) / pred.shape[0] - all_f1 = 2 * all_precision * all_recall / (all_precision + all_recall + 1e-10) + all_f1 = ( + 2 + * all_precision + * all_recall + / (all_precision + all_recall + 1e-10) + ) return all_f1.max() From a1b3a74d407d4d60377d20c24dda135ac6587050 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 16 Nov 2023 05:18:22 -0800 Subject: [PATCH 056/106] set in_memory=True as default for GO datasets --- proteinworkshop/config/dataset/go-bp.yaml | 1 + proteinworkshop/config/dataset/go-cc.yaml | 1 + proteinworkshop/config/dataset/go-mf.yaml | 1 + 3 files changed, 3 insertions(+) diff --git a/proteinworkshop/config/dataset/go-bp.yaml b/proteinworkshop/config/dataset/go-bp.yaml index 91db6159..9d5d8a8a 100644 --- a/proteinworkshop/config/dataset/go-bp.yaml +++ b/proteinworkshop/config/dataset/go-bp.yaml @@ -11,4 +11,5 @@ datamodule: split: "BP" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True num_classes: 1943 # Number of classes diff --git a/proteinworkshop/config/dataset/go-cc.yaml b/proteinworkshop/config/dataset/go-cc.yaml index 0bbaaf69..4f787ee5 100644 --- a/proteinworkshop/config/dataset/go-cc.yaml +++ b/proteinworkshop/config/dataset/go-cc.yaml @@ -11,4 +11,5 @@ datamodule: split: "CC" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True num_classes: 320 # Number of classes diff --git a/proteinworkshop/config/dataset/go-mf.yaml b/proteinworkshop/config/dataset/go-mf.yaml index f4f4c2e4..2b333e16 100644 --- a/proteinworkshop/config/dataset/go-mf.yaml +++ b/proteinworkshop/config/dataset/go-mf.yaml @@ -11,4 +11,5 @@ datamodule: split: "MF" # Split of the dataset to use (`BP`, `MF`, `CC`) transforms: ${transforms} # Transforms to apply to dataset examples overwrite: False # Whether to overwrite existing dataset files + in_memory: True num_classes: 489 # Number of classes From 06364b251aa2f3536f0261175de0497c6ae61429 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 16 Nov 2023 17:28:37 -0800 Subject: [PATCH 057/106] clean up EC dataset --- proteinworkshop/datasets/ec_reaction.py | 32 ++++++++++++------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/proteinworkshop/datasets/ec_reaction.py b/proteinworkshop/datasets/ec_reaction.py index bac16578..c4cd2e6e 100644 --- a/proteinworkshop/datasets/ec_reaction.py +++ b/proteinworkshop/datasets/ec_reaction.py @@ -8,7 +8,6 @@ import wget from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger -from torch_geometric.data import Dataset from proteinworkshop.datasets.base import ProteinDataModule, ProteinDataset @@ -93,7 +92,9 @@ def parse_labels(self) -> Dict[str, str]: ) return dict(class_map.values) - def _get_dataset(self, split: str) -> Dataset: + def _get_dataset( + self, split: Literal["training", "validation", "testing"] + ) -> ProteinDataset: df = self.parse_dataset(split) return ProteinDataset( root=str(self.data_dir), @@ -107,20 +108,18 @@ def _get_dataset(self, split: str) -> Dataset: in_memory=self.in_memory, ) - def train_dataset(self) -> Dataset: + def train_dataset(self) -> ProteinDataset: return self._get_dataset("training") - def val_dataset(self) -> Dataset: + def val_dataset(self) -> ProteinDataset: return self._get_dataset("validation") - def test_dataset(self) -> Dataset: + def test_dataset(self) -> ProteinDataset: return self._get_dataset("testing") def train_dataloader(self) -> ProteinDataLoader: - if not hasattr(self, "train_ds"): - self.train_ds = self.train_dataset() return ProteinDataLoader( - self.train_ds, + self.train_dataset(), batch_size=self.batch_size, shuffle=True, pin_memory=self.pin_memory, @@ -128,10 +127,8 @@ def train_dataloader(self) -> ProteinDataLoader: ) def val_dataloader(self) -> ProteinDataLoader: - if not hasattr(self, "val_ds"): - self.val_ds = self.val_dataset() return ProteinDataLoader( - self.val_ds, + self.val_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, @@ -139,10 +136,8 @@ def val_dataloader(self) -> ProteinDataLoader: ) def test_dataloader(self) -> ProteinDataLoader: - if not hasattr(self, "test_ds"): - self.test_ds = self.test_dataset() return ProteinDataLoader( - self.test_ds, + self.test_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, @@ -210,13 +205,16 @@ def parse_dataset(self, split: str) -> pd.DataFrame: from proteinworkshop import constants cfg = omegaconf.OmegaConf.load( - constants.SRC_PATH / "config" / "dataset" / "ec.yaml" + constants.SRC_PATH / "config" / "dataset" / "ec_reaction.yaml" ) - cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "EnzymeCommission" # type: ignore + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "ECReaction" # type: ignore cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore + cfg.datamodule.transforms = [] ds = hydra.utils.instantiate(cfg) print(ds) dl = ds["datamodule"].val_dataloader() - print(ds["datamodule"].val_ds[1]) + for batch in dl: + print(batch) + dl = ds["datamodule"].test_dataloader() for batch in dl: print(batch) From 91cbc2fae19025aed13681785942caf652fc6293 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 16 Nov 2023 17:47:33 -0800 Subject: [PATCH 058/106] clean up GO dataset --- proteinworkshop/datasets/go.py | 42 +++++++++++++++------------------- 1 file changed, 19 insertions(+), 23 deletions(-) diff --git a/proteinworkshop/datasets/go.py b/proteinworkshop/datasets/go.py index c75dc6fb..ffa64e8c 100644 --- a/proteinworkshop/datasets/go.py +++ b/proteinworkshop/datasets/go.py @@ -8,10 +8,9 @@ import pandas as pd import torch import wget +from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger as log from sklearn.preprocessing import LabelEncoder -from torch_geometric.data import Dataset -from torch_geometric.loader import DataLoader from tqdm import tqdm from proteinworkshop.datasets.base import ProteinDataModule, ProteinDataset @@ -129,7 +128,9 @@ def parse_labels(self) -> Dict[str, torch.Tensor]: log.info(f"Encoded {len(labels)} labels for task {self.split}.") return labels - def _get_dataset(self, split: str) -> Dataset: + def _get_dataset( + self, split: Literal["training", "validation", "testing"] + ) -> ProteinDataset: df = self.parse_dataset(split) log.info("Initialising Graphein dataset...") return ProteinDataset( @@ -144,42 +145,36 @@ def _get_dataset(self, split: str) -> Dataset: in_memory=self.in_memory, ) - def train_dataset(self) -> Dataset: + def train_dataset(self) -> ProteinDataset: return self._get_dataset("training") - def val_dataset(self) -> Dataset: + def val_dataset(self) -> ProteinDataset: return self._get_dataset("validation") - def test_dataset(self) -> Dataset: + def test_dataset(self) -> ProteinDataset: return self._get_dataset("testing") - def train_dataloader(self) -> DataLoader: - if not hasattr(self, "train_ds"): - self.train_ds = self.train_dataset() - return DataLoader( - self.train_ds, + def train_dataloader(self) -> ProteinDataLoader: + return ProteinDataLoader( + self.train_dataset(), batch_size=self.batch_size, shuffle=True, pin_memory=self.pin_memory, num_workers=self.num_workers, ) - def val_dataloader(self) -> DataLoader: - if not hasattr(self, "val_ds"): - self.val_ds = self.val_dataset() - return DataLoader( - self.val_ds, + def val_dataloader(self) -> ProteinDataLoader: + return ProteinDataLoader( + self.val_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, num_workers=self.num_workers, ) - def test_dataloader(self) -> DataLoader: - if not hasattr(self, "test_ds"): - self.test_ds = self.test_dataset() - return DataLoader( - self.test_ds, + def test_dataloader(self) -> ProteinDataLoader: + return ProteinDataLoader( + self.test_dataset(), batch_size=self.batch_size, shuffle=False, pin_memory=self.pin_memory, @@ -206,7 +201,9 @@ def download(self): def exclude_pdbs(self): pass - def parse_dataset(self, split: str) -> pd.DataFrame: + def parse_dataset( + self, split: Literal["training", "validation", "testing"] + ) -> pd.DataFrame: # sourcery skip: remove-unnecessary-else, swap-if-else-branches, switch """ Parses the raw dataset files to Pandas DataFrames. @@ -287,6 +284,5 @@ def parse_dataset(self, split: str) -> pd.DataFrame: print(ds) ds.datamodule.setup() dl = ds["datamodule"].val_dataloader() - print(ds["datamodule"].val_ds[1]) for batch in dl: print(batch) From 40baf0c095031c1af62086291f0d58ad2f72fd03 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 16 Nov 2023 17:49:02 -0800 Subject: [PATCH 059/106] improve instantiation test --- proteinworkshop/datasets/go.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/proteinworkshop/datasets/go.py b/proteinworkshop/datasets/go.py index ffa64e8c..64041cb0 100644 --- a/proteinworkshop/datasets/go.py +++ b/proteinworkshop/datasets/go.py @@ -286,3 +286,6 @@ def parse_dataset( dl = ds["datamodule"].val_dataloader() for batch in dl: print(batch) + dl = ds["datamodule"].test_dataloader() + for batch in dl: + print(batch) From d85c6f0467af96dc973ffbf670d18bc2000f0b1a Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 16 Nov 2023 19:28:29 -0800 Subject: [PATCH 060/106] set ec to in memory --- proteinworkshop/config/dataset/ec_reaction.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/proteinworkshop/config/dataset/ec_reaction.yaml b/proteinworkshop/config/dataset/ec_reaction.yaml index 36a3bcc1..d67271e4 100644 --- a/proteinworkshop/config/dataset/ec_reaction.yaml +++ b/proteinworkshop/config/dataset/ec_reaction.yaml @@ -10,4 +10,5 @@ datamodule: shuffle_labels: False # Whether to shuffle labels for permutation testing transforms: ${transforms} overwrite: False + in_memory: True num_classes: 384 From 82e40246a9358f7b70c12c4fccc2407fdf44ba74 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Fri, 17 Nov 2023 07:13:32 -0800 Subject: [PATCH 061/106] fix GO labelling --- proteinworkshop/datasets/go.py | 32 ++++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/proteinworkshop/datasets/go.py b/proteinworkshop/datasets/go.py index 64041cb0..43004b48 100644 --- a/proteinworkshop/datasets/go.py +++ b/proteinworkshop/datasets/go.py @@ -8,6 +8,7 @@ import pandas as pd import torch import wget +from graphein.protein.tensor.data import Protein from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger as log from sklearn.preprocessing import LabelEncoder @@ -140,7 +141,9 @@ def _get_dataset( chains=list(df.chain), graph_labels=list(list(df.label)), overwrite=self.overwrite, - transform=self.transform, + transform=self.labeller + if self.transform is None + else self.compose_transforms([self.labeller] + [self.transform]), format=self.format, in_memory=self.in_memory, ) @@ -258,9 +261,30 @@ def parse_dataset( data["label"] = data["label"].sample(frac=1).values # logger.info(f"Found {len(data)} examples in {split} after removing nonstandard proteins") + self.labeller = GOLabeller(data) return data.sample(frac=1) # Shuffle dataset for batches +class GOLabeller: + """ + This labeller applies the graph labels to each example as a transform. + + This is required as chains can be used across tasks (e.g. CC, BP or MF) with + different labels. + """ + + def __init__(self, label_df: pd.DataFrame): + self.labels = label_df + + def __call__(self, data: Protein) -> Protein: + pdb, chain = data.id.split("_") + label = self.labels.loc[ + (self.labels.pdb == pdb) & (self.labels.chain == chain) + ].label.item() + data.graph_y = label + return data + + if __name__ == "__main__": import pathlib @@ -270,7 +294,7 @@ def parse_dataset( log.info("Imported libs") cfg = omegaconf.OmegaConf.load( - constants.SRC_PATH / "config" / "dataset" / "go-cc.yaml" + constants.SRC_PATH / "config" / "dataset" / "go-bp.yaml" ) # cfg = omegaconf.OmegaConf.load(constants.SRC_PATH / "config" / "dataset" / "go-mf.yaml") # cfg = omegaconf.OmegaConf.load(constants.SRC_PATH / "config" / "dataset" / "go-bp.yaml") @@ -282,7 +306,11 @@ def parse_dataset( ds = hydra.utils.instantiate(cfg) print(ds) + # labels = ds["datamodule"].parse_labels() ds.datamodule.setup() + dl = ds["datamodule"].train_dataloader() + for batch in dl: + print(batch) dl = ds["datamodule"].val_dataloader() for batch in dl: print(batch) From 2e9d73c3e7a9b7b6be414d647285189093181795 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sat, 18 Nov 2023 13:53:57 -0800 Subject: [PATCH 062/106] fix metrics memory leak --- proteinworkshop/metrics/__init__.py | 0 proteinworkshop/metrics/auprc.py | 4 ++-- proteinworkshop/metrics/f1_max.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 proteinworkshop/metrics/__init__.py diff --git a/proteinworkshop/metrics/__init__.py b/proteinworkshop/metrics/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/proteinworkshop/metrics/auprc.py b/proteinworkshop/metrics/auprc.py index e6290700..2acf9c01 100644 --- a/proteinworkshop/metrics/auprc.py +++ b/proteinworkshop/metrics/auprc.py @@ -26,8 +26,8 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: :param target: Tensor of targets :type target: torch.Tensor """ - self.preds.append(preds) - self.targets.append(target) + self.preds.append(preds.detach()) + self.targets.append(target.detach()) def compute(self) -> torch.Tensor: """Computes the AUPRC metric. diff --git a/proteinworkshop/metrics/f1_max.py b/proteinworkshop/metrics/f1_max.py index d9f318ce..61681c6b 100644 --- a/proteinworkshop/metrics/f1_max.py +++ b/proteinworkshop/metrics/f1_max.py @@ -28,8 +28,8 @@ def __init__(self, num_classes: int, compute_on_cpu: bool = True) -> None: self.compute_on_cpu = compute_on_cpu def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: - self.preds.append(preds) - self.targets.append(target) + self.preds.append(preds.detach()) + self.targets.append(target.detach()) def compute(self) -> Any: """Computes the F1Max metric. From 64658e1c969e00bc45e2e5d775276a624cb03db9 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Sun, 19 Nov 2023 15:57:10 -0800 Subject: [PATCH 063/106] add ec_Reaction sweep --- .../config/sweeps/baseline_ec_reaction.yaml | 64 +++++++++++++++++++ proteinworkshop/models/base.py | 59 ++++------------- 2 files changed, 78 insertions(+), 45 deletions(-) create mode 100644 proteinworkshop/config/sweeps/baseline_ec_reaction.yaml diff --git a/proteinworkshop/config/sweeps/baseline_ec_reaction.yaml b/proteinworkshop/config/sweeps/baseline_ec_reaction.yaml new file mode 100644 index 00000000..cfb98ac5 --- /dev/null +++ b/proteinworkshop/config/sweeps/baseline_ec_reaction.yaml @@ -0,0 +1,64 @@ +program: proteinworkshop/train.py +method: grid +name: baseline_ec_reaction +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: [multiclass_graph_classification] + + dataset: + values: [ec_reaction] + + dataset.datamodule.batch_size: + value: 8 + + encoder: + values: [schnet, gear_net_edge, egnn, gcpnet, tfn, mace] + + + features: + values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] + + scheduler: + value: plateau + + extras.enforce_tags: + value: False + + +aux_task: + values: [none, nn_sequence, nn_structure_r3] #nn_structure_torsion, nn_structure_r3] + + trainer.max_epochs: + value: 300 + + test: + value: True + + trainer: + value: gpu + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_seed_${seed}" + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.graph_label.dropout: + value: ${hparams.hparams.decoder_dropout} + + #seed: + # values: [13, 42, 99] + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index b21fb26c..f18bbd3e 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -37,9 +37,7 @@ def forward(self, batch: Batch) -> torch.Tensor: ... @abc.abstractmethod - def training_step( - self, batch: Batch, batch_idx: torch.Tensor - ) -> torch.Tensor: + def training_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: """Implement training step. :param batch: Mini-batch of data. @@ -52,9 +50,7 @@ def training_step( ... @abc.abstractmethod - def validation_step( - self, batch: Batch, batch_idx: torch.Tensor - ) -> torch.Tensor: + def validation_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: """Implement validation step. :param batch: Mini-batch of data. @@ -112,9 +108,7 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: for output in self.config.task.supervise_on: if output == "node_label": labels["node_label"] = batch.node_y - if isinstance( - self.losses["node_label"], torch.nn.BCEWithLogitsLoss - ): + if isinstance(self.losses["node_label"], torch.nn.BCEWithLogitsLoss): labels["node_label"] = F.one_hot( labels["node_label"], num_classes=self.config.dataset.num_classes, @@ -122,9 +116,7 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: elif output == "graph_label": labels["graph_label"] = batch.graph_y if ( - isinstance( - self.losses["graph_label"], torch.nn.BCEWithLogitsLoss - ) + isinstance(self.losses["graph_label"], torch.nn.BCEWithLogitsLoss) and batch.graph_y.ndim == 1 ): labels["graph_label"] = F.one_hot( @@ -176,9 +168,7 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: return Label(labels) @beartype - def compute_loss( - self, y_hat: ModelOutput, y: Label - ) -> Dict[str, torch.Tensor]: + def compute_loss(self, y_hat: ModelOutput, y: Label) -> Dict[str, torch.Tensor]: """ Compute loss by iterating over all outputs. @@ -215,13 +205,9 @@ def configure_optimizers(self): # sourcery skip: extract-method if self.config.get("scheduler"): logger.info("Instantiating scheduler...") - scheduler = hydra.utils.instantiate( - self.config.scheduler, optimiser - ) + scheduler = hydra.utils.instantiate(self.config.scheduler, optimiser) scheduler = OmegaConf.to_container(scheduler) - scheduler["scheduler"] = scheduler["scheduler"]( - optimizer=optimiser - ) + scheduler["scheduler"] = scheduler["scheduler"](optimizer=optimiser) optimiser_config = { "optimizer": optimiser, "lr_scheduler": scheduler, @@ -252,9 +238,7 @@ def _build_output_decoders(self) -> nn.ModuleDict: decoders[output_head] = hydra.utils.instantiate(cfg) return decoders - def configure_losses( - self, loss_dict: Dict[str, str] - ) -> Dict[str, Callable]: + def configure_losses(self, loss_dict: Dict[str, str]) -> Dict[str, Callable]: """ Configures losses from config. Returns a dictionary of losses mapping each output name to its respective loss function. @@ -325,12 +309,7 @@ def configure_metrics(self): for stage in {"train", "val", "test"}: metric = hydra.utils.instantiate(metric_conf) if output == "residue_type": - if metric in { - "auprc", - "f1_score", - "f1_max", - "roc_auc", - }: + if metric_name not in {"accuracy", "perplexity"}: continue metric.num_classes = 23 metric.task = "multiclass" @@ -357,9 +336,7 @@ def configure_metrics(self): setattr(self, "metric_names", metric_names) @beartype - def log_metrics( - self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch - ): + def log_metrics(self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch): """ Logs metrics to logger. @@ -439,9 +416,7 @@ def __init__(self, cfg: DictConfig) -> None: logger.info(self.featuriser) logger.info("Instantiating task transform...") - self.task_transform = hydra.utils.instantiate( - cfg.get("task.transform") - ) + self.task_transform = hydra.utils.instantiate(cfg.get("task.transform")) logger.info(self.task_transform) self.save_hyperparameters() @@ -463,9 +438,7 @@ def _create_example_batch(self) -> ProteinBatch: ] for p in proteins: setattr(p, "x", torch.zeros(p.coords.shape[0])) - setattr( - p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1) - ) + setattr(p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1)) batch = ProteinBatch.from_data_list(proteins) return self.featurise(batch) @@ -504,16 +477,12 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: emb_type = self.decoder[ output_head ].input # node_embedding or graph_embedding - output[output_head] = self.decoder[output_head]( - output[emb_type] - ) + output[output_head] = self.decoder[output_head](output[emb_type]) return self.compute_output(output, batch) @beartype - def transform_encoder_output( - self, output: EncoderOutput, batch - ) -> EncoderOutput: + def transform_encoder_output(self, output: EncoderOutput, batch) -> EncoderOutput: """ Modifies graph encoder output. From 075c00b86dfffba9ae44b2eadd3268794b55410b Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Sun, 19 Nov 2023 19:39:24 -0600 Subject: [PATCH 064/106] Ignore local Conda env in project directory --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 6f4bf06c..8758df9c 100644 --- a/.gitignore +++ b/.gitignore @@ -143,6 +143,7 @@ proteinworkshop/data/* !proteinworkshop/data/.gitkeep logs/ +ProteinWorkshop/ wandb/ .DS_Store .env From dc3ec556b31febd2f715397aeee27f1cee321b64 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 20 Nov 2023 07:20:14 -0800 Subject: [PATCH 065/106] add missing mace ca_angles hparams --- proteinworkshop/config/hparams/mace_ca_angles.yaml | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 proteinworkshop/config/hparams/mace_ca_angles.yaml diff --git a/proteinworkshop/config/hparams/mace_ca_angles.yaml b/proteinworkshop/config/hparams/mace_ca_angles.yaml new file mode 100644 index 00000000..caddde5e --- /dev/null +++ b/proteinworkshop/config/hparams/mace_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.0010 + decoder_dropout: 0.1 From e3354a32b25749ed9a5f55dc03a394f4494ef54e Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 21 Nov 2023 12:39:19 -0800 Subject: [PATCH 066/106] add addev config --- .../baseline_antibody_developability.yaml | 64 +++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 proteinworkshop/config/sweeps/baseline_antibody_developability.yaml diff --git a/proteinworkshop/config/sweeps/baseline_antibody_developability.yaml b/proteinworkshop/config/sweeps/baseline_antibody_developability.yaml new file mode 100644 index 00000000..75a16ca5 --- /dev/null +++ b/proteinworkshop/config/sweeps/baseline_antibody_developability.yaml @@ -0,0 +1,64 @@ +program: proteinworkshop/train.py +method: grid +name: baseline_antibody_developability +metric: # Does not matter, as we are using sweep to run the experiment. + goal: minimize + name: val/loss/total + +parameters: + task: + values: [binary_graph_classification] + + dataset: + value: [antibody_developability] + + dataset.datamodule.batch_size: + value: 8 + + encoder: + values: [schnet, gear_net_edge, egnn, gcpnet, tfn, mace] + + features: + values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] + + scheduler: + values: [plateau] + + logger: + value: wandb + + trainer: + value: gpu + + extras.enforce_tags: + value: False + + +aux_task: + #values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + values: [none, nn_sequence, nn_structure_r3] + + trainer.max_epochs: + value: 600 + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}_seed_${seed}" + + test: + value: True + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.graph_label.dropout: + value: ${hparams.hparams.decoder_dropout} + + #seed: + # values: [13, 42, 121] + +command: + - ${env} + - HYDRA_FULL_ERROR=1 + - WANDB_START_METHOD=thread + - python + - ${program} + - ${args_no_hyphens} From 415e6d310866cf861c46fe9450ea2c38d0911dd5 Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Tue, 21 Nov 2023 14:41:25 -0600 Subject: [PATCH 067/106] A dataset loading script for antibody_developability.py --- .../datasets/antibody_developability.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/proteinworkshop/datasets/antibody_developability.py b/proteinworkshop/datasets/antibody_developability.py index e5ab4e0f..b4caa94d 100644 --- a/proteinworkshop/datasets/antibody_developability.py +++ b/proteinworkshop/datasets/antibody_developability.py @@ -227,3 +227,29 @@ def test_dataloader(self) -> ProteinDataLoader: pin_memory=self.pin_memory, shuffle=False, ) + +if __name__ == "__main__": + import pathlib + + import hydra + + from proteinworkshop import constants + + cfg = omegaconf.OmegaConf.load( + constants.SRC_PATH / "config" / "dataset" / "antibody_developability.yaml" + ) + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "AntibodyDevelopability" # type: ignore + cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore + cfg.datamodule.transforms = [] + ds = hydra.utils.instantiate(cfg) + print(ds) + ds["datamodule"].parse_dataset() + dl = ds["datamodule"].train_dataloader() + for batch in dl: + print(batch) + dl = ds["datamodule"].val_dataloader() + for batch in dl: + print(batch) + dl = ds["datamodule"].test_dataloader() + for batch in dl: + print(batch) From 4879e500a3dd3fcefdb0370c202e831aa006a433 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 13:17:10 -0800 Subject: [PATCH 068/106] add esm BB config --- proteinworkshop/config/hparams/esm_ca_bb.yaml | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 proteinworkshop/config/hparams/esm_ca_bb.yaml diff --git a/proteinworkshop/config/hparams/esm_ca_bb.yaml b/proteinworkshop/config/hparams/esm_ca_bb.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_bb.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 From 06bb80f75ff8963a5e763ba367614f930a091b55 Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Wed, 22 Nov 2023 15:59:24 -0600 Subject: [PATCH 069/106] Add ESM config for all feature schemes --- proteinworkshop/config/hparams/esm_ca_angles.yaml | 3 +++ proteinworkshop/config/hparams/esm_ca_base.yaml | 3 +++ proteinworkshop/config/hparams/esm_ca_sc.yaml | 3 +++ proteinworkshop/config/hparams/esm_ca_seq.yaml | 3 +++ 4 files changed, 12 insertions(+) create mode 100644 proteinworkshop/config/hparams/esm_ca_angles.yaml create mode 100644 proteinworkshop/config/hparams/esm_ca_base.yaml create mode 100644 proteinworkshop/config/hparams/esm_ca_sc.yaml create mode 100644 proteinworkshop/config/hparams/esm_ca_seq.yaml diff --git a/proteinworkshop/config/hparams/esm_ca_angles.yaml b/proteinworkshop/config/hparams/esm_ca_angles.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_angles.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_base.yaml b/proteinworkshop/config/hparams/esm_ca_base.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_base.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_sc.yaml b/proteinworkshop/config/hparams/esm_ca_sc.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_sc.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 diff --git a/proteinworkshop/config/hparams/esm_ca_seq.yaml b/proteinworkshop/config/hparams/esm_ca_seq.yaml new file mode 100644 index 00000000..e301ceb0 --- /dev/null +++ b/proteinworkshop/config/hparams/esm_ca_seq.yaml @@ -0,0 +1,3 @@ +hparams: + lr: 0.001 + decoder_dropout: 0.3 From c9c344544e3de0fd067635e42499d71166c6ae49 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 14:22:10 -0800 Subject: [PATCH 070/106] add ppi prediction task updates --- proteinworkshop/config/task/ppi_site_prediction.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/proteinworkshop/config/task/ppi_site_prediction.yaml b/proteinworkshop/config/task/ppi_site_prediction.yaml index 7ad91343..6ce75afa 100644 --- a/proteinworkshop/config/task/ppi_site_prediction.yaml +++ b/proteinworkshop/config/task/ppi_site_prediction.yaml @@ -1,6 +1,7 @@ # @package _global_ defaults: + - _self_ - override /metrics: - accuracy - f1_score @@ -27,7 +28,7 @@ callbacks: task: task: "classification" classification_type: "binary" - metric_average: "macro" + metric_average: "micro" losses: node_label: bce From f8ea13bb582d9ca798c824cf2935b14095203413 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 14:24:11 -0800 Subject: [PATCH 071/106] add ppi sweep config --- .../config/sweeps/baseline_ppi.yaml | 26 ++++++++++++++----- 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/proteinworkshop/config/sweeps/baseline_ppi.yaml b/proteinworkshop/config/sweeps/baseline_ppi.yaml index f3346d94..8c931ad5 100644 --- a/proteinworkshop/config/sweeps/baseline_ppi.yaml +++ b/proteinworkshop/config/sweeps/baseline_ppi.yaml @@ -9,15 +9,14 @@ parameters: task: values: [ppi_site_prediction] - dataset: values: [masif_site] encoder: - values: [schnet, dimenet_plus_plus, gear_net_edge, egnn, gcpnet] + values: [schnet, gear_net_edge, egnn, gcpnet, tfn, mace] - optimiser.optimizer.lr: - values: [0.0001] + dataset.datamodule.batch_size: + value: 16 features: values: [ca_base, ca_seq, ca_angles, ca_bb, ca_sc] @@ -29,10 +28,25 @@ parameters: value: False +aux_task: - values: [none, nn_sequence, nn_structure_torsion, nn_structure_r3] + values: [none, nn_sequence, nn_structure_r3] trainer.max_epochs: - value: 150 + value: 300 + + test: + value: True + + logger: + value: wandb + + name: + value: "${hydra:runtime.choices.encoder}_${hydra:runtime.choices.features}_${hydra:runtime.choices.aux_task}" + + optimiser.optimizer.lr: + value: ${hparams.hparams.lr} + + decoder.node_label.dropout: + value: ${hparams.hparams.decoder_dropout} command: - ${env} From 15fc31b812f366e7886db89942c1df49fc940f9f Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Wed, 22 Nov 2023 16:48:27 -0600 Subject: [PATCH 072/106] Update test script for masif_dataset.py --- proteinworkshop/datasets/masif_site.py | 29 ++++++++++++++++---------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/proteinworkshop/datasets/masif_site.py b/proteinworkshop/datasets/masif_site.py index 7b890914..98144d6f 100644 --- a/proteinworkshop/datasets/masif_site.py +++ b/proteinworkshop/datasets/masif_site.py @@ -197,15 +197,22 @@ def parse_dataset(self): from proteinworkshop import constants - config = omegaconf.OmegaConf.load( - "../proteinworkshop/config/dataset/masif_site.yaml" + cfg = omegaconf.OmegaConf.load( + constants.SRC_PATH / "config" / "dataset" / "masif_site.yaml" ) - config.datamodule.path = pathlib.Path(constants.DATA_PATH) / "MasifSite" # type: ignore - config.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore - config.datamodule.transforms = None - print(config) - - dm = hydra.utils.instantiate(config) - print(dm["datamodule"]) - dm["datamodule"].setup() - dm["datamodule"].test_dataloader() + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "MasifSite" # type: ignore + cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore + cfg.datamodule.transforms = [] + ds = hydra.utils.instantiate(cfg) + print(ds) + ds["datamodule"].setup() + ds["datamodule"].parse_dataset() + dl = ds["datamodule"].train_dataloader() + for batch in dl: + print(batch) + dl = ds["datamodule"].val_dataloader() + for batch in dl: + print(batch) + dl = ds["datamodule"].test_dataloader() + for batch in dl: + print(batch) From b30d40fcd22fbc08c02043dbcefce2e587074e10 Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Wed, 22 Nov 2023 16:53:37 -0600 Subject: [PATCH 073/106] Update path for masif_site in test script --- proteinworkshop/datasets/masif_site.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/datasets/masif_site.py b/proteinworkshop/datasets/masif_site.py index 98144d6f..83db8cb3 100644 --- a/proteinworkshop/datasets/masif_site.py +++ b/proteinworkshop/datasets/masif_site.py @@ -200,7 +200,7 @@ def parse_dataset(self): cfg = omegaconf.OmegaConf.load( constants.SRC_PATH / "config" / "dataset" / "masif_site.yaml" ) - cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "MasifSite" # type: ignore + cfg.datamodule.path = pathlib.Path(constants.DATA_PATH) / "masif_site" # type: ignore cfg.datamodule.pdb_dir = pathlib.Path(constants.DATA_PATH) / "pdb" # type: ignore cfg.datamodule.transforms = [] ds = hydra.utils.instantiate(cfg) From 01caa96d800ef82c410b2b9db97ea8f7d419e4cf Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 15:17:24 -0800 Subject: [PATCH 074/106] mask additional attributes in PPI site prediction --- proteinworkshop/tasks/ppi_site_prediction.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index 839f4f01..6dbad947 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -30,9 +30,7 @@ def __init__(self, radius: float = 3.5, ca_only: bool = True) -> None: self.fill_value = 1e-5 self.ca_only = ca_only charstr: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - self.chain_map: Dict[str, int] = { - charstr[i]: i for i in range(len(charstr)) - } + self.chain_map: Dict[str, int] = {charstr[i]: i for i in range(len(charstr))} def __call__(self, data: Union[Protein, Data]): # Map the chain labels to integers @@ -61,9 +59,7 @@ def __call__(self, data: Union[Protein, Data]): # Unwrap the coordinates other_chains = other_chains.reshape(-1, 3) # Remove any rows with 1e-5 - other_chains = other_chains[ - ~torch.all(other_chains == self.fill_value, dim=1) - ] + other_chains = other_chains[~torch.all(other_chains == self.fill_value, dim=1)] # Create a KDTree # If Ca only, we only see if the interacting chains are within the @@ -101,6 +97,12 @@ def __call__(self, data: Union[Protein, Data]): if data.x is not None: data.x = data.x[mask] + if data.seq_pos is not None: + data.seq_pos = data.seq_pos[mask] + + if data.amino_acid_one_hot is not None: + data.amino_acid_one_hot = data.amino_acid_one_hot[mask] + return data From 9f50f04e9ee480c1f72df0a8cf8480d94dba1721 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 15:53:58 -0800 Subject: [PATCH 075/106] resolve sequence tokenization --- .../models/graph_encoders/esm_embeddings.py | 94 ++++++++++++------- 1 file changed, 60 insertions(+), 34 deletions(-) diff --git a/proteinworkshop/models/graph_encoders/esm_embeddings.py b/proteinworkshop/models/graph_encoders/esm_embeddings.py index 882d1be2..2bc96276 100644 --- a/proteinworkshop/models/graph_encoders/esm_embeddings.py +++ b/proteinworkshop/models/graph_encoders/esm_embeddings.py @@ -1,19 +1,20 @@ """Modified from TorchDrug.""" -import esm import os -import torch +from typing import Dict, List, Optional, Set, Tuple, Union +import torch from beartype import beartype -from torch_geometric.data import Batch -from torch_geometric.utils import to_dense_batch -from tqdm import tqdm -from typing import Dict, List, Optional, Union, Set, Tuple -from graphein.protein.tensor.data import ProteinBatch from graphein.protein.resi_atoms import RESI_THREE_TO_1 +from graphein.protein.tensor.data import ProteinBatch from loguru import logger from six.moves.urllib.request import urlretrieve -from proteinworkshop.models.utils import get_aggregation from torch import nn +from torch_geometric.data import Batch +from torch_geometric.utils import to_dense_batch +from tqdm import tqdm + +import esm +from proteinworkshop.models.utils import get_aggregation from proteinworkshop.types import EncoderOutput @@ -38,7 +39,10 @@ def _compute_md5(file_name: str, chunk_size: int = 65536) -> str: @beartype def _download( - url: str, path: str, save_file: Optional[str] = None, md5: Optional[str] = None + url: str, + path: str, + save_file: Optional[str] = None, + md5: Optional[str] = None, ): """ Download a file from the specified url. @@ -55,7 +59,9 @@ def _download( save_file = save_file[: save_file.find("?")] save_file = os.path.join(path, save_file) - if not os.path.exists(save_file) or (md5 is not None and _compute_md5(save_file) != md5): + if not os.path.exists(save_file) or ( + md5 is not None and _compute_md5(save_file) != md5 + ): logger.info(f"Downloading {url} to {save_file}") urlretrieve(url, save_file) return save_file @@ -129,14 +135,14 @@ class EvolutionaryScaleModeling(nn.Module): max_input_length = 1024 - 2 def __init__( - self, - path: Union[str, os.PathLike], - model: str = "ESM-2-650M", - readout: str = "mean", - mlp_post_embed: bool = True, - dropout: float = 0.1, - finetune: bool = False - ): + self, + path: Union[str, os.PathLike], + model: str = "ESM-2-650M", + readout: str = "mean", + mlp_post_embed: bool = True, + dropout: float = 0.1, + finetune: bool = False, + ): super(EvolutionaryScaleModeling, self).__init__() path = os.path.expanduser(path) if not os.path.exists(path): @@ -160,24 +166,29 @@ def __init__( nn.ReLU(), nn.Dropout(dropout), ) - + if not self.finetune: self.model.eval() self.readout = get_aggregation(readout) + self.residue_map = RESI_THREE_TO_1 + self.residue_map["UNK"] = "" + @property @beartype def required_batch_attributes(self) -> Set[str]: """ Return the requied attributes for each batch. - + :return: set of required attributes """ return {"residues", "id", "coords", "batch"} @beartype - def load_weight(self, path: str, model: str) -> Tuple[nn.Module, esm.data.Alphabet]: + def load_weight( + self, path: str, model: str + ) -> Tuple[nn.Module, esm.data.Alphabet]: """ Load ESM model weights and their corresponding alphabet. @@ -192,7 +203,9 @@ def load_weight(self, path: str, model: str) -> Tuple[nn.Module, esm.data.Alphab if model != "ESM-1v" and not model.startswith("ESM-2"): regression_model = f"{model}-regression" regression_file = _download( - self.url[regression_model], path, md5=self.md5[regression_model] + self.url[regression_model], + path, + md5=self.md5[regression_model], ) regression_data = torch.load(regression_file, map_location="cpu") else: @@ -201,15 +214,22 @@ def load_weight(self, path: str, model: str) -> Tuple[nn.Module, esm.data.Alphab return esm.pretrained.load_model_and_alphabet_core( model_name, model_data, regression_data ) - + @beartype - def esm_embed(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[torch.device, str]] = None) -> torch.Tensor: + def esm_embed( + self, + batch: Union[Batch, ProteinBatch], + device: Optional[Union[torch.device, str]] = None, + ) -> torch.Tensor: """ Compute residue ESM embeddings for input proteins """ device = device if device is not None else batch.coords.device - seqs = ["".join([RESI_THREE_TO_1[s] for s in seq]) for seq in batch.residues] + seqs = [ + "".join([self.residue_map[s] for s in seq]) + for seq in batch.residues + ] seqs = ["".join(seq) for seq in seqs] data = list(tuple(zip(batch.id, seqs))) @@ -220,17 +240,23 @@ def esm_embed(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[to node_embedding = output["representations"][self.repr_layer] # NOTE: tokens `0` and `N` are always beginning-of-sequence and end-of-sequence tokens, # so the first (real) residue is token `1` and the last is `N - 1`. - node_embedding = node_embedding[:, 1:node_embedding.shape[1] - 1, :] + node_embedding = node_embedding[:, 1 : node_embedding.shape[1] - 1, :] _, batch_mask = to_dense_batch( - x=torch.rand(batch.coords.shape[0], self.output_dim, device=device), + x=torch.rand( + batch.coords.shape[0], self.output_dim, device=device + ), batch=batch.batch, ) node_embedding = node_embedding[batch_mask] return node_embedding @beartype - def forward(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[torch.device, str]] = None) -> EncoderOutput: + def forward( + self, + batch: Union[Batch, ProteinBatch], + device: Optional[Union[torch.device, str]] = None, + ) -> EncoderOutput: """ Compute the residue representations and the graph representation(s). @@ -250,19 +276,19 @@ def forward(self, batch: Union[Batch, ProteinBatch], device: Optional[Union[torc if self.mlp_post_embed: # combine ESM embeddings with node features node_embedding = self.mlp( - torch.concatenate( - [node_embedding, batch.x], - dim=-1 - ) + torch.concatenate([node_embedding, batch.x], dim=-1) ) graph_embedding = self.readout(node_embedding, batch.batch) return EncoderOutput( - {"graph_embedding": graph_embedding, - "node_embedding": node_embedding} + { + "graph_embedding": graph_embedding, + "node_embedding": node_embedding, + } ) + if __name__ == "__main__": from proteinworkshop.datasets.utils import create_example_batch From 74004f81966a6c2f238d04370c86260ba1be8ff2 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 16:16:36 -0800 Subject: [PATCH 076/106] refactor chain identification --- proteinworkshop/tasks/ppi_site_prediction.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index 6dbad947..bb6349c7 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -30,18 +30,18 @@ def __init__(self, radius: float = 3.5, ca_only: bool = True) -> None: self.fill_value = 1e-5 self.ca_only = ca_only charstr: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - self.chain_map: Dict[str, int] = {charstr[i]: i for i in range(len(charstr))} + self.chain_map: Dict[str, int] = { + charstr[i]: i for i in range(len(charstr)) + } def __call__(self, data: Union[Protein, Data]): # Map the chain labels to integers target_chains = [] + chain_strs = [res.split(":")[0] for res in data.residue_id] + for chain in data.graph_y: - try: - target_chains.append(self.chain_map[chain]) - except KeyError: - # Sometimes chains are stored as integers - target_chains.append(int(chain) - 1) + target_chains.append(chain_strs.index(chain)) target_chains = torch.tensor(target_chains) target_indices = torch.where(torch.isin(data.chains, target_chains))[0] @@ -59,7 +59,9 @@ def __call__(self, data: Union[Protein, Data]): # Unwrap the coordinates other_chains = other_chains.reshape(-1, 3) # Remove any rows with 1e-5 - other_chains = other_chains[~torch.all(other_chains == self.fill_value, dim=1)] + other_chains = other_chains[ + ~torch.all(other_chains == self.fill_value, dim=1) + ] # Create a KDTree # If Ca only, we only see if the interacting chains are within the From 9d9994c5dcb44d8a9943c27b0762d9730c0bd3e6 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 16:25:38 -0800 Subject: [PATCH 077/106] fix error in error fix --- proteinworkshop/tasks/ppi_site_prediction.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index bb6349c7..c6c09b05 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -30,15 +30,14 @@ def __init__(self, radius: float = 3.5, ca_only: bool = True) -> None: self.fill_value = 1e-5 self.ca_only = ca_only charstr: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - self.chain_map: Dict[str, int] = { - charstr[i]: i for i in range(len(charstr)) - } + self.chain_map: Dict[str, int] = {charstr[i]: i for i in range(len(charstr))} def __call__(self, data: Union[Protein, Data]): # Map the chain labels to integers target_chains = [] chain_strs = [res.split(":")[0] for res in data.residue_id] + chain_strs = np.unique(chain_strs) for chain in data.graph_y: target_chains.append(chain_strs.index(chain)) @@ -59,9 +58,7 @@ def __call__(self, data: Union[Protein, Data]): # Unwrap the coordinates other_chains = other_chains.reshape(-1, 3) # Remove any rows with 1e-5 - other_chains = other_chains[ - ~torch.all(other_chains == self.fill_value, dim=1) - ] + other_chains = other_chains[~torch.all(other_chains == self.fill_value, dim=1)] # Create a KDTree # If Ca only, we only see if the interacting chains are within the From ee8b3cf09f47cd8a5d86fc32581309a2ae089de6 Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Wed, 22 Nov 2023 18:40:45 -0600 Subject: [PATCH 078/106] Fix fix of a fix --- proteinworkshop/tasks/ppi_site_prediction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index c6c09b05..51036e7f 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -37,7 +37,7 @@ def __call__(self, data: Union[Protein, Data]): target_chains = [] chain_strs = [res.split(":")[0] for res in data.residue_id] - chain_strs = np.unique(chain_strs) + chain_strs = list(np.unique(chain_strs)) for chain in data.graph_y: target_chains.append(chain_strs.index(chain)) From d827c84a3bc26acfcbacfc7601e10c4056174b2b Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 17:38:01 -0800 Subject: [PATCH 079/106] exclude erroneous examples --- proteinworkshop/datasets/masif_site.py | 29 +++++++++++++++++--------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/proteinworkshop/datasets/masif_site.py b/proteinworkshop/datasets/masif_site.py index 83db8cb3..ea89f74f 100644 --- a/proteinworkshop/datasets/masif_site.py +++ b/proteinworkshop/datasets/masif_site.py @@ -1,6 +1,6 @@ import os import pathlib -from typing import Callable, Iterable, Literal, Optional +from typing import Callable, Iterable, Literal, Optional, List import omegaconf import wget @@ -61,8 +61,8 @@ def __init__( def setup(self, stage: Optional[str] = None): self.download() - def exclude_pdbs(self): - pass + def exclude_pdbs(self) -> List[str]: + return ["1EXB_ABDC", "3LVK_AC"] def download(self): if not os.path.exists(self.path / "training.txt"): @@ -161,6 +161,9 @@ def parse_dataset(self): test = f.readlines() test = [x.strip() for x in test] + train = [x for x in train if x not in self.exclude_pdbs()] + test = [x for x in test if x not in self.exclude_pdbs()] + # Train / val split train, val = train_test_split(train, test_size=self.val_fraction) @@ -207,12 +210,18 @@ def parse_dataset(self): print(ds) ds["datamodule"].setup() ds["datamodule"].parse_dataset() + import torch + dl = ds["datamodule"].train_dataloader() - for batch in dl: - print(batch) - dl = ds["datamodule"].val_dataloader() - for batch in dl: - print(batch) - dl = ds["datamodule"].test_dataloader() - for batch in dl: + for i, batch in enumerate(dl): print(batch) + bad_seq = torch.argwhere(batch["amino_acid_one_hot"][:, -2] == 1) + + if bad_seq.sum() > 10: + break + # dl = ds["datamodule"].val_dataloader() + # for batch in dl: + # print(batch) + # dl = ds["datamodule"].test_dataloader() + # for batch in dl: + # print(batch) From e4b4d626a87429966ac160152c8ad378edba1f91 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 17:41:55 -0800 Subject: [PATCH 080/106] fix edge cases --- proteinworkshop/tasks/ppi_site_prediction.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index c6c09b05..15fa98f3 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -37,7 +37,7 @@ def __call__(self, data: Union[Protein, Data]): target_chains = [] chain_strs = [res.split(":")[0] for res in data.residue_id] - chain_strs = np.unique(chain_strs) + chain_strs = list(np.unique(chain_strs)) for chain in data.graph_y: target_chains.append(chain_strs.index(chain)) @@ -96,10 +96,10 @@ def __call__(self, data: Union[Protein, Data]): if data.x is not None: data.x = data.x[mask] - if data.seq_pos is not None: + if hasattr(data, "seq_pos"): data.seq_pos = data.seq_pos[mask] - if data.amino_acid_one_hot is not None: + if hasattr(data, "amino_acid_one_hot"): data.amino_acid_one_hot = data.amino_acid_one_hot[mask] return data From eb44c49567e10a6b717141b2801edf2f4e42f9cc Mon Sep 17 00:00:00 2001 From: Jamasb Date: Wed, 22 Nov 2023 17:42:13 -0800 Subject: [PATCH 081/106] fix edge cases --- proteinworkshop/tasks/ppi_site_prediction.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/proteinworkshop/tasks/ppi_site_prediction.py b/proteinworkshop/tasks/ppi_site_prediction.py index 15fa98f3..af0f40de 100644 --- a/proteinworkshop/tasks/ppi_site_prediction.py +++ b/proteinworkshop/tasks/ppi_site_prediction.py @@ -30,7 +30,9 @@ def __init__(self, radius: float = 3.5, ca_only: bool = True) -> None: self.fill_value = 1e-5 self.ca_only = ca_only charstr: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - self.chain_map: Dict[str, int] = {charstr[i]: i for i in range(len(charstr))} + self.chain_map: Dict[str, int] = { + charstr[i]: i for i in range(len(charstr)) + } def __call__(self, data: Union[Protein, Data]): # Map the chain labels to integers @@ -58,7 +60,9 @@ def __call__(self, data: Union[Protein, Data]): # Unwrap the coordinates other_chains = other_chains.reshape(-1, 3) # Remove any rows with 1e-5 - other_chains = other_chains[~torch.all(other_chains == self.fill_value, dim=1)] + other_chains = other_chains[ + ~torch.all(other_chains == self.fill_value, dim=1) + ] # Create a KDTree # If Ca only, we only see if the interacting chains are within the From 758a292b2d3087ed6ccb8a3ff8bcf47c1a3edcc2 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Mon, 27 Nov 2023 01:09:38 -0800 Subject: [PATCH 082/106] add model io utils --- proteinworkshop/model_io.py | 124 ++++++++++++++++++++++++++++++++++++ 1 file changed, 124 insertions(+) create mode 100644 proteinworkshop/model_io.py diff --git a/proteinworkshop/model_io.py b/proteinworkshop/model_io.py new file mode 100644 index 00000000..168e236a --- /dev/null +++ b/proteinworkshop/model_io.py @@ -0,0 +1,124 @@ +"""Utilities for loading models.""" +import collections +import os +import pathlib +from typing import Any, Dict, Tuple, Union + +import omegaconf +import torch +from loguru import logger + +import wandb +from proteinworkshop.models.base import BenchMarkModel + + +def load_model_from_checkpoint( + ckpt_path: Union[str, os.PathLike], + load_state: bool = False, + load_weights: bool = True, +) -> Tuple[BenchMarkModel, omegaconf.DictConfig]: + """Loads a model from a checkpoint. + + :param ckpt_path: Path to the checkpoint. + :type ckpt_path: Union[str, os.PathLike] + :param load_weights: Whether to load the weights from the checkpoint. + :type load_weights: bool + :return: The model. + """ + config = load_config_from_checkpoint(ckpt_path) + model = BenchMarkModel(config) + + if load_state: + return model.load_from_checkpoint(ckpt_path), config + + if load_weights: + ckpt = torch.load(ckpt_path) + return load_weights(model, ckpt["state_dict"]), config + else: + return model, config + + +def load_config_from_checkpoint( + ckpt_path: Union[str, os.PathLike] +) -> omegaconf.DictConfig: + """Loads a config from a checkpoint. + + :param ckpt_path: Path to the checkpoint. + :type ckpt_path: Union[str, os.PathLike] + :return: The config. + """ + ckpt = torch.load(ckpt_path) + config = ckpt["hyper_parameters"]["cfg"] + config = omegaconf.OmegaConf.create(config) + return config + + +def load_model_from_wandb( + run_id: str, + entity: str, + project: str, + version: str = "v0", + load_state: bool = False, + load_weights: bool = True, +) -> Dict[str, Any]: + """Loads a model from wandb.""" + api = wandb.Api() + run_name = f"{entity}/{project}/{run_id}" + run = api.run(run_name) + + # Download config + files = run.files() + downloaded = False + for file in files: + if file.name == "config.yaml": + file.download() + logger.info(f"Download config for run {run_id}") + downloaded = True + + if not downloaded: + logger.error(f"Failed to download config for {run_name}") + + # TODO: Automatically select best model + ckpt_ref = f"{entity}/{project}/model-{run_id}:{version}" + artifact = api.artifact(ckpt_ref, type="model") + artifact_dir = artifact.download() + + return load_model_from_checkpoint( + pathlib.Path(artifact_dir) / "model.ckpt" + ) + + +def load_weights( + model: BenchMarkModel, state_dict: Dict[str, Any] +) -> BenchMarkModel: + """Load weights from a state dict into a model. + + :param model: The model. + :type model: BenchMarkModel + :param state_dict: The state dict containing weights. + :type state_dict: Dict[str, Any] + :return: The model with loaded weights. + """ + encoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("encoder"): + encoder_weights[k.replace("encoder.", "")] = v + err = model.encoder.load_state_dict(encoder_weights, strict=False) + logger.warning(f"Error loading encoder weights: {err}") + + decoder_weights = collections.OrderedDict() + for k, v in state_dict.items(): + if k.startswith("decoder"): + decoder_weights[k.replace("decoder.", "")] = v + err = model.decoder.load_state_dict(decoder_weights, strict=False) + logger.warning(f"Error loading decoder weights: {err}") + + logger.info("Freezing encoder!") + for param in model.encoder.parameters(): + param.requires_grad = False + + logger.info("Freezing decoder!") + for param in model.decoder.parameters(): + param.requires_grad = False + + return model From 595b5ac4380e7f1fddec3d80e5b3fe89b3b5e541 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Thu, 30 Nov 2023 06:11:43 -0800 Subject: [PATCH 083/106] standardise default features for train and finetune configs #61 --- proteinworkshop/config/finetune.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/config/finetune.yaml b/proteinworkshop/config/finetune.yaml index fafca6b4..988233ab 100644 --- a/proteinworkshop/config/finetune.yaml +++ b/proteinworkshop/config/finetune.yaml @@ -9,7 +9,7 @@ num_workers: 16 # number of subprocesses to use for data loading. defaults: - env: default - dataset: cath - - features: ca_base + - features: ca_seq - encoder: egnn - decoder: default - transforms: none From 381040cf3b3d10a1a5d0651787e86893541bf303 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 11:58:53 -0800 Subject: [PATCH 084/106] refactor to new recommended jaxtyping/beartype syntax --- docs/source/tutorials.rst | 5 +- notebooks/adding_new_task_tutorial.ipynb | 2 +- proteinworkshop/datasets/atom3d_datamodule.py | 10 +- .../datasets/components/atom3d_dataset.py | 2 +- .../datasets/components/ppi_dataset.py | 16 +-- proteinworkshop/features/edge_features.py | 14 +-- proteinworkshop/features/edges.py | 6 +- proteinworkshop/features/factory.py | 5 +- proteinworkshop/features/node_features.py | 14 +-- proteinworkshop/features/representation.py | 22 ++--- proteinworkshop/features/sequence_features.py | 4 +- proteinworkshop/features/utils.py | 5 +- proteinworkshop/models/base.py | 66 +++++++++---- .../graph_encoders/components/radial.py | 5 +- .../graph_encoders/components/wrappers.py | 5 +- .../models/graph_encoders/gcpnet.py | 35 ++----- proteinworkshop/models/graph_encoders/gnn.py | 5 +- proteinworkshop/models/graph_encoders/gvp.py | 5 +- .../models/graph_encoders/identity.py | 5 +- .../models/graph_encoders/layers/gcp.py | 98 ++++++------------- proteinworkshop/models/graph_encoders/mace.py | 5 +- proteinworkshop/models/graph_encoders/tfn.py | 5 +- proteinworkshop/models/utils.py | 17 ++-- proteinworkshop/tasks/sequence_denoising.py | 2 +- proteinworkshop/tasks/structural_denoising.py | 4 +- 25 files changed, 154 insertions(+), 208 deletions(-) diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst index 2c24f7ae..784cb0cf 100644 --- a/docs/source/tutorials.rst +++ b/docs/source/tutorials.rst @@ -45,7 +45,7 @@ Evaluating a pre-trained Model from graphein.protein.tensor.data import ProteinBatch from proteinworkshop.models.utils import get_aggregation from jaxtyping import jaxtyped - from beartype import beartype + from beartype import beartype as typechecker class IdentityModel(nn.Module): @@ -58,8 +58,7 @@ Evaluating a pre-trained Model """This property describes the required attributes of the input batch.""" return {"x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> Dict[str, torch.Tensor]: """ This method does the forward pass of the model. diff --git a/notebooks/adding_new_task_tutorial.ipynb b/notebooks/adding_new_task_tutorial.ipynb index d9cf4622..4b82ff9a 100644 --- a/notebooks/adding_new_task_tutorial.ipynb +++ b/notebooks/adding_new_task_tutorial.ipynb @@ -67,7 +67,7 @@ " def required_attributes(self) -> Set[str]:\n", " return {\"residue_type\"}\n", "\n", - " @beartype\n", + " @jaxtyped(typechecker=typechecker)\n", " def __call__(self, x: Union[Data, Protein]) -> Union[Data, Protein]:\n", " x.residue_type_uncorrupted = copy.deepcopy(x.residue_type)\n", " # Get indices of residues to corrupt\n", diff --git a/proteinworkshop/datasets/atom3d_datamodule.py b/proteinworkshop/datasets/atom3d_datamodule.py index ccfa1dc1..3fe19eeb 100644 --- a/proteinworkshop/datasets/atom3d_datamodule.py +++ b/proteinworkshop/datasets/atom3d_datamodule.py @@ -11,7 +11,7 @@ import atom3d.datasets.datasets as da import lightning as L import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.dataloader import ProteinDataLoader from loguru import logger as log from torch.utils.data import Dataset @@ -34,7 +34,7 @@ def set_worker_sharing_strategy(worker_id: int): torch.multiprocessing.set_sharing_strategy(SHARING_STRATEGY) -@beartype +@typechecker def get_data_path( dataset: str, lba_split: int = 30, @@ -57,7 +57,7 @@ def get_data_path( return data_paths[dataset] -@beartype +@typechecker def get_test_data_path( dataset: str, lba_split: int = 30, @@ -85,7 +85,7 @@ def get_test_data_path( return data_paths[dataset] -@beartype +@typechecker def get_task_split( task: str, lba_split: int = 30, @@ -317,7 +317,7 @@ def setup(self, stage: Optional[str] = None): self.data_test, ) = self.get_datasets() - @beartype + @typechecker def get_dataloader( self, dataset: Union[da.LMDBDataset, PPIDataset, RESDataset], diff --git a/proteinworkshop/datasets/components/atom3d_dataset.py b/proteinworkshop/datasets/components/atom3d_dataset.py index e2a7d27a..7422caad 100644 --- a/proteinworkshop/datasets/components/atom3d_dataset.py +++ b/proteinworkshop/datasets/components/atom3d_dataset.py @@ -28,7 +28,7 @@ NUM_ATOM_TYPES = len(_atom_types_dict) -@beartype +@typechecker def _element_mapping(x: str) -> int: return _atom_types_dict.get(x, 8) diff --git a/proteinworkshop/datasets/components/ppi_dataset.py b/proteinworkshop/datasets/components/ppi_dataset.py index bc3985be..431dc4e2 100644 --- a/proteinworkshop/datasets/components/ppi_dataset.py +++ b/proteinworkshop/datasets/components/ppi_dataset.py @@ -7,7 +7,7 @@ import scipy import torch from atom3d.datasets import LMDBDataset -from beartype import beartype +from beartype import beartype as typechecker from torch.utils.data import IterableDataset from torch_geometric.data import Data @@ -23,14 +23,14 @@ ] -@beartype +@typechecker def get_res(df: pd.DataFrame) -> pd.DataFrame: """Get all residues.""" # Adapted from: https://github.com/drorlab/atom3d/blob/master/examples/ppi/dataset/neighbors.py return df[PPI_DF_INDEX_COLUMNS].drop_duplicates() -@beartype +@typechecker def _get_idx_to_res_mapping( df: pd.DataFrame, ) -> Tuple[pd.DataFrame, pd.Series]: @@ -43,7 +43,7 @@ def _get_idx_to_res_mapping( return idx_to_res, res_to_idx -@beartype +@typechecker def get_subunits( ensemble: pd.DataFrame, ) -> Tuple[ @@ -79,7 +79,7 @@ def get_subunits( return names, (bdf0, bdf1, udf0, udf1) -@beartype +@typechecker def get_negatives( neighbors, df0: pd.DataFrame, df1: pd.DataFrame ) -> pd.DataFrame: @@ -170,7 +170,7 @@ def __iter__(self): ) return gen - @beartype + @typechecker def _df_to_graph( self, struct_df: pd.DataFrame, chain_res: Iterable, label: float ) -> Optional[Data]: @@ -208,7 +208,7 @@ def _df_to_graph( return data - @beartype + @typechecker def _dataset_generator( self, indices: List[int], shuffle: bool = True ) -> Generator[Tuple[Data, Data], None, None]: @@ -247,7 +247,7 @@ def _dataset_generator( continue yield graph1, graph2 - @beartype + @typechecker def _create_labels( self, positives: pd.DataFrame, diff --git a/proteinworkshop/features/edge_features.py b/proteinworkshop/features/edge_features.py index f7fcbc62..e7877750 100644 --- a/proteinworkshop/features/edge_features.py +++ b/proteinworkshop/features/edge_features.py @@ -3,7 +3,7 @@ import numpy as np import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.types import CoordTensor, EdgeTensor from jaxtyping import jaxtyped from omegaconf import ListConfig @@ -20,8 +20,7 @@ """List of edge features that can be computed.""" -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_scalar_edge_features( x: Union[Data, Batch], features: Union[List[str], ListConfig] ) -> torch.Tensor: @@ -55,8 +54,7 @@ def compute_scalar_edge_features( return torch.cat(feats, dim=1) -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_vector_edge_features( x: Union[Data, Batch], features: Union[List[str], ListConfig] ) -> Union[Data, Batch]: @@ -71,8 +69,7 @@ def compute_vector_edge_features( return x -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_edge_distance( pos: CoordTensor, edge_index: EdgeTensor ) -> torch.Tensor: @@ -91,8 +88,7 @@ def compute_edge_distance( ) -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def pos_emb(edge_index: EdgeTensor, num_pos_emb: int = 16): # From https://github.com/jingraham/neurips19-graph-protein-design d = edge_index[0] - edge_index[1] diff --git a/proteinworkshop/features/edges.py b/proteinworkshop/features/edges.py index d6015a79..4c888892 100644 --- a/proteinworkshop/features/edges.py +++ b/proteinworkshop/features/edges.py @@ -4,13 +4,13 @@ import graphein.protein.tensor.edges as gp import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein, ProteinBatch from omegaconf import ListConfig from torch_geometric.data import Batch, Data -@beartype +@typechecker def compute_edges( x: Union[Data, Batch, Protein, ProteinBatch], edge_types: Union[ListConfig, List[str]], @@ -81,7 +81,7 @@ def compute_edges( return edges, indxs -@beartype +@typechecker def sequence_edges( b: Union[Data, Batch, Protein, ProteinBatch], chains: Optional[torch.Tensor] = None, diff --git a/proteinworkshop/features/factory.py b/proteinworkshop/features/factory.py index 63ae4300..9cc94ec0 100644 --- a/proteinworkshop/features/factory.py +++ b/proteinworkshop/features/factory.py @@ -2,7 +2,7 @@ import torch import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch, get_random_batch from jaxtyping import jaxtyped from loguru import logger @@ -75,8 +75,7 @@ def __init__( if "sequence_positional_encoding" in self.scalar_node_features: self.positional_encoding = PositionalEncoding(16) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, batch: Union[Batch, ProteinBatch] ) -> Union[Batch, ProteinBatch]: diff --git a/proteinworkshop/features/node_features.py b/proteinworkshop/features/node_features.py index dd4c669b..07013fac 100644 --- a/proteinworkshop/features/node_features.py +++ b/proteinworkshop/features/node_features.py @@ -3,7 +3,7 @@ import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.angles import ( alpha, dihedrals, @@ -25,8 +25,7 @@ from .utils import _normalize -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_scalar_node_features( x: Union[Batch, Data, Protein, ProteinBatch], node_features: Union[ListConfig, List[ScalarNodeFeature]], @@ -86,8 +85,7 @@ def compute_scalar_node_features( return torch.cat(feats, dim=1) if feats else x.x -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_vector_node_features( x: Union[Batch, Data, Protein, ProteinBatch], vector_features: Union[ListConfig, List[str]], @@ -114,8 +112,7 @@ def compute_vector_node_features( return x -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_surface_feat( coords: Union[CoordTensor, AtomTensor], k: int, sigma: List[float] ): @@ -150,8 +147,7 @@ def compute_surface_feat( return torch.cat(feat, dim=1) -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def orientations( X: Union[CoordTensor, AtomTensor], ca_idx: int = 1 ) -> OrientationTensor: diff --git a/proteinworkshop/features/representation.py b/proteinworkshop/features/representation.py index a8fed802..3cd7b573 100644 --- a/proteinworkshop/features/representation.py +++ b/proteinworkshop/features/representation.py @@ -2,7 +2,7 @@ from typing import Literal, Tuple import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.types import AtomTensor, CoordTensor from jaxtyping import jaxtyped from torch_geometric.data import Batch, Data @@ -11,8 +11,7 @@ from proteinworkshop.configs.config import ExperimentConfigurationError -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def get_full_atom_coords( atom_tensor: AtomTensor, fill_value: float = 1e-5 ) -> Tuple[CoordTensor, torch.Tensor, torch.Tensor]: @@ -40,8 +39,7 @@ def get_full_atom_coords( return coords, residue_index, atom_type -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def transform_representation( x: Batch, representation_type: Literal["CA", "BB", "FA", "BB_SC", "CA_SC"] ) -> Batch: @@ -87,7 +85,7 @@ def transform_representation( ) -@beartype +@typechecker def _ca_to_fa_repr(x: Data) -> Data: """Converts CA representation to full atom representation.""" coords, residue_index, atom_type = get_full_atom_coords(x.coords) @@ -101,7 +99,7 @@ def _ca_to_fa_repr(x: Data) -> Data: return x -@beartype +@typechecker def _ca_to_bb_repr(x: Data) -> Data: """Converts CA representation to backbone representation.""" x.pos = x.coords[:, :4, :].reshape(-1, 3) @@ -117,7 +115,7 @@ def _ca_to_bb_repr(x: Data) -> Data: return x -@beartype +@typechecker def ca_to_bb_repr(batch: Batch) -> Batch: # sourcery skip: assign-if-exp """ Converts a batch of CA representations to backbone representations. I.e. @@ -180,7 +178,7 @@ def ca_to_bb_repr(batch: Batch) -> Batch: # sourcery skip: assign-if-exp return batch -@beartype +@typechecker def ca_to_bb_sc_repr(batch: Batch) -> Batch: """Converts a batch of CA representations to backbone + sidechain representations.""" # Get centroids @@ -190,7 +188,7 @@ def ca_to_bb_sc_repr(batch: Batch) -> Batch: return ca_to_fa_repr(batch) -@beartype +@typechecker def ca_to_ca_sc_repr(batch: Batch) -> Batch: """Converts a batch of CA representations to C + sidechain representations.""" # Get centroids @@ -200,7 +198,7 @@ def ca_to_ca_sc_repr(batch: Batch) -> Batch: return batch -@beartype +@typechecker def coarsen_sidechain(x: Data, aggr: str = "mean") -> CoordTensor: """Returns tensor of sidechain centroids: L x 3""" # sourcery skip: remove-unnecessary-else, swap-if-else-branches @@ -216,7 +214,7 @@ def coarsen_sidechain(x: Data, aggr: str = "mean") -> CoordTensor: return sc_points -@beartype +@typechecker def ca_to_fa_repr(batch: Batch) -> Batch: # sourcery skip: assign-if-exp """Converts a batch of CA representations to full atom representations.""" if "sidechain_torsion" in batch.keys: diff --git a/proteinworkshop/features/sequence_features.py b/proteinworkshop/features/sequence_features.py index 58815f89..e012107c 100644 --- a/proteinworkshop/features/sequence_features.py +++ b/proteinworkshop/features/sequence_features.py @@ -3,11 +3,11 @@ import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from torch_geometric.data import Batch, Data -@beartype +@typechecker def amino_acid_one_hot( x: Union[Batch, Data], num_classes: int = 23 ) -> torch.Tensor: diff --git a/proteinworkshop/features/utils.py b/proteinworkshop/features/utils.py index 2d6b7190..e2f4e1c3 100644 --- a/proteinworkshop/features/utils.py +++ b/proteinworkshop/features/utils.py @@ -1,10 +1,9 @@ import torch -from beartype import beartype +from beartype import beartype as typechecker from jaxtyping import jaxtyped -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def _normalize(tensor: torch.Tensor, dim: int = -1) -> torch.Tensor: """ Safely normalize a Tensor. Adapted from: diff --git a/proteinworkshop/models/base.py b/proteinworkshop/models/base.py index f18bbd3e..3c0149d0 100644 --- a/proteinworkshop/models/base.py +++ b/proteinworkshop/models/base.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.angles import dihedrals from graphein.protein.tensor.data import ProteinBatch, get_random_protein from loguru import logger @@ -37,7 +37,9 @@ def forward(self, batch: Batch) -> torch.Tensor: ... @abc.abstractmethod - def training_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: + def training_step( + self, batch: Batch, batch_idx: torch.Tensor + ) -> torch.Tensor: """Implement training step. :param batch: Mini-batch of data. @@ -50,7 +52,9 @@ def training_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: ... @abc.abstractmethod - def validation_step(self, batch: Batch, batch_idx: torch.Tensor) -> torch.Tensor: + def validation_step( + self, batch: Batch, batch_idx: torch.Tensor + ) -> torch.Tensor: """Implement validation step. :param batch: Mini-batch of data. @@ -108,7 +112,9 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: for output in self.config.task.supervise_on: if output == "node_label": labels["node_label"] = batch.node_y - if isinstance(self.losses["node_label"], torch.nn.BCEWithLogitsLoss): + if isinstance( + self.losses["node_label"], torch.nn.BCEWithLogitsLoss + ): labels["node_label"] = F.one_hot( labels["node_label"], num_classes=self.config.dataset.num_classes, @@ -116,7 +122,9 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: elif output == "graph_label": labels["graph_label"] = batch.graph_y if ( - isinstance(self.losses["graph_label"], torch.nn.BCEWithLogitsLoss) + isinstance( + self.losses["graph_label"], torch.nn.BCEWithLogitsLoss + ) and batch.graph_y.ndim == 1 ): labels["graph_label"] = F.one_hot( @@ -167,8 +175,10 @@ def get_labels(self, batch: Union[Batch, ProteinBatch]) -> Label: return Label(labels) - @beartype - def compute_loss(self, y_hat: ModelOutput, y: Label) -> Dict[str, torch.Tensor]: + @typechecker + def compute_loss( + self, y_hat: ModelOutput, y: Label + ) -> Dict[str, torch.Tensor]: """ Compute loss by iterating over all outputs. @@ -205,9 +215,13 @@ def configure_optimizers(self): # sourcery skip: extract-method if self.config.get("scheduler"): logger.info("Instantiating scheduler...") - scheduler = hydra.utils.instantiate(self.config.scheduler, optimiser) + scheduler = hydra.utils.instantiate( + self.config.scheduler, optimiser + ) scheduler = OmegaConf.to_container(scheduler) - scheduler["scheduler"] = scheduler["scheduler"](optimizer=optimiser) + scheduler["scheduler"] = scheduler["scheduler"]( + optimizer=optimiser + ) optimiser_config = { "optimizer": optimiser, "lr_scheduler": scheduler, @@ -238,7 +252,9 @@ def _build_output_decoders(self) -> nn.ModuleDict: decoders[output_head] = hydra.utils.instantiate(cfg) return decoders - def configure_losses(self, loss_dict: Dict[str, str]) -> Dict[str, Callable]: + def configure_losses( + self, loss_dict: Dict[str, str] + ) -> Dict[str, Callable]: """ Configures losses from config. Returns a dictionary of losses mapping each output name to its respective loss function. @@ -335,8 +351,10 @@ def configure_metrics(self): metric_names.append(f"{metric_name}") setattr(self, "metric_names", metric_names) - @beartype - def log_metrics(self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch): + @typechecker + def log_metrics( + self, loss, y_hat: ModelOutput, y: Label, stage: str, batch: Batch + ): """ Logs metrics to logger. @@ -416,7 +434,9 @@ def __init__(self, cfg: DictConfig) -> None: logger.info(self.featuriser) logger.info("Instantiating task transform...") - self.task_transform = hydra.utils.instantiate(cfg.get("task.transform")) + self.task_transform = hydra.utils.instantiate( + cfg.get("task.transform") + ) logger.info(self.task_transform) self.save_hyperparameters() @@ -438,11 +458,13 @@ def _create_example_batch(self) -> ProteinBatch: ] for p in proteins: setattr(p, "x", torch.zeros(p.coords.shape[0])) - setattr(p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1)) + setattr( + p, "seq_pos", torch.arange(p.coords.shape[0]).unsqueeze(-1) + ) batch = ProteinBatch.from_data_list(proteins) return self.featurise(batch) - @beartype + @typechecker def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: """ Implements the forward pass of the model. @@ -477,12 +499,16 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> ModelOutput: emb_type = self.decoder[ output_head ].input # node_embedding or graph_embedding - output[output_head] = self.decoder[output_head](output[emb_type]) + output[output_head] = self.decoder[output_head]( + output[emb_type] + ) return self.compute_output(output, batch) - @beartype - def transform_encoder_output(self, output: EncoderOutput, batch) -> EncoderOutput: + @typechecker + def transform_encoder_output( + self, output: EncoderOutput, batch + ) -> EncoderOutput: """ Modifies graph encoder output. @@ -508,7 +534,7 @@ def transform_encoder_output(self, output: EncoderOutput, batch) -> EncoderOutpu return output - @beartype + @typechecker def compute_output(self, output: ModelOutput, batch: Batch) -> ModelOutput: """ Computes output from model output. @@ -540,7 +566,7 @@ def compute_output(self, output: ModelOutput, batch: Batch) -> ModelOutput: return output - @beartype + @typechecker def _do_step( self, batch: Batch, diff --git a/proteinworkshop/models/graph_encoders/components/radial.py b/proteinworkshop/models/graph_encoders/components/radial.py index 2a4b6a50..1474930f 100644 --- a/proteinworkshop/models/graph_encoders/components/radial.py +++ b/proteinworkshop/models/graph_encoders/components/radial.py @@ -6,7 +6,7 @@ import numpy as np import torch -from beartype import beartype +from beartype import beartype as typechecker from jaxtyping import Float, jaxtyped @@ -95,8 +95,7 @@ def __repr__(self) -> str: return f"{self.__class__.__name__}(p={self.p}, r_max={self.r_max})" -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def compute_rbf( distances: Float[torch.Tensor, " num_edges"], min_distance: float = 0.0, diff --git a/proteinworkshop/models/graph_encoders/components/wrappers.py b/proteinworkshop/models/graph_encoders/components/wrappers.py index 704c33e3..3caae133 100644 --- a/proteinworkshop/models/graph_encoders/components/wrappers.py +++ b/proteinworkshop/models/graph_encoders/components/wrappers.py @@ -1,5 +1,5 @@ import torch -from beartype import beartype +from beartype import beartype as typechecker from jaxtyping import Bool, jaxtyped @@ -80,8 +80,7 @@ def repeat(self, n, c: int = 1, y: int = 1): def clone(self): return ScalarVector(self.scalar.clone(), self.vector.clone()) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def mask(self, node_mask: Bool[torch.Tensor, " n_nodes"]): return ScalarVector( self.scalar * node_mask[:, None], diff --git a/proteinworkshop/models/graph_encoders/gcpnet.py b/proteinworkshop/models/graph_encoders/gcpnet.py index 02e85c3d..35026c3f 100644 --- a/proteinworkshop/models/graph_encoders/gcpnet.py +++ b/proteinworkshop/models/graph_encoders/gcpnet.py @@ -4,7 +4,7 @@ import hydra import torch import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from omegaconf import DictConfig @@ -87,24 +87,14 @@ def __init__( self.predict_node_rep = module_cfg.predict_node_rep # Feature dimensionalities - edge_input_dims = ScalarVector( - model_cfg.e_input_dim, model_cfg.xi_input_dim - ) - node_input_dims = ScalarVector( - model_cfg.h_input_dim, model_cfg.chi_input_dim - ) - self.edge_dims = ScalarVector( - model_cfg.e_hidden_dim, model_cfg.xi_hidden_dim - ) - self.node_dims = ScalarVector( - model_cfg.h_hidden_dim, model_cfg.chi_hidden_dim - ) + edge_input_dims = ScalarVector(model_cfg.e_input_dim, model_cfg.xi_input_dim) + node_input_dims = ScalarVector(model_cfg.h_input_dim, model_cfg.chi_input_dim) + self.edge_dims = ScalarVector(model_cfg.e_hidden_dim, model_cfg.xi_hidden_dim) + self.node_dims = ScalarVector(model_cfg.h_hidden_dim, model_cfg.chi_hidden_dim) # Position-wise operations self.centralize = partial(centralize, key="pos") - self.localize = partial( - localize, norm_pos_diff=module_cfg.norm_pos_diff - ) + self.localize = partial(localize, norm_pos_diff=module_cfg.norm_pos_diff) self.decentralize = partial(decentralize, key="pos") # Input embeddings @@ -156,11 +146,10 @@ def __init__( def required_batch_attributes(self) -> List[str]: return ["edge_index", "pos", "x", "batch"] - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the GCPNet encoder. - + Returns the node embedding and graph embedding in a dictionary. :param batch: Batch of data to encode. @@ -174,9 +163,7 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: :rtype: EncoderOutput """ # Centralize node positions to make them translation-invariant - pos_centroid, batch.pos = self.centralize( - batch, batch_index=batch.batch - ) + pos_centroid, batch.pos = self.centralize(batch, batch_index=batch.batch) # Install `h`, `chi`, `e`, and `xi` using corresponding features built by the `FeatureFactory` batch.h, batch.chi, batch.e, batch.xi = ( @@ -218,9 +205,7 @@ def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: _, centralized_node_pos = self.centralize( batch, batch_index=batch.batch ) - batch.f_ij = self.localize( - centralized_node_pos, batch.edge_index - ) + batch.f_ij = self.localize(centralized_node_pos, batch.edge_index) encoder_outputs["pos"] = batch.pos # (n, 3) -> (batch_size, 3) # Summarize intermediate node representations as final predictions diff --git a/proteinworkshop/models/graph_encoders/gnn.py b/proteinworkshop/models/graph_encoders/gnn.py index 4bfc3d79..3e067b87 100644 --- a/proteinworkshop/models/graph_encoders/gnn.py +++ b/proteinworkshop/models/graph_encoders/gnn.py @@ -1,7 +1,7 @@ from typing import List, Set, Union import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -166,8 +166,7 @@ def required_batch_attributes(self) -> Set[str]: else: return {"x", "edge_index", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the GNN encoder. diff --git a/proteinworkshop/models/graph_encoders/gvp.py b/proteinworkshop/models/graph_encoders/gvp.py index 3aca7ef9..78dc26bb 100644 --- a/proteinworkshop/models/graph_encoders/gvp.py +++ b/proteinworkshop/models/graph_encoders/gvp.py @@ -2,7 +2,7 @@ import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -129,8 +129,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"edge_index", "pos", "x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the GVP-GNN encoder. diff --git a/proteinworkshop/models/graph_encoders/identity.py b/proteinworkshop/models/graph_encoders/identity.py index 9aba9b1d..799ec542 100644 --- a/proteinworkshop/models/graph_encoders/identity.py +++ b/proteinworkshop/models/graph_encoders/identity.py @@ -1,7 +1,7 @@ from typing import Set, Union import torch.nn as nn -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -33,8 +33,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the IdentityModel encoder. diff --git a/proteinworkshop/models/graph_encoders/layers/gcp.py b/proteinworkshop/models/graph_encoders/layers/gcp.py index 7f823b7f..1644f13c 100644 --- a/proteinworkshop/models/graph_encoders/layers/gcp.py +++ b/proteinworkshop/models/graph_encoders/layers/gcp.py @@ -18,7 +18,7 @@ import torch import torch_scatter -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import Bool, Float, Int64, jaxtyped from omegaconf import DictConfig, OmegaConf @@ -45,8 +45,7 @@ def __init__(self, drop_rate: float): super().__init__() self.drop_rate = drop_rate - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, x: torch.Tensor) -> torch.Tensor: """ :param x: `torch.Tensor` corresponding to vector channels @@ -75,8 +74,7 @@ def __init__(self, drop_rate: float, use_gcp_dropout: bool = True): VectorDropout(drop_rate) if use_gcp_dropout else nn.Identity() ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, x: Union[torch.Tensor, ScalarVector] ) -> Union[torch.Tensor, ScalarVector]: @@ -88,9 +86,7 @@ def forward( return x elif isinstance(x, torch.Tensor): return self.scalar_dropout(x) - return ScalarVector( - self.scalar_dropout(x[0]), self.vector_dropout(x[1]) - ) + return ScalarVector(self.scalar_dropout(x[0]), self.vector_dropout(x[1])) class GCPLayerNorm(nn.Module): @@ -110,8 +106,7 @@ def __init__( self.eps = eps @staticmethod - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def norm_vector( v: torch.Tensor, use_gcp_norm: bool = True, eps: float = 1e-8 ) -> torch.Tensor: @@ -120,14 +115,11 @@ def norm_vector( vector_norm = torch.clamp( torch.sum(torch.square(v), dim=-1, keepdim=True), min=eps ) - vector_norm = torch.sqrt( - torch.mean(vector_norm, dim=-2, keepdim=True) - ) + vector_norm = torch.sqrt(torch.mean(vector_norm, dim=-2, keepdim=True)) v_norm = v / vector_norm return v_norm - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, x: Union[torch.Tensor, ScalarVector] ) -> Union[torch.Tensor, ScalarVector]: @@ -189,9 +181,7 @@ def __init__( else max(self.vector_input_dim, self.vector_output_dim) ) - scalar_vector_frame_dim = ( - scalarization_vectorization_output_dim * 3 - ) + scalar_vector_frame_dim = scalarization_vectorization_output_dim * 3 self.vector_down = nn.Linear( self.vector_input_dim, self.hidden_dim, bias=False ) @@ -208,9 +198,7 @@ def __init__( ) if feedforward_out else nn.Linear( - self.hidden_dim - + self.scalar_input_dim - + scalar_vector_frame_dim, + self.hidden_dim + self.scalar_input_dim + scalar_vector_frame_dim, self.scalar_output_dim, ) ) @@ -240,13 +228,10 @@ def __init__( else nn.Linear(self.scalar_input_dim, self.scalar_output_dim) ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def create_zero_vector( self, - scalar_rep: Float[ - torch.Tensor, "batch_num_entities merged_scalar_dim" - ], + scalar_rep: Float[torch.Tensor, "batch_num_entities merged_scalar_dim"], ) -> Float[torch.Tensor, "batch_num_entities o 3"]: return torch.zeros( scalar_rep.shape[0], @@ -256,8 +241,7 @@ def create_zero_vector( ) @staticmethod - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def scalarize( vector_rep: Float[torch.Tensor, "batch_num_entities 3 3"], edge_index: Int64[torch.Tensor, "2 batch_num_edges"], @@ -289,23 +273,17 @@ def scalarize( ) local_scalar_rep_i = local_scalar_rep_i.transpose(-1, -2) else: - local_scalar_rep_i = torch.matmul(frames, vector_rep_i).transpose( - -1, -2 - ) + local_scalar_rep_i = torch.matmul(frames, vector_rep_i).transpose(-1, -2) # potentially enable E(3)-equivariance and, thereby, chirality-invariance if enable_e3_equivariance: # avoid corrupting gradients with an in-place operation local_scalar_rep_i_copy = local_scalar_rep_i.clone() - local_scalar_rep_i_copy[:, :, 1] = torch.abs( - local_scalar_rep_i[:, :, 1] - ) + local_scalar_rep_i_copy[:, :, 1] = torch.abs(local_scalar_rep_i[:, :, 1]) local_scalar_rep_i = local_scalar_rep_i_copy # reshape frame-derived geometric scalars - local_scalar_rep_i = local_scalar_rep_i.reshape( - vector_rep_i.shape[0], 9 - ) + local_scalar_rep_i = local_scalar_rep_i.reshape(vector_rep_i.shape[0], 9) if node_inputs: # for node inputs, summarize all edge-wise geometric scalars using an average @@ -320,13 +298,10 @@ def scalarize( return local_scalar_rep_i - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def vectorize( self, - scalar_rep: Float[ - torch.Tensor, "batch_num_entities merged_scalar_dim" - ], + scalar_rep: Float[torch.Tensor, "batch_num_entities merged_scalar_dim"], vector_hidden_rep: Float[torch.Tensor, "batch_num_entities 3 n"], ) -> Float[torch.Tensor, "batch_num_entities o 3"]: vector_rep = self.vector_up(vector_hidden_rep) @@ -342,8 +317,7 @@ def vectorize( return vector_rep - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, s_maybe_v: Union[ @@ -462,8 +436,7 @@ def __init__( enable_e3_equivariance=cfg.enable_e3_equivariance, ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, batch: Union[Batch, ProteinBatch] ) -> Tuple[ @@ -494,21 +467,15 @@ def forward( ) # [n_edges, 3] edge_lengths = torch.linalg.norm(edge_vectors, dim=-1) # [n_edges, 1] edge_rep = ScalarVector( - torch.cat( - (edge_rep.scalar, self.radial_embedding(edge_lengths)), dim=-1 - ), + torch.cat((edge_rep.scalar, self.radial_embedding(edge_lengths)), dim=-1), edge_rep.vector, ) edge_rep = ( - edge_rep.scalar - if not self.edge_embedding.vector_input_dim - else edge_rep + edge_rep.scalar if not self.edge_embedding.vector_input_dim else edge_rep ) node_rep = ( - node_rep.scalar - if not self.node_embedding.vector_input_dim - else node_rep + node_rep.scalar if not self.node_embedding.vector_input_dim else node_rep ) if self.pre_norm: @@ -537,7 +504,7 @@ def forward( return node_rep, edge_rep -@beartype +@typechecker def get_GCP_with_custom_cfg( input_dims: Any, output_dims: Any, cfg: DictConfig, **kwargs ): @@ -621,8 +588,7 @@ def __init__( nn.Linear(output_dims.scalar, 1), nn.Sigmoid() ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def message( self, node_rep: ScalarVector, @@ -674,8 +640,7 @@ def message( return message_residual.flatten() - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def aggregate( self, message: Float[torch.Tensor, "batch_num_edges message_dim"], @@ -688,8 +653,7 @@ def aggregate( ) return aggregate - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, node_rep: ScalarVector, @@ -723,9 +687,7 @@ def __init__( if nonlinearities is None: nonlinearities = cfg.nonlinearities self.pre_norm = layer_cfg.pre_norm - self.predict_node_positions = getattr( - cfg, "predict_node_positions", False - ) + self.predict_node_positions = getattr(cfg, "predict_node_positions", False) self.node_positions_weight = getattr(cfg, "node_positions_weight", 1.0) self.update_positions_with_vector_sum = getattr( cfg, "update_positions_with_vector_sum", False @@ -815,8 +777,7 @@ def __init__( enable_e3_equivariance=cfg.enable_e3_equivariance, ) - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def derive_x_update( self, node_rep: ScalarVector, @@ -838,8 +799,7 @@ def derive_x_update( return x_update - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward( self, node_rep: Tuple[ diff --git a/proteinworkshop/models/graph_encoders/mace.py b/proteinworkshop/models/graph_encoders/mace.py index f1d0de6f..5b99180d 100644 --- a/proteinworkshop/models/graph_encoders/mace.py +++ b/proteinworkshop/models/graph_encoders/mace.py @@ -3,7 +3,7 @@ import e3nn import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -206,8 +206,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"edge_index", "pos", "x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the MACE encoder. diff --git a/proteinworkshop/models/graph_encoders/tfn.py b/proteinworkshop/models/graph_encoders/tfn.py index d23e845c..38f8caa4 100644 --- a/proteinworkshop/models/graph_encoders/tfn.py +++ b/proteinworkshop/models/graph_encoders/tfn.py @@ -4,7 +4,7 @@ import e3nn import torch import torch.nn.functional as F -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import jaxtyped from torch_geometric.data import Batch @@ -155,8 +155,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"edge_index", "pos", "x", "batch"} - @jaxtyped - @beartype + @jaxtyped(typechecker=typechecker) def forward(self, batch: Union[Batch, ProteinBatch]) -> EncoderOutput: """Implements the forward pass of the TFN encoder. diff --git a/proteinworkshop/models/utils.py b/proteinworkshop/models/utils.py index 89cc6673..d8f6f4fd 100644 --- a/proteinworkshop/models/utils.py +++ b/proteinworkshop/models/utils.py @@ -4,7 +4,7 @@ import torch.nn as nn import torch.nn.functional as F import torch_scatter -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import ProteinBatch from jaxtyping import Bool, Float, Int64, jaxtyped from omegaconf import DictConfig @@ -173,8 +173,7 @@ def flatten_list(l: List[List]) -> List: # noqa: E741 return [item for sublist in l for item in sublist] -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def centralize( batch: Union[Batch, ProteinBatch], key: str, @@ -210,8 +209,7 @@ def centralize( return entities_centroid, entities_centered -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def decentralize( batch: Union[Batch, ProteinBatch], key: str, @@ -230,8 +228,7 @@ def decentralize( return entities_centered -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def localize( pos: Float[torch.Tensor, "batch_num_nodes 3"], edge_index: Int64[torch.Tensor, "2 batch_num_edges"], @@ -311,8 +308,7 @@ def localize( return f_ij -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def safe_norm( x: torch.Tensor, dim: int = -1, @@ -326,8 +322,7 @@ def safe_norm( return norm + eps -@jaxtyped -@beartype +@jaxtyped(typechecker=typechecker) def is_identity( nonlinearity: Optional[Union[Callable, nn.Module]] = None ) -> bool: diff --git a/proteinworkshop/tasks/sequence_denoising.py b/proteinworkshop/tasks/sequence_denoising.py index e7f0e74f..10db2b8c 100644 --- a/proteinworkshop/tasks/sequence_denoising.py +++ b/proteinworkshop/tasks/sequence_denoising.py @@ -41,7 +41,7 @@ def __init__( def required_attributes(self) -> Set[str]: return {"residue_type"} - @beartype + @typechecker def __call__(self, x: Union[Data, Protein]) -> Union[Data, Protein]: x.residue_type_uncorrupted = copy.deepcopy(x.residue_type) # Get indices of residues to corrupt diff --git a/proteinworkshop/tasks/structural_denoising.py b/proteinworkshop/tasks/structural_denoising.py index c1c25b42..0a03b654 100644 --- a/proteinworkshop/tasks/structural_denoising.py +++ b/proteinworkshop/tasks/structural_denoising.py @@ -3,7 +3,7 @@ from typing import Literal, Set, Union import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein from torch_geometric.data import Data from torch_geometric.transforms import BaseTransform @@ -36,7 +36,7 @@ def __init__( def required_attributes(self) -> Set[str]: return {"coords"} - @beartype + @typechecker def __call__(self, x: Union[Data, Protein]) -> Union[Data, Protein]: """Adds noise to the coordinates of a protein structure. From 9fbc041371dde73dad9a9124f0001d827b67bc5a Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:06:07 -0800 Subject: [PATCH 085/106] typechecker refactor for esm --- .../models/graph_encoders/esm_embeddings.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/proteinworkshop/models/graph_encoders/esm_embeddings.py b/proteinworkshop/models/graph_encoders/esm_embeddings.py index 2bc96276..8ab18118 100644 --- a/proteinworkshop/models/graph_encoders/esm_embeddings.py +++ b/proteinworkshop/models/graph_encoders/esm_embeddings.py @@ -3,7 +3,7 @@ from typing import Dict, List, Optional, Set, Tuple, Union import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.resi_atoms import RESI_THREE_TO_1 from graphein.protein.tensor.data import ProteinBatch from loguru import logger @@ -18,7 +18,7 @@ from proteinworkshop.types import EncoderOutput -@beartype +@typechecker def _compute_md5(file_name: str, chunk_size: int = 65536) -> str: """ Compute MD5 of the file. @@ -37,7 +37,7 @@ def _compute_md5(file_name: str, chunk_size: int = 65536) -> str: return md5.hexdigest() -@beartype +@typechecker def _download( url: str, path: str, @@ -176,7 +176,7 @@ def __init__( self.residue_map["UNK"] = "" @property - @beartype + @typechecker def required_batch_attributes(self) -> Set[str]: """ Return the requied attributes for each batch. @@ -185,7 +185,7 @@ def required_batch_attributes(self) -> Set[str]: """ return {"residues", "id", "coords", "batch"} - @beartype + @typechecker def load_weight( self, path: str, model: str ) -> Tuple[nn.Module, esm.data.Alphabet]: @@ -215,7 +215,7 @@ def load_weight( model_name, model_data, regression_data ) - @beartype + @typechecker def esm_embed( self, batch: Union[Batch, ProteinBatch], @@ -251,7 +251,7 @@ def esm_embed( node_embedding = node_embedding[batch_mask] return node_embedding - @beartype + @typechecker def forward( self, batch: Union[Batch, ProteinBatch], From 69d04744e0f9cb50fa3f10478f7e018117d2c428 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:08:28 -0800 Subject: [PATCH 086/106] typechecker refactor for dataset base --- proteinworkshop/datasets/base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/proteinworkshop/datasets/base.py b/proteinworkshop/datasets/base.py index 2101ba75..1f58dbfa 100644 --- a/proteinworkshop/datasets/base.py +++ b/proteinworkshop/datasets/base.py @@ -20,7 +20,7 @@ import numpy as np import pandas as pd import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein import verbose from graphein.protein.tensor.dataloader import ProteinDataLoader from graphein.protein.tensor.io import protein_to_pyg @@ -99,7 +99,7 @@ def obsolete_pdbs(self) -> Dict[str, str]: """ return get_obsolete_mapping() - @beartype + @typechecker def compose_transforms(self, transforms: Iterable[Callable]) -> T.Compose: """Compose an iterable of Transforms into a single transform. From 248478b6af9099a511b6fecbf727414688c2ef73 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:09:02 -0800 Subject: [PATCH 087/106] lint --- proteinworkshop/datasets/masif_site.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/datasets/masif_site.py b/proteinworkshop/datasets/masif_site.py index ea89f74f..30d9d2e9 100644 --- a/proteinworkshop/datasets/masif_site.py +++ b/proteinworkshop/datasets/masif_site.py @@ -1,6 +1,6 @@ import os import pathlib -from typing import Callable, Iterable, Literal, Optional, List +from typing import Callable, Iterable, List, Literal, Optional import omegaconf import wget From e7123f6eb937c587f82f6588ace7bf80cd3588bd Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:13:28 -0800 Subject: [PATCH 088/106] remove merge artifact from poetry.lock --- poetry.lock | 1 - 1 file changed, 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 3247a7a0..e71f2174 100644 --- a/poetry.lock +++ b/poetry.lock @@ -492,7 +492,6 @@ dev = ["annoy", "black (==22.3.0)", "flake8", "flask", "flask-compress", "ipytho insights = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter"] test = ["parameterized", "pytest", "pytest-cov"] tutorials = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter", "torchtext", "torchvision"] -======= name = "bokeh" version = "3.3.0" description = "Interactive plots and applications in the browser from Python" From 51732590c6fde3539b87d366c4e1d3bafb0e06ae Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:23:47 -0800 Subject: [PATCH 089/106] fix beartype import --- proteinworkshop/tasks/sequence_denoising.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/tasks/sequence_denoising.py b/proteinworkshop/tasks/sequence_denoising.py index 10db2b8c..f6fd1aa5 100644 --- a/proteinworkshop/tasks/sequence_denoising.py +++ b/proteinworkshop/tasks/sequence_denoising.py @@ -3,7 +3,7 @@ from typing import Literal, Set, Union import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein from torch_geometric.data import Data from torch_geometric.transforms import BaseTransform From a0a775aa8c52800aa3f93434797fb9c59f8b88ac Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:25:23 -0800 Subject: [PATCH 090/106] fix broken lock file --- poetry.lock | 2 ++ 1 file changed, 2 insertions(+) diff --git a/poetry.lock b/poetry.lock index e71f2174..5bd2359d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -492,6 +492,8 @@ dev = ["annoy", "black (==22.3.0)", "flake8", "flask", "flask-compress", "ipytho insights = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter"] test = ["parameterized", "pytest", "pytest-cov"] tutorials = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter", "torchtext", "torchvision"] + +[[package]] name = "bokeh" version = "3.3.0" description = "Interactive plots and applications in the browser from Python" From 6be97aaaec177bcca9784c76e1c5e9489b1c16b0 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 12:51:03 -0800 Subject: [PATCH 091/106] fix broken poetry.lock and update jaxtyping dependency --- poetry.lock | 2733 +++++++++++++++++++++++------------------------- pyproject.toml | 2 +- 2 files changed, 1337 insertions(+), 1398 deletions(-) diff --git a/poetry.lock b/poetry.lock index 5bd2359d..04d7a651 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,112 +1,100 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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3d39e27bcb8c755ed92c896e10055e6fb27fec03 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 13:04:46 -0800 Subject: [PATCH 092/106] fix broken poetry.lock and update jaxtyping dependency --- poetry.lock | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/poetry.lock b/poetry.lock index 04d7a651..b7f9d840 100644 --- a/poetry.lock +++ b/poetry.lock @@ -5871,4 +5871,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = ">= 3.9, < 3.11" -content-hash = "7fe16ddbc57a36d0906d75cda9b8e5698a8ce8a4e6aa54203bdd5166ce301a0f" +content-hash = "7a1cf7c5d80a28705ce6bcb18b79e5077677a5751aa5e6be6b72467c1baed34a" diff --git a/pyproject.toml b/pyproject.toml index db848c9f..0ebabd58 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,7 +51,7 @@ lightning = "^2.0.7" python-dotenv = "^1.0.0" wget = "^3.2" opt-einsum = "^3.3.0" -torch = {version="^2.1.0", source="torch"} +torch = {version="^2.1.2", source="torch"} pyrootutils = "^1.0.4" torch-geometric = "^2.4.0" hydra-colorlog = "^1.2.0" From d7d22b91c5b12b3a81eafbbda2f6c6161a45fbd8 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 13:31:44 -0800 Subject: [PATCH 093/106] use mamba in test workflow --- .github/workflows/code-tests.yaml | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/.github/workflows/code-tests.yaml b/.github/workflows/code-tests.yaml index 92767204..9e5a8468 100644 --- a/.github/workflows/code-tests.yaml +++ b/.github/workflows/code-tests.yaml @@ -25,10 +25,14 @@ jobs: steps: - name: Checkout repository uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2.3.1 + - name: Setup miniconda + uses: conda-incubator/setup-miniconda@v2 with: + auto-update-conda: true + miniforge-variant: Mambaforge + channels: "conda-forge, pytorch, pyg" python-version: ${{ matrix.python-version }} + use-mamba: true - id: cache-dependencies name: Cache dependencies uses: actions/cache@v2.1.7 From be5dd407fda5ea2dd85a0295c728431af16cbbe7 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 13:39:20 -0800 Subject: [PATCH 094/106] fix pyg wheel link for torch > 2.1.0 --- proteinworkshop/scripts/install_pyg.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/proteinworkshop/scripts/install_pyg.py b/proteinworkshop/scripts/install_pyg.py index cb3928c8..4ef97b46 100644 --- a/proteinworkshop/scripts/install_pyg.py +++ b/proteinworkshop/scripts/install_pyg.py @@ -26,6 +26,8 @@ def _install_pyg(force_reinstall: bool = False): # torch_version = "2.0.0" if cuda_version == "cu116": raise ValueError("PyTorch 2.0.0 does not support CUDA 11.6") + if torch_version.startswith("2.1"): + torch_version = "2.1.0" if torch_version.startswith("1.13"): if cuda_version == "cu118": From 735640b91f5c4f2dbbf2a588020769a38ee9938a Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 13:50:56 -0800 Subject: [PATCH 095/106] update tests --- proteinworkshop/tasks/multihot_label_encoding.py | 5 ++++- tests/features/test_features.py | 5 ++++- tests/task/test_instantiate_transforms.py | 9 ++++++++- 3 files changed, 16 insertions(+), 3 deletions(-) diff --git a/proteinworkshop/tasks/multihot_label_encoding.py b/proteinworkshop/tasks/multihot_label_encoding.py index 113ad967..3708bb55 100644 --- a/proteinworkshop/tasks/multihot_label_encoding.py +++ b/proteinworkshop/tasks/multihot_label_encoding.py @@ -1,4 +1,4 @@ -from typing import Union +from typing import Union, Set import torch from graphein.protein.tensor.data import Protein @@ -17,6 +17,9 @@ class MultiHotLabelEncoding(T.BaseTransform): def __init__(self, num_classes: int) -> None: self.num_classes = num_classes + def required_batch_attributes(self) -> Set[str]: + return {"graph_y"} + def __call__(self, data: Union[Protein, Data]) -> Union[Protein, Data]: labels = torch.zeros((1, self.num_classes)) labels[:, data.graph_y] = 1 diff --git a/tests/features/test_features.py b/tests/features/test_features.py index 9eb23a45..87e924ce 100644 --- a/tests/features/test_features.py +++ b/tests/features/test_features.py @@ -1,5 +1,5 @@ import os - +import torch import omegaconf from hydra.utils import instantiate @@ -29,6 +29,9 @@ def test_feature_shapes(example_batch): cfg = omegaconf.OmegaConf.load(config_path) featuriser = instantiate(cfg) + example_batch.seq_pos = torch.arange( + example_batch.coords.shape[0], dtype=torch.long + ) out = featuriser(example_batch) out_features = out.x diff --git a/tests/task/test_instantiate_transforms.py b/tests/task/test_instantiate_transforms.py index 7fcbc63b..76b027be 100644 --- a/tests/task/test_instantiate_transforms.py +++ b/tests/task/test_instantiate_transforms.py @@ -9,10 +9,13 @@ TRANSFORMS = os.listdir(TRANSFORM_CONFIG_DIR) -def test_instantiate_encoders(): +def test_instantiate_transforms(): for t in TRANSFORMS: config_path = TRANSFORM_CONFIG_DIR / t cfg = omegaconf.OmegaConf.load(config_path) + + if t == "multihot_label_encoding.yaml": + cfg.num_classes = 2 transform = instantiate(cfg) if t == "none.yaml": @@ -25,6 +28,10 @@ def test_transform_call(example_batch): for t in TRANSFORMS: config_path = TRANSFORM_CONFIG_DIR / t cfg = omegaconf.OmegaConf.load(config_path) + + if t == "multihot_label_encoding.yaml": + cfg.num_classes = 2 + transform = instantiate(cfg) t = t.removesuffix(".yaml") From 8d32aa282a772afa2bd273d1ebb5ace468e46634 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 13:51:12 -0800 Subject: [PATCH 096/106] lint --- proteinworkshop/tasks/multihot_label_encoding.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/tasks/multihot_label_encoding.py b/proteinworkshop/tasks/multihot_label_encoding.py index 3708bb55..cf72e653 100644 --- a/proteinworkshop/tasks/multihot_label_encoding.py +++ b/proteinworkshop/tasks/multihot_label_encoding.py @@ -1,4 +1,4 @@ -from typing import Union, Set +from typing import Set, Union import torch from graphein.protein.tensor.data import Protein From b15bdf2528d89601cdb7011e8b320ed01358ad6f Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 13:57:11 -0800 Subject: [PATCH 097/106] fix test --- tests/task/test_instantiate_transforms.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/task/test_instantiate_transforms.py b/tests/task/test_instantiate_transforms.py index 76b027be..63db1c68 100644 --- a/tests/task/test_instantiate_transforms.py +++ b/tests/task/test_instantiate_transforms.py @@ -15,7 +15,7 @@ def test_instantiate_transforms(): cfg = omegaconf.OmegaConf.load(config_path) if t == "multihot_label_encoding.yaml": - cfg.num_classes = 2 + cfg.multihot_label_encoding.num_classes = 2 transform = instantiate(cfg) if t == "none.yaml": @@ -30,7 +30,7 @@ def test_transform_call(example_batch): cfg = omegaconf.OmegaConf.load(config_path) if t == "multihot_label_encoding.yaml": - cfg.num_classes = 2 + cfg.multihot_label_encoding.num_classes = 2 transform = instantiate(cfg) From a0782c419f5a891ab30073e44b9a5cc63c9c01e1 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 14:03:19 -0800 Subject: [PATCH 098/106] set dummy labels on example_batch --- proteinworkshop/datasets/utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/proteinworkshop/datasets/utils.py b/proteinworkshop/datasets/utils.py index 81b06e06..1d0f73d4 100644 --- a/proteinworkshop/datasets/utils.py +++ b/proteinworkshop/datasets/utils.py @@ -10,6 +10,7 @@ from typing import List, Optional import biotite.database.rcsb as rcsb +import torch import torch.nn.functional as F from graphein.protein.tensor.data import ProteinBatch, get_random_protein from tqdm import tqdm @@ -136,6 +137,8 @@ def create_example_batch(n: int = 4) -> ProteinBatch: batch.pos = batch.coords[:, 1, :] batch.x = F.one_hot(batch.residue_type, num_classes=23).float() + batch.graph_y = torch.randint(0, 2, (n, 1)) + batch.x_vector_attr = orientations(batch.pos) batch.edge_attr = pos_emb(batch.edge_index, 9) batch.edge_vector_attr = _normalize( From cc4364edf6db4930546040e336d1084b59deaec9 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 14:35:07 -0800 Subject: [PATCH 099/106] fix zenodo url --- tests/test_cli.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tests/test_cli.py b/tests/test_cli.py index f9aa7964..07943a85 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,10 +1,8 @@ import shutil -import pytest import requests - def is_tool(name: str) -> bool: """Checks whether ``name`` is on ``PATH`` and is marked as an executable. @@ -31,13 +29,17 @@ def test_commands_found(): "install_pyg_workshop" ), "Workshop PyG installation CLI tool not found" + def test_download_urls(): """Assert downloads are found.""" - from proteinworkshop.scripts.download_processed_data import dataset_fname_map, _ZENODO_RECORD + from proteinworkshop.scripts.download_processed_data import ( + _ZENODO_RECORD, + dataset_fname_map, + ) fnames = list(set(dataset_fname_map.values())) for f in fnames: - url = f"https://zenodo.org/record/{_ZENODO_RECORD}/files/{f}.tar.gz?download=1" + url = f"https://zenodo.org/records/{_ZENODO_RECORD}/files/{f}.tar.gz?download=1" response = requests.head(url) assert response.status_code == 200, f"URL {url} not found." From cefcb8b157c456eb44cc312998854c92c59cac5b Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 14:42:13 -0800 Subject: [PATCH 100/106] fix zenodo url --- proteinworkshop/scripts/download_processed_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/scripts/download_processed_data.py b/proteinworkshop/scripts/download_processed_data.py index 303c3568..1fa3bb97 100644 --- a/proteinworkshop/scripts/download_processed_data.py +++ b/proteinworkshop/scripts/download_processed_data.py @@ -102,7 +102,7 @@ def download_processed_data(dataset_name: str, data_dir: Optional[str] = None): logger.info( f"Downloading {dataset_name} dataset from Zenodo Record {_ZENODO_RECORD}" ) - zenodo_url = f"https://zenodo.org/record/{_ZENODO_RECORD}/files/{fname}.tar.gz?download=1" + zenodo_url = f"https://zenodo.org/records/{_ZENODO_RECORD}/files/{fname}.tar.gz?download=1" wget.download(zenodo_url, out=str(data_dir)) else: logger.info( From c322df6073f47e4e0ad8e9f9e1b6750bf19036de Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 14:49:47 -0800 Subject: [PATCH 101/106] fix beartype import name --- proteinworkshop/datasets/components/atom3d_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/proteinworkshop/datasets/components/atom3d_dataset.py b/proteinworkshop/datasets/components/atom3d_dataset.py index 7422caad..57c7cdc1 100644 --- a/proteinworkshop/datasets/components/atom3d_dataset.py +++ b/proteinworkshop/datasets/components/atom3d_dataset.py @@ -7,7 +7,7 @@ import numpy as np import pandas as pd import torch -from beartype import beartype +from beartype import beartype as typechecker from graphein.protein.tensor.data import Protein from graphein.protein.tensor.io import protein_to_pyg from loguru import logger as log From 3e735c83c861405e37cd76b3835f48a3d0ed0c17 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 14:51:19 -0800 Subject: [PATCH 102/106] add changelog --- CHANGELOG.md | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7a19ad8e..14328430 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,33 @@ +### 0.2.6 (Unreleased) + + + +#### Datasets + +* Adds to antibody-specific datasets using the IGFold corpuses for paired OAS and Jaffe 2022 [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Set `in_memory=True` as default for most (small) datasets for improved performance [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fix `num_classes` for GO datamodules * Set `in_memory=True` as default for most (downstream) datasets for improved performance [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fixes GO labelling [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + + +### Features +* Improves positional encoding performance by adding a `seq_pos` attribute on `Data/Protein` objects in the base dataset getter. [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + +### Models +* Adds CDConv implementation [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds tuned hparams for models [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + +### Framework +* Refactors beartype/jaxtyping to use latest recommended syntax [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds explainability module for performing attribution on a trained model [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Change default finetuning features in config: `ca_base` -> `ca_seq` [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Add optional hparam entry point to finetuning config [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fixes GPU memory accumulation for some metrics [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Updates zenodo URL for processed datasets to reflect upstream API change [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds multi-hot label encoding transform [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Fixes auto PyG install for `torch>2.1.0` [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) +* Adds `proteinworkshop.model_io` containing utils for loading trained models [#53](https://github.com/a-r-j/ProteinWorkshop/pull/53/) + ### 0.2.5 (25/09/2024) * Implement ESM embedding encoder ([#33](https://github.com/a-r-j/ProteinWorkshop/pull/33), [#41](https://github.com/a-r-j/ProteinWorkshop/pull/33)) From 19dd12d3129344f2f0598eb70c6d02f57065d771 Mon Sep 17 00:00:00 2001 From: Jamasb Date: Tue, 26 Dec 2023 15:16:04 -0800 Subject: [PATCH 103/106] add attribution to toc --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ae47d9c9..bba5388c 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,7 @@ Configuration files to run the experiments described in the manuscript are provi - [Running a sweep/experiment](#running-a-sweepexperiment) - [Embedding a dataset](#embedding-a-dataset) - [Visualising a dataset's embeddings](#visualising-pre-trained-model-embeddings-for-a-given-dataset) + - [Performing attribution of a pre-trained model](#performing-attribution-of-a-pre-trained-model) - [Verifying a config](#verifying-a-config) - [Using `proteinworkshop` modules functionally](#using-proteinworkshop-modules-functionally) - [Models](#models) From 22fca65e271a90f8308b90adfd3326daddee26be Mon Sep 17 00:00:00 2001 From: Alex Morehead Date: Wed, 27 Dec 2023 21:09:43 -0600 Subject: [PATCH 104/106] Update install instructions to PyTorch 2.1.2+, and sync docs with README.md line-by-line --- README.md | 15 ++++---- docs/source/configs/dataset.rst | 6 ++-- docs/source/configs/features.rst | 4 +-- .../configs/framework_components/env.rst | 4 +-- docs/source/configs/model.rst | 35 ++++++++++++++----- docs/source/installation.rst | 4 +-- .../source/modules/proteinworkshop.models.rst | 11 ++++++ docs/source/quickstart.rst | 4 +-- .../downstream_component.html | 3 +- .../pretrain_component.html | 3 +- docs/source/tutorials.rst | 4 +-- 11 files changed, 61 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index bba5388c..7a7b553e 100644 --- a/README.md +++ b/README.md @@ -68,14 +68,11 @@ Below, we outline how one may set up a virtual environment for `proteinworkshop` ### From PyPI -`proteinworkshop` is available for install [from PyPI](https://pypi.org/project/proteinworkshop/). This enables training of specific configurations via the CLI **or** using individual components from the benchmark, such as datasets, featurisers, or transforms, as drop-ins to other projects. Make sure to install [PyTorch](https://pytorch.org/) (specifically version `2.0.0`) using its official `pip` installation instructions, with CUDA support as desired. +`proteinworkshop` is available for install [from PyPI](https://pypi.org/project/proteinworkshop/). This enables training of specific configurations via the CLI **or** using individual components from the benchmark, such as datasets, featurisers, or transforms, as drop-ins to other projects. Make sure to install [PyTorch](https://pytorch.org/) (specifically version `2.1.2` or newer) using its official `pip` installation instructions, with CUDA support as desired. ```bash # install `proteinworkshop` from PyPI -pip install proteinworkshop --no-cache-dir - -# e.g., install PyTorch with CUDA 11.8 support on Linux -pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 --no-cache-dir +pip install proteinworkshop # install PyTorch Geometric using the (now-installed) CLI workshop install pyg @@ -87,7 +84,7 @@ export DATA_PATH="where/you/want/data/" # e.g., `export DATA_PATH="proteinworksh However, for full exploration we recommend cloning the repository and building from source. ### Building from source -With a local virtual environment activated (e.g., one created with `conda create -n proteinworkshop python=3.9`): +With a local virtual environment activated (e.g., one created with `conda create -n proteinworkshop python=3.10`): 1. Clone and install the project ```bash @@ -96,11 +93,11 @@ With a local virtual environment activated (e.g., one created with `conda create pip install -e . ``` -2. Install [PyTorch](https://pytorch.org/) (specifically version `2.0.0`) using its official `pip` installation instructions, with CUDA support as desired (N.B. make sure to add `--no-cache-dir` to the end of the `pip` installation command) +2. Install [PyTorch](https://pytorch.org/) (specifically version `2.1.2` or newer) using its official `pip` installation instructions, with CUDA support as desired ```bash # e.g., to install PyTorch with CUDA 11.8 support on Linux: - pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118 --no-cache-dir + pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 torchaudio==2.1.2+cu118 --index-url https://download.pytorch.org/whl/cu118 ``` 3. Then use the newly-installed `proteinworkshop` CLI to install [PyTorch Geometric](https://pyg.org/) @@ -257,7 +254,7 @@ See the `visualise` section of `proteinworkshop/config/visualise.yaml` for addit We provide a utility in `proteinworkshop/explain.py` for performing attribution of a pre-trained model using integrated gradients. -This will write PDB files for all the structures in a dataset for a supervised task with residue-level attributions in the b_factor column. To visualise the attributions, we recommend using the [Protein Viewer VSCode extension](https://marketplace.visualstudio.com/items?itemName=ArianJamasb.protein-viewer) and changing the 3D representation to colour by `Uncertainty/Disorder`. +This will write PDB files for all the structures in a dataset for a supervised task with residue-level attributions in the `b_factor` column. To visualise the attributions, we recommend using the [Protein Viewer VSCode extension](https://marketplace.visualstudio.com/items?itemName=ArianJamasb.protein-viewer) and changing the 3D representation to colour by `Uncertainty/Disorder`. To run the attribution: diff --git a/docs/source/configs/dataset.rst b/docs/source/configs/dataset.rst index cd6a9aa6..d9133965 100644 --- a/docs/source/configs/dataset.rst +++ b/docs/source/configs/dataset.rst @@ -31,8 +31,8 @@ Unlabelled Datasets .. mdinclude:: ../../../README.md - :start-line: 331 - :end-line: 373 + :start-line: 361 + :end-line: 406 :py:class:`ASTRAL ` (``astral``) @@ -116,7 +116,7 @@ This is a dataset of approximately 3 million protein structures from the AlphaFo Species-Specific Datasets ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -TODO +Stay tuned! Graph-level Datasets diff --git a/docs/source/configs/features.rst b/docs/source/configs/features.rst index a84a0bc9..b515b17c 100644 --- a/docs/source/configs/features.rst +++ b/docs/source/configs/features.rst @@ -7,8 +7,8 @@ Features :width: 400 .. mdinclude:: ../../../README.md - :start-line: 426 - :end-line: 475 + :start-line: 459 + :end-line: 508 Default Features diff --git a/docs/source/configs/framework_components/env.rst b/docs/source/configs/framework_components/env.rst index 109b43f6..93d4b333 100644 --- a/docs/source/configs/framework_components/env.rst +++ b/docs/source/configs/framework_components/env.rst @@ -2,8 +2,8 @@ Environment ------------ .. mdinclude:: ../../../../README.md - :start-line: 109 - :end-line: 111 + :start-line: 108 + :end-line: 110 .. literalinclude:: ../../../../.env.example :language: bash diff --git a/docs/source/configs/model.rst b/docs/source/configs/model.rst index 46a4560d..e5785d4d 100644 --- a/docs/source/configs/model.rst +++ b/docs/source/configs/model.rst @@ -34,14 +34,14 @@ Invariant Encoders ============================= .. mdinclude:: ../../../README.md - :start-line: 295 - :end-line: 302 + :start-line: 319 + :end-line: 326 :py:class:`SchNet ` (``schnet``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ SchNet is one of the most popular and simplest instantiation of E(3) invariant message passing GNNs. SchNet constructs messages through element-wise multiplication of scalar features modulated by a radial filter conditioned on the pairwise distance :math:`\Vert \vec{\vx}_{ij} \Vert`` between two neighbours. -Scalar features are update from iteration :math:`t`` to :math:`t+1` via: +Scalar features are updated from iteration :math:`t`` to :math:`t+1` via: .. math:: \begin{align} @@ -114,7 +114,7 @@ where :math:`\mathrm{FC(\cdot)}` denotes a linear transformation upon the messag -:py:class`CDConv ` (``cdconv``) +:py:class:`CDConv ` (``cdconv``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ CDConv is an SE(3) invariant architecture that uses independent learnable weights for sequential displacement, whilst directly encoding geometric displacements. @@ -130,8 +130,8 @@ Vector-Equivariant Encoders ============================= .. mdinclude:: ../../../README.md - :start-line: 306 - :end-line: 312 + :start-line: 330 + :end-line: 336 :py:class:`EGNN ` (``egnn``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -183,8 +183,8 @@ Tensor-Equivariant Encoders ============================= .. mdinclude:: ../../../README.md - :start-line: 314 - :end-line: 319 + :start-line: 338 + :end-line: 343 :py:class:`Tensor Field Networks ` (``tfn``) @@ -227,6 +227,25 @@ In our formalism, this corresponds to: :caption: config/encoder/mace.yaml +Sequence-Based Encoders +============================= + +.. mdinclude:: ../../../README.md + :start-line: 345 + :end-line: 349 + + +:py:class:`Evolutionary Scale Modeling ` (``esm``) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Evolutionary Scale Modeling is a series of Transformer-based protein sequence encoders (Vaswani et al., 2017) that has been successfully used in protein structure prediction (Lin et al., 2023), protein design (Verkuil et al., 2022), and beyond. +This model class has commonly been used as a baseline for protein-related representation learning tasks, and we included it in our benchmark for this reason. + +.. literalinclude:: ../../../proteinworkshop/config/encoder/esm.yaml + :language: yaml + :caption: config/encoder/esm.yaml + + Decoder Models ============================= diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 8410b28b..ba9256ab 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -5,5 +5,5 @@ Installation :doc:`/configs/framework_components/env` .. mdinclude:: ../../README.md - :start-line: 64 - :end-line: 109 + :start-line: 66 + :end-line: 108 diff --git a/docs/source/modules/proteinworkshop.models.rst b/docs/source/modules/proteinworkshop.models.rst index 563b213b..d7f8e156 100644 --- a/docs/source/modules/proteinworkshop.models.rst +++ b/docs/source/modules/proteinworkshop.models.rst @@ -118,6 +118,17 @@ Multi-Atomic Cluster Expansion (``mace``) :show-inheritance: +Sequence-Based Encoders +============================= + +Evolutionary Scale Modeling (``esm``) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. automodule:: proteinworkshop.models.graph_encoders.esm_embeddings + :members: + :undoc-members: + :show-inheritance: + + Decoders ============================= diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index aa49ef93..6cc38c63 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -2,8 +2,8 @@ Quickstart --------------------- .. mdinclude:: ../../README.md - :start-line: 130 - :end-line: 289 + :start-line: 129 + :end-line: 313 .. toctree:: :maxdepth: 3 diff --git a/docs/source/quickstart_component/downstream_component.html b/docs/source/quickstart_component/downstream_component.html index f2f7720c..fdf10f55 100644 --- a/docs/source/quickstart_component/downstream_component.html +++ b/docs/source/quickstart_component/downstream_component.html @@ -92,7 +92,8 @@ ['gvp', "GVP"], ['gcpnet', 'GCPNet'], ['tfn', 'TFN'], - ['mace', 'MACE'] + ['mace', 'MACE'], + ['esm', 'ESM'] ]; var taskList = [ diff --git a/docs/source/quickstart_component/pretrain_component.html b/docs/source/quickstart_component/pretrain_component.html index a6ce8107..2eeffa2d 100644 --- a/docs/source/quickstart_component/pretrain_component.html +++ b/docs/source/quickstart_component/pretrain_component.html @@ -85,7 +85,8 @@ ['gvp', "GVP"], ['gcpnet', 'GCPNet'], ['tfn', 'TFN'], - ['mace', 'MACE'] + ['mace', 'MACE'], + ['esm', 'ESM'] ]; var taskList = [ diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst index 784cb0cf..e412a5a0 100644 --- a/docs/source/tutorials.rst +++ b/docs/source/tutorials.rst @@ -2,8 +2,8 @@ Tutorials --------------------- .. mdinclude:: ../../README.md - :start-line: 119 - :end-line: 128 + :start-line: 118 + :end-line: 127 Training a New Model From 3014dc386336cd1e26c4fda757e5c01d4015006b Mon Sep 17 00:00:00 2001 From: Arian Jamasb Date: Thu, 28 Dec 2023 14:58:16 +0000 Subject: [PATCH 105/106] fix malformed HTML in quickstart components --- docs/source/quickstart_component/downstream_component.html | 2 +- docs/source/quickstart_component/pretrain_component.html | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/quickstart_component/downstream_component.html b/docs/source/quickstart_component/downstream_component.html index fdf10f55..b475b3e5 100644 --- a/docs/source/quickstart_component/downstream_component.html +++ b/docs/source/quickstart_component/downstream_component.html @@ -87,7 +87,7 @@ var modelList = [ ['schnet', "SchNet"], ['dimenetpp', 'DimeNet'], - ['cdconv', "CDConv"] + ['cdconv', "CDConv"], ['egnn', 'EGNN'], ['gvp', "GVP"], ['gcpnet', 'GCPNet'], diff --git a/docs/source/quickstart_component/pretrain_component.html b/docs/source/quickstart_component/pretrain_component.html index 2eeffa2d..f93dc168 100644 --- a/docs/source/quickstart_component/pretrain_component.html +++ b/docs/source/quickstart_component/pretrain_component.html @@ -80,7 +80,7 @@ var modelList = [ ['schnet', "SchNet"], ['dimenetpp', 'DimeNet'], - ['cdconv', "CDConv"] + ['cdconv', "CDConv"], ['egnn', 'EGNN'], ['gvp', "GVP"], ['gcpnet', 'GCPNet'], From a46c4ed1c3f69322ad8455b99eafa74ecbe0a5fa Mon Sep 17 00:00:00 2001 From: Arian Jamasb Date: Thu, 28 Dec 2023 14:59:31 +0000 Subject: [PATCH 106/106] minor fixes to docs --- docs/source/conf.py | 3 ++- docs/source/configs/model.rst | 2 +- docs/source/index.rst | 5 +++-- docs/source/modules/proteinworkshop.metrics.rst | 4 ++++ 4 files changed, 10 insertions(+), 4 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 4828bb25..fd6cb62d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -23,6 +23,7 @@ "sphinx.ext.autosummary", "sphinx.ext.intersphinx", "sphinx.ext.viewcode", + "sphinx.ext.doctest", "sphinx_copybutton", "sphinx_inline_tabs", "sphinxcontrib.gtagjs", @@ -32,7 +33,7 @@ "nbsphinx_link", "sphinx.ext.napoleon", "sphinx_codeautolink", - "sphinxcontrib.jquery" + "sphinxcontrib.jquery", # "sphinx_autorun", ] diff --git a/docs/source/configs/model.rst b/docs/source/configs/model.rst index e5785d4d..a32c2f0f 100644 --- a/docs/source/configs/model.rst +++ b/docs/source/configs/model.rst @@ -236,7 +236,7 @@ Sequence-Based Encoders :py:class:`Evolutionary Scale Modeling ` (``esm``) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Evolutionary Scale Modeling is a series of Transformer-based protein sequence encoders (Vaswani et al., 2017) that has been successfully used in protein structure prediction (Lin et al., 2023), protein design (Verkuil et al., 2022), and beyond. This model class has commonly been used as a baseline for protein-related representation learning tasks, and we included it in our benchmark for this reason. diff --git a/docs/source/index.rst b/docs/source/index.rst index 1ec868be..638b7dfa 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,6 +42,7 @@ Welcome to Protein Workshop's documentation! configs/task configs/features configs/transforms + configs/metrics framework ml_components @@ -57,9 +58,9 @@ Welcome to Protein Workshop's documentation! modules/proteinworkshop.tasks modules/proteinworkshop.features modules/proteinworkshop.utils - modules/protein_workshop.constants + modules/proteinworkshop.constants modules/proteinworkshop.types - + modules/proteinworkshop.metrics Indices and tables ================== diff --git a/docs/source/modules/proteinworkshop.metrics.rst b/docs/source/modules/proteinworkshop.metrics.rst index e69de29b..9a5a409e 100644 --- a/docs/source/modules/proteinworkshop.metrics.rst +++ b/docs/source/modules/proteinworkshop.metrics.rst @@ -0,0 +1,4 @@ +protein_workshop.metrics +------------------------- + +Stay tuned!