SkinTokens / src /tokenizer /tokenizer_part.py
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Public release: SkinTokens 路 TokenRig demo
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from dataclasses import dataclass, field
from numpy import ndarray
from typing import Dict, Tuple, Union, List, Optional
import numpy as np
from .spec import Tokenizer, TokenizeInput, DetokenizeOutput
from .spec import make_skeleton
from ..data.order import Order
@dataclass
class TokenizerPart(Tokenizer):
# cls token id
cls_token_id: Dict[str, int]
# parts token id
parts_token_id: Dict[str, int]
part_token_to_name: Dict[int, str]
cls_token_to_name: Dict[int, str]
parts_token_id_name: List[str]
# normalization range
continuous_range: Tuple[float, float]
# coordinate discrete
num_discrete: int
token_id_branch: int
token_id_bos: int
token_id_eos: int
token_id_pad: int
token_id_spring: int
token_id_cls_none: int
_vocab_size: int
order: Optional[Order]=None
@classmethod
def parse(
cls,
**kwargs,
):
num_discrete = kwargs.pop('num_discrete')
continuous_range = kwargs.pop('continuous_range')
cls_token_id = kwargs.pop('cls_token_id')
parts_token_id = kwargs.pop('parts_token_id')
order = kwargs.get('order')
if order is not None:
assert isinstance(order, Order)
_offset = num_discrete
token_id_branch = _offset + 0
token_id_bos = _offset + 1
token_id_eos = _offset + 2
token_id_pad = _offset + 3
_offset += 4
token_id_spring = _offset + 0
_offset += 1
assert None not in parts_token_id
for i in parts_token_id:
parts_token_id[i] += _offset
_offset += len(parts_token_id)
token_id_cls_none = _offset + 0
_offset += 1
for i in cls_token_id:
cls_token_id[i] += _offset
_offset += len(cls_token_id)
_vocab_size = _offset
parts_token_id_name = [x for x in parts_token_id]
part_token_to_name = {v: k for k, v in parts_token_id.items()}
assert len(part_token_to_name) == len(parts_token_id), 'names with same token found in parts_token_id'
part_token_to_name[token_id_spring] = None
cls_token_to_name = {v: k for k, v in cls_token_id.items()}
assert len(cls_token_to_name) == len(cls_token_id), 'names with same token found in cls_token_id'
return TokenizerPart(
num_discrete=num_discrete,
continuous_range=continuous_range,
cls_token_id=cls_token_id,
parts_token_id=parts_token_id,
order=order,
token_id_branch=token_id_branch,
token_id_bos=token_id_bos,
token_id_eos=token_id_eos,
token_id_pad=token_id_pad,
token_id_spring=token_id_spring,
token_id_cls_none=token_id_cls_none,
parts_token_id_name=parts_token_id_name,
part_token_to_name=part_token_to_name,
cls_token_to_name=cls_token_to_name,
_vocab_size=_vocab_size,
)
def make_cls_head(self, **kwargs) -> List[int]:
cls = kwargs.get('cls', None)
if cls is not None:
return [self.cls_name_to_token(cls=cls)]
return [self.token_id_cls_none]
def cls_name_to_token(self, cls: str) -> int:
if cls not in self.cls_token_id:
return self.token_id_cls_none
return self.cls_token_id[cls]
def part_name_to_token(self, part: str) -> int:
assert part in self.parts_token_id, f"do not find part name `{part}` in tokenizer"
return self.parts_token_id[part]
def next_posible_token(self, ids: ndarray) -> List[int]:
if ids.shape[0] == 0 or ids.ndim == 0:
return [self.token_id_bos]
assert ids.ndim == 1, "expect an array"
state = 'expect_bos'
for id in ids:
if state == 'expect_bos':
assert id == self.token_id_bos, 'ids do not start with bos'
state = 'expect_cls_or_part_or_joint'
elif state == 'expect_cls_or_part_or_joint':
if id < self.num_discrete:
state = 'expect_joint_2'
elif id == self.token_id_cls_none or id in self.cls_token_id.values():
state = 'expect_part_or_joint'
else: # a part
state = 'expect_joint'
elif state == 'expect_part_or_joint':
if id < self.num_discrete:
state = 'expect_joint_2'
else:
state = 'expect_part_or_joint'
elif state == 'expect_joint_2':
state = 'expect_joint_3'
elif state == 'expect_joint_3':
state = 'expect_branch_or_part_or_joint'
elif state == 'expect_branch_or_part_or_joint':
if id == self.token_id_branch:
state = 'expect_joint'
elif id < self.num_discrete:
state = 'expect_joint_2'
else: # find a part
state = 'expect_joint'
elif state == 'expect_joint':
state = 'expect_joint_2'
else:
assert 0, state
s = []
def add_cls():
s.append(self.token_id_cls_none)
for v in self.cls_token_id.values():
s.append(v)
def add_part():
s.append(self.token_id_spring)
for v in self.parts_token_id.values():
s.append(v)
def add_joint():
for i in range(self.num_discrete):
s.append(i)
def add_branch():
s.append(self.token_id_branch)
def add_eos():
s.append(self.token_id_eos)
def add_bos():
s.append(self.token_id_bos)
if state == 'expect_bos':
add_bos()
elif state == 'expect_cls_or_part_or_joint':
add_cls()
add_part()
add_joint()
elif state == 'expect_cls':
add_cls()
elif state == 'expect_part_or_joint':
add_part()
add_joint()
add_eos()
elif state == 'expect_joint_2':
add_joint()
elif state == 'expect_joint_3':
add_joint()
elif state == 'expect_branch_or_part_or_joint':
add_joint()
add_part()
add_branch()
add_eos()
elif state == 'expect_joint':
add_joint()
else:
assert 0, state
return s
def bones_in_sequence(self, ids: ndarray):
assert ids.ndim == 1, "expect an array"
s = 0
is_branch = False
state = 'expect_bos'
for id in ids:
if state == 'expect_bos':
assert id == self.token_id_bos, 'ids do not start with bos'
state = 'expect_cls_or_part_or_joint'
elif state == 'expect_cls_or_part_or_joint':
if id < self.num_discrete:
state = 'expect_joint_2'
elif id == self.token_id_cls_none or id in self.cls_token_id.values():
state = 'expect_part_or_joint'
else: # a part
state = 'expect_joint'
elif state == 'expect_part_or_joint':
if id < self.num_discrete:
state = 'expect_joint_2'
else:
state = 'expect_part_or_joint'
elif state == 'expect_joint_2':
state = 'expect_joint_3'
elif state == 'expect_joint_3':
if not is_branch:
s += 1
is_branch = False
state = 'expect_branch_or_part_or_joint'
elif state == 'expect_branch_or_part_or_joint':
if id == self.token_id_branch:
state = 'expect_joint'
is_branch = True
elif id < self.num_discrete:
state = 'expect_joint_2'
else: # find a part
state = 'expect_joint'
elif state == 'expect_joint':
state = 'expect_joint_2'
else:
assert 0, state
if id == self.token_id_eos:
break
return s
def tokenize(self, input: TokenizeInput) -> ndarray:
num_bones = input.num_bones
bones = discretize(t=input.bones, continuous_range=self.continuous_range, num_discrete=self.num_discrete)
branch = input.branch
tokens = [self.token_id_bos]
if input.cls is None or input.cls not in self.cls_token_id:
tokens.append(self.token_id_cls_none)
else:
tokens.append(self.cls_token_id[input.cls])
if self.order is not None and input.cls is not None and input.joint_names is not None:
_, parts_bias = self.order.arrange_names(cls=input.cls, names=input.joint_names, parents=input.parents)
else:
parts_bias = []
for i in range(num_bones):
# add parts token id
if i in parts_bias:
part = parts_bias[i]
if part is None:
tokens.append(self.token_id_spring)
else:
assert part in self.parts_token_id, f"do not find part name {part} in tokenizer {self.__class__}"
tokens.append(self.parts_token_id[part])
if branch[i]:
tokens.append(self.token_id_branch)
tokens.append(bones[i, 0])
tokens.append(bones[i, 1])
tokens.append(bones[i, 2])
tokens.append(bones[i, 3])
tokens.append(bones[i, 4])
tokens.append(bones[i, 5])
else:
tokens.append(bones[i, 3])
tokens.append(bones[i, 4])
tokens.append(bones[i, 5])
tokens.append(self.token_id_eos)
return np.array(tokens, dtype=np.int64)
def detokenize(self, ids: ndarray, **kwargs) -> DetokenizeOutput:
assert isinstance(ids, ndarray), 'expect ids to be ndarray'
if ids[0] != self.token_id_bos:
raise ValueError(f"first token is not bos")
trailing_pad = 0
while trailing_pad < ids.shape[0] and ids[-trailing_pad-1] == self.token_id_pad:
trailing_pad += 1
if ids[-1-trailing_pad] != self.token_id_eos:
raise ValueError(f"last token is not eos")
ids = ids[1:-1-trailing_pad]
joints = []
p_joints = []
tails_dict = {}
parts = []
i = 0
is_branch = False
last_joint = None
num_bones = 0
cls = None
while i < len(ids):
if ids[i] < self.num_discrete:
if is_branch:
p_joint = undiscretize(t=ids[i:i+3], continuous_range=self.continuous_range, num_discrete=self.num_discrete)
current_joint = undiscretize(t=ids[i+3:i+6], continuous_range=self.continuous_range, num_discrete=self.num_discrete)
joints.append(current_joint)
p_joints.append(p_joint)
i += 6
else:
current_joint = undiscretize(t=ids[i:i+3], continuous_range=self.continuous_range, num_discrete=self.num_discrete)
joints.append(current_joint)
if len(p_joints) == 0: # root
p_joints.append(current_joint)
p_joint = current_joint
else:
assert last_joint is not None
p_joints.append(last_joint)
p_joint = last_joint
i += 3
if last_joint is not None:
tails_dict[num_bones-1] = current_joint
last_joint = current_joint
num_bones += 1
is_branch = False
elif ids[i]==self.token_id_branch:
is_branch = True
last_joint = None
i += 1
elif ids[i]==self.token_id_spring or ids[i] in self.parts_token_id.values():
parts.append(self.part_token_to_name[ids[i]])
i += 1
elif ids[i] in self.cls_token_id.values():
cls = ids[i]
i += 1
elif ids[i] == self.token_id_cls_none:
cls = None
i += 1
else:
raise ValueError(f"unexpected token found: {ids[i]}")
joints = np.stack(joints)
p_joints = np.stack(p_joints)
# leaf is ignored in this tokenizer so need to extrude tails for leaf and branch
bones, tails, available_bones_id, parents = make_skeleton(
joints=joints,
p_joints=p_joints,
tails_dict=tails_dict,
convert_leaf_bones_to_tails=False,
extrude_tail_for_leaf=True,
extrude_tail_for_branch=True,
)
bones = bones[available_bones_id]
tails = tails[available_bones_id]
if cls in self.cls_token_to_name:
cls = self.cls_token_to_name[cls]
else:
cls = None
if self.order is not None:
joint_names = self.order.make_names(cls=cls, parts=parts, num_bones=num_bones)
else:
joint_names = [f"bone_{i}" for i in range(num_bones)]
return DetokenizeOutput(
tokens=ids,
bones=bones,
parents=parents,
cls=cls,
joint_names=joint_names,
continuous_range=self.continuous_range,
)
def get_require_parts(self) -> List[str]:
return self.parts_token_id_name
@property
def vocab_size(self):
return self._vocab_size
@property
def pad(self):
return self.token_id_pad
@property
def bos(self):
return self.token_id_bos
@property
def eos(self):
return self.token_id_eos
def discretize(
t: ndarray,
continuous_range: Tuple[float, float],
num_discrete: int,
) -> ndarray:
lo, hi = continuous_range
assert hi >= lo
t = (t - lo) / (hi - lo)
t *= num_discrete
return np.clip(t.round(), 0, num_discrete - 1).astype(np.int64)
def undiscretize(
t: ndarray,
continuous_range: Tuple[float, float],
num_discrete: int,
) -> ndarray:
lo, hi = continuous_range
assert hi >= lo
t = t.astype(np.float32) + 0.5
t /= num_discrete
return t * (hi - lo) + lo