File size: 11,115 Bytes
bf31071 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 |
"""Processor for handling multimodal swipe inputs (path + text)."""
import numpy as np
import torch
from transformers import ProcessorMixin
class SwipeProcessor(ProcessorMixin):
"""
Processor for handling multimodal swipe inputs (path coordinates + text).
This processor combines path coordinate preprocessing with text tokenization,
creating the inputs needed for SwipeTransformer models.
Args:
tokenizer: SwipeTokenizer instance
max_path_len (int): Maximum path length. Defaults to 64.
max_char_len (int): Maximum character length. Defaults to 38.
"""
attributes = ["tokenizer"]
tokenizer_class = "AutoTokenizer" # Will use auto_map from tokenizer_config.json
def __init__(self, tokenizer=None, max_path_len: int = 64, max_char_len: int = 38):
self.tokenizer = tokenizer
self.max_path_len = max_path_len
self.max_char_len = max_char_len
# Attributes expected by newer transformers (not used for swipe models)
self.chat_template = None
self.audio_tokenizer = None
self.feature_extractor = None
self.image_processor = None
def __call__(
self,
path_coords: list[list[list[float]]] | torch.Tensor | np.ndarray | None = None,
text: str | list[str] | None = None,
padding: bool | str = True,
truncation: bool = True,
max_length: int | None = None,
return_tensors: str | None = "pt",
**kwargs,
):
"""
Process path coordinates and text into model inputs.
Args:
path_coords: List of paths or tensor [batch, path_len, 3]
Each point is (x, y, time). Can be None if only processing text.
text: String or list of strings to encode. Can be None if only processing paths.
padding: Whether to pad sequences. Can be True/False or "max_length"
truncation: Whether to truncate sequences
max_length: Maximum sequence length for text (overrides max_char_len)
return_tensors: "pt" for PyTorch, "np" for NumPy, None for lists
**kwargs: Additional keyword arguments
Returns:
Dictionary with:
- path_coords: [batch, max_path_len, 3] (if path_coords provided)
- input_ids: [batch, max_char_len] (if text provided)
- attention_mask: [batch, total_seq_len]
"""
if path_coords is None and text is None:
raise ValueError("Must provide either path_coords or text (or both)")
# Determine batch size
if path_coords is not None:
# Handle path coordinates
if isinstance(path_coords, (list, tuple)):
# Check if it's a batch or single path
if len(path_coords) > 0 and isinstance(path_coords[0][0], (list, tuple)):
# Batch of paths [[path1], [path2], ...]
path_coords = torch.tensor(path_coords, dtype=torch.float32)
else:
# Single path [[x,y,t], [x,y,t], ...]
path_coords = torch.tensor([path_coords], dtype=torch.float32)
elif isinstance(path_coords, np.ndarray):
path_coords = torch.from_numpy(path_coords).float()
if path_coords.dim() == 2:
# Single path, add batch dimension
path_coords = path_coords.unsqueeze(0)
elif isinstance(path_coords, torch.Tensor):
if path_coords.dim() == 2:
# Single path, add batch dimension
path_coords = path_coords.unsqueeze(0)
batch_size = path_coords.shape[0]
elif text is not None:
if isinstance(text, str):
batch_size = 1
text = [text]
else:
batch_size = len(text)
else:
batch_size = 1
result = {}
# Process path coordinates
if path_coords is not None:
current_path_len = path_coords.shape[1]
# Truncate if needed
if truncation and current_path_len > self.max_path_len:
path_coords = path_coords[:, : self.max_path_len, :]
current_path_len = self.max_path_len
# Pad if needed
if padding and current_path_len < self.max_path_len:
pad_len = self.max_path_len - current_path_len
path_coords = torch.cat([path_coords, torch.zeros(batch_size, pad_len, 3)], dim=1)
# Create path mask (1 = real data, 0 = padding)
path_mask = torch.ones(batch_size, self.max_path_len, dtype=torch.long)
if padding and current_path_len < self.max_path_len:
path_mask[:, current_path_len:] = 0
result["path_coords"] = path_coords
# Store path_mask internally for attention_mask construction
_path_mask = path_mask
else:
# No path coords provided, create empty/zero tensors
path_coords = torch.zeros(batch_size, self.max_path_len, 3)
_path_mask = torch.zeros(batch_size, self.max_path_len, dtype=torch.long)
result["path_coords"] = path_coords
# Process text
if text is not None:
# Ensure text is a list
if isinstance(text, str):
text = [text]
# Tokenize text
text_max_length = max_length if max_length is not None else self.max_char_len
# First tokenize without padding/truncation to add EOS
encoded_raw = self.tokenizer(
text,
padding=False,
truncation=False,
return_tensors=None, # Get lists first
**kwargs,
)
# Add EOS token after each word (matching training dataset behavior)
eos_id = self.tokenizer.eos_token_id
for i in range(len(encoded_raw["input_ids"])):
# Add EOS if not already present
if encoded_raw["input_ids"][i][-1] != eos_id:
encoded_raw["input_ids"][i].append(eos_id)
# Now apply padding and truncation
max_len_needed = max(len(ids) for ids in encoded_raw["input_ids"])
if truncation and max_len_needed > text_max_length:
# Truncate but preserve EOS at the end
for i in range(len(encoded_raw["input_ids"])):
if len(encoded_raw["input_ids"][i]) > text_max_length:
encoded_raw["input_ids"][i] = (
encoded_raw["input_ids"][i][: text_max_length - 1] + [eos_id]
)
# Pad sequences
if padding:
pad_id = self.tokenizer.pad_token_id
for i in range(len(encoded_raw["input_ids"])):
seq_len = len(encoded_raw["input_ids"][i])
if seq_len < text_max_length:
encoded_raw["input_ids"][i].extend([pad_id] * (text_max_length - seq_len))
# Create attention mask (1 for real tokens + EOS, 0 for padding)
_char_mask = []
for ids in encoded_raw["input_ids"]:
mask = [1 if token_id != self.tokenizer.pad_token_id else 0 for token_id in ids]
_char_mask.append(mask)
# Convert to tensors if requested
if return_tensors == "pt":
result["input_ids"] = torch.tensor(encoded_raw["input_ids"], dtype=torch.long)
_char_mask = torch.tensor(_char_mask, dtype=torch.long)
elif return_tensors == "np":
result["input_ids"] = np.array(encoded_raw["input_ids"], dtype=np.int64)
_char_mask = np.array(_char_mask, dtype=np.int64)
else:
result["input_ids"] = encoded_raw["input_ids"]
else:
# No text provided, create padding tokens
if return_tensors == "pt":
char_tokens = torch.full(
(batch_size, self.max_char_len), self.tokenizer.pad_token_id, dtype=torch.long
)
_char_mask = torch.zeros(batch_size, self.max_char_len, dtype=torch.long)
elif return_tensors == "np":
char_tokens = np.full(
(batch_size, self.max_char_len), self.tokenizer.pad_token_id, dtype=np.int64
)
_char_mask = np.zeros((batch_size, self.max_char_len), dtype=np.int64)
else:
char_tokens = [
[self.tokenizer.pad_token_id] * self.max_char_len for _ in range(batch_size)
]
_char_mask = [[0] * self.max_char_len for _ in range(batch_size)]
result["input_ids"] = char_tokens
# Create combined attention mask: [CLS] + path + [SEP] + chars
# Sequence structure: [CLS:1] + _path_mask + [SEP:1] + _char_mask
if return_tensors == "pt":
cls_mask = torch.ones(batch_size, 1, dtype=torch.long)
sep_mask = torch.ones(batch_size, 1, dtype=torch.long)
attention_mask = torch.cat([cls_mask, _path_mask, sep_mask, _char_mask], dim=1)
elif return_tensors == "np":
cls_mask = np.ones((batch_size, 1), dtype=np.int64)
sep_mask = np.ones((batch_size, 1), dtype=np.int64)
attention_mask = np.concatenate([cls_mask, _path_mask, sep_mask, _char_mask], axis=1)
else:
cls_mask = [[1] for _ in range(batch_size)]
sep_mask = [[1] for _ in range(batch_size)]
attention_mask = [
cls + path.tolist() + sep + char
for cls, path, sep, char in zip(
cls_mask, _path_mask, sep_mask, _char_mask, strict=False
)
]
result["attention_mask"] = attention_mask
# Convert to requested format
if return_tensors == "np":
for key in result:
if isinstance(result[key], torch.Tensor):
result[key] = result[key].numpy()
elif return_tensors is None:
for key in result:
if isinstance(result[key], torch.Tensor):
result[key] = result[key].tolist()
return result
def batch_decode(self, token_ids, **kwargs):
"""
Decode token IDs to strings.
Args:
token_ids: Token IDs to decode
**kwargs: Additional arguments passed to tokenizer
Returns:
List of decoded strings
"""
return self.tokenizer.batch_decode(token_ids, **kwargs)
def decode(self, token_ids, **kwargs):
"""
Decode single sequence of token IDs to string.
Args:
token_ids: Token IDs to decode
**kwargs: Additional arguments passed to tokenizer
Returns:
Decoded string
"""
return self.tokenizer.decode(token_ids, **kwargs)
|