OmniSVG-3B / tokenizer.py
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Update tokenizer.py
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import numpy as np
import torch
import yaml
from typing import List, Tuple, Dict, Optional, Union
from deepsvg.difflib.tensor import SVGTensor
from deepsvg.svglib.svg import SVG
from deepsvg.svglib.geom import Bbox
class SVGTokenizer:
"""SVG tokenizer - supports both 8B and 4B models via config.yaml"""
def __init__(self, config_path: str = "./config.yaml", model_size: str = None):
"""
Initialize SVGTokenizer.
Args:
config_path: Path to config.yaml
model_size: Model size ("8B" or "4B"). If None, uses default from config.
"""
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)
# Determine model size
self.model_size = model_size or self.config.get('default_model_size', '8B')
if self.model_size not in self.config.get('models', {}):
raise ValueError(f"Invalid model_size: {self.model_size}. Must be one of: {list(self.config.get('models', {}).keys())}")
self._load_config()
self.pixel2xy = self._create_pixel2xy_mapping()
def _get_model_specific_config(self, *keys):
"""Get model-specific config value, with fallback to shared config."""
model_cfg = self.config.get('models', {}).get(self.model_size, {})
# Navigate through nested keys in model-specific config
value = model_cfg
for key in keys:
if isinstance(value, dict) and key in value:
value = value[key]
else:
value = None
break
# If not found in model-specific, try shared config
if value is None:
value = self.config
for key in keys:
if isinstance(value, dict) and key in value:
value = value[key]
else:
return None
return value
def _load_config(self):
"""Load all constants from configuration file with model-specific overrides."""
# ========== Token-related configs ==========
# Model-specific tokens
self.NUM_MASK_AND_EOM = self._get_model_specific_config('tokens', 'num_mask_and_eom')
self.BASE_OFFSET = self._get_model_specific_config('tokens', 'base_offset')
# Shared tokens
tokens_cfg = self.config['tokens']
self.NUM_SVG_END = tokens_cfg['svg_end']
self.NUM_END_TOKEN = tokens_cfg['num_end_token']
# ========== Coordinate-related configs ==========
# Model-specific coordinates
self.PIX_PAD = self._get_model_specific_config('coordinates', 'pix_pad_offset')
self.COORD_PAD = self._get_model_specific_config('coordinates', 'coord_pad_offset')
# Shared coordinates
coords_cfg = self.config['coordinates']
self.BBOX = coords_cfg['bbox']
# ========== Color-related configs ==========
colors_cfg = self.config['colors']
self.COLOR_TOKEN_START_RAW = colors_cfg['color_token_start']
self.MAX_COLOR_TOKENS = colors_cfg['max_color_tokens']
# Model-specific colors
self.COLOR_START_OFFSET = self._get_model_specific_config('colors', 'color_start_offset')
self.COLOR_END_OFFSET = self._get_model_specific_config('colors', 'color_end_offset')
# ========== SVG command values ==========
commands_cfg = self.config['svg_commands']
self.CMD_MOVE = commands_cfg['move']
self.CMD_LINE = commands_cfg['line']
self.CMD_CURVE = commands_cfg['curve']
self.CMD_ARC = commands_cfg['arc']
self.CMD_CLOSE = commands_cfg['close']
# ========== Model-related configs ==========
model_cfg = self.config['model']
self.BOS_TOKEN_ID = model_cfg['bos_token_id']
self.EOS_TOKEN_ID = model_cfg['eos_token_id']
self.PAD_TOKEN_ID = model_cfg['pad_token_id']
# ========== Arc parameter configs ==========
arc_cfg = self.config.get('arc', {})
self.ARC_PARAM_OFFSET = arc_cfg.get('param_offset', 44500)
self.ARC_PARAM_RANGE = arc_cfg.get('param_range', 100)
self.ARC_PARAM_START = self.ARC_PARAM_OFFSET + self.BASE_OFFSET
# ========== Derived constants ==========
self.PIXEL_OFFSET = (self.NUM_MASK_AND_EOM - self.BASE_OFFSET +
self.NUM_SVG_END - self.CMD_MOVE)
# Command token range
self.CMD_TOKEN_START = self.NUM_MASK_AND_EOM + self.NUM_SVG_END
self.CMD_TOKEN_END = self.PIX_PAD + self.NUM_SVG_END
# Coordinate token start
self.COORD_TOKEN_START = self.PIX_PAD + self.NUM_SVG_END
# Color-coordinate boundary
self.COLOR_COORD_BOUNDARY = self.COLOR_TOKEN_START_RAW + 1 + self.BASE_OFFSET
# Color threshold for raster_svg
self.COLOR_THRESHOLD = self.COLOR_TOKEN_START_RAW - self.PIXEL_OFFSET + 1
def _create_pixel2xy_mapping(self) -> Dict[int, np.ndarray]:
"""Create pixel to xy mapping following dataset.py logic."""
pixel2xy = {}
x = np.linspace(0, self.BBOX - 1, self.BBOX)
y = np.linspace(0, self.BBOX - 1, self.BBOX)
xx, yy = np.meshgrid(x, y)
xy_grid = (np.array((xx.ravel(), yy.ravel())).T).astype(int)
for pixel, xy in enumerate(xy_grid):
pixel2xy[pixel] = xy + self.COORD_PAD + self.NUM_SVG_END
return pixel2xy
def token_to_color(self, color_token: int) -> str:
"""Convert token to color following dataset.py logic."""
try:
if color_token == self.COLOR_TOKEN_START_RAW:
return "none"
elif color_token == self.COLOR_TOKEN_START_RAW + 1:
return "currentColor"
color_index = color_token - (self.COLOR_TOKEN_START_RAW + 2)
if color_index < 0 or color_index >= self.MAX_COLOR_TOKENS:
print(f"Warning: Color token {color_token} out of range")
return "#808080"
r = (color_index >> 8) & 0xF
g = (color_index >> 4) & 0xF
b = color_index & 0xF
r = (r << 4) | r
g = (g << 4) | g
b = (b << 4) | b
return f"#{r:02x}{g:02x}{b:02x}"
except Exception as e:
print(f"Error in token_to_color: {e}")
return "#808080"
def process_generated_tokens(self, output_ids: torch.Tensor) -> np.ndarray:
"""Process generated tokens following dataset.py logic."""
# Remove bos/eos
generated_pixels = output_ids[:, 1:-1].cpu().numpy().flatten()
sample_xys = []
for pixel in generated_pixels:
try:
# 1. Command tokens: CMD_TOKEN_START <= pixel < CMD_TOKEN_END
if self.CMD_TOKEN_START <= pixel < self.CMD_TOKEN_END:
xy = np.array([pixel - self.BASE_OFFSET,
pixel - self.BASE_OFFSET]).astype(int)
sample_xys.append(xy)
# 2. Coordinate tokens: COORD_TOKEN_START <= pixel < COLOR_COORD_BOUNDARY
elif self.COORD_TOKEN_START <= pixel < self.COLOR_COORD_BOUNDARY:
pixel_index = pixel - self.COORD_TOKEN_START
if pixel_index in self.pixel2xy:
xy = self.pixel2xy[pixel_index] - self.BASE_OFFSET
sample_xys.append(xy)
# 3. Arc parameters: ARC_PARAM_START + 1 <= pixel < ARC_PARAM_START + 1 + ARC_PARAM_RANGE
elif (self.ARC_PARAM_START + 1 <= pixel <
self.ARC_PARAM_START + 1 + self.ARC_PARAM_RANGE):
value = pixel - self.ARC_PARAM_START - 1
xy = np.array([value, value]).astype(int)
sample_xys.append(xy)
# 4. Color tokens: COLOR_COORD_BOUNDARY <= pixel < ARC_PARAM_START
elif self.COLOR_COORD_BOUNDARY <= pixel < self.ARC_PARAM_START:
xy = np.array([pixel - self.BASE_OFFSET,
pixel - self.BASE_OFFSET]).astype(int)
sample_xys.append(xy)
except Exception as e:
print(f"Error processing pixel {pixel}: {e}")
continue
if sample_xys:
return np.vstack(sample_xys)
else:
return np.array([]).reshape(0, 2)
def raster_svg(self, pixels: np.ndarray) -> Tuple[List[List[torch.Tensor]], List[int]]:
"""Convert pixels to SVG tensors following dataset.py logic."""
try:
if len(pixels) == 0:
return [[]], []
# Key step: subtract PIXEL_OFFSET
pixels = pixels - self.PIXEL_OFFSET
svg_tensors = []
color_tensors = []
path_tensor = []
i = 0
while i < len(pixels):
try:
pix = pixels[i]
# Move command
if pix[0] == self.CMD_MOVE:
if i + 2 >= len(pixels):
break
cmd_tensor = np.zeros(14)
cmd_tensor[0] = 0 # Move command index
cmd_tensor[12:14] = pixels[i+2]
path_tensor.append(cmd_tensor.tolist())
i += 3
# Line command
elif pix[0] == self.CMD_LINE:
if i + 1 >= len(pixels):
break
cmd_tensor = np.zeros(14)
cmd_tensor[0] = 1 # Line command index
cmd_tensor[12:14] = pixels[i+1]
path_tensor.append(cmd_tensor.tolist())
i += 2
# Curve command
elif pix[0] == self.CMD_CURVE:
if i + 3 >= len(pixels):
break
cmd_tensor = np.zeros(14)
cmd_tensor[0] = 2 # Curve command index
cmd_tensor[8:10] = pixels[i+1]
cmd_tensor[10:12] = pixels[i+2]
cmd_tensor[12:14] = pixels[i+3]
path_tensor.append(cmd_tensor.tolist())
i += 4
# Arc command
elif pix[0] == self.CMD_ARC:
if i + 5 >= len(pixels):
break
cmd_tensor = np.zeros(14)
cmd_tensor[0] = 3 # Arc command index
radius = pixels[i+1]
x_axis_rot = pixels[i+2][0] + self.PIXEL_OFFSET
large_arc_flg = pixels[i+3][0] + self.PIXEL_OFFSET
sweep_flg = pixels[i+4][0] + self.PIXEL_OFFSET
end_pos = pixels[i+5]
cmd_tensor[1:3] = radius
cmd_tensor[3] = x_axis_rot
cmd_tensor[4] = large_arc_flg
cmd_tensor[5] = sweep_flg
cmd_tensor[12:14] = end_pos
path_tensor.append(cmd_tensor.tolist())
i += 6
# Close command
elif pix[0] == self.CMD_CLOSE:
if i + 1 >= len(pixels):
break
cmd_tensor = np.zeros(14)
cmd_tensor[0] = 6 # Close command index
cmd_tensor[12:14] = pixels[i+1]
path_tensor.append(cmd_tensor.tolist())
i += 2
# Color token: pix[0] >= COLOR_THRESHOLD
elif pix[0] >= self.COLOR_THRESHOLD:
if path_tensor:
svg_tensors.append(torch.tensor(path_tensor))
# Reverse transform: restore original color token
color_token = int(pix[0] + self.PIXEL_OFFSET - 1)
color_tensors.append(color_token)
path_tensor = []
i += 1
else:
i += 1
except (IndexError, TypeError) as e:
print(f"Error at position {i}: {e}")
break
# Handle remaining path (without color)
if path_tensor:
svg_tensors.append(torch.tensor(path_tensor))
return [svg_tensors], color_tensors
except Exception as e:
print(f"Error in raster_svg: {e}")
import traceback
traceback.print_exc()
return [[]], []
def apply_colors_to_svg(self, svg_tensors: List[torch.Tensor],
colors: Optional[List[int]]) -> SVG:
"""Apply colors and create final SVG."""
paths = []
if not svg_tensors:
raise ValueError("No valid SVG tensors")
colors = colors or []
for i, path_tensor in enumerate(svg_tensors):
try:
path = SVGTensor.from_data(path_tensor)
path = SVG.from_tensor(path.data, viewbox=Bbox(self.BBOX))
actual_color = self.token_to_color(colors[i]) if i < len(colors) else "none"
for path_group in path:
path_group.color = actual_color
path_group.stroke_color = "none"
path.fill_(True)
paths.append(path)
except Exception as e:
print(f"Error processing path {i}: {e}")
continue
if not paths:
raise ValueError("No valid paths generated")
path_groups = paths[0].svg_path_groups
for i in range(1, len(paths)):
path_groups.extend(paths[i].svg_path_groups)
return SVG(path_groups, viewbox=Bbox(self.BBOX))