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b1e87cf 9e3a5e2 b1e87cf | 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 | import io
import math
import tarfile
import zipfile
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import sympy as sp
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from plot2eq.core.tokenizer import Tokenizer
from plot2eq.data_utils.augmentation import HandDrawnAugmentation
class SymbolicDataset(Dataset):
def __init__(
self,
data_dir: Path | str,
map_location: str | None = None,
drawn_augmentation: bool = False,
max_drift_scale=0.005,
max_wobble_scale=0.015,
p=0.8,
) -> None:
self.drawn_augmentation = drawn_augmentation
self.max_drift_scale = max_drift_scale
self.max_wobble_scale = max_wobble_scale
self.p = p
self.transform = HandDrawnAugmentation(max_drift_scale, max_wobble_scale, p)
self.tokenizer = Tokenizer()
self.data_dir = Path(data_dir)
self.map_location = map_location
if map_location is None:
self.map_location = "cuda" if torch.cuda.is_available() else "cpu"
elif not map_location.startswith(("cuda", "cpu")):
raise ValueError(f"Invalid map_location: {map_location}!")
self.points = []
self.tokens = []
self.file_names = []
if self.data_dir.is_dir():
self.file_names = sorted(
[f.name for f in self.data_dir.iterdir() if f.name.endswith(".pt")]
)
if not self.file_names:
raise RuntimeError(f"Не найдено ни одного .pt файла в папке {data_dir}")
print(f"Загружаем {len(self.file_names)} файлов в память (из директории)")
for f in tqdm(self.file_names):
file_path = self.data_dir / f
data = torch.load(
file_path, map_location=self.map_location, weights_only=True
)
self.points.append(data["points"])
self.tokens.append(data["tokens"])
elif self.data_dir.is_file() and self.data_dir.suffix.lower() == ".zip":
with zipfile.ZipFile(self.data_dir, "r") as z:
self.file_names = sorted([f for f in z.namelist() if f.endswith(".pt")])
if not self.file_names:
raise RuntimeError(
f"Не найдено ни одного .pt файла в архиве {data_dir}"
)
print(
f"Загружаем {len(self.file_names)} файлов в память (из ZIP-архива)"
)
for f_name in tqdm(self.file_names):
with z.open(f_name) as f:
buffer = io.BytesIO(f.read())
data = torch.load(
buffer, map_location=self.map_location, weights_only=True
)
self.points.append(data["points"])
self.tokens.append(data["tokens"])
elif self.data_dir.is_file() and self.data_dir.name.endswith(
(".tar", ".tar.gz", ".tgz")
):
mode = "r:gz" if self.data_dir.name.endswith(("gz", "tgz")) else "r"
with tarfile.open(self.data_dir, mode) as tar:
members = [m for m in tar.getmembers() if m.name.endswith(".pt")]
members.sort(key=lambda m: m.name)
self.file_names = [m.name for m in members]
if not self.file_names:
raise RuntimeError(
f"Не найдено ни одного .pt файла в архиве {data_dir}"
)
print(
f"Загружаем {len(self.file_names)} файлов в память (из TAR-архива)"
)
for member in tqdm(members):
f = tar.extractfile(member)
if f is not None:
buffer = io.BytesIO(f.read())
data = torch.load(
buffer, map_location=self.map_location, weights_only=True
)
self.points.append(data["points"])
self.tokens.append(data["tokens"])
else:
raise ValueError(
f"Путь {self.data_dir} не существует, не является папкой или поддерживаемым архивом!"
)
self.points = torch.cat(self.points, dim=0)
self.tokens = torch.cat(self.tokens, dim=0)
def __len__(self):
return len(self.points)
def __getitem__(self, index):
points, tokens = self.points[index], self.tokens[index]
if self.drawn_augmentation:
points = self.transform(points)
return points, tokens
def visualize(
self, index: int, apply_transform: bool = False, title_prefix: str = ""
):
points = self.points[index]
tokens = self.tokens[index]
if apply_transform and self.transform is not None:
points = self.transform(points)
y_vals = points[0].cpu().numpy()
mask = points[1].cpu().numpy()
x_vals = np.linspace(0, 1, len(y_vals))
clean_tokens = [t.item() for t in tokens if t.item() not in (0, 1, 2)]
try:
expr = self.tokenizer.token_seq_to_expr(torch.tensor(clean_tokens))
latex_expr = sp.latex(expr)
title_str = f"${latex_expr}$"
except Exception as e:
title_str = f"Ошибка декодирования: {e}"
plt.figure(figsize=(8, 5))
plt.fill_between(
x_vals,
-0.05,
1.05,
where=(mask == 0.0),
color="red",
alpha=0.1,
label="Маска = 0 (Разрыв/Асимптота)",
transform=plt.gca().get_xaxis_transform(),
)
y_plot = y_vals.copy()
y_plot[mask == 0.0] = np.nan
plt.plot(
x_vals,
y_plot,
linewidth=2.5,
color="blue",
alpha=0.8,
label="Линия графика",
)
plt.title(f"{title_prefix}\n{title_str}", fontsize=16, pad=15)
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.grid(True, linestyle="--", alpha=0.6)
plt.legend(loc="upper right")
plt.tight_layout()
plt.show()
def visualize_batch(self, batch_size=9, indices=None, apply_transform=True):
"""
Отрисовывает сетку из batch_size графиков.
:param batch_size: Количество графиков
:param indices: Конкретные индексы для отрисовки (опционально)
:param apply_transform: Применять ли аугментацию
"""
if indices is None:
indices = np.random.choice(len(self.points), size=batch_size, replace=False)
else:
batch_size = len(indices)
grid_cols = math.ceil(math.sqrt(batch_size))
grid_rows = math.ceil(batch_size / grid_cols)
fig, axes = plt.subplots(
grid_rows, grid_cols, figsize=(5 * grid_cols, 4 * grid_rows)
)
if isinstance(axes, np.ndarray):
axes = axes.flatten()
else:
axes = [axes]
for idx, ax in zip(indices, axes):
points = self.points[idx]
tokens = self.tokens[idx]
if apply_transform and self.transform is not None:
points = self.transform(points)
y_vals = points[0].cpu().numpy()
mask = points[1].cpu().numpy()
x_vals = np.linspace(0, 1, len(y_vals))
clean_tokens = [t.item() for t in tokens if t.item() not in (0, 1, 2)]
try:
expr = self.tokenizer.token_seq_to_expr(torch.tensor(clean_tokens))
expr_str = str(expr)
except Exception as e:
expr_str = f"Ошибка: {e}"
ax.fill_between(
x_vals,
-0.05,
1.05,
where=(mask == 0.0),
color="red",
alpha=0.1,
transform=ax.get_xaxis_transform(),
)
y_plot = y_vals.copy()
y_plot[mask == 0.0] = np.nan
ax.plot(x_vals, y_plot, linewidth=2.5, color="blue", alpha=0.8)
ax.set_title(f"Idx: {idx}\n{expr_str}", fontsize=10, pad=8)
ax.set_xlim(-0.05, 1.05)
ax.set_ylim(-0.05, 1.05)
ax.grid(True, linestyle="--", alpha=0.6)
for i in range(batch_size, len(axes)):
axes[i].axis("off")
plt.tight_layout()
plt.show()
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