File size: 9,750 Bytes
ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 9a88738 ebcc7d1 |
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 |
import torch
from torch.utils.data import DataLoader
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import re
import cv2
import string
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from vit import LineDataset, collate_fn
from loguru import logger
import os
from configs import hf_token
class Inference:
def __init__(self, model_path, processor_path, target_size=(256, 64), batch_size=32):
"""
Initialize the TextGenerator with model and processor paths.
Args:
model_path (str): Path to the pre-trained model
processor_path (str): Path to the pre-trained processor
target_size (tuple): Target size for input images (height, width)
batch_size (int): Batch size for inference
"""
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.model_path = self._get_absolute_path(model_path)
# self.processor_path = self._get_absolute_path(processor_path)
self.model_path = model_path
self.processor_path = processor_path
self.target_size = target_size
self.batch_size = batch_size
# Initialize model and processor
self.processor = None
self.model = None
self._initialize_model()
def _get_absolute_path(self, path):
"""Convert relative path to absolute path"""
if os.path.isabs(path):
return path
# If it's a relative path, make it absolute relative to the current working directory
return os.path.join(os.getcwd(), path.lstrip('./'))
def _initialize_model(self):
"""Load and initialize the model and processor."""
logger.info("Loading model...")
# # Check if paths exist
# if not os.path.exists(self.model_path):
# raise FileNotFoundError(f"Model path not found: {self.model_path}")
# if not os.path.exists(self.processor_path):
# raise FileNotFoundError(f"Processor path not found: {self.processor_path}")
# # List all files in the model directory
# all_files = os.listdir(self.model_path)
# # Validate that we have the necessary files
# if not any(f in all_files for f in ['pytorch_model.bin', 'model.safetensors']):
# logger.error("No model weights file found! (pytorch_model.bin or model.safetensors)")
# raise FileNotFoundError("Model weights file missing")
# if 'config.json' not in all_files:
# logger.error("config.json file not found!")
# raise FileNotFoundError("config.json missing")
logger.info(f"Loading model from: {self.model_path}")
logger.info(f"Loading processor from: {self.processor_path}")
try:
# Load processor
self.processor = TrOCRProcessor.from_pretrained(self.processor_path, do_rescale=False, use_fast=True, token=hf_token)
logger.info("Processor loaded successfully")
# Try different loading methods for the model
logger.info("Attempting to load model...")
# Method 1: Try with explicit device mapping
try:
self.model = VisionEncoderDecoderModel.from_pretrained(
self.model_path,
use_safetensors=True,
device_map="auto" if torch.cuda.is_available() else None,
token=hf_token
)
logger.info("Model loaded with safetensors=True and device_map")
except Exception as e1:
logger.warning(f"Method 1 failed: {e1}")
# Method 2: Try without device mapping
try:
self.model = VisionEncoderDecoderModel.from_pretrained(
self.model_path,
use_safetensors=True,
token=hf_token
)
logger.info("Model loaded with safetensors=True")
except Exception as e2:
logger.warning(f"Method 2 failed: {e2}")
# Method 3: Try without safetensors
try:
self.model = VisionEncoderDecoderModel.from_pretrained(
self.model_path,
use_safetensors=True,
token=hf_token
)
logger.info("Model loaded with safetensors=False")
except Exception as e3:
logger.error(f"All loading methods failed: {e3}")
raise
# Move model to device if not already done by device_map
if not hasattr(self.model, 'device') or str(self.model.device) != str(self.device):
logger.info(f"Moving model to device: {self.device}")
self.model.to(self.device)
self.model.eval()
logger.info("Model loaded successfully and moved to device")
except Exception as e:
logger.error(f"Error loading model or processor: {e}")
import traceback
logger.error(f"Traceback: {traceback.format_exc()}")
raise
def preprocess_images(self, line_segments):
"""
Prepare line images for inference.
Args:
line_segments (dict): Dictionary containing line segment information
Returns:
tuple: (keys, line_images) - keys and corresponding images
"""
keys = list(line_segments.keys())
line_images = [line_segments[k]["image"] for k in keys]
return keys, line_images
def create_dataloader(self, line_images):
"""
Create DataLoader for inference.
Args:
line_images (list): List of line images
Returns:
DataLoader: Configured DataLoader for inference
"""
# Create dummy labels for inference
dummy_labels = [""] * len(line_images)
dataset = LineDataset(
self.processor,
self.model,
line_images,
dummy_labels,
self.target_size,
apply_augmentation=False
)
dataloader = DataLoader(
dataset,
batch_size=self.batch_size,
shuffle=False,
collate_fn=collate_fn
)
return dataloader
def generate_texts(self, dataloader):
"""
Generate texts from images using the model.
Args:
dataloader (DataLoader): DataLoader containing preprocessed images
Returns:
list: List of generated texts
"""
generated_texts = []
with torch.no_grad():
for batch in dataloader:
pixel_values = batch["pixel_values"].to(self.device)
generated_ids = self.model.generate(pixel_values)
generated_texts_batch = self.processor.batch_decode(
generated_ids,
skip_special_tokens=True
)
generated_texts.extend(generated_texts_batch)
return generated_texts
def update_line_segments(self, line_segments, keys, generated_texts):
"""
Update line segments dictionary with generated transcriptions.
Args:
line_segments (dict): Original line segments dictionary
keys (list): List of keys corresponding to the line segments
generated_texts (list): List of generated texts
Returns:
dict: Updated line segments dictionary with transcriptions
"""
for key, text in zip(keys, generated_texts):
line_segments[key]["transcription"] = text
return line_segments
def generate_texts_from_images(self, line_segments):
"""
Main method to generate texts from line segment images.
Args:
line_segments (dict): Dictionary containing line segment information
with "image" key for each segment
Returns:
dict: Updated line segments dictionary with "transcription" key added
"""
logger.info("Starting text generation from images...")
# Preprocess images
keys, line_images = self.preprocess_images(line_segments)
# Create dataloader
dataloader = self.create_dataloader(line_images)
# Generate texts
generated_texts = self.generate_texts(dataloader)
# Update line segments with transcriptions
updated_line_segments = self.update_line_segments(
line_segments, keys, generated_texts
)
return updated_line_segments
def generate_single_image(self, image):
"""
Generate text from a single image.
Args:
image: PIL Image or numpy array
Returns:
str: Generated text
"""
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
# Create a temporary line_segments-like structure
temp_segments = {"temp_key": {"image": image}}
# Use the main method
result = self.generate_texts_from_images(temp_segments)
return result["temp_key"]["transcription"]
|