File size: 9,069 Bytes
6df3c38 ee7e9d0 6df3c38 ee7e9d0 6df3c38 |
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 |
from .utils import load_model,load_processor,normalize_box,compare_boxes,adjacent
from .annotate_image import get_flattened_output,annotate_image
from PIL import Image,ImageDraw, ImageFont
import logging
import torch
import json
import os
logger = logging.getLogger(__name__)
class ModelHandler(object):
def __init__(self):
self.model = None
self.model_dir = None
self.device = 'cpu'
self.error = None
self.initialized = False
self._raw_input_data = None
self._processed_data = None
self._images_size = None
self.counter = 0
def initialize(self, context):
try:
logger.info("Loading transformer model")
self._context = context
properties = self._context
self.model_dir = properties.get("model_dir")
self.model = self.load(self.model_dir)
self.initialized = True
except Exception as e:
logger.error(f"Error initializing model: {str(e)}")
self.error = str(e)
def preprocess(self, batch):
try:
inference_dict = batch
self._raw_input_data = inference_dict
processor = load_processor()
images = [Image.open(path).convert("RGB")
for path in inference_dict['image_path']]
self._images_size = [img.size for img in images]
words = inference_dict['words']
boxes = [[normalize_box(box, images[i].size[0], images[i].size[1])
for box in doc] for i, doc in enumerate(inference_dict['bboxes'])]
encoded_inputs = processor(
images, words, boxes=boxes, return_tensors="pt", padding="max_length", truncation=True)
self._processed_data = encoded_inputs
return encoded_inputs
except Exception as e:
logger.error(f"Error in preprocessing: {str(e)}")
self.error = str(e)
return None
def load(self, model_dir):
try:
model = load_model(model_dir)
return model
except Exception as e:
logger.error(f"Error loading LayoutLMv3 model: {str(e)}")
self.error = str(e)
return None
def inference(self, model_input):
try:
with torch.no_grad():
inference_outputs = self.model(**model_input)
predictions = inference_outputs.logits.argmax(-1).tolist()
results = []
for i in range(len(predictions)):
tmp = dict()
tmp[f'output_{i}'] = predictions[i]
results.append(tmp)
return [results]
except Exception as e:
logger.error(f"Error in inference: {str(e)}")
self.error = str(e)
return None
def postprocess(self, inference_output):
try:
docs = []
k = 0
for page, doc_words in enumerate(self._raw_input_data['words']):
doc_list = []
width, height = self._images_size[page]
for i, doc_word in enumerate(doc_words, start=0):
word_tagging = None
word_labels = []
word = dict()
word['id'] = k
k += 1
word['text'] = doc_word
word['pageNum'] = page + 1
word['box'] = self._raw_input_data['bboxes'][page][i]
_normalized_box = normalize_box(
self._raw_input_data['bboxes'][page][i], width, height)
for j, box in enumerate(self._processed_data['bbox'].tolist()[page]):
if compare_boxes(box, _normalized_box):
if self.model.config.id2label[inference_output[0][page][f'output_{page}'][j]] != 'O':
word_labels.append(
self.model.config.id2label[inference_output[0][page][f'output_{page}'][j]][2:])
else:
word_labels.append('other')
if word_labels != []:
word_tagging = word_labels[0] if word_labels[0] != 'other' else word_labels[-1]
else:
word_tagging = 'other'
word['label'] = word_tagging
word['pageSize'] = {'width': width, 'height': height}
if word['label'] != 'other':
doc_list.append(word)
spans = []
def adjacents(entity): return [
adj for adj in doc_list if adjacent(entity, adj)]
output_test_tmp = doc_list[:]
for entity in doc_list:
if adjacents(entity) == []:
spans.append([entity])
output_test_tmp.remove(entity)
while output_test_tmp != []:
span = [output_test_tmp[0]]
output_test_tmp = output_test_tmp[1:]
while output_test_tmp != [] and adjacent(span[-1], output_test_tmp[0]):
span.append(output_test_tmp[0])
output_test_tmp.remove(output_test_tmp[0])
spans.append(span)
output_spans = []
for span in spans:
if span[0]['label'] in ['ITEMS', 'PRICE']:
output_span = {"text": ' '.join([entity['text'] for entity in span]) + '|',
"label": span[0]['label'],
"words": [{
'id': entity['id'],
'box': entity['box'],
'text': entity['text']
} for entity in span]
}
else:
output_span = {"text": ' '.join([entity['text'] for entity in span]),
"label": span[0]['label'],
"words": [{
'id': entity['id'],
'box': entity['box'],
'text': entity['text']
} for entity in span]
}
output_spans.append(output_span)
docs.append({f'output': output_spans})
return [json.dumps(docs, ensure_ascii=False)]
except Exception as e:
logger.error(f"Error in postprocessing: {str(e)}")
self.error = str(e)
return None
def handle(self, data, context):
"""
Call preprocess, inference, and post-process functions
:param data: input data
:param context: mms context
"""
try:
if not self.initialized:
self.initialize(context)
if data is None:
return None
model_input = self.preprocess(data)
if model_input is None:
return None
model_out = self.inference(model_input)
if model_out is None:
return None
inference_out = self.postprocess(model_out)[0]
# # Write the original inference output to a JSON file
# with open('temp/LayoutlMV3InferenceOutput.json', 'w') as inf_out:
# inf_out.write(inference_out)
# Load the original inference output from the JSON file
inference_out_list = json.loads(inference_out)
flattened_output_list = get_flattened_output(inference_out_list)
print('Ready for Annotation')
for i, flattened_output in enumerate(flattened_output_list):
annotate_image(data['image_path'][i], flattened_output)
# Create the labeled directory if it doesn't exist
labeled_dir = 'static/temp/labeled'
os.makedirs(labeled_dir, exist_ok=True)
# Loop through the list and create a new file for each JSON object
for index, output_json in enumerate(inference_out_list):
# Generate a filename based on the index
output_filename = os.path.join(labeled_dir, f'Output_{index}.json')
# Write the JSON object to the file
with open(output_filename, 'w') as output_file:
json.dump(output_json, output_file)
except Exception as e:
logger.error(f"Error handling request: {str(e)}")
self.error = str(e)
_service = ModelHandler()
def handle(data, context):
if not _service.initialized:
_service.initialize(context)
if data is None:
return None
return _service.handle(data, context)
|