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import os
import logging
import torch
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from label_studio_ml.model import LabelStudioMLBase
from lxml import etree
from uuid import uuid4
from PIL import Image
class Model(LabelStudioMLBase):
image_processor = AutoImageProcessor.from_pretrained("diegokauer/conditional-detr-coe-int")
model = AutoModelForObjectDetection.from_pretrained("diegokauer/conditional-detr-coe-int")
def __init__(self, **kwargs):
# don't forget to call base class constructor
super(Model, self).__init__(**kwargs)
# you can preinitialize variables with keys needed to extract info from tasks and annotations and form predictions
self.model = model
self.tokenizer = image_processor
self.id2label = model.config.id2label
def predict(self, tasks, **kwargs):
""" This is where inference happens: model returns
the list of predictions based on input list of tasks
"""
predictions = []
for task in tasks:
image_path = task["data"]["image"]
image = Image(image_path)
original_width, original_height = image.size
with torch.no_grad():
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
result_list = []
for score, label, box in zip(results['scores'], results['labels'], scores['boxes']):
label_id = str(uuid4())[:4]
x, y, x2, y2 = tuple(box)
result_list.append(
{
'id': id
'original_width': original_width,
'original_height': original_height,
'from_name': "label",
'to_name': "image",
'type': 'labels',
'score': score, # per-region score, visible in the editor
'value': {
'x': x,
'y': y,
'width': x2-x,
'height': y2-y,
'rotation': 0
'labels': [self.id2label[label]]
}
}
)
predictions.append({
'score': results['scores'].mean(), # prediction overall score, visible in the data manager columns
'model_version': 'diegokauer/conditional-detr-coe-int', # all predictions will be differentiated by model version
'result': result_list
})
return predictions |