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The following function applies the processor to the images and questions and formats the labels as described above:
import torch
def preprocess_data(examples):
image_paths = examples['image_id']
images = [Image.open(image_path) for image_path in image_paths]
texts = examples['question']
encoding = processor(images, texts, padding="max_length", truncation=True, return_tensors="pt")
for k, v in encoding.items():
encoding[k] = v.squeeze()
targets = []
for labels, scores in zip(examples['label.ids'], examples['label.weights']):
target = torch.zeros(len(id2label))
for label, score in zip(labels, scores):
target[label] = score
targets.append(target)
encoding["labels"] = targets
return encoding
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.map] function. |