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import json
import numpy as np
import plotly.express as px
import torch
from PIL import Image
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode
from transformers import BertTokenizerFast
class Transform(torch.nn.Module):
def __init__(self, image_size):
super().__init__()
self.transforms = torch.nn.Sequential(
Resize([image_size], interpolation=InterpolationMode.BICUBIC),
CenterCrop(image_size),
ConvertImageDtype(torch.float),
Normalize(
(0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711),
),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
with torch.no_grad():
x = self.transforms(x)
return x
transform = Transform(224)
def get_transformed_image(image):
if image.shape[-1] == 3 and isinstance(image, np.ndarray):
image = image.transpose(2, 0, 1)
image = torch.tensor(image)
return transform(image).unsqueeze(0).permute(0, 2, 3, 1).numpy()
bert_tokenizer = BertTokenizerFast.from_pretrained("bert-base-multilingual-uncased")
def get_text_attributes(text):
return bert_tokenizer([text], return_token_type_ids=True, return_tensors="np")
def get_top_5_predictions(logits, answer_reverse_mapping):
indices = np.argsort(logits)[-5:]
values = logits[indices]
labels = [answer_reverse_mapping[str(i)] for i in indices]
return labels, values
with open("translation_dict.json") as f:
translate_dict = json.load(f)
def translate_labels(labels, lang_id):
translated_labels = []
for label in labels:
if label == "<unk>":
translated_labels.append("<unk>")
elif lang_id == "en":
translated_labels.append(label)
else:
translated_labels.append(translate_dict[label][lang_id])
return translated_labels
def plotly_express_horizontal_bar_plot(values, labels):
fig = px.bar(
x=values,
y=labels,
text=[format(value, ".3%") for value in values],
title="Top-5 Predictions",
labels={"x": "Scores", "y": "Answers"},
orientation="h",
)
return fig
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