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from torchvision.io import read_image, ImageReadMode | |
import torch | |
import numpy as np | |
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize | |
from torchvision.transforms.functional import InterpolationMode | |
from transformers import BertTokenizerFast | |
import plotly.express as px | |
import json | |
from PIL import Image | |
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 |