import json import os import numpy as np import plotly.express as px import torch from torchvision.io import 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=None): indices = np.argsort(logits)[-5:] values = logits[indices] if answer_reverse_mapping is not None: labels = [answer_reverse_mapping[str(i)] for i in indices] else: labels = bert_tokenizer.convert_ids_to_tokens(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 == "": translated_labels.append("") 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 def read_markdown(path, parent="./sections/"): with open(os.path.join(parent, path)) as f: return f.read()