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| import json | |
| import os | |
| import numpy as np | |
| import streamlit as st | |
| 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 Toc: | |
| def __init__(self): | |
| self._items = [] | |
| self._placeholder = None | |
| def title(self, text): | |
| self._markdown(text, "h1") | |
| def header(self, text): | |
| self._markdown(text, "h2", " " * 2) | |
| def subheader(self, text): | |
| self._markdown(text, "h3", " " * 4) | |
| def subsubheader(self, text): | |
| self._markdown(text, "h4", " " * 8) | |
| def placeholder(self, sidebar=False): | |
| self._placeholder = st.sidebar.empty() if sidebar else st.empty() | |
| def generate(self): | |
| if self._placeholder: | |
| self._placeholder.markdown("\n".join(self._items), unsafe_allow_html=True) | |
| def _markdown(self, text, level, space=""): | |
| key = "".join(filter(str.isalnum, text)).lower() | |
| st.markdown(f"<{level} id='{key}'>{text}</{level}>", unsafe_allow_html=True) | |
| self._items.append(f"{space}* <a href='#{key}'>{text}</a>") | |
| 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 == "<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 | |
| def read_markdown(path, parent="./sections/"): | |
| with open(os.path.join(parent, path)) as f: | |
| return f.read() |