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| from .utils import ( | |
| get_text_attributes, | |
| get_top_5_predictions, | |
| get_transformed_image, | |
| plotly_express_horizontal_bar_plot, | |
| bert_tokenizer, | |
| ) | |
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import os | |
| import matplotlib.pyplot as plt | |
| from mtranslate import translate | |
| from .utils import read_markdown | |
| from .model.flax_clip_vision_bert.modeling_clip_vision_bert import ( | |
| FlaxCLIPVisionBertForMaskedLM, | |
| ) | |
| def softmax(logits): | |
| return np.exp(logits) / np.sum(np.exp(logits), axis=0) | |
| def app(state): | |
| mlm_state = state | |
| with st.beta_expander("Usage"): | |
| st.write(read_markdown("mlm_usage.md")) | |
| st.write(read_markdown("mlm_intro.md")) | |
| # @st.cache(persist=False) # TODO: Make this work with mlm_state. Currently not supported. | |
| def predict(transformed_image, caption_inputs): | |
| outputs = mlm_state.mlm_model(pixel_values=transformed_image, **caption_inputs) | |
| indices = np.where(caption_inputs["input_ids"] == bert_tokenizer.mask_token_id)[1][0] | |
| preds = outputs.logits[0][indices] | |
| scores = np.array(preds) | |
| return scores | |
| # @st.cache(persist=False) | |
| def load_model(ckpt): | |
| return FlaxCLIPVisionBertForMaskedLM.from_pretrained(ckpt) | |
| mlm_checkpoints = ["flax-community/clip-vision-bert-cc12m-70k"] | |
| dummy_data = pd.read_csv("cc12m_data/vqa_val.tsv", sep="\t") | |
| first_index = 15 | |
| # Init Session mlm_state | |
| if mlm_state.mlm_image_file is None: | |
| mlm_state.mlm_image_file = dummy_data.loc[first_index, "image_file"] | |
| caption = dummy_data.loc[first_index, "caption"].strip("- ") | |
| ids = bert_tokenizer.encode(caption) | |
| ids[np.random.randint(1, len(ids) - 1)] = bert_tokenizer.mask_token_id | |
| mlm_state.caption = bert_tokenizer.decode(ids[1:-1]) | |
| mlm_state.caption_lang_id = dummy_data.loc[first_index, "lang_id"] | |
| image_path = os.path.join("cc12m_data/resized_images_vqa", mlm_state.mlm_image_file) | |
| image = plt.imread(image_path) | |
| mlm_state.mlm_image = image | |
| if mlm_state.mlm_model is None: | |
| # Display Top-5 Predictions | |
| with st.spinner("Loading model..."): | |
| mlm_state.mlm_model = load_model(mlm_checkpoints[0]) | |
| if st.button( | |
| "Get a random example", | |
| help="Get a random example from the 100 `seeded` image-text pairs.", | |
| ): | |
| sample = dummy_data.sample(1).reset_index() | |
| mlm_state.mlm_image_file = sample.loc[0, "image_file"] | |
| caption = sample.loc[0, "caption"].strip("- ") | |
| ids = bert_tokenizer.encode(caption) | |
| ids[np.random.randint(1, len(ids) - 1)] = bert_tokenizer.mask_token_id | |
| mlm_state.caption = bert_tokenizer.decode(ids[1:-1]) | |
| mlm_state.caption_lang_id = sample.loc[0, "lang_id"] | |
| image_path = os.path.join("cc12m_data/resized_images_vqa", mlm_state.mlm_image_file) | |
| image = plt.imread(image_path) | |
| mlm_state.mlm_image = image | |
| transformed_image = get_transformed_image(mlm_state.mlm_image) | |
| new_col1, new_col2 = st.beta_columns([5, 5]) | |
| # Display Image | |
| new_col1.image(mlm_state.mlm_image, use_column_width="always") | |
| # Display caption | |
| new_col2.write("Write your text with exactly one [MASK] token.") | |
| caption = new_col2.text_input( | |
| label="Text", | |
| value=mlm_state.caption, | |
| help="Type your masked caption regarding the image above in one of the four languages.", | |
| ) | |
| new_col2.markdown( | |
| f"""**English Translation**: {caption if mlm_state.caption_lang_id == "en" else translate(caption, 'en')}""" | |
| ) | |
| caption_inputs = get_text_attributes(caption) | |
| # Display Top-5 Predictions | |
| with st.spinner("Predicting..."): | |
| scores = predict(transformed_image, dict(caption_inputs)) | |
| scores = softmax(scores) | |
| labels, values = get_top_5_predictions(scores) | |
| # newer_col1, newer_col2 = st.beta_columns([6,4]) | |
| fig = plotly_express_horizontal_bar_plot(values, labels) | |
| st.dataframe(pd.DataFrame({"Tokens":labels, "English Translation": list(map(lambda x: translate(x),labels))}).T) | |
| st.plotly_chart(fig, use_container_width=True) | |