import streamlit as st import pickle import pandas as pd import numpy as np import random import torch from matplotlib.backends.backend_agg import RendererAgg from backend.disentangle_concepts import * import torch_utils import dnnlib import legacy _lock = RendererAgg.lock st.set_page_config(layout='wide') BACKGROUND_COLOR = '#bcd0e7' SECONDARY_COLOR = '#bce7db' st.title('Disentanglement studies on the Textile Dataset') st.markdown( """ This is a demo of the Disentanglement studies on the [Oxford Vases Dataset](https://www.robots.ox.ac.uk/~vgg/data/oxbuildings/). """, unsafe_allow_html=False,) annotations_file = './data/vase_annotated_files/seeds0000-20000.pkl' with open(annotations_file, 'rb') as f: annotations = pickle.load(f) if 'image_id' not in st.session_state: st.session_state.image_id = 0 if 'concept_ids' not in st.session_state: st.session_state.concept_ids =['AMPHORA'] if 'space_id' not in st.session_state: st.session_state.space_id = 'W' # def on_change_random_input(): # st.session_state.image_id = st.session_state.image_id # ----------------------------- INPUT ---------------------------------- st.header('Input') input_col_1, input_col_2, input_col_3 = st.columns(3) # --------------------------- INPUT column 1 --------------------------- with input_col_1: with st.form('text_form'): # image_id = st.number_input('Image ID: ', format='%d', step=1) st.write('**Choose two options to disentangle**') type_col = st.selectbox('Concept category:', tuple(['Provenance', 'Shape Name', 'Fabric', 'Technique'])) ann_df = pd.read_csv(f'./data/vase_annotated_files/sim_{type_col}_seeds0000-20000.csv') labels = list(ann_df.columns) labels.remove('ID') labels.remove('Unnamed: 0') concept_ids = st.multiselect('Concepts:', tuple(labels), max_selections=2, default=[labels[2], labels[3]]) st.write('**Choose a latent space to disentangle**') space_id = st.selectbox('Space:', tuple(['W', 'Z'])) choose_text_button = st.form_submit_button('Choose the defined concept and space to disentangle') if choose_text_button: concept_ids = list(concept_ids) st.session_state.concept_ids = concept_ids space_id = str(space_id) st.session_state.space_id = space_id # st.write(image_id, st.session_state.image_id) # ---------------------------- SET UP OUTPUT ------------------------------ epsilon_container = st.empty() st.header('Output') st.subheader('Concept vector') # perform attack container # header_col_1, header_col_2, header_col_3, header_col_4, header_col_5 = st.columns([1,1,1,1,1]) # output_col_1, output_col_2, output_col_3, output_col_4, output_col_5 = st.columns([1,1,1,1,1]) header_col_1, header_col_2 = st.columns([5,1]) output_col_1, output_col_2 = st.columns([5,1]) st.subheader('Derivations along the concept vector') # prediction error container error_container = st.empty() smoothgrad_header_container = st.empty() # smoothgrad container smooth_head_1, smooth_head_2, smooth_head_3, smooth_head_4, smooth_head_5 = st.columns([1,1,1,1,1]) smoothgrad_col_1, smoothgrad_col_2, smoothgrad_col_3, smoothgrad_col_4, smoothgrad_col_5 = st.columns([1,1,1,1,1]) # ---------------------------- DISPLAY COL 1 ROW 1 ------------------------------ with output_col_1: separation_vector, number_important_features, imp_nodes, performance = get_separation_space(concept_ids, annotations, ann_df, latent_space=st.session_state.space_id, samples=150) # st.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.3f}% confidence') st.write('Concept vector', separation_vector) header_col_1.write(f'Concept {st.session_state.concept_ids} - Space {st.session_state.space_id} - Number of relevant nodes: {number_important_features} - Val classification performance: {performance}')# - Nodes {",".join(list(imp_nodes))}') # ----------------------------- INPUT column 2 & 3 ---------------------------- with input_col_2: with st.form('image_form'): # image_id = st.number_input('Image ID: ', format='%d', step=1) st.write('**Choose or generate a random image to test the disentanglement**') chosen_image_id_input = st.empty() image_id = chosen_image_id_input.number_input('Image ID:', format='%d', step=1, value=st.session_state.image_id) choose_image_button = st.form_submit_button('Choose the defined image') random_id = st.form_submit_button('Generate a random image') if random_id: image_id = random.randint(0, 20000) st.session_state.image_id = image_id chosen_image_id_input.number_input('Image ID:', format='%d', step=1, value=st.session_state.image_id) if choose_image_button: image_id = int(image_id) st.session_state.image_id = int(image_id) # st.write(image_id, st.session_state.image_id) with input_col_3: with st.form('Variate along the disentangled concept'): st.write('**Set range of change**') chosen_epsilon_input = st.empty() epsilon = chosen_epsilon_input.number_input('Lambda:', min_value=1, step=1) epsilon_button = st.form_submit_button('Choose the defined lambda') st.write('**Select hierarchical levels to manipulate**') layers = st.multiselect('Layers:', tuple(range(14))) if len(layers) == 0: layers = None print(layers) layers_button = st.form_submit_button('Choose the defined layers') # ---------------------------- DISPLAY COL 2 ROW 1 ------------------------------ #model = torch.load('./data/model_files/pytorch_model.bin', map_location=torch.device('cpu')) with dnnlib.util.open_url('./data/vase_model_files/network-snapshot-003800.pkl') as f: model = legacy.load_network_pkl(f)['G_ema'].to('cpu') # type: ignore if st.session_state.space_id == 'Z': original_image_vec = annotations['z_vectors'][st.session_state.image_id] else: original_image_vec = annotations['w_vectors'][st.session_state.image_id] img = generate_original_image(original_image_vec, model, latent_space=st.session_state.space_id) top_pred = ann_df.loc[st.session_state.image_id, labels].astype(float).idxmax() # input_image = original_image_dict['image'] # input_label = original_image_dict['label'] # input_id = original_image_dict['id'] with smoothgrad_col_3: st.image(img) smooth_head_3.write(f'Base image, predicted as {top_pred}') images, lambdas = regenerate_images(model, original_image_vec, separation_vector, min_epsilon=-(int(epsilon)), max_epsilon=int(epsilon), latent_space=st.session_state.space_id, layers=layers) with smoothgrad_col_1: st.image(images[0]) smooth_head_1.write(f'Change of {np.round(lambdas[0], 2)}') with smoothgrad_col_2: st.image(images[1]) smooth_head_2.write(f'Change of {np.round(lambdas[1], 2)}') with smoothgrad_col_4: st.image(images[3]) smooth_head_4.write(f'Change of {np.round(lambdas[3], 2)}') with smoothgrad_col_5: st.image(images[4]) smooth_head_5.write(f'Change of {np.round(lambdas[4], 2)}')