working version

data/stored_vectors/scores_colors_hsv.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:93f5789a80465ca7b21713819bc444d72239fa1b7ae56adf69e3323e0f3bedd1
-size 974247
+oid sha256:2d237e5efaac5afd98d777681cbfbf77bf7c41b8e4f221557fc588ab17e5e42b
+size 974255

pages/1_Textiles_Disentanglement.py

@@ -34,6 +34,13 @@ with open(annotations_file, 'rb') as f:
 concept_vectors = pd.read_csv('./data/stored_vectors/scores_colors_hsv.csv')
 concept_vectors['vector'] = [np.array([float(xx) for xx in x]) for x in concept_vectors['vector'].str.split(', ')]
 concept_vectors['score'] = concept_vectors['score'].astype(float)
+
+concept_vectors['sign'] = [True if 'sign:True' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['extremes'] = [True if 'extremes method:True' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['regularization'] = [float(val.split(',')[1].strip('regularization: ')) if 'regularization:' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['cl_method'] = [val.split(',')[0].strip('classification method:') if 'classification method:' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['num_factors'] = [int(val.split(',')[1].strip('number of factors:')) if 'number of factors:' in val else False for val in concept_vectors['kwargs']]
+
 concept_vectors = concept_vectors.sort_values('score', ascending=False).reset_index()
 
 with dnnlib.util.open_url('./data/textile_model_files/network-snapshot-005000.pkl') as f:
@@ -53,14 +60,25 @@ if 'saturation_lambda' not in st.session_state:
     st.session_state.saturation_lambda = 0
 if 'value_lambda' not in st.session_state:
     st.session_state.value_lambda = 0
-    
+if 'sign' not in st.session_state:
+    st.session_state.sign = False
+if 'extremes' not in st.session_state:
+    st.session_state.extremes = False
+if 'regularization' not in st.session_state:
+    st.session_state.regularization = False
+if 'cl_method' not in st.session_state:
+    st.session_state.cl_method = False
+if 'num_factors' not in st.session_state:
+    st.session_state.num_factors = False
+if 'best' not in st.session_state:
+    st.session_state.best = True
     
 # 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_col_1, input_col_2, input_col_3, input_col_4 = st.columns(4)
 # --------------------------- INPUT column 1 ---------------------------
 with input_col_1:
    with st.form('image_form'):
@@ -80,44 +98,69 @@ with input_col_1:
             
         if choose_image_button:
             image_id = int(image_id)
-            st.session_state.image_id = int(image_id)
+            st.session_state.image_id = image_id
 
 with input_col_2:
     with st.form('text_form_1'):
         
         st.write('**Choose color to vary**')
-        type_col = st.selectbox('Color:', tuple(COLORS_LIST))
+        type_col = st.selectbox('Color:', tuple(COLORS_LIST), index=7)
         colors_button = st.form_submit_button('Choose the defined color')
         
         st.write('**Set range of change**')
         chosen_color_lambda_input = st.empty()
-        color_lambda = chosen_color_lambda_input.number_input('Lambda:', min_value=0, step=1, value=7)
-        color_lambda_button = st.form_submit_button('Choose the defined lambda')
+        color_lambda = chosen_color_lambda_input.number_input('Lambda:', min_value=-100, step=1, value=7)
+        color_lambda_button = st.form_submit_button('Choose the defined lambda for color')
           
         if colors_button or color_lambda_button:
+            st.session_state.image_id = image_id
             st.session_state.concept_ids = type_col
             st.session_state.color_lambda = color_lambda
             
-            
-            
         
 with input_col_3:
     with st.form('text_form'):
         
         st.write('**Saturation variation**')
         chosen_saturation_lambda_input = st.empty()
-        saturation_lambda = chosen_saturation_lambda_input.number_input('Lambda:', min_value=0, step=1, key=0)
-        saturation_lambda_button = st.form_submit_button('Choose the defined lambda for Saturation')
+        saturation_lambda = chosen_saturation_lambda_input.number_input('Lambda:', min_value=-100, step=1, key=0, value=0)
+        saturation_lambda_button = st.form_submit_button('Choose the defined lambda for saturation')
         
         st.write('**Value variation**')
         chosen_value_lambda_input = st.empty()
-        value_lambda = chosen_value_lambda_input.number_input('Lambda:', min_value=0, step=1, key=1)
-        value_lambda_button = st.form_submit_button('Choose the defined lambda for Value')
+        value_lambda = chosen_value_lambda_input.number_input('Lambda:', min_value=-100, step=1, key=1, value=0)
+        value_lambda_button = st.form_submit_button('Choose the defined lambda for salue')
         
         if saturation_lambda_button or value_lambda_button:
             st.session_state.saturation_lambda = int(saturation_lambda)
             st.session_state.value_lambda = int(value_lambda)
             
+with input_col_4:         
+    with st.form('text_form_2'):
+        st.write('Use best options')
+        best = st.selectbox('Option:', tuple([True, False]), index=0)
+        st.write('Options for StyleSpace (not available for Saturation and Value)')
+        sign = st.selectbox('Sign option:', tuple([True, False]), index=1)
+        num_factors = st.selectbox('Number of factors option:', tuple([1, 5, 10, 20, False]), index=4)
+        st.write('Options for InterFaceGAN (not available for Saturation and Value)')
+        cl_method = st.selectbox('Classification method option:', tuple(['LR', 'SVM', False]), index=2)
+        regularization = st.selectbox('Regularization option:', tuple([0.1, 1.0, False]), index=2)
+        st.write('Options for InterFaceGAN (only for Saturation and Value)')
+        extremes = st.selectbox('Extremes option:', tuple([True, False]), index=1)
+        
+        choose_options_button = st.form_submit_button('Choose the defined options')
+        # st.write('**Choose a latent space to disentangle**')
+        # # chosen_text_id_input = st.empty()
+        # # concept_id = chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
+        # space_id = st.selectbox('Space:', tuple(['Z', 'W']))
+        if choose_options_button:
+            st.session_state.sign = sign
+            st.session_state.num_factors = num_factors
+            st.session_state.cl_method = cl_method
+            st.session_state.regularization = regularization
+            st.session_state.extremes = extremes
+            st.session_state.best = best
+    
 # with input_col_4:
 #     with st.form('Network specifics:'):
 #         st.write('**Choose a latent space to use**')
@@ -153,9 +196,19 @@ with header_col_1:
     st.write(f'Original image')
 
 with header_col_2:
-    color_separation_vector, performance_color = concept_vectors[concept_vectors['color'] == st.session_state.concept_ids].reset_index().loc[0, ['vector', 'score']]
-    saturation_separation_vector, performance_saturation = concept_vectors[concept_vectors['color'] == 'Saturation'].reset_index().loc[0, ['vector', 'score']]
-    value_separation_vector, performance_value = concept_vectors[concept_vectors['color'] == 'Value'].reset_index().loc[0, ['vector', 'score']]
+    if st.session_state.best:
+        color_separation_vector, performance_color = concept_vectors[concept_vectors['color'] == st.session_state.concept_ids].reset_index().loc[0, ['vector', 'score']]
+        saturation_separation_vector, performance_saturation = concept_vectors[concept_vectors['color'] == 'Saturation'].reset_index().loc[0, ['vector', 'score']]
+        value_separation_vector, performance_value = concept_vectors[concept_vectors['color'] == 'Value'].reset_index().loc[0, ['vector', 'score']]
+    else:
+        tmp = concept_vectors[concept_vectors['color'] == st.session_state.concept_ids]
+        tmp = tmp[tmp['sign'] == st.session_state.sign][tmp['num_factors'] == st.session_state.num_factors][tmp['cl_method'] == st.session_state.cl_method][tmp['regularization'] == st.session_state.regularization]
+        color_separation_vector, performance_color = tmp.reset_index().loc[0, ['vector', 'score']]
+        tmp_value = concept_vectors[concept_vectors['color'] == 'Value'][concept_vectors['extremes'] == st.session_state.extremes]
+        value_separation_vector, performance_value = tmp_value.reset_index().loc[0, ['vector', 'score']]
+        tmp_sat = concept_vectors[concept_vectors['color'] == 'Saturation'][concept_vectors['extremes'] == st.session_state.extremes]
+        saturation_separation_vector, performance_saturation = tmp_sat.reset_index().loc[0, ['vector', 'score']]
+        
     st.write(f'Change in {st.session_state.concept_ids} of {np.round(st.session_state.color_lambda, 2)}, in saturation of {np.round(st.session_state.saturation_lambda, 2)}, in value of {np.round(st.session_state.value_lambda, 2)}. - Performance color vector: {performance_color}, saturation vector: {performance_saturation/100}, value vector: {performance_value/100}')
 
 # ---------------------------- DISPLAY COL 2 ROW 1 ------------------------------

pages/2_Colours_comparison.py

@@ -22,91 +22,124 @@ HIGHTLIGHT_COLOR = '#e7bcc5'
 st.set_page_config(layout='wide')
 
 
-st.title('Comparison among concept vectors')
-st.write('> **How do the concept vectors relate to each other?**')
-st.write('> **What is their join impact on the image?**')
-st.write("""Description to write""")
+st.title('Comparison among color directions')
+st.write('> **How do the color directions relate to each other?**')
+st.write('> **What is their joint impact on the image?**')
 
-    
-annotations_file = './data/annotated_files/seeds0000-50000.pkl'
+        
+annotations_file = './data/textile_annotated_files/seeds0000-100000_S.pkl'
 with open(annotations_file, 'rb') as f:
     annotations = pickle.load(f)
 
-ann_df = pd.read_csv('./data/annotated_files/sim_seeds0000-50000.csv')
-concepts = './data/concepts.txt'
+concept_vectors = pd.read_csv('./data/stored_vectors/scores_colors_hsv.csv')
+concept_vectors['vector'] = [np.array([float(xx) for xx in x]) for x in concept_vectors['vector'].str.split(', ')]
+concept_vectors['score'] = concept_vectors['score'].astype(float)
+concept_vectors['sign'] = [True if 'sign:True' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['extremes'] = [True if 'extremes method:True' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['regularization'] = [float(val.split(',')[1].strip('regularization: ')) if 'regularization:' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['cl_method'] = [val.split(',')[0].strip('classification method:') if 'classification method:' in val else False for val in concept_vectors['kwargs']]
+concept_vectors['num_factors'] = [int(val.split(',')[1].strip('number of factors:')) if 'number of factors:' in val else False for val in concept_vectors['kwargs']]
+concept_vectors = concept_vectors.sort_values('score', ascending=False).reset_index()
+
+with dnnlib.util.open_url('./data/textile_model_files/network-snapshot-005000.pkl') as f:
+    model = legacy.load_network_pkl(f)['G_ema'].to('cpu') # type: ignore
 
-with open(concepts) as f:
-    labels = [line.strip() for line in f.readlines()]
+COLORS_LIST = ['Gray', 'Red Orange', 'Yellow', 'Green', 'Light Blue', 'Blue', 'Purple', 'Pink', 'Saturation', 'Value']
 
 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 = ['Abstract', 'Representational']
+    st.session_state.concept_ids = [COLORS_LIST[-1], COLORS_LIST[-2], ]
+if 'sign' not in st.session_state:
+    st.session_state.sign = False
+if 'extremes' not in st.session_state:
+    st.session_state.extremes = False
+if 'regularization' not in st.session_state:
+    st.session_state.regularization = False
+if 'cl_method' not in st.session_state:
+    st.session_state.cl_method = False
+if 'num_factors' not in st.session_state:
+    st.session_state.num_factors = False
+
+
 if 'space_id' not in st.session_state:
-    st.session_state.space_id = 'Z'
-# def on_change_random_input():
-#     st.session_state.image_id = st.session_state.image_id
+    st.session_state.space_id = 'W'
 
 # ----------------------------- INPUT ----------------------------------
 st.header('Input')
-input_col_1, input_col_2, input_col_3 = st.columns(3)
+input_col_1, input_col_2 = st.columns([1,1])
 # --------------------------- 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 a series of concepts to compare**')
+        st.write('**Choose a series of colors to compare**')
         # chosen_text_id_input = st.empty()
         # concept_id = chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
-        concept_ids = st.multiselect('Concept:', tuple(labels))
-
-        st.write('**Choose a latent space to disentangle**')
-        # chosen_text_id_input = st.empty()
-        # concept_id = chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
-        space_id = st.selectbox('Space:', tuple(['Z', 'W']))
-
-        choose_text_button = st.form_submit_button('Choose the defined concept and space to disentangle')
+        concept_ids = st.multiselect('Color (including Saturation and Value):', tuple(COLORS_LIST), default=[COLORS_LIST[-1], COLORS_LIST[-2], ])
+        choose_text_button = st.form_submit_button('Choose the defined colors')
         
         if choose_text_button:
             st.session_state.concept_ids = list(concept_ids)
-            space_id = str(space_id)
-            st.session_state.space_id = space_id
-        # st.write(image_id, st.session_state.image_id)
-
+            
+            
+with input_col_2:
+    with st.form('text_form_1'):
+        st.write('Options for StyleSpace (not available for Saturation and Value)')
+        sign = st.selectbox('Sign option:', tuple([True, False]), index=1)
+        num_factors = st.selectbox('Number of factors option:', tuple([1, 5, 10, 20, False]), index=4)
+        st.write('Options for InterFaceGAN (not available for Saturation and Value)')
+        cl_method = st.selectbox('Classification method option:', tuple(['LR', 'SVM', False]), index=2)
+        regularization = st.selectbox('Regularization option:', tuple([0.1, 1.0, False]), index=2)
+        st.write('Options for InterFaceGAN (only for Saturation and Value)')
+        extremes = st.selectbox('Extremes option:', tuple([True, False]), index=1)
+        
+        choose_options_button = st.form_submit_button('Choose the defined options')
+        # st.write('**Choose a latent space to disentangle**')
+        # # chosen_text_id_input = st.empty()
+        # # concept_id = chosen_text_id_input.text_input('Concept:', value=st.session_state.concept_id)
+        # space_id = st.selectbox('Space:', tuple(['Z', 'W']))
+        if choose_options_button:
+            st.session_state.sign = sign
+            st.session_state.num_factors = num_factors
+            st.session_state.cl_method = cl_method
+            st.session_state.regularization = regularization
+            st.session_state.extremes = extremes
+            
 # ---------------------------- SET UP OUTPUT ------------------------------
 epsilon_container = st.empty()
-st.header('Output')
-st.subheader('Concept vector')
+st.header('Comparison')
+st.subheader('Color vectors')
 
-# 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([1,1])
-output_col_1, output_col_2 = st.columns([1,1])
+header_col_1, header_col_2 = st.columns([3,1])
+output_col_1, output_col_2 = st.columns([3,1])
 
-st.subheader('Derivations along the concept vector')
-
-# prediction error container
-error_container = st.empty()
-smoothgrad_header_container = st.empty()
+# ---------------------------- DISPLAY COL 1 ROW 1 ------------------------------
+tmp = concept_vectors[concept_vectors['color'].isin(st.session_state.concept_ids)]
+tmp = tmp[tmp['sign'] == st.session_state.sign][tmp['extremes'] == st.session_state.extremes][tmp['num_factors'] == st.session_state.num_factors][tmp['cl_method'] == st.session_state.cl_method][tmp['regularization'] == st.session_state.regularization]
+info = tmp.loc[:, ['vector', 'score', 'color', 'kwargs']].values
+concept_ids = [i[2] for i in info] #+ ' ' + i[3]
+    
+with header_col_1:
+    st.write('Similarity graph')
 
-# 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])
+with header_col_2:
+    st.write('Information')
+    
+with output_col_2:
+    for i,concept_id in enumerate(concept_ids):
+        st.write(f'Color {info[i][2]} - Settings: {info[i][3]} Performance of the color vector: {info[i][1]}')# - Nodes {",".join(list(imp_nodes))}')
 
-# ---------------------------- DISPLAY COL 1 ROW 1 ------------------------------
 with output_col_1:
-    vectors, nodes_in_common, performances = get_concepts_vectors(concept_ids, annotations, ann_df, latent_space=space_id)
-    header_col_1.write(f'Concepts {", ".join(concept_ids)} - Latent space {space_id} - Relevant nodes in common: {nodes_in_common} - Performance of the concept vectors: {performances}')# - Nodes {",".join(list(imp_nodes))}')
-
+    
     edges = []
     for i in range(len(concept_ids)):
         for j in range(len(concept_ids)):
-            if i != j:
+            if i != j and info[i][2] != info[j][2]:
                 print(f'Similarity between {concept_ids[i]} and {concept_ids[j]}')
-                similarity = cosine_similarity(vectors[i,:].reshape(1, -1), vectors[j,:].reshape(1, -1))
+                similarity = cosine_similarity(info[i][0].reshape(1, -1), info[j][0].reshape(1, -1)) 
                 print(np.round(similarity[0][0], 3))
-                edges.append((concept_ids[i], concept_ids[j], np.round(similarity[0][0], 3)))
+                edges.append((concept_ids[i], concept_ids[j], np.round(similarity[0][0] + 0.001, 3)))
 
     
     net = Network(height="750px", width="100%",)
@@ -142,123 +175,3 @@ with output_col_1:
 
     # Load HTML file in HTML component for display on Streamlit page
     components.html(HtmlFile.read(), height=435)
-
-with output_col_2:
-    with open('data/CLIP_vecs.pkl', 'rb') as f:
-        vectors_CLIP = pickle.load(f)
-        
-    # st.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.3f}% confidence')
-    #st.write('Concept vector', separation_vector)
-    header_col_2.write(f'Concepts {", ".join(concept_ids)} - Latent space CLIP')# - Nodes {",".join(list(imp_nodes))}')
-
-    edges_clip = []
-    for c1 in concept_ids:
-        for c2 in concept_ids:
-            if c1 != c2:
-                print(f'Similarity between {c1} and {c2}')
-                similarity = cosine_similarity(vectors_CLIP[c1].reshape(1, -1), vectors_CLIP[c2].reshape(1, -1))
-                print(np.round(similarity[0][0], 3))
-                edges_clip.append((c1, c2, np.round(float(np.round(similarity[0][0], 3)), 3)))
-
-
-    net_clip = Network(height="750px", width="100%",)
-    for e in edges_clip:
-        src = e[0]
-        dst = e[1]
-        w = e[2]
-
-        net_clip.add_node(src, src, title=src)
-        net_clip.add_node(dst, dst, title=dst)
-        net_clip.add_edge(src, dst, value=w, title=src + ' to ' + dst + ' similarity ' +str(w))
-    
-    # Generate network with specific layout settings
-    net_clip.repulsion(
-                        node_distance=420,
-                        central_gravity=0.33,
-                        spring_length=110,
-                        spring_strength=0.10,
-                        damping=0.95
-                       )
-
-    # Save and read graph as HTML file (on Streamlit Sharing)
-    try:
-        path = '/tmp'
-        net_clip.save_graph(f'{path}/pyvis_graph_clip.html')
-        HtmlFile = open(f'{path}/pyvis_graph_clip.html', 'r', encoding='utf-8')
-
-    # Save and read graph as HTML file (locally)
-    except:
-        path = '/html_files'
-        net_clip.save_graph(f'{path}/pyvis_graph_clip.html')
-        HtmlFile = open(f'{path}/pyvis_graph_clip.html', 'r', encoding='utf-8')
-
-    # Load HTML file in HTML component for display on Streamlit page
-    components.html(HtmlFile.read(), height=435)
-    
-# ----------------------------- 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, 50000)
-            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 concepts'):
-        st.write('**Set range of change**')
-        chosen_epsilon_input = st.empty()
-        epsilon = chosen_epsilon_input.number_input('Epsilon:', min_value=1, step=1)
-        epsilon_button = st.form_submit_button('Choose the defined epsilon')
-
-# # ---------------------------- DISPLAY COL 2 ROW 1 ------------------------------
-
-
-with dnnlib.util.open_url('./data/model_files/network-snapshot-010600.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)
-# 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')
-
-
-images, lambdas = generate_joint_effect(model, original_image_vec, vectors, min_epsilon=-(int(epsilon)), max_epsilon=int(epsilon), latent_space=st.session_state.space_id)
-
-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)}')