remove st.tabs

.gitattributes

@@ -1,4 +1,4 @@
 *.pkl filter=lfs diff=lfs merge=lfs -text
-*.json filter=lfs diff=lfs merge=lfs -text
 .csv filter=lfs diff=lfs merge=lfs -text
 data/** filter=lfs diff=lfs merge=lfs -text
+Visual-Explanation-Methods-PyTorch/** filter=lfs diff=lfs merge=lfs -text

pages/1_Maximally_activating_patches.py

@@ -64,98 +64,96 @@ props = {
     }
 }
 
-convnext_tab, resnet_tab, mobilenet_tab = st.tabs(['ConvNeXt', 'ResNet', 'MobileNet'])
 
-with convnext_tab:
-    col1, col2 = st.columns((2,5))
-    col1.markdown("#### Architecture")
-    col1.write('')
-    col1.write('Click on a layer below to generate top-k maximally activating image patches')
-    col1.graphviz_chart(convnext_graph)
+col1, col2 = st.columns((2,5))
+col1.markdown("#### Architecture")
+col1.write('')
+col1.write('Click on a layer below to generate top-k maximally activating image patches')
+col1.graphviz_chart(convnext_graph)
+
+with col2:
+    st.markdown("#### Output")
+    nodes = on_click_graph(key='toggle_buttons', **props)
+
+# -------------------------- DISPLAY OUTPUT -----------------------------------
+
+if nodes != None:
+    clicked_node_title = nodes["choice"]["node_title"]
+    clicked_node_id = nodes["choice"]["node_id"]
+    display_text, activation_key = chosen_node_text(clicked_node_title)
+    col2.write(f'**Chosen layer:** {display_text}')
+    # col2.write(f'**Activation key:** {activation_key}')
+
+    hightlight_syle = f'''
+        <style>
+            div[data-stale]:has(iframe) {{
+                height: 0;
+            }}
+            #{clicked_node_id}>polygon {{
+                fill: {HIGHTLIGHT_COLOR};
+                stroke: {HIGHTLIGHT_COLOR};
+            }}
+        </style>
+    '''
+    col2.markdown(hightlight_syle, unsafe_allow_html=True)
 
     with col2:
-        st.markdown("#### Output")
-        nodes = on_click_graph(key='toggle_buttons', **props)
-
-    # -------------------------- DISPLAY OUTPUT -----------------------------------
-
-    if nodes != None:
-        clicked_node_title = nodes["choice"]["node_title"]
-        clicked_node_id = nodes["choice"]["node_id"]
-        display_text, activation_key = chosen_node_text(clicked_node_title)
-        col2.write(f'**Chosen layer:** {display_text}')
-        # col2.write(f'**Activation key:** {activation_key}')
-
-        hightlight_syle = f'''
-            <style>
-                div[data-stale]:has(iframe) {{
-                    height: 0;
-                }}
-                #{clicked_node_id}>polygon {{
-                    fill: {HIGHTLIGHT_COLOR};
-                    stroke: {HIGHTLIGHT_COLOR};
-                }}
-            </style>
-        '''
-        col2.markdown(hightlight_syle, unsafe_allow_html=True)
-
-        with col2:
-            layer_infos = None
-            with st.form('top_k_form'):
-                activation_path = './data/activation/convnext_activation.json'
-                activation = load_activation(activation_path)
-                num_channels = activation[activation_key].shape[1]
-
-                top_k = st.slider('Choose K for top-K maximally activating patches', 1,20, value=10)
-                channel_start, channel_end = st.slider(
-                    'Choose channel range of this layer (recommend to choose small range less than 30)',
-                    1, num_channels, value=(1, 30))
-                summit_button = st.form_submit_button('Generate image patches')
-                if summit_button:
+        layer_infos = None
+        with st.form('top_k_form'):
+            activation_path = './data/activation/convnext_activation.json'
+            activation = load_activation(activation_path)
+            num_channels = activation[activation_key].shape[1]
+
+            top_k = st.slider('Choose K for top-K maximally activating patches', 1,20, value=10)
+            channel_start, channel_end = st.slider(
+                'Choose channel range of this layer (recommend to choose small range less than 30)',
+                1, num_channels, value=(1, 30))
+            summit_button = st.form_submit_button('Generate image patches')
+            if summit_button:
+                
+                activation = activation[activation_key][:top_k,:,:]
+                layer_infos = load_layer_infos('./data/layer_infos/convnext_layer_infos.json')
+                # st.write(channel_start, channel_end)
+                # st.write(activation.shape, activation.shape[1])
+
+        if layer_infos != None:
+            num_cols, num_rows = top_k, channel_end - channel_start + 1
+            # num_rows = activation.shape[1]
+            top_k_coor_max_ = activation
+            st.markdown(f"#### Top-{top_k} maximally activating image patches of {num_rows} channels ({channel_start}-{channel_end})")
+
+            for row in range(channel_start, channel_end+1):
+                if row == channel_start:
+                    top_margin = 50
+                    fig = make_subplots(
+                        rows=1, cols=num_cols, 
+                        subplot_titles=tuple([f"#{i+1}" for i in range(top_k)]), shared_yaxes=True)
+                else:
+                    top_margin = 0
+                    fig = make_subplots(rows=1, cols=num_cols)
+                for col in range(1, num_cols+1):
+                    k, c = col-1, row-1
+                    img_index = int(top_k_coor_max_[k, c, 3])
+                    activation_value = top_k_coor_max_[k, c, 0]
+                    img = dataset_dict[img_index//10_000][img_index%10_000]['image']
+                    class_label = dataset_dict[img_index//10_000][img_index%10_000]['label']
+                    class_id = dataset_dict[img_index//10_000][img_index%10_000]['id']
+
+                    idx_x, idx_y = top_k_coor_max_[k, c, 1], top_k_coor_max_[k, c, 2]
+                    x1, x2, y1, y2 = get_receptive_field_coordinates(layer_infos, activation_key, idx_x, idx_y)
+                    img = np.array(img)[y1:y2, x1:x2, :]
                     
-                    activation = activation[activation_key][:top_k,:,:]
-                    layer_infos = load_layer_infos('./data/layer_infos/convnext_layer_infos.json')
-                    # st.write(channel_start, channel_end)
-                    # st.write(activation.shape, activation.shape[1])
-
-            if layer_infos != None:
-                num_cols, num_rows = top_k, channel_end - channel_start + 1
-                # num_rows = activation.shape[1]
-                top_k_coor_max_ = activation
-                st.markdown(f"#### Top-{top_k} maximally activating image patches of {num_rows} channels ({channel_start}-{channel_end})")
-
-                for row in range(channel_start, channel_end+1):
-                    if row == channel_start:
-                        top_margin = 50
-                        fig = make_subplots(
-                            rows=1, cols=num_cols, 
-                            subplot_titles=tuple([f"#{i+1}" for i in range(top_k)]), shared_yaxes=True)
-                    else:
-                        top_margin = 0
-                        fig = make_subplots(rows=1, cols=num_cols)
-                    for col in range(1, num_cols+1):
-                        k, c = col-1, row-1
-                        img_index = int(top_k_coor_max_[k, c, 3])
-                        activation_value = top_k_coor_max_[k, c, 0]
-                        img = dataset_dict[img_index//10_000][img_index%10_000]['image']
-                        class_label = dataset_dict[img_index//10_000][img_index%10_000]['label']
-                        class_id = dataset_dict[img_index//10_000][img_index%10_000]['id']
-    
-                        idx_x, idx_y = top_k_coor_max_[k, c, 1], top_k_coor_max_[k, c, 2]
-                        x1, x2, y1, y2 = get_receptive_field_coordinates(layer_infos, activation_key, idx_x, idx_y)
-                        img = np.array(img)[y1:y2, x1:x2, :]
-                        
-                        hovertemplate = f"""Top-{col}<br>Activation value: {activation_value:.5f}<br>Class Label: {class_label}<br>Class id: {class_id}<br>Image id: {img_index}"""
-                        fig.add_trace(go.Image(z=img, hovertemplate=hovertemplate), row=1, col=col)
-                        fig.update_xaxes(showticklabels=False, showgrid=False)
-                        fig.update_yaxes(showticklabels=False, showgrid=False)
-                        fig.update_layout(margin={'b':0, 't':top_margin, 'r':0, 'l':0})
-                        fig.update_layout(showlegend=False, yaxis_title=row)
-                        fig.update_layout(height=100, plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)')
-                        fig.update_layout(hoverlabel=dict(bgcolor="#e9f2f7"))
-                    st.plotly_chart(fig, use_container_width=True)
-
-
-    else:
-        col2.markdown(f'Chosen layer: <code>None</code>', unsafe_allow_html=True)
-        col2.markdown("""<style>div[data-stale]:has(iframe) {height: 0};""", unsafe_allow_html=True)
+                    hovertemplate = f"""Top-{col}<br>Activation value: {activation_value:.5f}<br>Class Label: {class_label}<br>Class id: {class_id}<br>Image id: {img_index}"""
+                    fig.add_trace(go.Image(z=img, hovertemplate=hovertemplate), row=1, col=col)
+                    fig.update_xaxes(showticklabels=False, showgrid=False)
+                    fig.update_yaxes(showticklabels=False, showgrid=False)
+                    fig.update_layout(margin={'b':0, 't':top_margin, 'r':0, 'l':0})
+                    fig.update_layout(showlegend=False, yaxis_title=row)
+                    fig.update_layout(height=100, plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)')
+                    fig.update_layout(hoverlabel=dict(bgcolor="#e9f2f7"))
+                st.plotly_chart(fig, use_container_width=True)
+
+
+else:
+    col2.markdown(f'Chosen layer: <code>None</code>', unsafe_allow_html=True)
+    col2.markdown("""<style>div[data-stale]:has(iframe) {height: 0};""", unsafe_allow_html=True)

requirements.txt

@@ -10,7 +10,8 @@ Pillow==9.3.0
 plotly==5.11.0
 scipy==1.9.3
 setuptools==65.5.0
-streamlit==1.15.2
+# streamlit==1.15.2
+streamlit==1.10.0
 torch==1.10.1
 torchvision==0.11.2
 tqdm==4.64.1