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| import gradio as gr | |
| import helpers.models as h_models | |
| import helpers.listeners as listeners | |
| # Custom css | |
| css = """div[data-testid="block-label"] {z-index: var(--layer-3)}""" | |
| def main(): | |
| with gr.Blocks(title="Feature Visualization Generator", | |
| css=css, | |
| theme=gr.themes.Soft(primary_hue="blue", | |
| secondary_hue="blue", | |
| )) as demo: | |
| # Session state init | |
| model, model_layers, selected_layer, ft_map_sizes, \ | |
| thresholds, channel_max, nodeX_max, nodeY_max, \ | |
| node_max = (gr.State(None) for _ in range(9)) | |
| # GUI Elements | |
| with gr.Row(): # Upper banner | |
| gr.Markdown("""# Feature Visualization Generator\n | |
| Feature Visualizations (FV's) answer questions | |
| about what a network—or parts of a network—are | |
| looking for by generating examples. | |
| ([Read more about it here](https://distill.pub/2017/feature-visualization/)) | |
| FVs are a part of a wider field called Explainable | |
| Artificial Intelligence (XAI) This generator aims | |
| to make it easier to explore different concepts | |
| used in FV generation and allow for experimentation. | |
| Currently Convolutional and Linear layers were tested.\n\n | |
| **Start by selecting a model from the drop down.**""") | |
| with gr.Row(): # Lower inputs and outputs | |
| with gr.Column(): # Inputs | |
| gr.Markdown("""## Model Settings""") | |
| model_dd = gr.Dropdown(label="Model", | |
| info="Select a model. Some models take longer to setup", | |
| choices=[m.name for m in h_models.ModelTypes]) | |
| layer_dd = gr.Dropdown(label="Layer", | |
| info="Select a layer. List will change depending on layer type selected", | |
| interactive=True, | |
| visible=False) | |
| layer_text = gr.Markdown("""## Layer Settings (Optional)""", | |
| visible=False) | |
| with gr.Row(): # Inputs specific to layer selection | |
| channel_num = gr.Number(label="Channel", | |
| info="Please choose a layer", | |
| precision=0, | |
| minimum=0, | |
| interactive=True, | |
| visible=False, | |
| value=None) | |
| node_num = gr.Number(label="Node", | |
| info="Please choose a layer", | |
| precision=0, | |
| minimum=0, | |
| interactive=True, | |
| visible=False, | |
| value=None) | |
| nodeX_num = gr.Number(label="Node X", | |
| info="Please choose a layer", | |
| precision=0, | |
| minimum=0, | |
| interactive=True, | |
| visible=False, | |
| value=None) | |
| nodeY_num = gr.Number(label="Node Y", | |
| info="Please choose a layer", | |
| precision=0, | |
| minimum=0, | |
| interactive=True, | |
| visible=False, | |
| value=None) | |
| gr.Markdown("""## Visualization Settings""") | |
| lr_sl = gr.Slider(label="Learning Rate", | |
| info="How aggresive each \"step\" towards the visualization is", | |
| minimum=0.000001, | |
| maximum=3, | |
| step=0.000001, | |
| value=0.125) | |
| epoch_num = gr.Number(label="Epochs", | |
| info="How many steps (epochs) to perform", | |
| precision=0, | |
| minimum=1, | |
| value=200) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("""## Image Settings""") | |
| img_num = gr.Number(label="Image Size", | |
| info="Image is square (<value> by <value>)", | |
| precision=0, | |
| minimum=1, | |
| value=227) | |
| chan_decor_ck = gr.Checkbox(label="Channel Decorrelation", | |
| info="Reduces channel-to-channel correlations", | |
| value=True) | |
| spacial_decor_ck = gr.Checkbox(label="Spacial Decorrelation (FFT)", | |
| info="Reduces pixel-to-pixel correlations", | |
| value=True) | |
| sd_num = gr.Number(label="Standard Deviation", | |
| info="The STD of the randomly generated starter image", | |
| value=0.01) | |
| with gr.Column(variant="panel"): | |
| gr.Markdown("""## Transform Settings (WIP)""") | |
| preprocess_ck = gr.Checkbox(label="Preprocess", | |
| info="Enable or disable preprocessing via transformations", | |
| value=True, | |
| interactive=True) | |
| transform_choices = [t.value for t in h_models.TransformTypes] | |
| transforms_dd = gr.Dropdown(label="Applied Transforms", | |
| info="Transforms to apply", | |
| choices=transform_choices, | |
| multiselect=True, | |
| value=transform_choices, | |
| interactive=True) | |
| # Transform specific settings | |
| pad_col = gr.Column() | |
| with pad_col: | |
| gr.Markdown("""### Pad Settings""") | |
| with gr.Row(): | |
| pad_num = gr.Number(label="Padding", | |
| info="How many pixels of padding", | |
| minimum=0, | |
| value=12, | |
| precision=0, | |
| interactive=True) | |
| mode_rad = gr.Radio(label="Mode", | |
| info="Constant fills padded pixels with a value. Reflect fills with edge pixels", | |
| choices=["Constant", "Reflect"], | |
| value="Constant", | |
| interactive=True) | |
| constant_num = gr.Number(label="Constant Fill Value", | |
| info="Value to fill padded pixels", | |
| value=0.5, | |
| interactive=True) | |
| jitter_col = gr.Column() | |
| with jitter_col: | |
| gr.Markdown("""### Jitter Settings""") | |
| with gr.Row(): | |
| jitter_num = gr.Number(label="Jitter", | |
| info="How much to jitter image by", | |
| minimum=1, | |
| value=8, | |
| precision=0, | |
| interactive=True) | |
| rand_scale_col = gr.Column() | |
| with rand_scale_col: | |
| gr.Markdown("""### Random Scale Settings""") | |
| with gr.Row(): | |
| scale_num = gr.Number(label="Max scale", | |
| info="How much to scale (from 1.0) in both directions (+ and -)", | |
| minimum=0, | |
| value=0.1, | |
| interactive=True) | |
| rand_rotate_col = gr.Column() | |
| with rand_rotate_col: | |
| gr.Markdown("""### Random Rotate Settings""") | |
| with gr.Row(): | |
| rotate_num = gr.Number(label="Max angle", | |
| info="How much to rotate in both directions (+ and -)", | |
| minimum=0, | |
| value=10, | |
| precision=0, | |
| interactive=True) | |
| ad_jitter_col = gr.Column() | |
| with ad_jitter_col: | |
| gr.Markdown("""### Additional Jitter Settings""") | |
| with gr.Row(): | |
| ad_jitter_num = gr.Number(label="Jitter", | |
| info="How much to jitter image by", | |
| minimum=1, | |
| value=4, | |
| precision=0, | |
| interactive=True) | |
| confirm_btn = gr.Button("Generate", visible=False) | |
| with gr.Column(): # Output | |
| gr.Markdown("""## Feature Visualization Output""") | |
| with gr.Row(): | |
| images_gal = gr.Gallery(show_label=False, | |
| preview=True, | |
| allow_preview=True) | |
| # Event listener binding | |
| model_dd.select(lambda: gr.Dropdown.update(visible=True), | |
| outputs=layer_dd) | |
| model_dd.select(listeners.on_model, | |
| inputs=[model, model_layers, ft_map_sizes], | |
| outputs=[layer_dd, model, model_layers, ft_map_sizes]) | |
| # TODO: Make button invisible always until layer selection | |
| layer_dd.select(lambda: gr.Button.update(visible=True), | |
| outputs=confirm_btn) | |
| layer_dd.select(listeners.on_layer, | |
| inputs=[selected_layer, model_layers, ft_map_sizes], | |
| outputs=[layer_text, | |
| channel_num, | |
| nodeX_num, | |
| nodeY_num, | |
| node_num, | |
| selected_layer, | |
| channel_max, | |
| nodeX_max, | |
| nodeY_max, | |
| node_max]) | |
| channel_num.blur(listeners.check_input, inputs=[channel_num, channel_max]) | |
| nodeX_num.blur(listeners.check_input, inputs=[nodeX_num, nodeX_max]) | |
| nodeY_num.blur(listeners.check_input, inputs=[nodeY_num, nodeY_max]) | |
| node_num.blur(listeners.check_input, inputs=[node_num, node_max]) | |
| images_gal.select(listeners.update_img_label, | |
| inputs=thresholds, | |
| outputs=images_gal) | |
| preprocess_ck.select(lambda status: (gr.update(visible=status), | |
| gr.update(visible=status), | |
| gr.update(visible=status), | |
| gr.update(visible=status), | |
| gr.update(visible=status), | |
| gr.update(visible=status)), | |
| inputs=preprocess_ck, | |
| outputs=[transforms_dd, | |
| pad_col, | |
| jitter_col, | |
| rand_scale_col, | |
| rand_rotate_col, | |
| ad_jitter_col]) | |
| transforms_dd.change(listeners.on_transform, | |
| inputs=transforms_dd, | |
| outputs=[pad_col, | |
| jitter_col, | |
| rand_scale_col, | |
| rand_rotate_col, | |
| ad_jitter_col]) | |
| mode_rad.select(listeners.on_pad_mode, | |
| outputs=constant_num) | |
| confirm_btn.click(listeners.generate, | |
| inputs=[lr_sl, | |
| epoch_num, | |
| img_num, | |
| channel_num, | |
| nodeX_num, | |
| nodeY_num, | |
| node_num, | |
| selected_layer, | |
| model, | |
| thresholds, | |
| chan_decor_ck, | |
| spacial_decor_ck, | |
| sd_num, | |
| transforms_dd, | |
| pad_num, | |
| mode_rad, | |
| constant_num, | |
| jitter_num, | |
| scale_num, | |
| rotate_num, | |
| ad_jitter_num], | |
| outputs=[images_gal, thresholds]) | |
| demo.queue().launch() | |
| main() | |