1.0.0b1@gradio

.gitattributes

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.gitignore

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README.md

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+---
+title: WildTorch
+emoji: 🔥
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 4.26.0
+app_file: app.py
+pinned: false
+license: mit
+---
+# WildTorch
+
+[![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch)
+[![Read the Docs](https://readthedocs.org/projects/wildtorch/badge/)](https://wildtorch.readthedocs.io/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10968647.svg)](https://doi.org/10.5281/zenodo.10968647)
+
+WildTorch: Leveraging GPU Acceleration for High-Fidelity, Stochastic Wildfire Simulations with PyTorch
+
+GitHub: [https://github.com/xiazeyu/WildTorch](https://github.com/xiazeyu/WildTorch)
+
+### Installation
+
+Install with minimal dependencies:
+
+```shell
+pip install wildtorch
+```
+
+Install with full dependencies (includes visualization and logging):
+
+```shell
+pip install 'wildtorch[full]'
+```
+
+### Quick Start
+
+```shell
+pip install 'wildtorch[full]'
+```
+
+```python
+import wildtorch as wt
+
+wildfire_map = wt.dataset.generate_empty_dataset()
+
+simulator = wt.WildTorchSimulator(
+    wildfire_map=wildfire_map,
+    simulator_constants=wt.SimulatorConstants(p_continue_burn=0.7),
+    initial_ignition=wt.utils.create_ignition(shape=wildfire_map[0].shape),
+)
+
+logger = wt.logger.Logger()
+
+for i in range(200):
+    simulator.step()
+    logger.log_stats(
+        step=i,
+        num_cells_on_fire=wt.metrics.cell_on_fire(simulator.fire_state).item(),
+        num_cells_burned_out=wt.metrics.cell_burned_out(simulator.fire_state).item(),
+    )
+    logger.snapshot_simulation(simulator)
+
+logger.save_logs()
+logger.save_snapshots()
+
+```
+
+### Demo
+
+See Our Live Demo at [Hugging Face Space](https://xiazeyu-wildtorch.hf.space/).
+
+### API Documents
+
+See at Our [Read the Docs](https://wildtorch.readthedocs.io/).

app.py

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+from typing import cast
+import uuid
+import os
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+import torch
+
+import wildtorch as wt
+
+USE_OFFLINE_DATA = False
+ENABLE_DOWNLOAD_SNAPSHOTS = False
+
+if USE_OFFLINE_DATA:
+    wildfire_sim_maps = torch.load('wildfire_sim_maps.pt')
+else:
+    wildfire_sim_maps = wt.dataset.load_wildfire_sim_maps()
+    # torch.save(wildfire_sim_maps, 'wildfire_sim_maps.pt')
+
+DEFAULT_SHAPE = (512, 512)
+DEFAULT_STATE = {
+    'ds': {
+        'name': None,
+        'shape': None,
+        'data': None,
+    },
+    'constants': {
+        'p_h': 0.58,
+        'c_1': 0.045,
+        'c_2': 0.131,
+        'a': 0.078,
+        'theta_w': 0,
+        'v': 10,
+        'p_firebreak': 0.9,
+        'p_continue_burn': 0.6,
+        'device': torch.device('cpu'),
+        'dtype': torch.float32,
+    },
+    'ignition': None,
+    'out_video_path': None,
+    'snapshots_path': None,
+    'checkpoint': None,
+    'logger': None,
+}
+
+with (gr.Blocks() as demo):
+    def remove_state_files(in_state):
+        if in_state['out_video_path'] is not None:
+            os.remove(in_state['out_video_path'])
+        if in_state['snapshots_path'] is not None:
+            os.remove(in_state['snapshots_path'])
+
+
+    state_var = gr.State(DEFAULT_STATE, delete_callback=remove_state_files)
+    with gr.Tabs(selected='tab_1') as tabs:
+        with gr.Tab("1. Datasets", interactive=True, id='tab_1') as tab_1:
+            sel_dataset = gr.Dropdown(cast(list, wildfire_sim_maps['name']) + ['empty'], label='Dataset')
+            with gr.Row() as shape_row:
+                sel_shape_h = gr.Number(label="Map Height", visible=False)
+                sel_shape_w = gr.Number(label="Map Width", visible=False)
+
+            with gr.Row() as preview_row:
+                canopy_img = gr.Image(label="canopy")
+                density_img = gr.Image(label="density")
+                slope_img = gr.Image(label="slope")
+
+            tab_1_confirm_btn = gr.Button("Confirm", interactive=True)
+
+
+            @tab_1_confirm_btn.click(inputs=[state_var], outputs=[state_var, tabs])
+            def jump_to_tab_2(in_state):
+                return in_state, gr.Tabs(selected='tab_2')
+
+        with gr.Tab("2. Simulation Constants and Initial Ignition", interactive=False, id='tab_2') as tab_2:
+            with gr.Row():
+                sel_p_h = gr.Slider(label="p_h",
+                                    info="The probability that a burnable cell adjacent to a burning cell will "
+                                         "catch fire at the next time step under normal conditions",
+                                    value=DEFAULT_STATE['constants']['p_h'], minimum=0, maximum=1, step=0.01,
+                                    interactive=True)
+                sel_p_continue_burn = gr.Slider(label="p_continue_burn",
+                                                info="The probability that a burning cell will continue to burn "
+                                                     "at the next time step",
+                                                value=DEFAULT_STATE['constants']['p_continue_burn'], minimum=0,
+                                                maximum=1,
+                                                step=0.01,
+                                                interactive=True)
+            with gr.Row():
+                sel_a = gr.Slider(label="a",
+                                  info="The coefficient of ground elevation",
+                                  value=DEFAULT_STATE['constants']['a'], minimum=0, maximum=1, step=0.001,
+                                  interactive=True)
+                sel_p_firebreak = gr.Slider(label="p_firebreak",
+                                            info="The probability that a burnable cell will not catch fire even "
+                                                 "if it is adjacent to a burning cell",
+                                            value=DEFAULT_STATE['constants']['p_firebreak'], minimum=0, maximum=1,
+                                            step=0.01, interactive=True)
+            with gr.Row():
+                sel_c_1 = gr.Slider(label="c_1",
+                                    info="The coefficient of wind velocity",
+                                    value=DEFAULT_STATE['constants']['c_1'], minimum=0, maximum=1, step=0.001,
+                                    interactive=True)
+                sel_c_2 = gr.Slider(label="c_2",
+                                    info="The coefficient of wind direction",
+                                    value=DEFAULT_STATE['constants']['c_2'], minimum=0, maximum=1, step=0.001,
+                                    interactive=True)
+            with gr.Row():
+                sel_theta_w = gr.Slider(label="theta_w",
+                                        info="The direction of the wind in degrees, measured clockwise from north",
+                                        value=DEFAULT_STATE['constants']['theta_w'], minimum=0, maximum=360, step=1,
+                                        interactive=True)
+                sel_v = gr.Slider(label="v",
+                                  info="The wind velocity, unit in m/s",
+                                  value=DEFAULT_STATE['constants']['v'], minimum=0, maximum=60, step=1,
+                                  interactive=True)
+            with gr.Row():
+                sel_device = gr.Dropdown(label="device", choices=['cpu', 'cuda', 'mps'],
+                                         info="The device to use",
+                                         value='cpu', allow_custom_value=True, interactive=True)
+                sel_dtype = gr.Dropdown(label="data type", choices=['float16', 'float32', 'float64'],
+                                        info="The data type to use",
+                                        value='float32', interactive=True)
+
+
+            @gr.on(triggers=[sel_p_h.input, sel_c_1.input, sel_c_2.input, sel_a.input,
+                             sel_theta_w.input, sel_v.input, sel_p_firebreak.input,
+                             sel_p_continue_burn.input, sel_device.input, sel_dtype.input],
+                   inputs=[state_var, sel_p_h, sel_c_1, sel_c_2, sel_a, sel_theta_w, sel_v,
+                           sel_p_firebreak, sel_p_continue_burn, sel_device, sel_dtype],
+                   outputs=[state_var])
+            def update_constants_state(in_state, in_p_h, in_c_1, in_c_2, in_a, in_theta_w, in_v, in_p_firebreak,
+                                       in_p_continue_burn,
+                                       in_device, in_dtype):
+                in_state['constants']['p_h'] = in_p_h
+                in_state['constants']['c_1'] = in_c_1
+                in_state['constants']['c_2'] = in_c_2
+                in_state['constants']['a'] = in_a
+                in_state['constants']['theta_w'] = in_theta_w
+                in_state['constants']['v'] = in_v
+                in_state['constants']['p_firebreak'] = in_p_firebreak
+                in_state['constants']['p_continue_burn'] = in_p_continue_burn
+                in_state['constants']['device'] = torch.device(in_device)
+                in_state['constants']['dtype'] = {
+                    'float16': torch.float16,
+                    'float32': torch.float32,
+                    'float64': torch.float64,
+                }[in_dtype]
+                return in_state
+
+
+            sel_ignition_mode = gr.Dropdown(label="Initial Ignition", choices=['random', 'center', 'custom'],
+                                            interactive=True)
+
+            gr.Markdown(
+                'to use custom ignition, please use the crop to fix the size, and then draw on the image. Please '
+                'click on the green button once done. Drawing on the black will be good choices.')
+            with gr.Row():
+                custom_ignition_paint = gr.Paint(label="custom ignition", image_mode='L', interactive=True,
+                                                 brush=gr.Brush(default_size=3, color_mode='fixed'))
+                ignition_img_over_map = gr.Image(label="ignition over map")
+
+
+            @gr.on(triggers=[sel_shape_h.input, sel_shape_w.input],
+                   inputs=[state_var, sel_shape_h, sel_shape_w],
+                   outputs=[state_var, canopy_img, density_img, slope_img, tab_2, custom_ignition_paint])
+            def update_preview_row(in_state, in_h, in_w):
+                shape = in_h, in_w
+                data = wt.dataset.generate_empty_dataset(shape)
+                in_state['ds']['shape'] = shape
+                in_state['ds']['data'] = data
+                in_state['ignition'] = None
+                return in_state, gr.Image(wt.utils.colorize_array(np.array(data[0]))), gr.Image(
+                    wt.utils.colorize_array(np.array(data[1]))), gr.Image(
+                    wt.utils.colorize_array(np.array(data[2]))), gr.Tab(interactive=True), gr.ImageEditor(
+                    crop_size=(shape[1], shape[0]))
+
+
+            @sel_dataset.change(inputs=[state_var, sel_dataset],
+                                outputs=[state_var, sel_shape_h, sel_shape_w, tab_2, custom_ignition_paint,
+                                         canopy_img, density_img, slope_img])
+            def update_shape_row(in_state, in_dataset):
+                if in_dataset == 'empty':
+                    shape = DEFAULT_SHAPE
+                    data = wt.dataset.generate_empty_dataset(shape)
+                    editable = True
+                else:
+                    idx_dict = {item['name']: index for index, item in enumerate(wildfire_sim_maps)}
+                    shape = tuple(cast(torch.Tensor, wildfire_sim_maps[idx_dict[in_dataset]]['shape']).tolist())
+                    data = wt.dataset.transform_wildfire_sim_map(wildfire_sim_maps[idx_dict[in_dataset]])
+                    editable = False
+                in_state['ds']['name'] = in_dataset
+                in_state['ds']['shape'] = shape
+                in_state['ds']['data'] = data
+                return in_state, gr.Number(value=shape[0], interactive=editable, visible=True), gr.Number(
+                    value=shape[1],
+                    interactive=editable,
+                    visible=True), gr.Tab(interactive=True), gr.ImageEditor(interactive=True,
+                                                                            crop_size=(shape[1], shape[0])), gr.Image(
+                    wt.utils.colorize_array(np.array(data[0]))), gr.Image(
+                    wt.utils.colorize_array(np.array(data[1]))), gr.Image(
+                    wt.utils.colorize_array(np.array(data[2])))
+
+
+            tab_2_confirm_btn = gr.Button("Confirm", interactive=False)
+
+
+            @sel_ignition_mode.input(
+                inputs=[state_var, sel_ignition_mode, custom_ignition_paint],
+                outputs=[state_var, ignition_img_over_map, tab_2_confirm_btn])
+            def update_ignition_img(in_state, in_mode, in_custom):
+                ignition = torch.zeros(in_state['ds']['shape'], dtype=torch.bool)
+
+                if in_mode == 'random':
+                    ignition = wt.utils.create_ignition(shape=in_state['ds']['shape'], mode='random-single')
+                elif in_mode == 'center':
+                    ignition = wt.utils.create_ignition(shape=in_state['ds']['shape'], mode='center')
+                elif in_mode == 'custom':
+                    if in_custom['composite'] is not None:
+                        ignition_ndarray = in_custom['composite'] != 0
+                        ignition = torch.tensor(ignition_ndarray)
+                    else:
+                        return in_state, gr.Image(
+                            wt.utils.colorize_array(wt.utils.compose_vis_wildfire_map(in_state['ds']['data']),
+                                                    cmap='grey')), gr.Button(interactive=False)
+
+                in_state['ignition'] = ignition
+                ignition_ndarray = wt.utils.to_ndarray(ignition)
+
+                ignition__over_map = wt.utils.overlay_arrays(
+                    wt.utils.colorize_array(ignition_ndarray),
+                    wt.utils.colorize_array(wt.utils.compose_vis_wildfire_map(in_state['ds']['data']),
+                                            cmap='grey'),
+                    0.5
+                )
+
+                return in_state, gr.Image((ignition__over_map * 255).astype(np.uint8)), gr.Button(interactive=True)
+
+
+            @custom_ignition_paint.change(
+                inputs=[state_var, custom_ignition_paint],
+                outputs=[state_var, sel_ignition_mode, ignition_img_over_map, tab_2_confirm_btn])
+            def update_ignition_img_over_map(in_state, in_custom):
+                if in_custom['composite'] is not None:
+                    ignition_ndarray = in_custom['composite'] != 0
+                    ignition = torch.tensor(ignition_ndarray)
+                else:
+                    return in_state, gr.Dropdown(), gr.Image(), gr.Button()
+                in_state['ignition'] = ignition
+
+                ignition__over_map = wt.utils.overlay_arrays(
+                    wt.utils.colorize_array(ignition_ndarray),
+                    wt.utils.colorize_array(wt.utils.compose_vis_wildfire_map(in_state['ds']['data']),
+                                            cmap='grey'),
+                    0.5
+                )
+
+                return in_state, gr.Dropdown(value='custom'), gr.Image(
+                    (ignition__over_map * 255).astype(np.uint8)), gr.Button(interactive=True)
+
+        with gr.Tab("3. Simulation Control", interactive=False, id='tab_3') as tab_3:
+            @tab_2_confirm_btn.click(inputs=[state_var], outputs=[state_var, tabs, tab_3])
+            def update_tab_34_components(in_state):
+                return in_state, gr.Tabs(selected='tab_3'), gr.Tab(interactive=True)
+
+
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("## Memory Control")
+                    checkpoint_cb = gr.Checkbox(label="Checkpoint -> Memory", value=False, interactive=True)
+                    run_from_cp_cb = gr.Checkbox(label="Begin from Memory", value=False, interactive=True)
+                    reset_btn = gr.Button("Reset Memory", interactive=True)
+                with gr.Column():
+                    gr.Markdown("## Misc Control")
+                    sel_steps = gr.Number(label="Number of Steps", value=200, minimum=1, step=1, interactive=True)
+                    auto_run_cb = gr.Checkbox(label="Auto Run", value=False, interactive=True)
+                    auto_reseed_cb = gr.Checkbox(label="Auto Regenerate Seed when open Tab", value=False,
+                                                 interactive=True)
+                    track_p_burn_cb = gr.Checkbox(label="Track p(burn), slow", value=False, interactive=True)
+                with gr.Column():
+                    gr.Markdown("## Random Seed Control")
+                    sel_seed = gr.Number(label="Random Seed", value=torch.Generator().seed(), minimum=0, step=1,
+                                         interactive=True)
+                    random_seed_btn = gr.Button("Randomize Seed", interactive=True)
+
+
+                @random_seed_btn.click(inputs=[state_var], outputs=[state_var, sel_seed])
+                def randomize_seed(in_state):
+                    return in_state, torch.Generator().seed()
+
+            with gr.Row():
+                run_btn = gr.Button("Run Simulation", interactive=True)
+                download_snap_btn = gr.DownloadButton(label="Download Snapshots", interactive=False, visible=False)
+
+            progress_bar = gr.Progress(track_tqdm=True)
+
+            with gr.Row():
+                output_video = gr.Video(label="Simulation Video", interactive=False, autoplay=True)
+
+                stats_plot = gr.LinePlot(title="Simulation Stats", interactive=True, height=600,
+                                         width=600, )
+
+        with gr.Tab("4. Advanced Simulation", interactive=False, id='tab_4') as tab_4:
+
+            sel_tab4_step = gr.Slider(label='Step', minimum=0, step=1, value=0, interactive=True)
+            with gr.Row():
+                cof_tb = gr.Textbox(label='cell_on_fire', interactive=False)
+                cbo_tb = gr.Textbox(label='cell_burned_out', interactive=False)
+            with gr.Row():
+                fire_state_img = gr.Image(label="Fire State", interactive=False)
+                p_burn_plot = gr.Image(label="p(burn)", interactive=False)  # gr.Plot is bad at presenting
+            stats_df = gr.DataFrame()
+
+
+            @sel_tab4_step.input(inputs=[state_var, sel_tab4_step],
+                                 outputs=[state_var, fire_state_img, p_burn_plot, cof_tb, cbo_tb])
+            def update_tab4_step(in_state, in_user_step):
+
+                o_fsi, o_pbp = gr.Image(), gr.Image()
+                o_cof_tb, o_cbo_tb = gr.Textbox(), gr.Textbox()
+
+                if in_state['logger'] is not None:
+                    snapshot = in_state['logger'].snapshots[in_user_step]
+                    log = in_state['logger'].logs[in_user_step]
+                    o_fsi = gr.Image(
+                        value=wt.utils.colorize_array(wt.utils.compose_vis_fire_state(snapshot['fire_state'])))
+                    if len(in_state['logger'].p_burns) > 0:
+                        p_burn_arr = in_state['logger'].p_burns[in_user_step].cpu().numpy()
+                        o_pbp = gr.Image(
+                            value=wt.utils.colorize_array(p_burn_arr))
+                    o_cof_tb = gr.Textbox(value=str(log['num_cells_on_fire']))
+                    o_cbo_tb = gr.Textbox(value=str(log['num_cells_burned_out']))
+
+                return in_state, o_fsi, o_pbp, o_cof_tb, o_cbo_tb
+
+
+            @tab_3.select(
+                inputs=[state_var, auto_run_cb, sel_steps, sel_seed, auto_reseed_cb, checkpoint_cb, run_from_cp_cb,
+                        track_p_burn_cb],
+                outputs=[state_var, output_video, tab_4, stats_plot, download_snap_btn, sel_seed, sel_tab4_step,
+                         stats_df])
+            def auto_run_simulation(in_state, in_auto_run, in_steps, in_seed, in_auto_reseed, in_checkpoint_cb,
+                                    in_run_from_cp_cb, in_track_p_burn_cb):
+                o_s = in_state
+                o_v = gr.Video()
+                o_t = gr.Tab()
+                o_lp = gr.LinePlot()
+                o_dsb = gr.DownloadButton()
+                o_ts = gr.Slider()
+                o_sdf = pd.DataFrame()
+                if in_auto_reseed:
+                    in_seed = torch.Generator().seed()
+                if in_auto_run:
+                    o_s, o_v, o_t, o_lp, o_dsb, o_ts, o_sdf = run_simulation(in_state,
+                                                                             in_steps,
+                                                                             in_seed,
+                                                                             in_checkpoint_cb,
+                                                                             in_run_from_cp_cb,
+                                                                             in_track_p_burn_cb)
+                return o_s, o_v, o_t, o_lp, o_dsb, in_seed, o_ts, o_sdf
+
+
+            @reset_btn.click(inputs=[state_var],
+                             outputs=[state_var])
+            def reset_simulation(in_state):
+                if in_state['checkpoint'] is not None:
+                    in_state['checkpoint'] = None
+                    gr.Info('Checkpoint Cleared.')
+                return in_state
+
+
+            @run_btn.click(inputs=[state_var, sel_steps, sel_seed, checkpoint_cb, run_from_cp_cb, track_p_burn_cb],
+                           outputs=[state_var, output_video, tab_4, stats_plot, download_snap_btn, sel_tab4_step,
+                                    stats_df])
+            def run_simulation(in_state, in_steps, in_seed, in_checkpoint_cb, in_run_from_cp_cb, in_track_p_burn_cb,
+                               in_progress=gr.Progress(track_tqdm=True)):
+                if in_state['out_video_path'] is None:
+                    in_state['out_video_path'] = f'runs/{str(uuid.uuid4())}.mp4'
+                simulator = wt.WildTorchSimulator(
+                    wildfire_map=in_state['ds']['data'],
+                    simulator_constants=wt.SimulatorConstants(
+                        p_h=in_state['constants']['p_h'],
+                        c_1=in_state['constants']['c_1'],
+                        c_2=in_state['constants']['c_2'],
+                        a=in_state['constants']['a'],
+                        theta_w=in_state['constants']['theta_w'],
+                        v=in_state['constants']['v'],
+                        p_firebreak=in_state['constants']['p_firebreak'],
+                        p_continue_burn=in_state['constants']['p_continue_burn'],
+                        device=in_state['constants']['device'],
+                        dtype=in_state['constants']['dtype'],
+                    ),
+                    maximum_step=in_steps,
+                    initial_ignition=in_state['ignition'],
+                    seed=in_seed,
+                )
+
+                if in_state['checkpoint'] is not None and in_run_from_cp_cb:
+                    simulator.load_checkpoint(in_state['checkpoint'], restore_seed=False)
+
+                logger = wt.logger.Logger(disable_writing=True, verbose=False)
+
+                for i in in_progress.tqdm(range(in_steps)):
+                    simulator.step()
+                    logger.snapshot_simulation(simulator)
+                    logger.log_stats(
+                        step=i,
+                        num_cells_on_fire=wt.metrics.cell_on_fire(simulator.fire_state).item(),
+                        num_cells_burned_out=wt.metrics.cell_burned_out(simulator.fire_state).item(),
+                    )
+                    if in_track_p_burn_cb:
+                        logger.log_p_burn(simulator)
+
+                gr.Info('Simulation Completed. Generating Video ...')
+
+                in_state['logger'] = logger
+
+                if in_checkpoint_cb:
+                    in_state['checkpoint'] = simulator.checkpoint
+
+                if ENABLE_DOWNLOAD_SNAPSHOTS:
+                    logger.snapshots_filepath = in_state['snapshots_path'] = f'runs/{str(uuid.uuid4())}.pt'
+                    logger.save_snapshots()
+                    can_download_snapshots = True
+                else:
+                    can_download_snapshots = False
+
+                wt.utils.animate_snapshots(logger.snapshots, simulator.wildfire_map,
+                                           out_filename=in_state['out_video_path'])
+
+                m_stats_df = pd.DataFrame(logger.logs)
+                m_stats_df = m_stats_df.melt(id_vars=["step"], var_name="key", value_name="value")
+
+                o_stats_df = pd.DataFrame(logger.logs)
+                return in_state, gr.Video(value=in_state['out_video_path']), gr.Tab(interactive=True), gr.LinePlot(
+                    m_stats_df, x='step', y='value', color="key", color_legend_position="bottom",
+                    tooltip=["step", "key", "value"], container=False, ), gr.DownloadButton(
+                    value=in_state['snapshots_path'], interactive=can_download_snapshots,
+                    visible=can_download_snapshots), gr.Slider(maximum=in_steps - 1), o_stats_df
+
+demo.queue().launch()

requirements.txt

@@ -0,0 +1 @@
+wildtorch[full]