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"""
Gradio interface for plotting attention.
"""
import chess
import chess.pgn
import io
import gradio as gr
import os
from lczerolens import LczeroBoard, LczeroModel, Lens
from .. import constants
def get_model(model_name: str):
return LczeroModel.from_onnx_path(os.path.join(constants.ONNX_MODEL_DIRECTORY, model_name))
def get_gradients(model: LczeroModel, board: LczeroBoard, wdl_target: str):
lens = Lens.from_name("gradient")
wdl_index = {"win": 0, "draw": 1, "loss": 2}[wdl_target]
def init_target(model):
return getattr(model, "output/wdl").output[:, wdl_index]
results = lens.analyse(model, board, init_target=init_target)
return results["input_grad"]
def get_board(game_pgn:str, board_fen:str):
if game_pgn:
try:
board = LczeroBoard()
pgn = io.StringIO(game_pgn)
game = chess.pgn.read_game(pgn)
for move in game.mainline_moves():
board.push(move)
except Exception as e:
print(e)
gr.Warning("Error parsing PGN, using starting position.")
board = LczeroBoard()
else:
try:
board = LczeroBoard(board_fen)
except Exception as e:
print(e)
gr.Warning("Invalid FEN, using starting position.")
board = LczeroBoard()
return board
def render_gradients(board: LczeroBoard, gradients, average_over_planes:bool, begin_average_index:int, end_average_index:int, plane_index:int):
if average_over_planes:
heatmap = gradients[0, begin_average_index:end_average_index].mean(dim=0).view(64)
else:
heatmap = gradients[0, plane_index].view(64)
board.render_heatmap(
heatmap,
save_to=f"{constants.FIGURE_DIRECTORY}/gradients.svg",
)
return f"{constants.FIGURE_DIRECTORY}/gradients_board.svg", f"{constants.FIGURE_DIRECTORY}/gradients_colorbar.svg"
def initial_load(model_name: str, board_fen: str, game_pgn: str, wdl_target: str, average_over_planes:bool, begin_average_index:int, end_average_index:int, plane_index: int):
model = get_model(model_name)
board = get_board(game_pgn, board_fen)
gradients = get_gradients(model, board, wdl_target)
plots = render_gradients(board, gradients, average_over_planes, begin_average_index, end_average_index, plane_index)
return model, board, gradients, *plots
def on_board_change(model: LczeroModel, game_pgn: str, board_fen: str, wdl_target: str, average_over_planes:bool, begin_average_index:int, end_average_index:int, plane_index: int):
board = get_board(game_pgn, board_fen)
gradients = get_gradients(model, board, wdl_target)
plots = render_gradients(board, gradients, average_over_planes, begin_average_index, end_average_index, plane_index)
return board, gradients, *plots
def on_model_change(model_name: str, board: LczeroBoard, wdl_target: str, average_over_planes:bool, begin_average_index:int, end_average_index:int, plane_index: int):
model = get_model(model_name)
gradients = get_gradients(model, board, wdl_target)
plots = render_gradients(board, gradients, average_over_planes, begin_average_index, end_average_index, plane_index)
return model, gradients, *plots
def on_wdl_target_change(model: LczeroModel, board: LczeroBoard, wdl_target: str, average_over_planes:bool, begin_average_index:int, end_average_index:int, plane_index: int):
gradients = get_gradients(model, board, wdl_target)
plots = render_gradients(board, gradients, average_over_planes, begin_average_index, end_average_index, plane_index)
return gradients, *plots
with gr.Blocks() as interface:
with gr.Row():
with gr.Column():
with gr.Group():
gr.Markdown(
"Specify the game PGN or FEN string that you want to analyse (PGN overrides FEN)."
)
game_pgn = gr.Textbox(
label="Game PGN",
lines=1,
value="",
)
board_fen = gr.Textbox(
label="Board FEN",
lines=1,
max_lines=1,
value=chess.STARTING_FEN,
)
with gr.Group():
model_name = gr.Dropdown(
label="Model",
choices=constants.ONNX_MODEL_NAMES,
)
wdl_target = gr.Radio(
["win", "draw", "loss"], label="WDL target",
value="win",
)
with gr.Group():
average_over_planes = gr.Checkbox(label="Average over Planes", value=False)
with gr.Accordion("Average over planes", open=False):
begin_average_index = gr.Slider(
label="Begin average index",
minimum=0,
maximum=111,
step=1,
value=0,
)
end_average_index = gr.Slider(
label="End average index",
minimum=0,
maximum=111,
step=1,
value=111,
)
plane_index = gr.Slider(
label="Plane index",
minimum=0,
maximum=111,
step=1,
value=0,
)
with gr.Column():
image_board = gr.Image(label="Board", interactive=False)
colorbar = gr.Image(label="Colorbar", interactive=False)
model = gr.State(value=None)
board = gr.State(value=None)
gradients = gr.State(value=None)
interface.load(
initial_load,
inputs=[model_name, game_pgn, board_fen, wdl_target, average_over_planes, begin_average_index, end_average_index, plane_index],
outputs=[model, board, gradients, image_board, colorbar],
)
game_pgn.submit(
on_board_change,
inputs=[model, game_pgn, board_fen, wdl_target, average_over_planes, begin_average_index, end_average_index, plane_index],
outputs=[board, gradients, image_board, colorbar],
)
board_fen.submit(
on_board_change,
inputs=[model, game_pgn, board_fen, wdl_target, average_over_planes, begin_average_index, end_average_index, plane_index],
outputs=[board, gradients, image_board, colorbar],
)
model_name.change(
on_model_change,
inputs=[model_name, board, wdl_target, average_over_planes, begin_average_index, end_average_index, plane_index],
outputs=[model, gradients, image_board, colorbar],
)
wdl_target.change(
on_wdl_target_change,
inputs=[model, board, wdl_target, average_over_planes, begin_average_index, end_average_index, plane_index],
outputs=[gradients, image_board, colorbar],
)
for render_arg in [average_over_planes, begin_average_index, end_average_index, plane_index]:
render_arg.change(
render_gradients,
inputs=[board, gradients, average_over_planes, begin_average_index, end_average_index, plane_index],
outputs=[image_board, colorbar],
)
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