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"""
Gradio interface for visualizing the policy of a model.
"""
import chess
import chess.svg
import gradio as gr
import torch
from lczero.backends import Backend, GameState, Weights
from demo import constants, utils, visualisation
from lczerolens import move_encodings
from lczerolens.model import lczero as lczero_utils
from lczerolens.xai import PolicyLens
from lczerolens.board import LczeroBoard
def list_models():
"""
List the models in the model directory.
"""
models_info = utils.get_models_info(onnx=False)
return sorted([[model_info[0]] for model_info in models_info])
def on_select_model_df(
evt: gr.SelectData,
):
"""
When a model is selected, update the statement.
"""
return evt.value
def make_policy_plot(
board_fen,
action_seq,
view,
model_name,
depth,
use_softmax,
aggregate_topk,
render_bestk,
only_legal,
):
if model_name == "":
gr.Warning(
"Please select a model.",
)
return (
None,
None,
"",
)
try:
board = LczeroBoard(board_fen)
except ValueError:
board = LczeroBoard()
gr.Warning("Invalid FEN, using starting position.")
if action_seq:
try:
for action in action_seq.split():
board.push_uci(action)
except ValueError:
gr.Warning("Invalid action sequence, using starting position.")
board = LczeroBoard()
lczero_weights = Weights(f"{constants.LEELA_MODEL_DIRECTORY}/{model_name}")
lczero_backend = Backend(lczero_weights)
uci_moves = [move.uci() for move in board.move_stack]
lczero_game = GameState(moves=uci_moves)
policy, value = lczero_utils.prediction_from_backend(
lczero_backend,
lczero_game,
softmax=use_softmax,
only_legal=only_legal,
illegal_value=0,
)
pickup_agg, dropoff_agg = PolicyLens.aggregate_policy(policy, int(aggregate_topk))
if view == "from":
if board.turn == chess.WHITE:
heatmap = pickup_agg
else:
heatmap = pickup_agg.view(8, 8).flip(0).view(64)
else:
if board.turn == chess.WHITE:
heatmap = dropoff_agg
else:
heatmap = dropoff_agg.view(8, 8).flip(0).view(64)
us_them = (board.turn, not board.turn)
if only_legal:
legal_moves = [move_encodings.encode_move(move, us_them) for move in board.legal_moves]
filtered_policy = torch.zeros(1858)
filtered_policy[legal_moves] = policy[legal_moves]
if (filtered_policy < 0).any():
gr.Warning("Some legal moves have negative policy.")
topk_moves = torch.topk(filtered_policy, render_bestk)
else:
topk_moves = torch.topk(policy, render_bestk)
arrows = []
for move_index in topk_moves.indices:
move = move_encodings.decode_move(move_index, us_them)
arrows.append((move.from_square, move.to_square))
svg_board, fig = visualisation.render_heatmap(board, heatmap, arrows=arrows)
with open(f"{constants.FIGURE_DIRECTORY}/policy.svg", "w") as f:
f.write(svg_board)
raw_policy, _ = lczero_utils.prediction_from_backend(
lczero_backend,
lczero_game,
softmax=False,
only_legal=False,
illegal_value=0,
)
fig_dist = visualisation.render_policy_distribution(
raw_policy,
[move_encodings.encode_move(move, us_them) for move in board.legal_moves],
)
return (
f"{constants.FIGURE_DIRECTORY}/policy.svg",
fig,
(f"Value: {value:.2f}"),
fig_dist,
)
with gr.Blocks() as interface:
with gr.Row():
with gr.Column(scale=2):
model_df = gr.Dataframe(
headers=["Available models"],
datatype=["str"],
interactive=False,
type="array",
value=list_models,
)
with gr.Column(scale=1):
with gr.Row():
model_name = gr.Textbox(label="Selected model", lines=1, interactive=False, scale=7)
model_df.select(
on_select_model_df,
None,
model_name,
)
with gr.Row():
with gr.Column():
board_fen = gr.Textbox(
label="Board FEN",
lines=1,
max_lines=1,
value=chess.STARTING_FEN,
)
action_seq = gr.Textbox(
label="Action sequence",
lines=1,
max_lines=1,
value=("e2e3 b8c6 d2d4 e7e5 g1f3 d8e7 " "d4d5 e5e4 f3d4 c6e5 f2f4 e5g6"),
)
with gr.Group():
with gr.Row():
depth = gr.Radio(label="Depth", choices=[0], value=0)
use_softmax = gr.Checkbox(label="Use softmax", value=True)
with gr.Row():
aggregate_topk = gr.Slider(
label="Aggregate top k",
minimum=1,
maximum=1858,
step=1,
value=1858,
scale=3,
)
view = gr.Radio(
label="View",
choices=["from", "to"],
value="from",
scale=1,
)
with gr.Row():
render_bestk = gr.Slider(
label="Render best k",
minimum=1,
maximum=5,
step=1,
value=5,
scale=3,
)
only_legal = gr.Checkbox(label="Only legal", value=True, scale=1)
policy_button = gr.Button("Plot policy")
colorbar = gr.Plot(label="Colorbar")
game_info = gr.Textbox(label="Game info", lines=1, max_lines=1, value="")
with gr.Column():
image = gr.Image(label="Board")
density_plot = gr.Plot(label="Density")
policy_inputs = [
board_fen,
action_seq,
view,
model_name,
depth,
use_softmax,
aggregate_topk,
render_bestk,
only_legal,
]
policy_outputs = [image, colorbar, game_info, density_plot]
policy_button.click(make_policy_plot, inputs=policy_inputs, outputs=policy_outputs)
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