Spaces:
Running
Running
File size: 5,783 Bytes
3333fb8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
"""
Gradio interface for plotting attention.
"""
import chess
import chess.pgn
import io
import gradio as gr
import os
import torch
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_activations(model: LczeroModel, board: LczeroBoard):
lens = Lens.from_name("activation", "block\d/conv2/relu")
with torch.no_grad():
results = lens.analyse(model, board)
return [results[f"block{i}/conv2/relu_output"][0] for i in range(len(results))]
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_activations(board: LczeroBoard, activations, layer_index:int, channel_index:int):
if layer_index >= len(activations):
safe_layer_index = len(activations) - 1
gr.Warning(f"Layer index {layer_index} out of range, using last layer ({safe_layer_index}).")
else:
safe_layer_index = layer_index
if channel_index >= activations[safe_layer_index].shape[0]:
safe_channel_index = activations[safe_layer_index].shape[0] - 1
gr.Warning(f"Channel index {channel_index} out of range, using last channel ({safe_channel_index}).")
else:
safe_channel_index = channel_index
heatmap = activations[safe_layer_index][safe_channel_index].view(64)
board.render_heatmap(
heatmap,
save_to=f"{constants.FIGURE_DIRECTORY}/activations.svg",
)
return f"{constants.FIGURE_DIRECTORY}/activations_board.svg", f"{constants.FIGURE_DIRECTORY}/activations_colorbar.svg"
def initial_load(model_name: str, board_fen: str, game_pgn: str, layer_index: int, channel_index: int):
model = get_model(model_name)
board = get_board(game_pgn, board_fen)
activations = get_activations(model, board)
plots = render_activations(board, activations, layer_index, channel_index)
return model, board, activations, *plots
def on_board_change(model: LczeroModel, game_pgn: str, board_fen: str, layer_index: int, channel_index: int):
board = get_board(game_pgn, board_fen)
activations = get_activations(model, board)
plots = render_activations(board, activations, layer_index, channel_index)
return board, activations, *plots
def on_model_change(model_name: str, board: LczeroBoard, layer_index: int, channel_index: int):
model = get_model(model_name)
activations = get_activations(model, board)
plots = render_activations(board, activations, layer_index, channel_index)
return model, activations, *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,
)
layer_index = gr.Slider(
label="Layer index",
minimum=0,
maximum=19,
step=1,
value=0,
)
channel_index = gr.Slider(
label="Channel index",
minimum=0,
maximum=200,
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)
activations = gr.State(value=None)
interface.load(
initial_load,
inputs=[model_name, game_pgn, board_fen, layer_index, channel_index],
outputs=[model, board, activations, image_board, colorbar],
)
game_pgn.submit(
on_board_change,
inputs=[model, game_pgn, board_fen, layer_index, channel_index],
outputs=[board, activations, image_board, colorbar],
)
board_fen.submit(
on_board_change,
inputs=[model, game_pgn, board_fen, layer_index, channel_index],
outputs=[board, activations, image_board, colorbar],
)
model_name.change(
on_model_change,
inputs=[model_name, board, layer_index, channel_index],
outputs=[model, activations, image_board, colorbar],
)
layer_index.change(
render_activations,
inputs=[board, activations, layer_index, channel_index],
outputs=[image_board, colorbar],
)
channel_index.change(
render_activations,
inputs=[board, activations, layer_index, channel_index],
outputs=[image_board, colorbar],
)
|