def init():
MyDebugger.pre_fix = os.path.join(MyDebugger.pre_fix, "debug")
debugger = MyDebugger(f"region_tiling",
fix_rand_seed=config.rand_seed,
save_print_to_file=False)
plotter = Plotter()
return debugger, plotter
def show_all_onering_neibors():
# Driver code
MyDebugger.pre_fix = os.path.join(MyDebugger.pre_fix, "debug")
debugger = MyDebugger(f"gen_tile_graph_{config.env_name}", fix_rand_seed=0)
plotter = Plotter()
data_env = config.environment
result_tiles = []
align_tags = []
for base_tile in data_env.proto_tiles:
for align_tile in data_env.proto_tiles:
neighbour_tiles, align_tag = get_all_tiles(base_tile,
align_tile,
integer_align=True)
for i in range(len(neighbour_tiles)):
plotter.draw_contours(
debugger.file_path(f"{align_tag[i]}.png"), [
base_tile.get_plot_attribute(),
neighbour_tiles[i].get_plot_attribute()
])
def draw_solution(self, painter: QPainter):
tiles = self.current_best_solution.get_selected_tiles()
painter.setPen(self.pens[solution_color])
painter.setBrush(self.brushes[solution_color])
scale, translation = self._get_scale_translation(False)
polygons = [
Plotter.create_polygon(
np.array(t.exterior.coords) * scale + translation)
for t in tiles
]
for p in polygons:
painter.drawPolygon(p)
def draw_grid(self, scale, translation):
grid_painter = QPainter(self.pixmap)
grid_painter.setRenderHint(QPainter.Antialiasing)
grid_painter.setRenderHint(QPainter.SmoothPixmapTransform)
############ Draw grids #############################
grid_painter.setPen(self.grid_pen)
polygons = [
Plotter.create_polygon(
np.array(t.tile_poly.exterior.coords) * scale + translation)
for t in self.complete_graph.tiles
]
for p in polygons:
grid_painter.drawPolygon(p)
self.need_draw_grid = False
import os
from inputs import config
from solver.ml_solver.trainer import Trainer
import torch
from solver.ml_solver.ml_solver import ML_Solver
import numpy as np
from graph_networks.networks.TilinGNN import TilinGNN
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if __name__ == "__main__":
MyDebugger.pre_fix = os.path.join(MyDebugger.pre_fix, "debug")
debugger = MyDebugger(f"training_{config.experiment_id}",
fix_rand_seed=config.rand_seed,
save_print_to_file=False)
plotter = Plotter()
data_env = config.environment
data_env.load_complete_graph(config.complete_graph_size)
#### Network
network = TilinGNN(
adj_edge_features_dim=data_env.complete_graph.total_feature_dim,
network_depth=config.network_depth,
network_width=config.network_width).to(device)
## solver
ml_solver = ML_Solver(debugger,
device,
data_env.complete_graph,
network,
num_prob_maps=1)