122 lines
2.2 KiB
Python
122 lines
2.2 KiB
Python
import torch
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import numpy as np
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from pathlib import Path
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import json
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# All of the following are required to load
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# a pickled model.
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from celeste_ai.celeste import Celeste
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from celeste_ai.network import DQN
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from celeste_ai.network import Transition
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def best_action(
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model_file: Path,
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out_filename: Path,
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*,
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device = torch.device("cpu"),
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draw_path = True
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):
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if not model_file.is_file():
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raise Exception(f"Bad model file {model_file}")
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out_filename.parent.mkdir(exist_ok = True, parents = True)
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# Create and load model
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policy_net = DQN(
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len(Celeste.state_number_map),
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len(Celeste.action_space)
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).to(device)
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checkpoint = torch.load(
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model_file,
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map_location = device
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)
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policy_net.load_state_dict(checkpoint["policy_state_dict"])
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# Compute preditions
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p = np.zeros((128, 128), dtype=np.float32)
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with torch.no_grad():
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for r in range(len(p)):
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for c in range(len(p[r])):
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x = c / 128.0
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y = r / 128.0
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k = np.asarray(policy_net(
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torch.tensor(
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[x, y],
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dtype = torch.float32,
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device = device
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).unsqueeze(0)
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)[0])
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p[r][c] = np.argmax(k)
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cmap = mpl.colors.ListedColormap(
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[
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"forestgreen",
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"firebrick",
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"lightgreen",
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"salmon",
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"darkturquoise",
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"sandybrown",
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"olive",
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"darkorchid",
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"mediumvioletred"
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]
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)
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# Plot predictions
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fig, axs = plt.subplots(1, 1, figsize = (20, 20))
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ax = axs
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ax.set(
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adjustable = "box",
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aspect = "equal",
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title = "Best Action"
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)
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plot = ax.pcolor(
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p,
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cmap = cmap,
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vmin = 0,
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vmax = 8
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)
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if draw_path:
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d = None
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with Path("model_data/solved_4layer/paths.json").open("r") as f:
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for l in f.readlines():
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t = json.loads(l)
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if t["current_image"] == model_file.name:
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break
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d = t
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assert d is not None
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plt.plot(
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[max(0,x["xpos"]) for x in d["hist"]],
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[max(0,x["ypos"] + 5) for x in d["hist"]],
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marker = "",
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markersize = 0,
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linestyle = "-",
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linewidth = 5,
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color = "white",
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solid_capstyle = "round",
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solid_joinstyle = "round"
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)
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ax.invert_yaxis()
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cbar = fig.colorbar(plot, ticks = list(range(0, 9)))
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cbar.ax.set_yticklabels(Celeste.action_space)
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fig.savefig(out_filename)
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plt.close()
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