81 lines
1.5 KiB
Python
81 lines
1.5 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.pyplot as plt
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from multiprocessing import Pool
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from celeste import Celeste
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from main import DQN
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from main import Transition
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# Use cpu, this script is faster in parallel.
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compute_device = torch.device("cpu")
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input_model = Path("model_data/current")
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src_dir = input_model / "model_archive"
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out_dir = input_model_dir / "plots/predicted_value"
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out_dir.mkdir(parents = True, exist_ok = True)
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def plot(src):
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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(compute_device)
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checkpoint = torch.load(
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src,
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map_location = compute_device
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)
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policy_net.load_state_dict(checkpoint["policy_state_dict"])
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fig, axs = plt.subplots(2, 4, figsize = (20, 10))
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# Compute preditions
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p = np.zeros((128, 128, 8), 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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k = np.asarray(policy_net(
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torch.tensor(
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[c, r, 60, 80],
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dtype = torch.float32,
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device = compute_device
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).unsqueeze(0)
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)[0])
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p[r][c] = k
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# Plot predictions
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for a in range(len(axs.ravel())):
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ax = axs.ravel()[a]
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ax.set(
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adjustable = "box",
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aspect = "equal",
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title = Celeste.action_space[a]
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)
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plot = ax.pcolor(
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p[:,:,a],
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cmap = "Greens",
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vmin = 0,
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)
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ax.invert_yaxis()
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fig.colorbar(plot)
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print(src)
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fig.savefig(out_dir / f"{src.stem}.png")
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plt.close()
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if __name__ == "__main__":
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with Pool(5) as p:
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p.map(plot, list(src_dir.iterdir()))
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