Reorganized celeste code

celeste/celeste_ai/plotting/__init__.py

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+from .plot_actual_reward import actual_reward
+from .plot_predicted_reward import predicted_reward

celeste/celeste_ai/plotting/plot_actual_reward.py

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+import torch
+import numpy as np
+from pathlib import Path
+import matplotlib.pyplot as plt
+from multiprocessing import Pool
+
+# All of the following are required to load
+# a pickled model.
+from celeste_ai.celeste import Celeste
+from celeste_ai.network import DQN
+from celeste_ai.network import Transition
+
+def actual_reward(
+	model_file: Path,
+	target_point: tuple[int, int],
+	out_filename: Path,
+	*,
+	device = torch.device("cpu")
+):
+	if not model_file.is_file():
+		raise Exception(f"Bad model file {model_file}")
+	out_filename.parent.mkdir(exist_ok = True, parents = True)
+
+
+	checkpoint = torch.load(
+		model_file,
+		map_location = device
+	)
+	memory = checkpoint["memory"]
+
+
+	r = np.zeros((128, 128, 8), dtype=np.float32)
+	for m in memory:
+		x, y, x_target, y_target = list(m.state[0])
+
+		action = m.action[0].item()
+		x = int(x.item())
+		y = int(y.item())
+		x_target = int(x_target.item())
+		y_target = int(y_target.item())
+
+		# Only plot memory related to this point
+		if (x_target, y_target) != target_point:
+			continue
+
+		if m.reward[0].item() == 1:
+			r[y][x][action] += 1
+		else:
+			r[y][x][action] -= 1
+
+
+	fig, axs = plt.subplots(2, 4, figsize = (20, 10))
+
+
+	for a in range(len(axs.ravel())):
+		ax = axs.ravel()[a]
+		ax.set(
+			adjustable = "box",
+			aspect = "equal",
+			title = Celeste.action_space[a]
+		)
+
+		plot = ax.pcolor(
+			r[:,:,a],
+			cmap = "seismic_r",
+			vmin = -10,
+			vmax = 10
+		)
+
+		# Draw target point on plot
+		ax.plot(
+			target_point[0],
+			target_point[1],
+			"k."
+		)
+
+		ax.invert_yaxis()
+		fig.colorbar(plot)
+
+	fig.savefig(out_filename)
+	plt.close()

celeste/celeste_ai/plotting/plot_predicted_reward.py

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+import torch
+import numpy as np
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+# All of the following are required to load
+# a pickled model.
+from celeste_ai.celeste import Celeste
+from celeste_ai.network import DQN
+from celeste_ai.network import Transition
+
+
+def predicted_reward(
+	model_file: Path,
+	out_filename: Path,
+	*,
+	device = torch.device("cpu")
+):
+	if not model_file.is_file():
+		raise Exception(f"Bad model file {model_file}")
+	out_filename.parent.mkdir(exist_ok = True, parents = True)
+
+	# Create and load model
+	policy_net = DQN(
+		len(Celeste.state_number_map),
+		len(Celeste.action_space)
+	).to(device)
+	checkpoint = torch.load(
+		model_file,
+		map_location = device
+	)
+	policy_net.load_state_dict(checkpoint["policy_state_dict"])
+
+
+
+	# Compute preditions
+	p = np.zeros((128, 128, 8), dtype=np.float32)
+	with torch.no_grad():
+		for r in range(len(p)):
+			for c in range(len(p[r])):
+				k = np.asarray(policy_net(
+					torch.tensor(
+						[c, r, 60, 80],
+						dtype = torch.float32,
+						device = device
+					).unsqueeze(0)
+				)[0])
+				p[r][c] = k
+
+
+	# Plot predictions
+	fig, axs = plt.subplots(2, 4, figsize = (20, 10))
+	for a in range(len(axs.ravel())):
+		ax = axs.ravel()[a]
+		ax.set(
+			adjustable = "box",
+			aspect = "equal",
+			title = Celeste.action_space[a]
+		)
+
+		plot = ax.pcolor(
+			p[:,:,a],
+			cmap = "Greens",
+			vmin = 0,
+		)
+
+		ax.invert_yaxis()
+		fig.colorbar(plot)
+
+	fig.savefig(out_filename)
+	plt.close()
+
+
+
+
+
+