cleanup

celeste/plots.py

@@ -8,43 +8,21 @@ from celeste import Celeste
 from main import DQN
 from main import Transition
 
-# Use cpu, the script is faster in parallel.
+# Use cpu, this script is faster in parallel.
 compute_device = torch.device("cpu")
 
-
-# Celeste env properties
-n_observations = len(Celeste.state_number_map)
-n_actions = len(Celeste.action_space)
-
-
 out_dir = Path("out/plots")
 out_dir.mkdir(parents = True, exist_ok = True)
 
-src_dir = Path("model_data/model_archive")
+src_dir = Path("model_data/current/model_archive")
-
-policy_net = DQN(
-	n_observations,
-	n_actions
-).to(compute_device)
-
-target_net = DQN(
-	n_observations,
-	n_actions
-).to(compute_device)
-
-optimizer = torch.optim.AdamW(
-	policy_net.parameters(),
-	lr = 0.01, # Hyperparameter: learning rate
-	amsgrad = True
-)
 
 
 def makeplt(i, net):
 	p = np.zeros((128, 128), dtype=np.float32)
 
+	with torch.no_grad():
 		for r in range(len(p)):
 			for c in range(len(p[r])):
-			with torch.no_grad():
 				k = net(
 					torch.tensor(
 						[c, r, 60, 80],
@@ -52,29 +30,38 @@ def makeplt(i, net):
 						device = compute_device
 					).unsqueeze(0)
 				)[0][i].item()
-
 				p[r][c] = k
 	return p
 
 
 
 def plot(src):
+	policy_net = DQN(
+		len(Celeste.state_number_map),
+		len(Celeste.action_space)
+	).to(compute_device)
+
 	checkpoint = torch.load(src)
 	policy_net.load_state_dict(checkpoint["policy_state_dict"])
 
 
-	fig, axs = plt.subplots(2, 4, figsize = (15, 10))
+	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")
+		ax.set(
+			adjustable = "box",
+			aspect = "equal"
+		)
+		ax.set_title(Celeste.action_space[a])
+		ax.invert_yaxis()
+
 		plot = ax.pcolor(
 			makeplt(a, policy_net),
 			cmap = "Greens",
 			vmin = 0,
 		)
-		ax.set_title(Celeste.action_space[a])
+
-		ax.invert_yaxis()
 		fig.colorbar(plot)
 	print(src)
 	fig.savefig(out_dir / f"{src.stem}.png")