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predict.py

@@ -174,7 +174,7 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
         ens.direct_cache = []
         for i in range(PRED_FRAMES):
             frame = np.transpose(predicted_np[i], (1, 2, 0))
-            frame = np.round(frame * 255 + 0.1).clip(0, 255).astype(np.uint8)
+            frame = np.round(frame * 255 + 0.2).clip(0, 255).astype(np.uint8)
             ens.direct_cache.append(frame)
 
         result = ens.direct_cache[ens.cache_step]
@@ -241,7 +241,7 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
         ens.direct_cache = []
         for i in range(PRED_FRAMES):
             frame = np.transpose(predicted_np[i], (1, 2, 0))
-            frame = np.round(frame * 255 + 0.1).clip(0, 255).astype(np.uint8)
+            frame = np.round(frame * 255 + 0.2).clip(0, 255).astype(np.uint8)
             ens.direct_cache.append(frame)
 
         result = ens.direct_cache[ens.cache_step]
@@ -273,7 +273,7 @@ def predict_next_frame(ens, context_frames: np.ndarray) -> np.ndarray:
         ens.direct_cache = []
         for i in range(PRED_FRAMES):
             frame = np.transpose(predicted_np[i], (1, 2, 0))
-            frame = np.round(frame * 255 + 0.1).clip(0, 255).astype(np.uint8)
+            frame = np.round(frame * 255 + 0.2).clip(0, 255).astype(np.uint8)
             ens.direct_cache.append(frame)
 
         result = ens.direct_cache[ens.cache_step]

sweep.py

@@ -1,42 +1,50 @@
-"""Sweep Pong AR residual scale."""
+"""Sweep rounding bias."""
 import subprocess
 import json
 import re
 
-predict_path = "/home/coder/experiments/2026-04-12-330000-pong-amp-sweep/predict.py"
+predict_path = "/home/coder/experiments/2026-04-12-332000-bias-resweep/predict.py"
 
 results = {}
-for scale in [1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07]:
+for bias in [0.0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.40, 0.50]:
     with open(predict_path, 'r') as f:
         content = f.read()
 
-    # Replace Pong AR residual_scale (only in Pong section, identified by "pong" model)
-    content = re.sub(
-        r'_predict_ar_frame\(ens\.models\["pong"\], ctx, last_t, residual_scale=[\d.]+\)',
-        f'_predict_ar_frame(ens.models["pong"], ctx, last_t, residual_scale={scale})',
-        content
-    )
+    if bias == 0.0:
+        content = re.sub(
+            r'np\.round\(frame \* 255 \+ [\d.]+\)',
+            'np.round(frame * 255)',
+            content
+        )
+    else:
+        # First handle the case where there's already a bias
+        content = re.sub(
+            r'np\.round\(frame \* 255 \+ [\d.]+\)',
+            f'np.round(frame * 255 + {bias})',
+            content
+        )
+        # Then handle the case where there's no bias (from bias=0.0 step)
+        content = re.sub(
+            r'np\.round\(frame \* 255\)\.clip',
+            f'np.round(frame * 255 + {bias}).clip',
+            content
+        )
 
     with open(predict_path, 'w') as f:
         f.write(content)
 
     result = subprocess.run(
-        ['python', 'task/score.py', '--model_path', '/home/coder/experiments/2026-04-12-330000-pong-amp-sweep'],
+        ['python', 'task/score.py', '--model_path', '/home/coder/experiments/2026-04-12-332000-bias-resweep'],
         capture_output=True, text=True, cwd='/home/coder'
     )
 
     for line in result.stdout.strip().split('\n'):
         if '"score"' in line:
             data = json.loads(line)
-            results[scale] = {
-                'score': data['score'],
-                'pong': data['per_game']['pong']['ssim'],
-                'sonic': data['per_game']['sonic']['ssim'],
-                'pp': data['per_game']['pole_position']['ssim']
-            }
-            print(f"Scale {scale}: overall={data['score']:.4f} pong={data['per_game']['pong']['ssim']:.4f}")
+            results[bias] = data['score']
+            print(f"Bias {bias:.2f}: overall={data['score']:.4f} pong={data['per_game']['pong']['ssim']:.4f} sonic={data['per_game']['sonic']['ssim']:.4f} pp={data['per_game']['pole_position']['ssim']:.4f}")
             break
 
 print("\n=== Summary ===")
-best_scale = max(results.keys(), key=lambda s: results[s]['pong'])
-print(f"Best Pong scale: {best_scale} with pong={results[best_scale]['pong']:.4f}, overall={results[best_scale]['score']:.4f}")
+best_bias = max(results.keys(), key=lambda b: results[b])
+print(f"Best bias: {best_bias} with overall={results[best_bias]:.4f}")