fixup! WIP: Attempt at switching

src/process.py

@@ -2,12 +2,26 @@ import contextlib
 from collections import deque
 from decimal import Decimal
 
+import numpy as np
 import torch
 from tqdm import tqdm
 
 from src.utils import reference_ae
 
 
+def probs_to_freqs(probs, total_freq=8192):
+    freqs = (probs * total_freq).round().long()
+
+    # Ensure no zero-frequency symbol if needed
+    freqs[freqs == 0] = 1
+
+    # Re-normalize so the sum matches total_freq
+    diff = total_freq - freqs.sum()
+    freqs[0] += diff  # fix the sum by adjusting the first bin
+
+    return freqs
+
+
 def compress(
         device,
         model_path: str,
@@ -51,15 +65,22 @@ def compress(
 
             with torch.inference_mode():
                 logits = model(context_tensor)
+                #normalize
+                mean = logits.mean(dim=-1, keepdim=True)
+                std = logits.std(dim=-1, keepdim=True)
+                logits = (logits - mean) / (std + 1e-6)
+                print(f"logits: {logits}")
                 probabilities = torch.softmax(logits[0], dim=-1)
-            probabilities = probabilities.detach().cpu().numpy()
+            print(f"probabilities: {probabilities}")
+            probabilities = probabilities.detach()
 
-            eps = 1e-10
+            eps = 1e-8
-            frequency_table = {i: float(probabilities[i]) + eps for i in range(len(probabilities))}
+            # np.add(probabilities, eps)
-            probability_table = reference_ae.SimpleFrequencyTable([0] * len(probabilities))
+            # frequency_table = {i: float(probabilities[i]) + eps for i in range(len(probabilities))}
-            probability_table = AE.get_probability_table(frequency_table)
+            probability_table = reference_ae.SimpleFrequencyTable(probs_to_freqs(probabilities))
+            # probability_table = AE.get_probability_table(frequency_table)
 
-            enc.write(frequency_table, byte)
+            enc.write(probability_table, byte)
 
             context.append(byte)