X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;fp=tasks.py;h=11879fd44866094711a76b5c7bdebebdda28fa41;hb=0b8185b90014369f0d39892e128ad04a7d9ae872;hp=f2b7709f1dc742979bad1e9a44e27da4525904d3;hpb=1eeba5d817d6e440a93895d42f6e580e9ba273fd;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index f2b7709..11879fd 100755
--- a/tasks.py
+++ b/tasks.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, os, tqdm
+import math, os, tqdm, warnings
 
 import torch, torchvision
 
@@ -1928,6 +1928,8 @@ class Escape(Task):
 
         result[:, it_len:] = -1
 
+        snapshots = []
+
         def ar(result, ar_mask, logit_biases=None):
             ar_mask = ar_mask.expand_as(result)
             result *= 1 - ar_mask
@@ -1941,6 +1943,8 @@ class Escape(Task):
                 device=self.device,
                 progress_bar_desc=None,
             )
+            warnings.warn("keeping thinking snapshots", RuntimeWarning)
+            snapshots.append(result[:10].detach().clone())
 
         # Generate iteration after iteration
 
@@ -1948,30 +1952,32 @@ class Escape(Task):
         optimistic_bias[escape.lookahead_reward2code(-1)] = -math.log(1e1)
         optimistic_bias[escape.lookahead_reward2code(1)] = math.log(1e1)
 
-        snapshots = []
-
         for u in tqdm.tqdm(
             range(it_len, result.size(1) - it_len + 1, it_len), desc="thinking"
         ):
+            lr, _, _, _ = escape.seq2episodes(result[:, :u], self.height, self.width)
+
             # Generate the lookahead_reward and state
-            ar_mask = (t >= u + index_lookahead_reward).long() * (
+            ar_mask = (t % it_len == index_lookahead_reward).long() * (
+                t <= u + index_lookahead_reward
+            ).long()
+            ar(result, ar_mask)
+
+            # Generate the lookahead_reward and state
+            ar_mask = (t >= u + index_states).long() * (
                 t < u + index_states + state_len
             ).long()
             ar(result, ar_mask)
-            snapshots.append(result[:10].detach().clone())
-            backup_lookahead_reward = result[:, u + index_lookahead_reward]
 
             # Re-generate the lookahead_reward
-            ar_mask = (t == u + index_lookahead_reward).long()
+            ar_mask = (t % it_len == index_lookahead_reward).long() * (
+                t <= u + index_lookahead_reward
+            ).long()
             ar(result, ar_mask, logit_biases=optimistic_bias)
-            snapshots.append(result[:10].detach().clone())
 
             # Generate the action and reward
             ar_mask = (t >= u + index_action).long() * (t <= u + index_reward).long()
             ar(result, ar_mask)
-            snapshots.append(result[:10].detach().clone())
-
-            result[:, u + index_lookahead_reward] = backup_lookahead_reward
 
         filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
         with open(filename, "w") as f: