X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=stack.py;h=312b39fda81b142b4dd9c10bc35259f8c8c7dc21;hb=76671c582f029aa67fce2626764b02e8d9e2dbeb;hp=dc494bb88d2428b0f173b7e59fbf0e0eea873226;hpb=16bf88f88bbab138c0dc33b4fbd2d88cf9db3ae5;p=picoclvr.git

diff --git a/stack.py b/stack.py
index dc494bb..312b39f 100755
--- a/stack.py
+++ b/stack.py
@@ -13,44 +13,52 @@ import torch, torchvision
 # CODE_VAL=val + 2 * nb_stacks
 
 
-def generate(nb, nb_steps, nb_stacks, nb_values):
+def generate_sequences(nb, nb_steps, nb_stacks, nb_values, device=torch.device("cpu")):
     stack = torch.empty(nb, nb_stacks, nb_steps, dtype=torch.int64)
-    stack_pointers = torch.zeros(nb, nb_stacks, dtype=torch.int64)
+    stack_counts = torch.zeros(nb, nb_stacks, dtype=torch.int64)
     k = torch.arange(nb)
     result = torch.empty(nb, 2 * nb_steps, dtype=torch.int64)
-    depth_counts = torch.zeros(nb, 2 * nb_steps, dtype=torch.int64)
+    recorded_stack_counts = torch.zeros(nb, 2 * nb_steps, dtype=torch.int64)
 
     for t in range(nb_steps):
         op = torch.randint(2, (nb,))
         st = torch.randint(nb_stacks, (nb,))
-        op = op * (stack_pointers[k, st] > 0)
+        op = op * (stack_counts[k, st] > 0)
         val_push = torch.randint(nb_values, (nb,))
         val_pop = stack[
             k,
             st,
-            (stack_pointers[k, st] - 1).clamp(min=0),
+            (stack_counts[k, st] - 1).clamp(min=0),
         ]
-        stack[k, st, stack_pointers[k, st]] = val_push
-        depth_counts[:, 2 * t + 1] = stack_pointers[k, st]
-        stack_pointers[k[op == 0], st[op == 0]] += 1
-        stack_pointers[k[op == 1], st[op == 1]] -= 1
+        stack[k, st, stack_counts[k, st]] = val_push
+        recorded_stack_counts[:, 2 * t + 1] = stack_counts[k, st]
+        stack_counts[k[op == 0], st[op == 0]] += 1
+        stack_counts[k[op == 1], st[op == 1]] -= 1
         result[:, 2 * t] = st * 2 + op
         result[:, 2 * t + 1] = (op * val_pop + (1 - op) * val_push) + 2 * nb_stacks
 
-    return result, depth_counts
+    return result.to(device), recorded_stack_counts.to(device)
 
 
-def seq_to_str(seq, depth_counts=None):
+def remove_poped_values(seq, nb_stacks):
+    m = torch.logical_and(seq % 2 == 1, seq < 2 * nb_stacks).long()
+    seq[:, 1:] = -m[:, :-1] + (1 - m[:, :-1]) * seq[:, 1:]
+
+
+def seq_to_str(seq, recorded_stack_counts=None):
     assert seq.size(0) % 2 == 0
     s = ""
     for t in range(seq.size(0) // 2):
         op = seq[2 * t]
-        op = f"POP_{op//2}" if op % 2 == 1 else f"PUSH_{op//2}"
-        val = seq[2 * t + 1] - 2 * nb_stacks
+        op = f"POP_{op//2}" if op % 2 == 1 else f"PSH_{op//2}"
+        if seq[2 * t + 1] == -1:
+            val = "?"
+        else:
+            val = seq[2 * t + 1] - 2 * nb_stacks
         if t > 0:
             s += " "
-        if depth_counts is not None:
-            s += f"[{depth_counts[2*t+1]}] "
+        if recorded_stack_counts is not None:
+            s += f"[{recorded_stack_counts[2*t+1]}] "
         s += f"{op} {val}"
     return s
 
@@ -59,9 +67,17 @@ def seq_to_str(seq, depth_counts=None):
 
 if __name__ == "__main__":
     nb, nb_steps, nb_stacks, nb_values = 150000, 10, 1, 5
-    seq, depth_counts = generate(
+    seq, recorded_stack_counts = generate_sequences(
         nb=nb, nb_steps=nb_steps, nb_stacks=nb_stacks, nb_values=nb_values
     )
 
     for n in range(min(10, seq.size(0))):
-        print(seq_to_str(seq[n], depth_counts[n]))
+        # print(seq_to_str(seq[n], recorded_stack_counts[n]))
+        print(seq_to_str(seq[n]))
+
+    print("--------------------------------------")
+
+    remove_poped_values(seq, nb_stacks)
+
+    for n in range(min(10, seq.size(0))):
+        print(seq_to_str(seq[n]))