X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=problems.py;h=9321194984caf3c5b483b68cb408c32f22061b44;hb=bd0903943d9b44e0de58835c63c7a59d72a65c94;hp=40598568806400b80abe9af5ba64d8d92110c07e;hpb=8cb67a3cf972dbba5741b5b48d531c1e84439745;p=picoclvr.git

diff --git a/problems.py b/problems.py
index 4059856..9321194 100755
--- a/problems.py
+++ b/problems.py
@@ -24,6 +24,8 @@ class Problem:
 
 
 ####################
+
+
 class ProblemDegradation(Problem):
     def __init__(self, nb_state_tokens=5, nb_time_steps=12, value_max=25, hard=False):
         assert value_max // nb_state_tokens >= 2
@@ -298,13 +300,21 @@ class ProblemMixing(Problem):
 
         # m = (torch.rand(y.size()).sort(dim=-1).indices < y.size(1) // 2).long()
 
-        i = torch.arange(self.height).reshape(1,-1,1).expand(nb,self.height,self.width)
-        j = torch.arange(self.width).reshape(1,1,-1).expand(nb,self.height,self.width)
+        i = (
+            torch.arange(self.height)
+            .reshape(1, -1, 1)
+            .expand(nb, self.height, self.width)
+        )
+        j = (
+            torch.arange(self.width)
+            .reshape(1, 1, -1)
+            .expand(nb, self.height, self.width)
+        )
 
-        ri = torch.randint(self.height, (nb,)).reshape(nb,1,1)
-        rj = torch.randint(self.width, (nb,)).reshape(nb,1,1)
+        ri = torch.randint(self.height, (nb,)).reshape(nb, 1, 1)
+        rj = torch.randint(self.width, (nb,)).reshape(nb, 1, 1)
 
-        m = 1 - torch.logical_or(i==ri,j==rj).long().flatten(1)
+        m = 1 - torch.logical_or(i == ri, j == rj).long().flatten(1)
 
         y = (y * m + self.height * self.width * (1 - m)).reshape(
             nb, self.height, self.width
@@ -313,16 +323,20 @@ class ProblemMixing(Problem):
         return y
 
     def start_error(self, x):
-        i = torch.arange(self.height).reshape(1,-1,1).expand_as(x)
-        j = torch.arange(self.width).reshape(1,1,-1).expand_as(x)
+        i = torch.arange(self.height, device=x.device).reshape(1, -1, 1).expand_as(x)
+        j = torch.arange(self.width, device=x.device).reshape(1, 1, -1).expand_as(x)
 
-        ri = (x == self.height * self.width).long().sum(dim=-1).argmax(-1).view(-1,1,1)
-        rj = (x == self.height * self.width).long().sum(dim=-2).argmax(-1).view(-1,1,1)
+        ri = (
+            (x == self.height * self.width).long().sum(dim=-1).argmax(-1).view(-1, 1, 1)
+        )
+        rj = (
+            (x == self.height * self.width).long().sum(dim=-2).argmax(-1).view(-1, 1, 1)
+        )
 
-        m = 1 - torch.logical_or(i==ri,j==rj).long().flatten(1)
+        m = 1 - torch.logical_or(i == ri, j == rj).long().flatten(1)
 
         x = x.flatten(1)
-        u = torch.arange(self.height * self.width, device = x.device).reshape(1, -1)
+        u = torch.arange(self.height * self.width, device=x.device).reshape(1, -1)
 
         d = (x - (m * u + (1 - m) * self.height * self.width)).abs().sum(-1)
         return d
@@ -390,7 +404,15 @@ class ProblemMixing(Problem):
         return " | ".join(
             [
                 " ".join(
-                    ["-".join([f"{x:02d}" if x < self.height * self.width else "**" for x in s]) for s in r.split(self.width)]
+                    [
+                        "-".join(
+                            [
+                                f"{x:02d}" if x < self.height * self.width else "**"
+                                for x in s
+                            ]
+                        )
+                        for s in r.split(self.width)
+                    ]
                 )
                 for r in seq.split(self.height * self.width)
             ]