From 40b56c9289bf9458ae5ef3c1990ccea851c6c3e2 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:07:07 +0900
Subject: cleanup some code

---
 modules/hypernetworks/hypernetwork.py | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 33827210..4072bf54 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 
+from collections import defaultdict, deque
 from statistics import stdev, mean
 
 class HypernetworkModule(torch.nn.Module):
@@ -269,15 +270,6 @@ def stack_conds(conds):
     return torch.stack(conds)
 
 
-def log_statistics(loss_info:dict, key, value):
-    if key not in loss_info:
-        loss_info[key] = [value]
-    else:
-        loss_info[key].append(value)
-        if len(loss_info[key]) > 1024:
-            loss_info[key].pop(0)
-
-
 def statistics(data):
     total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
     recent_data = data[-32:]
@@ -341,7 +333,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         weight.requires_grad = True
 
     size = len(ds.indexes)
-    loss_dict = {}
+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
@@ -383,7 +375,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
-                log_statistics(loss_dict, entry.filename, loss.item())
+                loss_dict[entry.filename].append(loss.item())
                 
             optimizer.zero_grad()
             weights[0].grad = None
-- 
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