Merge branch 'AUTOMATIC1111:master' into Inspiron

1 files changed, 24 insertions, 16 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 98a7b62e..d647ea55 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):

@@ -269,19 +270,18 @@ 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) > 1024:

-            loss_info.pop(0)

-

-

 def statistics(data):

-    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"

+    if len(data) < 2:

+        std = 0

+    else:

+        std = stdev(data)

+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"

     recent_data = data[-32:]

-    recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"

+    if len(recent_data) < 2:

+        std = 0

+    else:

+        std = stdev(recent_data)

+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"

     return total_information, recent_information

 

 

@@ -290,7 +290,7 @@ def report_statistics(loss_info:dict):
     for key in keys:

         try:

             print("Loss statistics for file " + key)

-            info, recent = statistics(loss_info[key])

+            info, recent = statistics(list(loss_info[key]))

             print(info)

             print(recent)

         except Exception as e:

@@ -341,8 +341,9 @@ 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_losses = [0]

     previous_mean_loss = 0

     print("Mean loss of {} elements".format(size))

 

@@ -364,7 +365,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for i, entries in pbar:

         hypernetwork.step = i + ititial_step

         if len(loss_dict) > 0:

-            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)

+            previous_mean_losses = [i[-1] for i in loss_dict.values()]

+            previous_mean_loss = mean(previous_mean_losses)

             

         scheduler.apply(optimizer, hypernetwork.step)

         if scheduler.finished:

@@ -383,7 +385,7 @@ 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

@@ -399,7 +401,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

         if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):

             raise RuntimeError("Loss diverged.")

-        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")

+        

+        if len(previous_mean_losses) > 1:

+            std = stdev(previous_mean_losses)

+        else:

+            std = 0

+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"

+        pbar.set_description(dataset_loss_info)

 

         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:

             # Before saving, change name to match current checkpoint.