Merge branch 'master' into modal-save-button-and-shortcut

1 files changed, 8 insertions, 5 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..2e84583b 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -25,6 +25,7 @@ from statistics import stdev, mean
 class HypernetworkModule(torch.nn.Module):

     multiplier = 1.0

     activation_dict = {

+        "linear": torch.nn.Identity,

         "relu": torch.nn.ReLU,

         "leakyrelu": torch.nn.LeakyReLU,

         "elu": torch.nn.ELU,

@@ -428,7 +429,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

             optimizer.step()

 

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

+        steps_done = hypernetwork.step + 1

+

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

             raise RuntimeError("Loss diverged.")

         

         if len(previous_mean_losses) > 1:

@@ -438,9 +441,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         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:

+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:

             # Before saving, change name to match current checkpoint.

-            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'

+            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'

             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')

             hypernetwork.save(last_saved_file)

 

@@ -449,8 +452,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             "learn_rate": scheduler.learn_rate

         })

 

-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:

-            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'

+        if images_dir is not None and steps_done % create_image_every == 0:

+            forced_filename = f'{hypernetwork_name}-{steps_done}'

             last_saved_image = os.path.join(images_dir, forced_filename)

 

             optimizer.zero_grad()