Save a csv containing the loss while training

3 files changed, 35 insertions, 2 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b6c06d49..6522078f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -5,6 +5,7 @@ import os
 import sys

 import traceback

 import tqdm

+import csv

 

 import torch

 

@@ -174,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)

 

 

-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):

+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, write_csv_every, template_file, preview_image_prompt):

     assert hypernetwork_name, 'hypernetwork not selected'

 

     path = shared.hypernetworks.get(hypernetwork_name, None)

@@ -256,6 +257,20 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')

             hypernetwork.save(last_saved_file)

 

+        print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}")

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

+            write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True

+            

+            with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:

+

+                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"])

+                

+                if write_csv_header:

+                    csv_writer.writeheader()

+

+                csv_writer.writerow({"step": hypernetwork.step, 

+                    "loss": f"{losses.mean():.7f}"})

+

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

             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')

 

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..25038a89 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm

 import html

 import datetime

+import csv

 

 from PIL import Image, PngImagePlugin

 

@@ -172,7 +173,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn

 

 

-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):

+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, write_csv_every, template_file, save_image_with_stored_embedding, preview_image_prompt):

     assert embedding_name, 'embedding not selected'

 

     shared.state.textinfo = "Initializing textual inversion training..."

@@ -256,6 +257,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')

             embedding.save(last_saved_file)

 

+        if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:

+            write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True

+

+            with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:

+

+                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"])

+                

+                if write_csv_header:

+                    csv_writer.writeheader()

+

+                csv_writer.writerow({"epoch": epoch_num + 1, 

+                    "epoch_step": epoch_step - 1, 

+                    "loss": f"{losses.mean():.7f}"})

+

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

             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')

 

diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..1195c2f1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1096,6 +1096,7 @@ def create_ui(wrap_gradio_gpu_call):
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)

                     steps = gr.Number(label='Max steps', value=100000, precision=0)

                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)

+                    write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0)

                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)

                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)

                     preview_image_prompt = gr.Textbox(label='Preview prompt', value="")

@@ -1174,6 +1175,7 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,

                 create_image_every,

                 save_embedding_every,

+                write_csv_every,

                 template_file,

                 save_image_with_stored_embedding,

                 preview_image_prompt,

@@ -1195,6 +1197,7 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,

                 create_image_every,

                 save_embedding_every,

+                write_csv_every,

                 template_file,

                 preview_image_prompt,

             ],