CalvinBackup/iperov-DeepFaceLab
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Upgraded to TF version 1.13.2

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Removed the wait at first launch for most graphics cards.

Increased speed of training by 10-20%, but you have to retrain all models from scratch.

SAEHD:

added option 'use float16'
	Experimental option. Reduces the model size by half.
	Increases the speed of training.
	Decreases the accuracy of the model.
	The model may collapse or not train.
	Model may not learn the mask in large resolutions.

true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
This commit is contained in:
Colombo
2020-01-25 21:58:19 +04:00
parent a3dfcb91b9
commit 76ca79216e
49 changed files with 1320 additions and 1297 deletions
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mainscripts/Trainer.py
+16 -16
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@@ -12,19 +12,19 @@ import cv2
import models
from core.interact import interact as io
def trainerThread (s2c, c2s, e,
def trainerThread (s2c, c2s, e,
model_class_name = None,
saved_models_path = None,
training_data_src_path = None,
training_data_dst_path = None,
pretraining_data_path = None,
pretrained_model_path = None,
no_preview=False,
pretraining_data_path = None,
pretrained_model_path = None,
no_preview=False,
force_model_name=None,
force_gpu_idxs=None,
cpu_only=None,
cpu_only=None,
execute_programs = None,
debug=False,
debug=False,
**kwargs):
while True:
try:
@@ -98,11 +98,11 @@ def trainerThread (s2c, c2s, e,
exec_prog = False
if prog_time > 0 and (cur_time - start_time) >= prog_time:
x[0] = 0
exec_prog = True
elif prog_time < 0 and (cur_time - last_time) >= -prog_time:
x[2] = cur_time
exec_prog = True
elif prog_time < 0 and (cur_time - last_time) >= -prog_time:
x[2] = cur_time
exec_prog = True
if exec_prog:
try:
exec(prog)
@@ -110,12 +110,12 @@ def trainerThread (s2c, c2s, e,
print("Unable to execute program: %s" % (prog) )
if not is_reached_goal:
if model.get_iter() == 0:
io.log_info("")
io.log_info("Trying to do the first iteration. If an error occurs, reduce the model parameters.")
io.log_info("")
iter, iter_time = model.train_one_iter()
loss_history = model.get_loss_history()
@@ -127,8 +127,8 @@ def trainerThread (s2c, c2s, e,
if shared_state['after_save']:
shared_state['after_save'] = False
last_save_time = time.time()
last_save_time = time.time()
mean_loss = np.mean ( [ np.array(loss_history[i]) for i in range(save_iter, iter) ], axis=0)
for loss_value in mean_loss:
@@ -145,10 +145,10 @@ def trainerThread (s2c, c2s, e,
io.log_info ('\r' + loss_string, end='')
else:
io.log_info (loss_string, end='\r')
if model.get_iter() == 1:
model_save()
if model.get_target_iter() != 0 and model.is_reached_iter_goal():
io.log_info ('Reached target iteration.')
model_save()