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import os
import sys
import traceback
import queue
import threading
import time
import numpy as np
import itertools
from pathlib import Path
from core import pathex
from core import imagelib
import cv2
import models
from core.interact import interact as io
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,
force_model_name=None,
force_gpu_idxs=None,
cpu_only=None,
silent_start=False,
execute_programs = None,
debug=False,
**kwargs):
while True:
try:
start_time = time.time()
save_interval_min = 25
if not training_data_src_path.exists():
training_data_src_path.mkdir(exist_ok=True, parents=True)
if not training_data_dst_path.exists():
training_data_dst_path.mkdir(exist_ok=True, parents=True)
if not saved_models_path.exists():
saved_models_path.mkdir(exist_ok=True, parents=True)
model = models.import_model(model_class_name)(
is_training=True,
saved_models_path=saved_models_path,
training_data_src_path=training_data_src_path,
training_data_dst_path=training_data_dst_path,
pretraining_data_path=pretraining_data_path,
pretrained_model_path=pretrained_model_path,
no_preview=no_preview,
force_model_name=force_model_name,
force_gpu_idxs=force_gpu_idxs,
cpu_only=cpu_only,
silent_start=silent_start,
debug=debug)
is_reached_goal = model.is_reached_iter_goal()
shared_state = { 'after_save' : False }
loss_string = ""
save_iter = model.get_iter()
def model_save():
if not debug and not is_reached_goal:
io.log_info ("Saving....", end='\r')
model.save()
shared_state['after_save'] = True
def model_backup():
if not debug and not is_reached_goal:
model.create_backup()
def send_preview():
if not debug:
previews = model.get_previews()
c2s.put ( {'op':'show', 'previews': previews, 'iter':model.get_iter(), 'loss_history': model.get_loss_history().copy() } )
else:
previews = [( 'debug, press update for new', model.debug_one_iter())]
c2s.put ( {'op':'show', 'previews': previews} )
e.set() #Set the GUI Thread as Ready
if model.get_target_iter() != 0:
if is_reached_goal:
io.log_info('Model already trained to target iteration. You can use preview.')
else:
io.log_info('Starting. Target iteration: %d. Press "Enter" to stop training and save model.' % ( model.get_target_iter() ) )
else:
io.log_info('Starting. Press "Enter" to stop training and save model.')
last_save_time = time.time()
execute_programs = [ [x[0], x[1], time.time() ] for x in execute_programs ]
for i in itertools.count(0,1):
if not debug:
cur_time = time.time()
for x in execute_programs:
prog_time, prog, last_time = x
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
if exec_prog:
try:
exec(prog)
except Exception as 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("")
if sys.platform[0:3] == 'win':
io.log_info("!!!")
io.log_info("Windows 10 users IMPORTANT notice. You should set this setting in order to work correctly.")
io.log_info("https://i.imgur.com/B7cmDCB.jpg")
io.log_info("!!!")
iter, iter_time = model.train_one_iter()
loss_history = model.get_loss_history()
time_str = time.strftime("[%H:%M:%S]")
if iter_time >= 10:
loss_string = "{0}[#{1:06d}][{2:.5s}s]".format ( time_str, iter, '{:0.4f}'.format(iter_time) )
else:
loss_string = "{0}[#{1:06d}][{2:04d}ms]".format ( time_str, iter, int(iter_time*1000) )
if shared_state['after_save']:
shared_state['after_save'] = False
mean_loss = np.mean ( loss_history[save_iter:iter], axis=0)
for loss_value in mean_loss:
loss_string += "[%.4f]" % (loss_value)
io.log_info (loss_string)
save_iter = iter
else:
for loss_value in loss_history[-1]:
loss_string += "[%.4f]" % (loss_value)
if io.is_colab():
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()
is_reached_goal = True
io.log_info ('You can use preview now.')
need_save = False
while time.time() - last_save_time >= save_interval_min*60:
last_save_time += save_interval_min*60
need_save = True
if not is_reached_goal and need_save:
model_save()
send_preview()
if i==0:
if is_reached_goal:
model.pass_one_iter()
send_preview()
if debug:
time.sleep(0.005)
while not s2c.empty():
input = s2c.get()
op = input['op']
if op == 'save':
model_save()
elif op == 'backup':
model_backup()
elif op == 'preview':
if is_reached_goal:
model.pass_one_iter()
send_preview()
elif op == 'close':
model_save()
i = -1
break
if i == -1:
break
model.finalize()
except Exception as e:
print ('Error: %s' % (str(e)))
traceback.print_exc()
break
c2s.put ( {'op':'close'} )
def main(**kwargs):
io.log_info ("Running trainer.\r\n")
no_preview = kwargs.get('no_preview', False)
s2c = queue.Queue()
c2s = queue.Queue()
e = threading.Event()
thread = threading.Thread(target=trainerThread, args=(s2c, c2s, e), kwargs=kwargs )
thread.start()
e.wait() #Wait for inital load to occur.
if no_preview:
while True:
if not c2s.empty():
input = c2s.get()
op = input.get('op','')
if op == 'close':
break
try:
io.process_messages(0.1)
except KeyboardInterrupt:
s2c.put ( {'op': 'close'} )
else:
wnd_name = "Training preview"
io.named_window(wnd_name)
io.capture_keys(wnd_name)
previews = None
loss_history = None
selected_preview = 0
update_preview = False
is_showing = False
is_waiting_preview = False
show_last_history_iters_count = 0
iter = 0
while True:
if not c2s.empty():
input = c2s.get()
op = input['op']
if op == 'show':
is_waiting_preview = False
loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
previews = input['previews'] if 'previews' in input.keys() else None
iter = input['iter'] if 'iter' in input.keys() else 0
if previews is not None:
max_w = 0
max_h = 0
for (preview_name, preview_rgb) in previews:
(h, w, c) = preview_rgb.shape
max_h = max (max_h, h)
max_w = max (max_w, w)
max_size = 800
if max_h > max_size:
max_w = int( max_w / (max_h / max_size) )
max_h = max_size
#make all previews size equal
for preview in previews[:]:
(preview_name, preview_rgb) = preview
(h, w, c) = preview_rgb.shape
if h != max_h or w != max_w:
previews.remove(preview)
previews.append ( (preview_name, cv2.resize(preview_rgb, (max_w, max_h))) )
selected_preview = selected_preview % len(previews)
update_preview = True
elif op == 'close':
break
if update_preview:
update_preview = False
selected_preview_name = previews[selected_preview][0]
selected_preview_rgb = previews[selected_preview][1]
(h,w,c) = selected_preview_rgb.shape
# HEAD
head_lines = [
'[s]:save [b]:backup [enter]:exit',
'[p]:update [space]:next preview [l]:change history range',
'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews) )
]
head_line_height = 15
head_height = len(head_lines) * head_line_height
head = np.ones ( (head_height,w,c) ) * 0.1
for i in range(0, len(head_lines)):
t = i*head_line_height
b = (i+1)*head_line_height
head[t:b, 0:w] += imagelib.get_text_image ( (head_line_height,w,c) , head_lines[i], color=[0.8]*c )
final = head
if loss_history is not None:
if show_last_history_iters_count == 0:
loss_history_to_show = loss_history
else:
loss_history_to_show = loss_history[-show_last_history_iters_count:]
lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iter, w, c)
final = np.concatenate ( [final, lh_img], axis=0 )
final = np.concatenate ( [final, selected_preview_rgb], axis=0 )
final = np.clip(final, 0, 1)
io.show_image( wnd_name, (final*255).astype(np.uint8) )
is_showing = True
key_events = io.get_key_events(wnd_name)
key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
if key == ord('\n') or key == ord('\r'):
s2c.put ( {'op': 'close'} )
elif key == ord('s'):
s2c.put ( {'op': 'save'} )
elif key == ord('b'):
s2c.put ( {'op': 'backup'} )
elif key == ord('p'):
if not is_waiting_preview:
is_waiting_preview = True
s2c.put ( {'op': 'preview'} )
elif key == ord('l'):
if show_last_history_iters_count == 0:
show_last_history_iters_count = 5000
elif show_last_history_iters_count == 5000:
show_last_history_iters_count = 10000
elif show_last_history_iters_count == 10000:
show_last_history_iters_count = 50000
elif show_last_history_iters_count == 50000:
show_last_history_iters_count = 100000
elif show_last_history_iters_count == 100000:
show_last_history_iters_count = 0
update_preview = True
elif key == ord(' '):
selected_preview = (selected_preview + 1) % len(previews)
update_preview = True
try:
io.process_messages(0.1)
except KeyboardInterrupt:
s2c.put ( {'op': 'close'} )
io.destroy_all_windows() |