mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-22 14:24:40 -07:00
better flask previews
This commit is contained in:
jh
parent
4c3c7b6033
commit
637bf69eb7
1 changed files with 255 additions and 203 deletions
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@ -3,8 +3,9 @@ import traceback
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import queue
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import threading
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import time
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from io import BytesIO
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import base64
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from enum import Enum
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from os.path import getmtime
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import numpy as np
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import itertools
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from pathlib import Path
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@ -14,20 +15,20 @@ import cv2
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import models
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from interact import interact as io
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from flask import Flask, send_file, Response, render_template, render_template_string, request, g
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from flask_caching import Cache
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# from flask_socketio import SocketIO
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def trainerThread(s2c, c2s, e, args, device_args):
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def trainerThread (s2c, c2s, e, args, device_args):
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while True:
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try:
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start_time = time.time()
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training_data_src_path = Path(args.get('training_data_src_dir', ''))
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training_data_dst_path = Path(args.get('training_data_dst_dir', ''))
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training_data_src_path = Path( args.get('training_data_src_dir', '') )
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training_data_dst_path = Path( args.get('training_data_dst_dir', '') )
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pretraining_data_path = args.get('pretraining_data_dir', '')
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pretraining_data_path = Path(pretraining_data_path) if pretraining_data_path is not None else None
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model_path = Path(args.get('model_path', ''))
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model_path = Path( args.get('model_path', '') )
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model_name = args.get('model_name', '')
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save_interval_min = 15
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debug = args.get('debug', '')
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@ -54,25 +55,24 @@ def trainerThread(s2c, c2s, e, args, device_args):
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is_reached_goal = model.is_reached_iter_goal()
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shared_state = {'after_save': False}
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shared_state = { 'after_save' : False }
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loss_string = ""
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save_iter = model.get_iter()
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save_iter = model.get_iter()
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def model_save():
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if not debug and not is_reached_goal:
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io.log_info("Saving....", end='\r')
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io.log_info ("Saving....", end='\r')
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model.save()
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shared_state['after_save'] = True
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def send_preview():
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if not debug:
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previews = model.get_previews()
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c2s.put({'op': 'show', 'previews': previews, 'iter': model.get_iter(),
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'loss_history': model.get_loss_history().copy()})
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c2s.put ( {'op':'show', 'previews': previews, 'iter':model.get_iter(), 'loss_history': model.get_loss_history().copy() } )
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else:
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previews = [('debug, press update for new', model.debug_one_iter())]
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c2s.put({'op': 'show', 'previews': previews})
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e.set() # Set the GUI Thread as Ready
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previews = [( 'debug, press update for new', model.debug_one_iter())]
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c2s.put ( {'op':'show', 'previews': previews} )
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e.set() #Set the GUI Thread as Ready
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if model.is_first_run():
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model_save()
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@ -81,16 +81,15 @@ def trainerThread(s2c, c2s, e, args, device_args):
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if is_reached_goal:
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io.log_info('Model already trained to target iteration. You can use preview.')
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else:
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io.log_info('Starting. Target iteration: %d. Press "Enter" to stop training and save model.' % (
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model.get_target_iter()))
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io.log_info('Starting. Target iteration: %d. Press "Enter" to stop training and save model.' % ( model.get_target_iter() ) )
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else:
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io.log_info('Starting. Press "Enter" to stop training and save model.')
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last_save_time = time.time()
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execute_programs = [[x[0], x[1], time.time()] for x in execute_programs]
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execute_programs = [ [x[0], x[1], time.time() ] for x in execute_programs ]
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for i in itertools.count(0, 1):
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for i in itertools.count(0,1):
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if not debug:
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cur_time = time.time()
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@ -100,7 +99,7 @@ def trainerThread(s2c, c2s, e, args, device_args):
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if prog_time > 0 and (cur_time - start_time) >= prog_time:
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x[0] = 0
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exec_prog = True
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elif prog_time < 0 and (cur_time - last_time) >= -prog_time:
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elif prog_time < 0 and (cur_time - last_time) >= -prog_time:
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x[2] = cur_time
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exec_prog = True
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@ -108,7 +107,7 @@ def trainerThread(s2c, c2s, e, args, device_args):
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try:
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exec(prog)
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except Exception as e:
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print("Unable to execute program: %s" % (prog))
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print("Unable to execute program: %s" % (prog) )
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if not is_reached_goal:
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iter, iter_time, batch_size = model.train_one_iter()
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@ -116,23 +115,20 @@ def trainerThread(s2c, c2s, e, args, device_args):
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loss_history = model.get_loss_history()
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time_str = time.strftime("[%H:%M:%S]")
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if iter_time >= 10:
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loss_string = "{0}[#{1:06d}][{2:.5s}s][bs: {3}]".format(time_str, iter,
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'{:0.4f}'.format(iter_time),
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batch_size)
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loss_string = "{0}[#{1:06d}][{2:.5s}s][bs: {3}]".format ( time_str, iter, '{:0.4f}'.format(iter_time), batch_size )
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else:
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loss_string = "{0}[#{1:06d}][{2:04d}ms][bs: {3}]".format(time_str, iter,
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int(iter_time * 1000), batch_size)
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loss_string = "{0}[#{1:06d}][{2:04d}ms][bs: {3}]".format ( time_str, iter, int(iter_time*1000), batch_size)
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if shared_state['after_save']:
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shared_state['after_save'] = False
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last_save_time = time.time() # upd last_save_time only after save+one_iter, because plaidML rebuilds programs after save https://github.com/plaidml/plaidml/issues/274
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last_save_time = time.time() #upd last_save_time only after save+one_iter, because plaidML rebuilds programs after save https://github.com/plaidml/plaidml/issues/274
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mean_loss = np.mean([np.array(loss_history[i]) for i in range(save_iter, iter)], axis=0)
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mean_loss = np.mean ( [ np.array(loss_history[i]) for i in range(save_iter, iter) ], axis=0)
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for loss_value in mean_loss:
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loss_string += "[%.4f]" % (loss_value)
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io.log_info(loss_string)
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io.log_info (loss_string)
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save_iter = iter
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else:
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@ -140,21 +136,21 @@ def trainerThread(s2c, c2s, e, args, device_args):
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loss_string += "[%.4f]" % (loss_value)
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if io.is_colab():
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io.log_info('\r' + loss_string, end='')
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io.log_info ('\r' + loss_string, end='')
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else:
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io.log_info(loss_string, end='\r')
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io.log_info (loss_string, end='\r')
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if model.get_target_iter() != 0 and model.is_reached_iter_goal():
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io.log_info('Reached target iteration.')
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io.log_info ('Reached target iteration.')
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model_save()
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is_reached_goal = True
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io.log_info('You can use preview now.')
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io.log_info ('You can use preview now.')
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if not is_reached_goal and (time.time() - last_save_time) >= save_interval_min * 60:
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if not is_reached_goal and (time.time() - last_save_time) >= save_interval_min*60:
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model_save()
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send_preview()
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if i == 0:
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if i==0:
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if is_reached_goal:
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model.pass_one_iter()
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send_preview()
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@ -179,169 +175,127 @@ def trainerThread(s2c, c2s, e, args, device_args):
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if i == -1:
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break
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model.finalize()
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except Exception as e:
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print('Error: %s' % (str(e)))
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print ('Error: %s' % (str(e)))
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traceback.print_exc()
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break
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c2s.put({'op': 'close'})
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c2s.put ( {'op':'close'} )
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class Preview:
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def __init__(self, c2s, s2c, preview_queue):
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self.c2s = c2s
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self.s2c = s2c
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self.preview_queue = preview_queue
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# self.wnd_name = "Training preview"
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# io.named_window(wnd_name)
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# io.capture_keys(wnd_name)
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class Zoom(Enum):
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ZOOM_25 = (1/4, '25%')
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ZOOM_33 = (1/3, '33%')
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ZOOM_50 = (1/2, '50%')
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ZOOM_67 = (2/3, '67%')
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ZOOM_75 = (3/4, '75%')
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ZOOM_80 = (4/5, '80%')
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ZOOM_90 = (9/10, '90%')
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ZOOM_100 = (1, '100%')
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ZOOM_110 = (11/10, '110%')
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ZOOM_125 = (5/4, '125%')
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ZOOM_150 = (3/2, '150%')
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ZOOM_175 = (7/4, '175%')
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ZOOM_200 = (2, '200%')
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ZOOM_250 = (5/2, '250%')
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ZOOM_300 = (3, '300%')
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ZOOM_400 = (4, '400%')
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ZOOM_500 = (5, '500%')
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self.previews = None
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self.loss_history = None
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self.selected_preview = 0
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self.update_preview = False
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self.is_showing = False
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self.is_waiting_preview = False
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self.show_last_history_iters_count = 0
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self.iter = 0
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self.batch_size = 1
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self.preview_min_height = 512
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self.preview_max_height = 1024
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self.close = False
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def __init__(self, scale, label):
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self.scale = scale
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self.label = label
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def get_preview(self):
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while not self.close:
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self.process_queue_items()
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self.update_preview_frame()
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def prev(self):
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cls = self.__class__
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members = list(cls)
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index = members.index(self) - 1
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if index < 0:
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return self
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return members[index]
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def process_queue_items(self):
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if not self.c2s.empty():
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input = self.c2s.get()
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op = input['op']
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if op == 'show':
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self.is_waiting_preview = False
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self.loss_history = input['loss_history'] if 'loss_history' in input.keys() else None
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self.previews = input['previews'] if 'previews' in input.keys() else None
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self.iter = input['iter'] if 'iter' in input.keys() else 0
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if self.previews is not None:
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self.resize_previews()
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self.selected_preview = self.selected_preview % len(self.previews)
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self.update_preview = True
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elif op == 'close':
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self.close = True
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elif op == 'update':
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self.update()
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elif op == 'next_preview':
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self.next_preview()
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elif op == 'change_history_range':
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self.change_history_range()
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def update_preview_frame(self):
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if self.update_preview:
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self.update_preview = False
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selected_preview_name = self.previews[self.selected_preview][0]
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selected_preview_rgb = self.previews[self.selected_preview][1]
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(h, w, c) = selected_preview_rgb.shape
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# HEAD
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head_lines = [
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'[s]:save [enter]:exit',
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'[p]:update [space]:next preview [l]:change history range',
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'Preview: "%s" [%d/%d]' % (selected_preview_name, self.selected_preview + 1, len(self.previews))
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]
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head_line_height = 15
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head_height = len(head_lines) * head_line_height
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head = np.ones((head_height, w, c)) * 0.1
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for i in range(0, len(head_lines)):
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t = i * head_line_height
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b = (i + 1) * head_line_height
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head[t:b, 0:w] += imagelib.get_text_image((head_line_height, w, c), head_lines[i], color=[0.8] * c)
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final = head
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if self.loss_history is not None:
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if self.show_last_history_iters_count == 0:
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loss_history_to_show = self.loss_history
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else:
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loss_history_to_show = self.loss_history[-self.show_last_history_iters_count:]
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lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, self.iter, self.batch_size, w,
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c)
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final = np.concatenate([final, lh_img], axis=0)
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final = np.concatenate([final, selected_preview_rgb], axis=0)
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final = np.clip(final, 0, 1)
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preview_pane = (final * 255).astype(np.uint8)
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retval, buffer = cv2.imencode('.jpg', preview_pane)
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# jpg_as_text = base64.b64encode(buffer)
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jpg_as_text = buffer.tostring()
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self.preview_queue.put(jpg_as_text)
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def resize_previews(self):
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preview_height = max((h for h, w, c in (im.shape for name, im in self.previews)))
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if preview_height > self.preview_max_height:
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preview_height = self.preview_max_height
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elif preview_height < self.preview_min_height:
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preview_height = self.preview_min_height
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# make all previews size equal
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for p in self.previews[:]:
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(preview_name, preview_rgb) = p
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(h, w, c) = preview_rgb.shape
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if h != preview_height:
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scale_factor = preview_height / float(h)
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self.previews.remove(p)
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self.previews.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_AREA)))
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self.selected_preview = self.selected_preview % len(self.previews)
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def save(self):
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self.s2c.put({'op': 'save'})
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def exit(self):
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self.s2c.put({'op': 'close'})
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def update(self):
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if not self.is_waiting_preview:
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self.is_waiting_preview = True
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self.s2c.put({'op': 'preview'})
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def next_preview(self):
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self.selected_preview = (self.selected_preview + 1) % len(self.previews)
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self.update_preview = True
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def change_history_range(self):
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if self.show_last_history_iters_count == 0:
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self.show_last_history_iters_count = 5000
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elif self.show_last_history_iters_count == 5000:
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self.show_last_history_iters_count = 10000
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elif self.show_last_history_iters_count == 10000:
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self.show_last_history_iters_count = 50000
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elif self.show_last_history_iters_count == 50000:
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self.show_last_history_iters_count = 100000
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elif self.show_last_history_iters_count == 100000:
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self.show_last_history_iters_count = 0
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self.update_preview = True
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def next(self):
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cls = self.__class__
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members = list(cls)
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index = members.index(self) + 1
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if index >= len(members):
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return self
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return members[index]
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def flask_thread(s2c, c2s, preview_queue):
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config = {
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"DEBUG": True, # some Flask specific configs
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"CACHE_TYPE": "simple", # Flask-Caching related configs
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"CACHE_DEFAULT_TIMEOUT": 300
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}
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def scale_previews(previews, zoom=Zoom.ZOOM_100):
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scaled = []
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for preview in previews:
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preview_name, preview_rgb = preview
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scale_factor = zoom.scale
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if scale_factor < 1:
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scaled.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_AREA)))
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elif scale_factor > 1:
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scaled.append((preview_name, cv2.resize(preview_rgb, (0, 0),
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fx=scale_factor,
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fy=scale_factor,
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interpolation=cv2.INTER_LANCZOS4)))
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else:
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scaled.append((preview_name, preview_rgb))
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return scaled
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def create_preview_pane_image(previews, selected_preview, loss_history,
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show_last_history_iters_count, iteration, batch_size, zoom=Zoom.ZOOM_100):
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scaled_previews = scale_previews(previews, zoom)
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selected_preview_name = scaled_previews[selected_preview][0]
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selected_preview_rgb = scaled_previews[selected_preview][1]
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h, w, c = selected_preview_rgb.shape
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# HEAD
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head_lines = [
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'[s]:save [enter]:exit [-/+]:zoom: %s' % zoom.label,
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'[p]:update [space]:next preview [l]:change history range',
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'Preview: "%s" [%d/%d]' % (selected_preview_name,selected_preview+1, len(previews))
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]
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head_line_height = int(15 * zoom.scale)
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head_height = len(head_lines) * head_line_height
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head = np.ones((head_height, w, c)) * 0.1
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for i in range(0, len(head_lines)):
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t = i * head_line_height
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b = (i+1) * head_line_height
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head[t:b, 0:w] += imagelib.get_text_image((head_line_height, w, c), head_lines[i], color=[0.8]*c)
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final = head
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||||
|
||||
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_height = int(100 * zoom.scale)
|
||||
lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, iteration, batch_size, w, c, lh_height)
|
||||
final = np.concatenate ( [final, lh_img], axis=0 )
|
||||
|
||||
final = np.concatenate([final, selected_preview_rgb], axis=0)
|
||||
final = np.clip(final, 0, 1)
|
||||
return (final*255).astype(np.uint8)
|
||||
|
||||
|
||||
def flask_thread(s2c, c2s, s2flask, args):
|
||||
# config = {
|
||||
# "DEBUG": True, # some Flask specific configs
|
||||
# "CACHE_TYPE": "simple", # Flask-Caching related configs
|
||||
# "CACHE_DEFAULT_TIMEOUT": 300
|
||||
# }
|
||||
app = Flask(__name__)
|
||||
app.config.from_mapping(config)
|
||||
cache = Cache(app)
|
||||
# app.config.from_mapping(config)
|
||||
# cache = Cache(app)
|
||||
template = """<html>
|
||||
<head>
|
||||
<title>Video Streaming Demonstration</title>
|
||||
<title>Flask Server Demonstration</title>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Video Streaming Demonstration</h1>
|
||||
|
|
@ -357,8 +311,17 @@ def flask_thread(s2c, c2s, preview_queue):
|
|||
</html>"""
|
||||
|
||||
def gen():
|
||||
if not preview_queue.empty():
|
||||
frame = preview_queue.get()
|
||||
model_path = Path(args.get('model_path', ''))
|
||||
print('[MainThread]', 'model_path:', model_path)
|
||||
filename = 'preview.jpg'
|
||||
preview_file = str(model_path / filename)
|
||||
print('[MainThread]', 'preview_file:', preview_file)
|
||||
frame = open(preview_file, 'rb').read()
|
||||
while True:
|
||||
try:
|
||||
frame = open(preview_file, 'rb').read()
|
||||
except:
|
||||
pass
|
||||
yield b'--frame\r\nContent-Type: image/jpeg\r\n\r\n'
|
||||
yield frame
|
||||
yield b'\r\n\r\n'
|
||||
|
|
@ -371,46 +334,135 @@ def flask_thread(s2c, c2s, preview_queue):
|
|||
elif 'exit' in request.form:
|
||||
s2c.put({'op': 'close'})
|
||||
elif 'update' in request.form:
|
||||
while not s2flask.empty():
|
||||
input = s2flask.get()
|
||||
c2s.put({'op': 'update'})
|
||||
while s2flask.empty():
|
||||
pass
|
||||
input = s2flask.get()
|
||||
elif 'next_preview' in request.form:
|
||||
c2s.put({'op': 'preview'})
|
||||
while not s2flask.empty():
|
||||
input = s2flask.get()
|
||||
c2s.put({'op': 'next_preview'})
|
||||
while s2flask.empty():
|
||||
pass
|
||||
input = s2flask.get()
|
||||
elif 'change_history_range' in request.form:
|
||||
while not s2flask.empty():
|
||||
input = s2flask.get()
|
||||
c2s.put({'op': 'change_history_range'})
|
||||
while s2flask.empty():
|
||||
pass
|
||||
input = s2flask.get()
|
||||
# return '', 204
|
||||
return render_template_string(template)
|
||||
|
||||
def queue_not_empty():
|
||||
return not preview_queue.empty()
|
||||
|
||||
# @app.route('/preview_image')
|
||||
# @cache.cached(timeout=300, unless=queue_not_empty)
|
||||
# def preview_image():
|
||||
# yield Response(preview_queue.get(),
|
||||
# mimetype='multipart/x-mixed-replace;boundary=frame')
|
||||
# return Response(gen(), mimetype='multipart/x-mixed-replace;boundary=frame')
|
||||
|
||||
@app.route('/preview_image')
|
||||
@cache.cached(timeout=300, unless=queue_not_empty)
|
||||
def preview_image():
|
||||
return Response(preview_queue.get(), mimetype='image/jpeg')
|
||||
model_path = Path(args.get('model_path', ''))
|
||||
filename = 'preview.jpg'
|
||||
preview_file = str(model_path / filename)
|
||||
return send_file(preview_file, mimetype='image/jpeg', cache_timeout=-1)
|
||||
|
||||
app.run(debug=True, use_reloader=False)
|
||||
app.run(debug=False, use_reloader=False)
|
||||
|
||||
|
||||
def main(args, device_args):
|
||||
io.log_info("Running trainer.\r\n")
|
||||
io.log_info ("Running trainer.\r\n")
|
||||
|
||||
no_preview = args.get('no_preview', False)
|
||||
|
||||
|
||||
s2c = queue.Queue()
|
||||
c2s = queue.Queue()
|
||||
preview_queue = queue.Queue()
|
||||
s2flask = queue.Queue()
|
||||
|
||||
e = threading.Event()
|
||||
thread = threading.Thread(target=trainerThread, args=(s2c, c2s, e, args, device_args))
|
||||
thread = threading.Thread(target=trainerThread, args=(s2c, c2s, e, args, device_args) )
|
||||
thread.start()
|
||||
|
||||
e.wait() # Wait for inital load to occur.
|
||||
e.wait() #Wait for inital load to occur.
|
||||
|
||||
flask_t = threading.Thread(target=flask_thread, args=(s2c, c2s, preview_queue))
|
||||
flask_t = threading.Thread(target=flask_thread, args=(s2c, c2s, s2flask, args))
|
||||
flask_t.start()
|
||||
|
||||
preview = Preview(c2s, s2c, preview_queue)
|
||||
preview.get_preview()
|
||||
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
|
||||
iteration = 0
|
||||
batch_size = 1
|
||||
zoom = Zoom.ZOOM_100
|
||||
|
||||
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
|
||||
iteration = input['iter'] if 'iter' in input.keys() else 0
|
||||
#batch_size = input['batch_size'] if 'iter' in input.keys() else 1
|
||||
if previews is not None:
|
||||
update_preview = True
|
||||
elif op == 'update':
|
||||
if not is_waiting_preview:
|
||||
is_waiting_preview = True
|
||||
s2c.put({'op': 'preview'})
|
||||
elif op == 'next_preview':
|
||||
selected_preview = (selected_preview + 1) % len(previews)
|
||||
update_preview = True
|
||||
elif op == 'change_history_range':
|
||||
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
|
||||
|
||||
if update_preview:
|
||||
update_preview = False
|
||||
selected_preview = selected_preview % len(previews)
|
||||
preview_pane_image = create_preview_pane_image(previews,
|
||||
selected_preview,
|
||||
loss_history,
|
||||
show_last_history_iters_count,
|
||||
iteration,
|
||||
batch_size,
|
||||
zoom)
|
||||
# io.show_image(wnd_name, preview_pane_image)
|
||||
model_path = Path(args.get('model_path', ''))
|
||||
filename = 'preview.jpg'
|
||||
preview_file = str(model_path / filename)
|
||||
cv2.imwrite(preview_file, preview_pane_image)
|
||||
s2flask.put({'op': 'show'})
|
||||
# socketio.emit('some event', {'data': 42})
|
||||
|
||||
# cv2.imshow(wnd_name, preview_pane_image)
|
||||
is_showing = True
|
||||
try:
|
||||
io.process_messages(0.01)
|
||||
except KeyboardInterrupt:
|
||||
s2c.put({'op': 'close'})
|
||||
|
||||
io.destroy_all_windows()
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue