import sys import traceback import queue import colorsys import time import numpy as np import itertools from pathlib import Path from utils import Path_utils from utils import image_utils import cv2 import models def trainerThread (input_queue, output_queue, training_data_src_dir, training_data_dst_dir, model_path, model_name, save_interval_min=10, debug=False, **in_options): while True: try: training_data_src_path = Path(training_data_src_dir) training_data_dst_path = Path(training_data_dst_dir) model_path = Path(model_path) if not training_data_src_path.exists(): print( 'Training data src directory does not exist.') return if not training_data_dst_path.exists(): print( 'Training data dst directory does not exist.') return if not model_path.exists(): model_path.mkdir(exist_ok=True) model = models.import_model(model_name)( model_path, training_data_src_path=training_data_src_path, training_data_dst_path=training_data_dst_path, debug=debug, **in_options) is_reached_goal = model.is_reached_epoch_goal() def model_save(): if not debug and not is_reached_goal: model.save() def send_preview(): if not debug: previews = model.get_previews() output_queue.put ( {'op':'show', 'previews': previews, 'epoch':model.get_epoch(), 'loss_history': model.get_loss_history().copy() } ) else: previews = [( 'debug, press update for new', model.debug_one_epoch())] output_queue.put ( {'op':'show', 'previews': previews} ) if model.is_first_run(): model_save() if model.get_target_epoch() != 0: if is_reached_goal: print ('Model already trained to target epoch. You can use preview.') else: print('Starting. Target epoch: %d. Press "Enter" to stop training and save model.' % ( model.get_target_epoch() ) ) else: print('Starting. Press "Enter" to stop training and save model.') last_save_time = time.time() for i in itertools.count(0,1): if not debug: if not is_reached_goal: loss_string = model.train_one_epoch() print (loss_string, end='\r') if model.get_target_epoch() != 0 and model.is_reached_epoch_goal(): print ('Reached target epoch.') model_save() is_reached_goal = True print ('You can use preview now.') if not is_reached_goal and (time.time() - last_save_time) >= save_interval_min*60: last_save_time = time.time() model_save() send_preview() if i==0: if is_reached_goal: model.pass_one_epoch() send_preview() if debug: time.sleep(0.005) while not input_queue.empty(): input = input_queue.get() op = input['op'] if op == 'save': model_save() elif op == 'preview': if is_reached_goal: model.pass_one_epoch() 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 output_queue.put ( {'op':'close'} ) def previewThread (input_queue, output_queue): previews = None loss_history = None selected_preview = 0 update_preview = False is_showing = False is_waiting_preview = False show_last_history_epochs_count = 0 epoch = 0 while True: if not input_queue.empty(): input = input_queue.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 epoch = input['epoch'] if 'epoch' 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 [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] += image_utils.get_text_image ( (w,head_line_height,c) , head_lines[i], color=[0.8]*c ) final = head if loss_history is not None: if show_last_history_epochs_count == 0: loss_history_to_show = loss_history else: loss_history_to_show = loss_history[-show_last_history_epochs_count:] lh_img = models.ModelBase.get_loss_history_preview(loss_history_to_show, epoch, 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) cv2.imshow ( 'Training preview', (final*255).astype(np.uint8) ) is_showing = True if is_showing: key = cv2.waitKey(100) else: time.sleep(0.1) key = 0 if key == ord('\n') or key == ord('\r'): output_queue.put ( {'op': 'close'} ) elif key == ord('s'): output_queue.put ( {'op': 'save'} ) elif key == ord('p'): if not is_waiting_preview: is_waiting_preview = True output_queue.put ( {'op': 'preview'} ) elif key == ord('l'): if show_last_history_epochs_count == 0: show_last_history_epochs_count = 5000 elif show_last_history_epochs_count == 5000: show_last_history_epochs_count = 10000 elif show_last_history_epochs_count == 10000: show_last_history_epochs_count = 50000 elif show_last_history_epochs_count == 50000: show_last_history_epochs_count = 100000 elif show_last_history_epochs_count == 100000: show_last_history_epochs_count = 0 update_preview = True elif key == ord(' '): selected_preview = (selected_preview + 1) % len(previews) update_preview = True cv2.destroyAllWindows() def main (training_data_src_dir, training_data_dst_dir, model_path, model_name, **in_options): print ("Running trainer.\r\n") output_queue = queue.Queue() input_queue = queue.Queue() import threading thread = threading.Thread(target=trainerThread, args=(output_queue, input_queue, training_data_src_dir, training_data_dst_dir, model_path, model_name), kwargs=in_options ) thread.start() previewThread (input_queue, output_queue)