def main(): # Parse arguments args = parser.add_cmdline_args(classifier.init_classifier_compression_arg_parser(True)).parse_args() app = ClassifierCompressorSampleApp(args, script_dir=os.path.dirname(__file__)) if app.handle_subapps(): return init_knowledge_distillation(app.args, app.model, app.compression_scheduler) app.run_training_loop() # Finally run results on the test set return app.test()
def main(individual): # Parse arguments #print(individual) args = parser.add_cmdline_args( classifier.init_classifier_compression_arg_parser(True)).parse_args() args.compress = 'FB_gene1.yaml' idx = 0 num = 0 #initial 5% yaml_list = [] for i in range(int(len(individual) / 7)): #各ビット数/7 num = 0 for j in range(7): #長さ if (individual[idx] == 1): num += pow(2, j) idx += 1 if num >= 100: num = 90 elif i == 59 and num <= 30: num = 31 elif num <= 5: num = 6 yaml_list.append(num / 100) print("yaml_list") print(yaml_list) write_yaml(yaml_list) app = ClassifierCompressorSampleApp(args, script_dir=os.path.dirname(__file__)) if app.handle_subapps(): return init_knowledge_distillation(app.args, app.model, app.compression_scheduler) app.run_training_loop() # Finally run results on the test set # return top1, top5, losssesが来る loaded_array = np.load( '/home/oza/pre-experiment/speeding/distiller/distiller/apputils/simple_gene.npz' ) accuracy = loaded_array['array_1'] sparce = loaded_array['array_2'] print("accuracy: " + str(accuracy)) print("sparce: " + str(sparce)) accuracy /= 100 sparce /= 100 #return app.test() score = accuracy * sparce print("score: " + str(score)) global max if (score > max): max = score print("max score: " + str(score)) print("max individual: " + str(yaml_list)) return accuracy * sparce
def main(): # Parse arguments args = parser.add_cmdline_args( classifier.init_classifier_compression_arg_parser(True)).parse_args() performance_tracker = apputils.SparsityAccuracyTracker( args.num_best_scores) #args.compress = 'OZA' #print(args.compress) #quit() app = ClassifierCompressorSampleApp(args, script_dir=os.path.dirname(__file__)) if app.handle_subapps(): return init_knowledge_distillation(app.args, app.model, app.compression_scheduler) app.run_training_loop() # Finally run results on the test set # return top1, top5, losssesが来る return app.test()