def test_tricks(test_trials): dataset = 'shopee-iet' data_dir = './' download_shopee(data_dir, dataset) for i in range(test_trials): print('Test:{}\n'.format(i)) target = config_choice(dataset, data_dir, tricks_combination[i]) classifier = task.fit(dataset=task.Dataset(target['dataset']), net=target['net'], optimizer=target['optimizer'], epochs=target['epochs'], ngpus_per_trial=target['ngpus_per_trial'], num_trials=target['num_trials'], batch_size=target['batch_size'], verbose=True, search_strategy='random', tricks=target['tricks'], lr_config=target['lr_config'], plot_results=True) test_dataset = task.Dataset(target['dataset'].replace( 'train', 'test/BabyPants'), train=False, scale_ratio_choice=[0.7, 0.8, 0.875]) inds, probs, probs_all = classifier.predict(test_dataset, set_prob_thresh=0.001) print(inds[0], probs[0], probs_all[0]) print('Top-1 val acc: %.3f' % classifier.results['best_reward'])
def task_dog_breed_identification(data_path, dataset): images_path = os.path.join(data_path, dataset, 'images_all') label_path = os.path.join(data_path, dataset, 'labels.csv') test_path = os.path.join(data_path, dataset, 'test') load_dataset = task.Dataset(images_path, label_file=label_path) @ag.obj(learning_rate=ag.space.Real(0.3, 0.5), momentum=ag.space.Real(0.90, 0.95), wd=ag.space.Real(1e-6, 1e-4, log=True), multi_precision=False) class NAG(optim.NAG): pass classifier = task.fit(dataset=load_dataset, net=ag.Categorical('standford_dog_resnext101_64x4d', 'standford_dog_resnet152_v1'), optimizer=NAG(), epochs=20, final_fit_epochs=180, num_trials=40, ngpus_per_trial=8, batch_size=48, verbose=False, ensemble=1) test_dataset = task.Dataset(test_path, train=False, crop_ratio=0.65) inds, probs, probs_all = classifier.predict(test_dataset, set_prob_thresh=0.001) ag.utils.generate_prob_csv(test_dataset, probs_all, custom='./submission.csv')
def test_classifier_save_load(): dataset = task.Dataset(name='FashionMNIST') test_dataset = task.Dataset(name='FashionMNIST', train=False) classifier = task.fit(dataset, epochs=1, ngpus_per_trial=1, verbose=False) classifier.save('classifier.ag') classifier2 = task.Classifier.load('classifier.ag') test_acc = classifier2.evaluate(test_dataset)
def test_ensemble(): dataset = task.Dataset(name='FashionMNIST') test_dataset = task.Dataset(name='FashionMNIST', train=False) classifier = task.fit(dataset, epochs=10, ngpus_per_trial=1, verbose=False, ensemble=2) test_acc = classifier.evaluate(test_dataset)
def test_tricks(test_trials): dataset = 'shopee-iet' data_dir = './' download_shopee(data_dir, dataset) for i in range(test_trials): print('Test:{}\n'.format(i)) target = config_choice(dataset, data_dir, tricks_combination[i]) classifier = task.fit(dataset=task.Dataset(target['dataset']), net=target['net'], optimizer=target['optimizer'], epochs=target['epochs'], ngpus_per_trial=target['ngpus_per_trial'], num_trials=target['num_trials'], batch_size=target['batch_size'], verbose=True, search_strategy='skopt', tricks=target['tricks'], lr_config=target['lr_config'], plot_results=True) # test_dataset = task.Dataset(target['dataset'].replace('images', 'test')) # inds, probs, probals_all= classifier.predict(test_dataset) # print(inds[0], probs[0], probals_all[0]) print('Top-1 val acc: %.3f' % classifier.results['best_reward'])
def predict_details(data_path, classifier, load_dataset): test_dataset = os.path.join(data_path, 'test') inds, probs, probs_all = classifier.predict(task.Dataset(test_dataset)) value = [] target_dataset = load_dataset.init() for i in inds: value.append(target_dataset.classes[i]) return inds, probs, probs_all, value
def main(): opt = parse_args() if not os.path.exists(opt.dataset): os.mkdir(opt.dataset) dataset_path = os.path.join(opt.data_dir, opt.dataset) local_path = os.path.dirname(__file__) output_directory = os.path.join(opt.dataset, 'checkpoint/') filehandler = logging.FileHandler(os.path.join(opt.dataset, 'summary.log')) streamhandler = logging.StreamHandler() logger = logging.getLogger('') logger.setLevel(logging.INFO) logger.addHandler(filehandler) logger.addHandler(streamhandler) logging.info(opt) target = config_choice(opt.data_dir, opt.dataset) load_dataset = task.Dataset(target['dataset']) classifier = task.fit(dataset=load_dataset, output_directory=output_directory, net=target['net'], optimizer=target['optimizer'], tricks=target['tricks'], lr_config=target['lr_config'], resume=opt.resume, epochs=opt.num_epochs, ngpus_per_trial=opt.ngpus_per_trial, num_trials=opt.num_trials, batch_size=opt.batch_size, verbose=True, plot_results=True) summary = classifier.fit_summary(output_directory=opt.dataset, verbosity=4) logging.info('Top-1 val acc: %.3f' % classifier.results['best_reward']) logger.info(summary) if opt.submission: test_dataset = task.Dataset(os.path.join(opt.data_dir, opt.dataset, 'test'), train=False) inds, probs, probs_all, value = predict_details( test_dataset, classifier, load_dataset) ag.utils.generate_csv_submission(dataset_path, opt.dataset, local_path, inds, probs_all, value, opt.custom)
def train(args): print("args {}".format(args)) is_distributed = len(args.hosts) > 1 dist_ip_addrs = [] if is_distributed: host_rank = args.hosts.index(args.current_host) ag.sagemaker_setup() if host_rank > 0: print('Host rank {} exit.'.format(host_rank)) return dist_ip_addrs = args.hosts dist_ip_addrs.pop(host_rank) dataset = task.Dataset(os.path.join(args.data_dir, 'train')) ngpus_per_trial = 1 if args.num_gpus > 0 else 0 classifier = task.fit(dataset, epochs=args.epochs, num_trials=args.num_trials, ngpus_per_trial=ngpus_per_trial, verbose=True, dist_ip_addrs=dist_ip_addrs, output_directory=args.model_dir)
import autogluon as ag from autogluon import ImageClassification as task dataset = task.Dataset('train') test_dataset = task.Dataset('va', train=False) print(ag.get_gpu_count()) time_limits = 8 * 60 * 60 epochs = 150 output = 'checkpoint/' resume = True classifier = task.fit(dataset, search_strategy='skopt', search_options={ 'base_estimator': 'RF', 'acq_func': 'EI' }, time_limits=time_limits, epochs=epochs, ngpus_per_trial=1, num_trials=1, output_directory=output, verbose=True, resume=resume, plot_results=True) print('Top-1 val acc: %.3f' % classifier.results[classifier.results['reward_attr']]) classifier.save('checkpoint/model.pth')
# Import Libraries """ pip install --upgrade mxnet-cu100 pip install autogluon import autogluon as ag from autogluon import ImageClassification as task """# Create AutoGluon Dataset""" !unzip shop.zip dataset = task.Dataset('data/train') test_dataset = task.Dataset('data/test', train=False) if ag.get_gpu_count() == 0: dataset = task.Dataset(name='FashionMNIST') test_dataset = task.Dataset(name='FashionMNIST', train=False) """# Use AutoGluon to Fit Models""" classifier = task.fit(dataset, epochs=5, ngpus_per_trial=1, verbose=False) print('Top-1 val acc: %.3f' % classifier.results['best_reward'])
y_pred = np.rint(model.predict(X_test)) print(accuracy_score(y_test, y_pred)) # %% """ # Experiment with AutoGluon: AutoML Toolkit for Deep Learning """ # %% import autogluon as ag from autogluon import ImageClassification as task # %% dataset = task.Dataset('./data') # %% #time_limits = 10 * 60 # 10mins #classifier = task.fit(dataset, time_limits=time_limits, ngpus_per_trial=1) classifier = task.fit(dataset, epochs=10, ngpus_per_trial=1, verbose=True) # %% print('Top-1 val acc: %.3f' % classifier.results['best_reward']) # %%