def main(): device = set_device(FLAGS.device) fluid.enable_dygraph(device) if FLAGS.dynamic else None train_dataset = MnistDataset(mode='train') val_dataset = MnistDataset(mode='test') inputs = [Input([None, 784], 'float32', name='image')] labels = [Input([None, 1], 'int64', name='label')] model = MNIST() optim = Momentum( learning_rate=FLAGS.lr, momentum=.9, parameter_list=model.parameters()) model.prepare( optim, CrossEntropy(), Accuracy(topk=(1, 2)), inputs, labels, device=FLAGS.device) if FLAGS.resume is not None: model.load(FLAGS.resume) model.fit(train_dataset, val_dataset, epochs=FLAGS.epoch, batch_size=FLAGS.batch_size, save_dir='mnist_checkpoint')
def train(): fluid.enable_dygraph(device) processor = SentaProcessor(data_dir=args.data_dir, vocab_path=args.vocab_path, random_seed=args.random_seed) num_labels = len(processor.get_labels()) num_train_examples = processor.get_num_examples(phase="train") max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count train_data_generator = processor.data_generator( batch_size=args.batch_size, padding_size=args.padding_size, places=device, phase='train', epoch=args.epoch, shuffle=False) eval_data_generator = processor.data_generator( batch_size=args.batch_size, padding_size=args.padding_size, places=device, phase='dev', epoch=args.epoch, shuffle=False) if args.model_type == 'cnn_net': model = CNN(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bow_net': model = BOW(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'gru_net': model = GRU(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bigru_net': model = BiGRU(args.vocab_size, args.batch_size, args.padding_size) optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr, parameter_list=model.parameters()) inputs = [Input([None, None], 'int64', name='doc')] labels = [Input([None, 1], 'int64', name='label')] model.prepare(optimizer, CrossEntropy(), Accuracy(topk=(1, )), inputs, labels, device=device) model.fit(train_data=train_data_generator, eval_data=eval_data_generator, batch_size=args.batch_size, epochs=args.epoch, save_dir=args.checkpoints, eval_freq=args.eval_freq, save_freq=args.save_freq)
def main(): device = set_device(FLAGS.device) fluid.enable_dygraph(device) if FLAGS.dynamic else None model_list = [x for x in models.__dict__["__all__"]] assert FLAGS.arch in model_list, "Expected FLAGS.arch in {}, but received {}".format( model_list, FLAGS.arch) model = models.__dict__[FLAGS.arch]( pretrained=FLAGS.eval_only and not FLAGS.resume) if FLAGS.resume is not None: model.load(FLAGS.resume) inputs = [Input([None, 3, 224, 224], 'float32', name='image')] labels = [Input([None, 1], 'int64', name='label')] train_dataset = ImageNetDataset(os.path.join(FLAGS.data, 'train'), mode='train', image_size=FLAGS.image_size, resize_short_size=FLAGS.resize_short_size) val_dataset = ImageNetDataset(os.path.join(FLAGS.data, 'val'), mode='val', image_size=FLAGS.image_size, resize_short_size=FLAGS.resize_short_size) optim = make_optimizer(np.ceil( len(train_dataset) * 1. / FLAGS.batch_size / ParallelEnv().nranks), parameter_list=model.parameters()) model.prepare(optim, CrossEntropy(), Accuracy(topk=(1, 5)), inputs, labels, FLAGS.device) if FLAGS.eval_only: model.evaluate(val_dataset, batch_size=FLAGS.batch_size, num_workers=FLAGS.num_workers) return output_dir = os.path.join( FLAGS.output_dir, FLAGS.arch, time.strftime('%Y-%m-%d-%H-%M', time.localtime())) if ParallelEnv().local_rank == 0 and not os.path.exists(output_dir): os.makedirs(output_dir) model.fit(train_dataset, val_dataset, batch_size=FLAGS.batch_size, epochs=FLAGS.epoch, save_dir=output_dir, num_workers=FLAGS.num_workers)
def infer(): fluid.enable_dygraph(device) processor = SentaProcessor(data_dir=args.data_dir, vocab_path=args.vocab_path, random_seed=args.random_seed) infer_data_generator = processor.data_generator( batch_size=args.batch_size, padding_size=args.padding_size, places=device, phase='infer', epoch=1, shuffle=False) if args.model_type == 'cnn_net': model_infer = CNN(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bow_net': model_infer = BOW(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'gru_net': model_infer = GRU(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bigru_net': model_infer = BiGRU(args.vocab_size, args.batch_size, args.padding_size) print('Do inferring ...... ') inputs = [Input([None, None], 'int64', name='doc')] model_infer.prepare(None, CrossEntropy(), Accuracy(topk=(1, )), inputs, device=device) model_infer.load(args.checkpoints, reset_optimizer=True) preds = model_infer.predict(test_data=infer_data_generator) preds = np.array(preds[0]).reshape((-1, 2)) if args.output_dir: with open(os.path.join(args.output_dir, 'predictions.json'), 'w') as w: for p in range(len(preds)): label = np.argmax(preds[p]) result = json.dumps({ 'index': p, 'label': label, 'probs': preds[p].tolist() }) w.write(result + '\n') print('Predictions saved at ' + os.path.join(args.output_dir, 'predictions.json'))
def fit(self, dynamic, is_mlp=False): device = set_device('gpu') fluid.enable_dygraph(device) if dynamic else None im_shape = (-1, 784) batch_size = 128 inputs = [Input(im_shape, 'float32', name='image')] labels = [Input([None, 1], 'int64', name='label')] train_dataset = MnistDataset(mode='train') val_dataset = MnistDataset(mode='test') test_dataset = TestMnistDataset() model = MNIST() if not is_mlp else MLP() optim = fluid.optimizer.Momentum(learning_rate=0.01, momentum=.9, parameter_list=model.parameters()) loss = CrossEntropy() if not is_mlp else MyCrossEntropy() model.prepare(optim, loss, Accuracy(), inputs, labels, device=device) cbk = ProgBarLogger(50) model.fit(train_dataset, val_dataset, epochs=2, batch_size=batch_size, callbacks=cbk) eval_result = model.evaluate(val_dataset, batch_size=batch_size) output = model.predict(test_dataset, batch_size=batch_size) np.testing.assert_equal(output[0].shape[0], len(test_dataset)) acc = get_predict_accuracy(output[0], val_dataset.labels) np.testing.assert_allclose(acc, eval_result['acc'])
def main(): device = set_device(FLAGS.device) fluid.enable_dygraph(device) if FLAGS.dynamic else None train_transform = Compose([ GroupScale(), GroupMultiScaleCrop(), GroupRandomCrop(), GroupRandomFlip(), NormalizeImage() ]) train_dataset = KineticsDataset( file_list=os.path.join(FLAGS.data, 'train_10.list'), pickle_dir=os.path.join(FLAGS.data, 'train_10'), label_list=os.path.join(FLAGS.data, 'label_list'), transform=train_transform) val_transform = Compose( [GroupScale(), GroupCenterCrop(), NormalizeImage()]) val_dataset = KineticsDataset( file_list=os.path.join(FLAGS.data, 'val_10.list'), pickle_dir=os.path.join(FLAGS.data, 'val_10'), label_list=os.path.join(FLAGS.data, 'label_list'), mode='val', transform=val_transform) pretrained = FLAGS.eval_only and FLAGS.weights is None model = tsm_resnet50(num_classes=train_dataset.num_classes, pretrained=pretrained) step_per_epoch = int(len(train_dataset) / FLAGS.batch_size \ / ParallelEnv().nranks) optim = make_optimizer(step_per_epoch, model.parameters()) inputs = [Input([None, 8, 3, 224, 224], 'float32', name='image')] labels = [Input([None, 1], 'int64', name='label')] model.prepare(optim, CrossEntropy(), metrics=Accuracy(topk=(1, 5)), inputs=inputs, labels=labels, device=FLAGS.device) if FLAGS.eval_only: if FLAGS.weights is not None: model.load(FLAGS.weights, reset_optimizer=True) model.evaluate(val_dataset, batch_size=FLAGS.batch_size, num_workers=FLAGS.num_workers) return if FLAGS.resume is not None: model.load(FLAGS.resume) model.fit(train_data=train_dataset, eval_data=val_dataset, epochs=FLAGS.epoch, batch_size=FLAGS.batch_size, save_dir='tsm_checkpoint', num_workers=FLAGS.num_workers, drop_last=True, shuffle=True)
def main(): """ Main Function """ args = Config(yaml_file='./config.yaml') args.build() args.Print() if not (args.do_train or args.do_val or args.do_infer): raise ValueError("For args `do_train`, `do_val` and `do_infer`, at " "least one of them must be True.") place = set_device("gpu" if args.use_cuda else "cpu") fluid.enable_dygraph(place) processor = EmoTectProcessor(data_dir=args.data_dir, vocab_path=args.vocab_path, random_seed=args.random_seed) num_labels = args.num_labels if args.model_type == 'cnn_net': model = CNN(args.vocab_size, args.max_seq_len) elif args.model_type == 'bow_net': model = BOW(args.vocab_size, args.max_seq_len) elif args.model_type == 'lstm_net': model = LSTM(args.vocab_size, args.max_seq_len) elif args.model_type == 'gru_net': model = GRU(args.vocab_size, args.max_seq_len) elif args.model_type == 'bigru_net': model = BiGRU(args.vocab_size, args.batch_size, args.max_seq_len) else: raise ValueError("Unknown model type!") inputs = [Input([None, args.max_seq_len], 'int64', name='doc')] optimizer = None labels = None if args.do_train: train_data_generator = processor.data_generator( batch_size=args.batch_size, places=place, phase='train', epoch=args.epoch, padding_size=args.max_seq_len) num_train_examples = processor.get_num_examples(phase="train") max_train_steps = args.epoch * num_train_examples // args.batch_size + 1 print("Num train examples: %d" % num_train_examples) print("Max train steps: %d" % max_train_steps) labels = [Input([None, 1], 'int64', name='label')] optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr, parameter_list=model.parameters()) test_data_generator = None if args.do_val: test_data_generator = processor.data_generator( batch_size=args.batch_size, phase='dev', epoch=1, places=place, padding_size=args.max_seq_len) elif args.do_val: test_data_generator = processor.data_generator( batch_size=args.batch_size, phase='test', epoch=1, places=place, padding_size=args.max_seq_len) elif args.do_infer: infer_data_generator = processor.data_generator( batch_size=args.batch_size, phase='infer', epoch=1, places=place, padding_size=args.max_seq_len) model.prepare(optimizer, CrossEntropy(), Accuracy(topk=(1, )), inputs, labels, device=place) if args.do_train: if args.init_checkpoint: model.load(args.init_checkpoint) elif args.do_val or args.do_infer: if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if" "only doing validation or infer!") model.load(args.init_checkpoint, reset_optimizer=True) if args.do_train: model.fit(train_data=train_data_generator, eval_data=test_data_generator, batch_size=args.batch_size, epochs=args.epoch, save_dir=args.checkpoints, eval_freq=args.eval_freq, save_freq=args.save_freq) elif args.do_val: eval_result = model.evaluate(eval_data=test_data_generator, batch_size=args.batch_size) print("Final eval result: acc: {:.4f}, loss: {:.4f}".format( eval_result['acc'], eval_result['loss'][0])) elif args.do_infer: preds = model.predict(test_data=infer_data_generator) preds = np.array(preds[0]).reshape((-1, args.num_labels)) if args.output_dir: with open(os.path.join(args.output_dir, 'predictions.json'), 'w') as w: for p in range(len(preds)): label = np.argmax(preds[p]) result = json.dumps({ 'index': p, 'label': label, 'probs': preds[p].tolist() }) w.write(result + '\n') print('Predictions saved at ' + os.path.join(args.output_dir, 'predictions.json'))