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train_ACC_classifier_train_on_unseen_images.py
169 lines (141 loc) · 11.9 KB
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train_ACC_classifier_train_on_unseen_images.py
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import argparse
# import data_for_augmentedimages_for_classifier as dataset
import data_for_ACC_classifier_train_on_unseen_images as dataset
from ACC_classification_builder import ExperimentBuilder
import numpy as np
parser = argparse.ArgumentParser(description='Welcome to GAN-Shot-Learning script')
parser.add_argument('--batch_size', nargs="?", type=int, default=10, help='batch_size for experiment')
parser.add_argument('--discriminator_inner_layers', nargs="?", type=int, default=3,
help='discr_number_of_conv_per_layer')
parser.add_argument('--generator_inner_layers', nargs="?", type=int, default=3, help='discr_number_of_conv_per_layer')
parser.add_argument('--experiment_title', nargs="?", type=str, default="densenet_generator_fc", help='Experiment name')
parser.add_argument('--continue_from_epoch', nargs="?", type=int, default=1, help='continue from checkpoint of epoch')
parser.add_argument('--num_of_gpus', nargs="?", type=int, default=1, help='discr_number_of_conv_per_layer')
parser.add_argument('--z_dim', nargs="?", type=int, default=100, help='The dimensionality of the z input')
parser.add_argument('--dropout_rate_value', type=float, default=0.5, help='dropout_rate_value')
parser.add_argument('--use_wide_connections', nargs="?", type=str, default="False",
help='Whether to use wide connections in discriminator')
parser.add_argument('--image_width', nargs="?", type=int, default=128)
parser.add_argument('--image_height', nargs="?", type=int, default=128)
parser.add_argument('--image_channel', nargs="?", type=int, default=3)
parser.add_argument('--matching', nargs="?", type=int, default=1)
parser.add_argument('--fce', nargs="?", type=int, default=0)
parser.add_argument('--full_context_unroll_k', nargs="?", type=int, default=4)
parser.add_argument('--average_per_class_embeddings', nargs="?", type=int, default=0)
parser.add_argument('--dataset', type=str, default='omniglot')
parser.add_argument('--loss_G', type=float, default=1)
parser.add_argument('--loss_D', type=float, default=1)
parser.add_argument('--loss_KL', type=float, default=0.0001)
parser.add_argument('--loss_CLA', type=float, default=1)
parser.add_argument('--loss_FSL', type=float, default=1)
parser.add_argument('--loss_recons_B', type=float, default=0.01)
parser.add_argument('--loss_matching_G', type=float, default=0.01)
parser.add_argument('--loss_matching_D', type=float, default=0.01)
parser.add_argument('--loss_sim', type=float, default=1e2)
parser.add_argument('--strategy', nargs="?", type=int, default=2)
###### generating data
parser.add_argument('--support_number', nargs="?", type=int, default=1, help='num_support')
parser.add_argument('--selected_classes', type=int, default=1)
##### general classifier parameters
parser.add_argument('--classification_total_epoch', type=int, default=500)
parser.add_argument('--general_classification_samples', type=int, default=5) ####=support number
##### fewshot classifier parameters
parser.add_argument('--episodes_number', type=int, default=10)
parser.add_argument('--few_shot_episode_classes', type=int, default=1) ###=selected_images
##### matchingGAN related parameters
parser.add_argument('--is_z2', nargs="?", type=int, default=0)
parser.add_argument('--is_z2_vae', nargs="?", type=int, default=0)
parser.add_argument('--restore_path', nargs="?", type=str, default="omniglot_dagan_experiment",
help='Experiment name')
parser.add_argument('--augmented_number', type=int, default=0)
parser.add_argument('--num_generations', nargs="?", type=int, default=0, help='num_generations')
parser.add_argument('--confidence', type=int, default=1)
parser.add_argument('--loss_d', type=int, default=1)
###### classifier related parameters
parser.add_argument('--pretrained_epoch', type=int, default=0)
parser.add_argument('--restore_classifier_path', nargs="?", type=str, default="omniglot_dagan_experiment",
help='Experiment name')
parser.add_argument('--pretrain', type=int, default=0)
parser.add_argument('--is_training', nargs="?", type=int, default=1)
parser.add_argument('--is_fewshot_setting', nargs="?", type=int, default=0)
args = parser.parse_args()
args_dict = vars(args)
for key in list(args_dict.keys()):
print(key, args_dict[key])
batch_size = args.batch_size
num_gpus = args.num_of_gpus
##### generating the batches data
support_num = args.support_number
selected_classes_num = args.few_shot_episode_classes
if args.dataset == 'omniglot':
print('omniglot')
data = dataset.OmniglotDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'vggfaces':
print('vggface')
data = dataset.VGGFaceDAGANDataset(batch_size=batch_size, last_training_class_index=1600, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'miniimagenet':
print('miniimagenet')
data = dataset.miniImagenetDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'emnist':
print('emnist')
data = dataset.emnistDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'figr':
print('figr')
data = dataset.FIGRDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,
is_training=args.is_training,
general_classification_samples=args.general_classification_samples,
selected_classes=args.selected_classes, image_size=args.image_width)
elif args.dataset == 'fc100':
data = dataset.FC100DAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'animals':
data = dataset.animalsDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'Selectanimals':
data = dataset.SelectanimalsDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'Generateanimals':
data = dataset.GenerateanimalsDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'SelectMOREanimals':
data = dataset.SelectMOREanimalsDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'flowers':
data = dataset.flowersDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'flowersselected':
data = dataset.flowersselectedDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'birds':
data = dataset.birdsDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'nabirds':
data = dataset.NAbirdsDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'food':
data = dataset.FoodDAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
elif args.dataset == 'cifar100':
data = dataset.CIFAR100DAGANDataset(batch_size=batch_size, last_training_class_index=900, reverse_channels=True,
num_of_gpus=num_gpus, gen_batches=1000, support_number=support_num,is_training=args.is_training,general_classification_samples=args.general_classification_samples,selected_classes=args.selected_classes,image_size=args.image_width)
print('curret test dataset is:', args.dataset)
print('current testing data size is:', np.shape(data.x_test))
experiment = ExperimentBuilder(parser, data=data)
experiment.run_experiment()