def __init__(self, parser, data):
tf.reset_default_graph()
args = parser.parse_args()
self.continue_from_epoch = args.continue_from_epoch
self.experiment_name = args.experiment_title
self.saved_models_filepath, self.log_path, self.save_image_path = build_experiment_folder(
self.experiment_name)
self.num_gpus = args.num_of_gpus
self.batch_size = args.batch_size
gen_depth_per_layer = args.generator_inner_layers
discr_depth_per_layer = args.discriminator_inner_layers
self.z_dim = args.z_dim
self.num_generations = args.num_generations
self.dropout_rate_value = args.dropout_rate_value
self.data = data
self.reverse_channels = False
# self.support_number = args.support_number
self.classification_total_epoch = args.classification_total_epoch
image_channel = data.image_channel
self.use_wide_connections = args.use_wide_connections
generator_layers = [64, 64, 128, 128]
self.discriminator_layers = [64, 64, 128, 128]
gen_inner_layers = [
gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer,
gen_depth_per_layer
]
self.discr_inner_layers = [
discr_depth_per_layer, discr_depth_per_layer,
discr_depth_per_layer, discr_depth_per_layer
]
generator_layer_padding = ["SAME", "SAME", "SAME", "SAME"]
image_height = data.image_height
image_width = data.image_width
image_channel = data.image_channel
self.support_number = args.support_number
self.selected_classes = args.selected_classes
self.general_classification_samples = args.general_classification_samples
self.classes = tf.placeholder(tf.int32)
self.selected_class = tf.placeholder(tf.int32)
self.number_support = tf.placeholder(tf.int32)
self.input_x_i = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, image_height, image_width,
image_channel
], 'batch')
self.input_y_i = tf.placeholder(
tf.float32,
[self.num_gpus, self.batch_size, self.data.selected_classes],
'y_inputs_bacth')
self.input_global_y_i = tf.placeholder(
tf.float32,
[self.num_gpus, self.batch_size, self.data.testing_classes],
'y_inputs_bacth_global')
self.input_x_j = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, self.data.selected_classes *
self.data.support_number, image_height, image_width, image_channel
], 'support')
self.input_y_j = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, self.data.selected_classes *
self.data.support_number, self.data.selected_classes
], 'y_inputs_support')
self.input_global_y_j = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, self.data.selected_classes *
self.data.support_number, self.data.testing_classes
], 'y_inputs_support_global')
self.input_x_j_selected = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, image_height, image_width,
image_channel
], 'support_discriminator')
self.input_global_y_j_selected = tf.placeholder(
tf.float32,
[self.num_gpus, self.batch_size, self.data.testing_classes],
'y_inputs_support_discriminator')
#### setting placehoder for the matchingGAN, mainly for the support images
self.input_y_i_dagan = tf.placeholder(
tf.float32,
[self.num_gpus, self.batch_size, self.selected_classes],
'y_inputs_bacth_dagan')
self.input_x_j_dagan = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, self.support_number, image_height,
image_width, image_channel
], 'support_dagan')
self.input_y_j_dagan = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, self.support_number,
self.data.selected_classes
], 'y_inputs_support_dagan')
self.input_global_y_j_dagan = tf.placeholder(
tf.float32, [
self.num_gpus, self.batch_size, self.support_number,
self.data.testing_classes
], 'y_inputs_support_global_dagan')
self.z_input = tf.placeholder(tf.float32,
[self.batch_size, self.z_dim], 'z-input')
self.z_input_2 = tf.placeholder(tf.float32,
[self.batch_size, self.z_dim],
'z-input_2')
self.feed_augmented = tf.placeholder(tf.int32)
self.feed_confidence = tf.placeholder(tf.int32)
self.feed_loss_d = tf.placeholder(tf.int32)
# self.selected_loss_d = tf.placeholder(tf.int32)
# self.selected_confidence = tf.placeholder(tf.int32)
# self.number_augmented = tf.placeholder(tf.int32)
self.training_phase = tf.placeholder(tf.bool, name='training-flag')
self.random_rotate = tf.placeholder(tf.bool, name='rotation-flag')
self.dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
self.z1z2_training = tf.placeholder(tf.bool, name='z1z2_training-flag')
self.is_z2 = args.is_z2
self.is_z2_vae = args.is_z2_vae
self.is_z2 = args.is_z2
self.is_z2_vae = args.is_z2_vae
self.loss_G = args.loss_G
self.loss_D = args.loss_D
self.loss_CLA = args.loss_CLA
self.loss_FSL = args.loss_FSL
self.loss_KL = args.loss_KL
self.loss_recons_B = args.loss_recons_B
self.loss_matching_G = args.loss_matching_G
self.loss_matching_D = args.loss_matching_D
self.loss_sim = args.loss_sim
self.strategy = args.strategy
self.is_fewshot_setting = args.is_fewshot_setting
# self.few_shot_episode_classes = args.few_shot_episode_classes
self.few_shot_episode_classes = args.selected_classes
self.confidence = args.confidence
self.loss_d = args.loss_d
self.augmented_number = args.augmented_number
self.matching = args.matching
self.fce = args.fce
self.full_context_unroll_k = args.full_context_unroll_k
self.average_per_class_embeddings = args.average_per_class_embeddings
self.restore_path = args.restore_path
self.restore_classifier_path = args.restore_classifier_path
self.episodes = args.episodes_number
if self.augmented_number > 0:
dagan = DAGAN(batch_size=self.batch_size, input_x_i=self.input_x_i, input_x_j=self.input_x_j_dagan,
input_y_i=self.input_y_i_dagan, input_y_j=self.input_y_j_dagan, input_global_y_i=self.input_global_y_i,
input_global_y_j=self.input_global_y_j_dagan,
input_x_j_selected=self.input_x_j_selected,
input_global_y_j_selected=self.input_global_y_j_selected, \
selected_classes=self.selected_classes, support_num=self.support_number,
classes=self.data.training_classes,
dropout_rate=self.dropout_rate, generator_layer_sizes=generator_layers,
generator_layer_padding=generator_layer_padding, num_channels=data.image_channel,
is_training=self.training_phase, augment=self.random_rotate,
discriminator_layer_sizes=self.discriminator_layers,
discr_inner_conv=self.discr_inner_layers, is_z2=self.is_z2, is_z2_vae=self.is_z2_vae,
gen_inner_conv=gen_inner_layers, num_gpus=self.num_gpus, z_dim=self.z_dim, z_inputs=self.z_input,
z_inputs_2=self.z_input_2,
use_wide_connections=args.use_wide_connections, fce=self.fce, matching=self.matching,
full_context_unroll_k=self.full_context_unroll_k,
average_per_class_embeddings=self.average_per_class_embeddings,
loss_G=self.loss_G, loss_D=self.loss_D, loss_KL=self.loss_KL, loss_recons_B=self.loss_recons_B,
loss_matching_G=self.loss_matching_G, loss_matching_D=self.loss_matching_D,
loss_CLA=self.loss_CLA, loss_FSL=self.loss_FSL, loss_sim=self.loss_sim,
z1z2_training=self.z1z2_training)
self.same_images = dagan.sample_same_images()
if self.is_fewshot_setting:
print('fewshot classifier categories:',
self.few_shot_episode_classes)
classifier = densenet_classifier(
input_x_i=self.input_x_i,
input_y=self.input_y_i,
classes=self.few_shot_episode_classes,
batch_size=self.batch_size,
layer_sizes=self.discriminator_layers,
inner_layers=self.discr_inner_layers,
num_gpus=self.num_gpus,
use_wide_connections=args.use_wide_connections,
is_training=self.training_phase,
augment=self.random_rotate,
dropout_rate=self.dropout_rate)
else:
print('general classifier categories:', self.data.testing_classes)
classifier = densenet_classifier(
input_x_i=self.input_x_i,
input_y=self.input_global_y_i,
classes=self.data.testing_classes,
batch_size=self.batch_size,
layer_sizes=self.discriminator_layers,
inner_layers=self.discr_inner_layers,
num_gpus=self.num_gpus,
use_wide_connections=args.use_wide_connections,
is_training=self.training_phase,
augment=self.random_rotate,
dropout_rate=self.dropout_rate)
self.summary, self.losses, self.accuracy, self.graph_ops = classifier.init_train(
)
self.total_train_batches = int(data.training_data_size /
(self.batch_size * self.num_gpus))
self.total_val_batches = int(data.validation_data_size /
(self.batch_size * self.num_gpus))
self.total_test_batches = int(data.testing_data_size /
(self.batch_size * self.num_gpus))
self.total_gen_batches = int(data.testing_data_size /
(self.batch_size * self.num_gpus))
self.init = tf.global_variables_initializer()
self.spherical_interpolation = False
self.tensorboard_update_interval = int(self.total_test_batches / 10 /
self.num_gpus)
self.z_vectors = np.random.normal(size=(10, self.z_dim))
self.z_vectors_2 = np.random.normal(size=(10, self.z_dim))
self.z_inputs = np.random.randn(self.batch_size, self.z_dim)
self.z_inputs_2 = np.random.randn(self.batch_size, self.z_dim)
self.total_test_items = int(
self.general_classification_samples / self.data.support_number) + 1
def __init__(self, parser, data):
tf.reset_default_graph()
args = parser.parse_args()
self.continue_from_epoch = args.continue_from_epoch
self.experiment_name = args.experiment_title
self.saved_models_filepath, self.log_path, self.save_image_path = build_experiment_folder(
self.experiment_name)
self.num_gpus = args.num_of_gpus
self.batch_size = args.batch_size
gen_depth_per_layer = args.generator_inner_layers
discr_depth_per_layer = args.discriminator_inner_layers
self.z_dim = args.z_dim
self.num_generations = args.num_generations
self.dropout_rate_value = args.dropout_rate_value
self.data = data
self.reverse_channels = False
self.support_number = args.support_number
self.classification_total_epoch = args.classification_total_epoch
image_channel = data.image_channel
self.use_wide_connections = args.use_wide_connections
self.pretrain = args.pretrain
generator_layers = [64, 64, 128, 128]
self.discriminator_layers = [64, 64, 128, 128]
gen_inner_layers = [
gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer,
gen_depth_per_layer
]
self.discr_inner_layers = [
discr_depth_per_layer, discr_depth_per_layer,
discr_depth_per_layer, discr_depth_per_layer
]
generator_layer_padding = ["SAME", "SAME", "SAME", "SAME"]
image_height = self.data.image_width
image_width = self.data.image_width
image_channels = self.data.image_channel
self.classes = tf.placeholder(tf.int32)
self.input_x_i = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size * self.data.selected_classes,
image_height, image_width, image_channels
], 'inputs-1')
self.input_y = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size * self.data.selected_classes,
self.data.selected_classes
], 'y_inputs-1')
self.input_x_j = tf.placeholder(tf.float32, [
self.num_gpus, self.batch_size, self.support_number, image_height,
image_width, image_channels
], 'inputs-2-same-class')
self.z_input = tf.placeholder(tf.float32,
[self.batch_size, self.z_dim], 'z-input')
self.z_input_2 = tf.placeholder(tf.float32,
[self.batch_size, self.z_dim],
'z-input_2')
self.training_phase = tf.placeholder(tf.bool, name='training-flag')
self.random_rotate = tf.placeholder(tf.bool, name='rotation-flag')
self.dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
self.matching = args.matching
self.fce = args.fce
self.full_context_unroll_k = args.full_context_unroll_k
self.average_per_class_embeddings = args.average_per_class_embeddings
self.total_train_batches = data.training_data_size / (self.batch_size *
self.num_gpus)
self.total_val_batches = data.validation_data_size / (self.batch_size *
self.num_gpus)
self.total_test_batches = 5 * 545 / (self.batch_size * self.num_gpus)
self.total_gen_batches = data.generation_data_size / (self.batch_size *
self.num_gpus)
self.spherical_interpolation = True
self.tensorboard_update_interval = int(self.total_test_batches / 10 /
self.num_gpus)
classifier = densenet_classifier(
input_x_i=self.input_x_i,
input_y=self.input_y,
classes=self.data.selected_classes,
batch_size=self.batch_size,
layer_sizes=self.discriminator_layers,
inner_layers=self.discr_inner_layers,
num_gpus=self.num_gpus,
use_wide_connections=self.use_wide_connections,
is_training=self.training_phase,
augment=self.random_rotate,
dropout_rate=self.dropout_rate)
print('classes', self.data.selected_classes)
self.summary, self.losses, self.accuracy, self.graph_ops = classifier.init_train(
)
self.init = tf.global_variables_initializer()