rnd.shuffle(TIMIT_lst)
p = Augmentor.Pipeline()
p.random_distortion(probability=1,
grid_width=4,
grid_height=4,
magnitude=8)
p.flip_left_right(probability=0.5)
# p.flip_top_bottom(probability=0.5)
# p.rotate90(probability=0.5)
# p.rotate270(probability=0.5)
dp = FaceScrubDataProvider(
top_dir + '/facescrub_128x128',
min_cluster_count=1,
max_cluster_count=5,
target_img_size=(128, 128),
min_element_count_per_cluster=2,
additional_augmentor=lambda x: p.sample_with_array(x))
dp.use_augmentation_for_test_data = False
dp.use_augmentation_for_validation_data = False
en = CnnEmbedding(output_size=256,
cnn_layers_per_block=1,
block_feature_counts=[32, 64, 128],
fc_layer_feature_counts=[256],
hidden_activation=LeakyReLU(),
final_activation=LeakyReLU(),
batch_norm_for_init_layer=False,
batch_norm_after_activation=True,
batch_norm_for_final_layer=True,
dropout_init=0.25,
magnitude=8)
p.flip_left_right(probability=0.5)
# p.flip_top_bottom(probability=0.5)
# p.rotate90(probability=0.5)
# p.rotate270(probability=0.5)
classes = list(range(530))
rand = Random()
rand.seed(1729)
rand.shuffle(classes)
dp = FaceScrubDataProvider(
top_dir + '/facescrub_128x128',
min_cluster_count=1,
max_cluster_count=5,
target_img_size=(128, 128),
min_element_count_per_cluster=2,
additional_augmentor=lambda x: p.sample_with_array(x),
train_classes=classes[0:424],
validate_classes=classes[424:(424 + 53)],
test_classes=classes[(424 + 53):(424 + 53 + 53)])
dp.use_augmentation_for_test_data = False
dp.use_augmentation_for_validation_data = False
def get_cnn(dataprovider=None):
if dataprovider is None:
dataprovider = dp
en = CnnEmbedding(output_size=256,
cnn_layers_per_block=1,
block_feature_counts=[32, 64, 128],
fc_layer_feature_counts=[256],
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.image.facescrub_data_provider import FaceScrubDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
dp = FaceScrubDataProvider(top_dir + '/test/facescrub_128x128',
min_element_count_per_cluster=4,
min_cluster_count=1,
max_cluster_count=10)
en = CnnEmbedding(output_size=256,
cnn_layers_per_block=1,
block_feature_counts=[64, 128, 256],
fc_layer_feature_counts=[256],
hidden_activation=LeakyReLU(),
final_activation=LeakyReLU(),
batch_norm_for_init_layer=False,
batch_norm_after_activation=True,
batch_norm_for_final_layer=True)
c_nn = ClusterNNTry00_V51(dp,
40,
en,
lstm_layers=7,