if __name__ == '__main__':
tf.compat.v1.enable_eager_execution()
gpu = tf.config.experimental.list_physical_devices('GPU')
print("Num GPUs Available: ", len(gpu))
if len(gpu) > 0:
tf.config.experimental.set_memory_growth(gpu[0], True)
tf.config.experimental.set_memory_growth(gpu[1], True)
# read dataset
path_root = os.path.abspath(os.path.dirname(__file__))
bird_data = DataSet("/Volumes/Watermelon") # DataSet(path_root)
phi_train = bird_data.get_phi(set=0)
w = bird_data.get_w(alpha=1) # (50*150)
train_class_list, test_class_list = bird_data.get_class_split(mode="easy")
train_ds, test_ds = bird_data.load_gpu(batch_size=BATCH_SIZE)
#path_root = os.path.abspath(os.path.dirname(__file__))
#database = DataSet("/Volumes/Watermelon") # path_root)
#PHI = database.get_phi()
#DS, DS_test = database.load_gpu(batch_size=5) # image_batch, label_batch
modelaki = FinalModel()
# define loss and opt functions
loss_fun = Loss().final_loss
step = tf.Variable(0, trainable=False)
boundaries = [187 * 5, 187 * 10]
values = [0.05, 0.005, 0.0005]
learning_rate_fn = PiecewiseConstantDecay(boundaries, values)
# Later, whenever we perform an optimization step, we pass in the step.
learning_rate = learning_rate_fn(step)
import sys
sys.path.append("../src")
from jointmodel import JFL
CHANNELS = 512
N_CLASSES = 200
SEMANTIC_SIZE = 28
BATCH_SIZE = 5
IMG_SIZE = 448
IMG_SHAPE = (IMG_SIZE, IMG_SIZE, 3)
# read dataset
database = DataSet("/Volumes/Watermelon")
PHI = database.get_phi()
DS, DS_test = database.load_gpu(batch_size=BATCH_SIZE)
tf.compat.v1.enable_eager_execution()
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
BUFFER_SIZE = 5
BATCH_SIZE_PER_REPLICA = 32
GLOBAL_BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync
EPOCHS = 30
train_dataset = DS
train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)
class FinalModel(Model):
