m_i, m_k, map_att, gtmap, score, y_true, y_pred, n_classes, batch_size = model(images)
loss = loss_fun(m_i, m_k, map_att, gtmap, score, y_true, y_pred, n_classes, batch_size)
print("Current TestLoss: {}".format(loss))
'''
# testing by running
if __name__ == '__main__':
# tf.compat.v1.enable_eager_execution()
gpu = tf.config.experimental.list_physical_devices('GPU')
print("Num GPUs Available: ", len(gpu))
# path_root = os.path.abspath(os.path.dirname(__file__))
database = DataSet('../basemodel') #"/content/gdrive/My Drive/data") # path_root)
# DS, DS_test = database.load_gpu() # image_batch, label_batch
DS = database.load(GPU=False, train=True, batch_size=4)
# DS_test = database.load(GPU=False, train=False, batch_size = 32)
modelaki = FinalModel()
loss_fun = Loss().loss_MA
opt_fun = tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9)
# ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=opt_fun, net=modelaki)
# manager = tf.train.CheckpointManager(ckpt, path_root + '/tf_ckpts',
# max_to_keep=3) # keep only the three most recent checkpoints
# ckpt.restore(manager.latest_checkpoint) # pickup training from where you left off
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.Accuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
#net = FinalModel()
#new_root = tf.train.Checkpoint(net=net)
#status = new_root.restore(tf.train.latest_checkpoint('./tf_ckpts/'))
net = FinalModel()
ckpt = tf.train.Checkpoint(step=tf.Variable(1, dtype=tf.int32),
optimizer=opt,
net=net)
ckpt.restore(tf.train.latest_checkpoint('./tf_ckpts/'))
#DATA
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 = bird_data.load(GPU=False, train=True, batch_size=32)
#test_ds = bird_data.load(GPU=False, train=False, batch_size=4) #.load_gpu(batch_size=4)
PHI = bird_data.get_phi(set=0)
for im, label in train_ds:
#im_path = "/Volumes/Watermelon/CUB_200_2011/CUB_200_2011/images/059.California_Gull/"
#img = tf.io.read_file(im_path)
#im = database.decode_img(img)
m0, m1, mask0, mask1, scores, phi, y_pred, C = net(
im, PHI) #tf.expand_dims(im,0)
nu = 50
ns = 150
W = tf.ones((nu, ns))
seen_classes = tf.ones((ns, 28))
unseen_classes = tf.ones((nu, 28))
phi_test = bird_data.get_phi(set=1)
