def test_glow():
print("[Test]: Glow")
from glow.config import JsonConfig
glow = models.Glow(JsonConfig("hparams/celeba_test.json"))
# img = cv2.imread("pictures/tsuki.jpeg")
# img = cv2.resize(img, (32, 32))
# img = (img / 255.0).astype(np.float32)
# img = img[:, :, ::-1].transpose(2, 0, 1)
# x = torch.Tensor([img]*8)
x = torch.Tensor(np.random.rand(8, 1, 32, 32))
print('x.size = ', x.size())
batch_size = 8
nb_digits = 10
y = torch.LongTensor(batch_size).random_() % nb_digits
print('y = ', y)
print('y.view(-1,1) = ', y.view(-1, 1))
y_onehot = torch.FloatTensor(batch_size, nb_digits)
y_onehot.zero_()
y_onehot.scatter_(1, y.view(-1, 1), 1)
print('y_onehot:', y_onehot)
z, det, y_logits = glow(x=x, y_onehot=y_onehot)
print(z.size())
print(det)
print(models.Glow.loss_generative(det))
print('y_logits = ', y_logits)
print(models.Glow.loss_class(y_logits, y))
def __init__(self, test_class_index, is_mean, K, class_number):
super(AssociateGlowGenerated, self).__init__()
self.glow = models.Glow(JsonConfig("hparams/omni_all_bg.json"),
test_class_index=test_class_index,
is_mean=is_mean,
K=K,
y_classes=class_number)
self.class_number = class_number
self.eval_index = test_class_index
self.criterion = nn.MSELoss()
self.ones_tensor = torch.ones((1, 32, 32)).float().cuda()
self.glow_generate = models.Glow(
JsonConfig("hparams/omni_all_bg.json"),
test_class_index=test_class_index,
is_mean=is_mean,
K=K,
y_classes=class_number)
def test_glow():
print("[Test]: Glow")
from glow.config import JsonConfig
glow = models.Glow(JsonConfig("hparams_celeba.json"))
img = cv2.imread("tsuki.jpeg")
img = cv2.resize(img, (64, 64))
img = (img / 255.0).astype(np.float32)
img = img[:, :, ::-1].transpose(2, 0, 1)
x = torch.Tensor([img] * 8)
y_onehot = torch.zeros((8, 40))
z, det, y_logits = glow(x=x, y_onehot=y_onehot)
print(z.size())
print(det)
print(models.Glow.loss_generative(det))
def test_glow():
print("[Test]: Glow")
from glow.config import JsonConfig
glow = models.Glow(JsonConfig("hparams/celeba_test.json"),
is_mean=True,
test_class_index=[1, 2],
K=4,
y_classes=10,
arc_loss=True)
# img = cv2.imread("pictures/tsuki.jpeg")
# img = cv2.resize(img, (32, 32))
# img = (img / 255.0).astype(np.float32)
# img = img[:, :, ::-1].transpose(2, 0, 1)
# x = torch.Tensor([img]*8)
# glow.set_z_add_random()
glow.cuda()
x = torch.Tensor(np.random.rand(12, 1, 32, 32))
print('x.size = ', x.size())
batch_size = 12
nb_digits = 10
y = torch.LongTensor(batch_size).random_() % nb_digits
print('y = ', y)
print('y.view(-1,1) = ', y.view(-1, 1))
y_onehot = torch.FloatTensor(batch_size, nb_digits)
y_onehot.zero_()
y_onehot.scatter_(1, y.view(-1, 1), 1)
print('y_onehot:', y_onehot)
z, det, y_logits = glow(x=x, y_onehot=y_onehot)
y_logits = glow.class_flow(z, y_onehot)
print('z.size() = ', z.size())
print('det = ', det)
print('y_logits = ', y_logits)
print(models.Glow.loss_generative(det))
# print('y_logits = ',y_logits)
print(models.Glow.loss_class(y_logits, y))