def __init__(self, entropy_bottleneck_channels, init_weights=True):
super().__init__()
self.entropy_bottleneck1 = EntropyBottleneck(
entropy_bottleneck_channels)
self.entropy_bottleneck2 = EntropyBottleneck(
entropy_bottleneck_channels)
if init_weights:
self._initialize_weights()
def test_forward_inference(self):
entropy_bottleneck = EntropyBottleneck(128)
entropy_bottleneck.eval()
x = torch.rand(1, 128, 32, 32)
y, y_likelihoods = entropy_bottleneck(x)
assert y.shape == x.shape
assert y_likelihoods.shape == x.shape
assert (y == torch.round(x)).all()
def test_forward_inference_ND(self):
entropy_bottleneck = EntropyBottleneck(128)
entropy_bottleneck.eval()
# Test from 1 to 5 dimensions
for i in range(1, 6):
x = torch.rand(1, 128, *([4] * i))
y, y_likelihoods = entropy_bottleneck(x)
assert y.shape == x.shape
assert y_likelihoods.shape == x.shape
assert (y == torch.round(x)).all()
def __init__(self, entropy_bottleneck_channels, init_weights=None):
super().__init__()
self.entropy_bottleneck = EntropyBottleneck(
entropy_bottleneck_channels)
if init_weights is not None:
warnings.warn(
"init_weights was removed as it was never functional",
DeprecationWarning,
)
def __init__(self,
entropy_bottleneck_channels,
scale=8,
init_weights=True):
super().__init__()
for m in range(scale):
self.add_module(f'entropy_bottleneck_{str(m)}',
EntropyBottleneck(entropy_bottleneck_channels))
if init_weights:
self._initialize_weights()
def test_forward_training(self):
entropy_bottleneck = EntropyBottleneck(128)
x = torch.rand(1, 128, 32, 32)
y, y_likelihoods = entropy_bottleneck(x)
assert isinstance(entropy_bottleneck, EntropyModel)
assert y.shape == x.shape
assert y_likelihoods.shape == x.shape
assert ((y - x) <= 0.5).all()
assert ((y - x) >= -0.5).all()
assert (y != torch.round(x)).any()
def test_compression_2D(self):
x = torch.rand(1, 128, 32, 32)
eb = EntropyBottleneck(128)
eb.update()
s = eb.compress(x)
x2 = eb.decompress(s, x.size()[2:])
assert torch.allclose(torch.round(x), x2)
def test_scripting(self):
entropy_bottleneck = EntropyBottleneck(128)
x = torch.rand(1, 128, 32, 32)
torch.manual_seed(32)
y0 = entropy_bottleneck(x)
m = torch.jit.script(entropy_bottleneck)
torch.manual_seed(32)
y1 = m(x)
assert torch.allclose(y0[0], y1[0])
assert torch.all(y1[1] == 0) # not yet supported
def test_compression_ND(self):
eb = EntropyBottleneck(128)
eb.update()
# Test 0D
x = torch.rand(1, 128)
s = eb.compress(x)
x2 = eb.decompress(s, [])
assert torch.allclose(torch.round(x), x2)
# Test from 1 to 5 dimensions
for i in range(1, 6):
x = torch.rand(1, 128, *([4] * i))
s = eb.compress(x)
x2 = eb.decompress(s, x.size()[2:])
assert torch.allclose(torch.round(x), x2)
def test_loss(self):
entropy_bottleneck = EntropyBottleneck(128)
loss = entropy_bottleneck.loss()
assert len(loss.size()) == 0
assert loss.numel() == 1
def test_script(self):
eb = EntropyBottleneck(32)
eb = torch.jit.script(eb)
x = torch.rand(1, 32, 4, 4)
x_q, likelihoods = eb(x)
assert (likelihoods == torch.zeros_like(x_q)).all()