def test_bvae_enc_dec():
# load an mnist image, x_0
image = pickle.load(open('torch_vae/sample_mnist_image', 'rb'))
image = torch.round(torch.tensor(image)).float()
# load vae params
model = BinaryVAE()
model.load_state_dict(
torch.load('torch_vae/saved_params/torch_binary_vae_params'))
latent_shape = (20,)
rec_net = tvae_utils.torch_fun_to_numpy_fun(model.encode)
gen_net = tvae_utils.torch_fun_to_numpy_fun(model.decode)
obs_append = tvae_utils.bernoulli_obs_append(obs_precision)
obs_pop = tvae_utils.bernoulli_obs_pop(obs_precision)
vae_append = util.vae_append(
latent_shape, gen_net, rec_net, obs_append,
prior_precision, q_precision)
vae_pop = util.vae_pop(
latent_shape, gen_net, rec_net, obs_pop,
prior_precision, q_precision)
# randomly generate some 'other' bits
other_bits = rng.randint(1 << 16, size=20, dtype=np.uint32)
state = rans.x_init
state = util.uniforms_append(16)(state, other_bits)
# ---------------------------- ENCODE ------------------------------------
state = vae_append(state, image)
compressed_message = rans.flatten(state)
print("Used " + str(32 * (len(compressed_message) - len(other_bits))) +
" bits.")
# ---------------------------- DECODE ------------------------------------
state = rans.unflatten(compressed_message)
state, image_ = vae_pop(state)
assert all(image == image_)
# recover the other bits from q(y|x_0)
state, recovered_bits = util.uniforms_pop(16, 20)(state)
assert all(other_bits == recovered_bits)
assert state == rans.x_init
latent_shape = (1, latent_dim)
model = BetaBinomialVAE(hidden_dim=200, latent_dim=latent_dim)
model.load_state_dict(
torch.load('torch_vae/saved_params/torch_vae_beta_binomial_params',
map_location=lambda storage, location: storage))
model.eval()
rec_net = tvae_utils.torch_fun_to_numpy_fun(model.encode)
gen_net = tvae_utils.torch_fun_to_numpy_fun(model.decode)
obs_append = tvae_utils.beta_binomial_obs_append(255, obs_precision)
obs_pop = tvae_utils.beta_binomial_obs_pop(255, obs_precision)
vae_append = util.vae_append(latent_shape, gen_net, rec_net, obs_append,
prior_precision, q_precision)
vae_pop = util.vae_pop(latent_shape, gen_net, rec_net, obs_pop,
prior_precision, q_precision)
# load some mnist images
mnist = datasets.MNIST('data/mnist',
train=False,
download=True,
transform=transforms.Compose([transforms.ToTensor()]))
images = mnist.test_data[:num_images]
images = [image.float().view(1, -1) for image in images]
# randomly generate some 'other' bits
other_bits = rng.randint(low=1 << 16, high=1 << 31, size=50, dtype=np.uint32)
state = rans.unflatten(other_bits)
print_interval = 10
