description="Very simple implementation of a VAE for teaching."
)
-parser.add_argument("--nb_epochs", type=int, default=25)
+parser.add_argument("--nb_epochs", type=int, default=100)
-parser.add_argument("--learning_rate", type=float, default=1e-3)
+parser.add_argument("--learning_rate", type=float, default=2e-4)
parser.add_argument("--batch_size", type=int, default=100)
parser.add_argument("--no_dkl", action="store_true")
-# With that option, do not follow the setup of the original VAE paper
-# of forcing the variance of X|Z to 1 during training and to 0 for
-# sampling, but optimize and use the variance.
-
-parser.add_argument("--no_hacks", action="store_true")
-
args = parser.parse_args()
log_file = open(args.log_filename, "w")
######################################################################
-def sample_gaussian(mu, log_var):
+def sample_gaussian(param):
+ mu, log_var = param
std = log_var.mul(0.5).exp()
return torch.randn(mu.size(), device=mu.device) * std + mu
-def log_p_gaussian(x, mu, log_var):
+def log_p_gaussian(x, param):
+ mu, log_var = param
var = log_var.exp()
return (
(-0.5 * ((x - mu).pow(2) / var) - 0.5 * log_var - 0.5 * math.log(2 * math.pi))
)
-def dkl_gaussians(mean_a, log_var_a, mean_b, log_var_b):
- mean_a, log_var_a = mean_a.flatten(1), log_var_a.flatten(1)
- mean_b, log_var_b = mean_b.flatten(1), log_var_b.flatten(1)
+def dkl_gaussians(param_a, param_b):
+ mean_a, log_var_a = param_a[0].flatten(1), param_a[1].flatten(1)
+ mean_b, log_var_b = param_b[0].flatten(1), param_b[1].flatten(1)
var_a = log_var_a.exp()
var_b = log_var_b.exp()
return 0.5 * (
def forward(self, z):
output = self.model(z.view(z.size(0), -1, 1, 1))
mu, log_var = output[:, 0:1], output[:, 1:2]
- if not args.no_hacks:
- log_var[...] = 0
+ # log_var.flatten(1)[...]=log_var.flatten(1)[:,:1]
return mu, log_var
######################################################################
-mean_p_Z = train_input.new_zeros(1, args.latent_dim)
-log_var_p_Z = mean_p_Z
+zeros = train_input.new_zeros(1, args.latent_dim)
+
+param_p_Z = zeros, zeros
for epoch in range(args.nb_epochs):
acc_loss = 0
for x in train_input.split(args.batch_size):
- mean_q_Z_given_x, log_var_q_Z_given_x = model_q_Z_given_x(x)
- z = sample_gaussian(mean_q_Z_given_x, log_var_q_Z_given_x)
- mean_p_X_given_z, log_var_p_X_given_z = model_p_X_given_z(z)
+ param_q_Z_given_x = model_q_Z_given_x(x)
+ z = sample_gaussian(param_q_Z_given_x)
+ param_p_X_given_z = model_p_X_given_z(z)
+ log_p_x_given_z = log_p_gaussian(x, param_p_X_given_z)
if args.no_dkl:
- log_q_z_given_x = log_p_gaussian(z, mean_q_Z_given_x, log_var_q_Z_given_x)
- log_p_x_z = log_p_gaussian(
- x, mean_p_X_given_z, log_var_p_X_given_z
- ) + log_p_gaussian(z, mean_p_Z, log_var_p_Z)
+ log_q_z_given_x = log_p_gaussian(z, param_q_Z_given_x)
+ log_p_z = log_p_gaussian(z, param_p_Z)
+ log_p_x_z = log_p_x_given_z + log_p_x_z
loss = -(log_p_x_z - log_q_z_given_x).mean()
else:
- log_p_x_given_z = log_p_gaussian(x, mean_p_X_given_z, log_var_p_X_given_z)
- dkl_q_Z_given_x_from_p_Z = dkl_gaussians(
- mean_q_Z_given_x, log_var_q_Z_given_x, mean_p_Z, log_var_p_Z
- )
+ dkl_q_Z_given_x_from_p_Z = dkl_gaussians(param_q_Z_given_x, param_p_Z)
loss = (-log_p_x_given_z + dkl_q_Z_given_x_from_p_Z).mean()
optimizer.zero_grad()
# Save the same images after encoding / decoding
-mean_q_Z_given_x, log_var_q_Z_given_x = model_q_Z_given_x(x)
-z = sample_gaussian(mean_q_Z_given_x, log_var_q_Z_given_x)
-mean_p_X_given_z, log_var_p_X_given_z = model_p_X_given_z(z)
-if args.no_hacks:
- x = sample_gaussian(mean_p_X_given_z, log_var_p_X_given_z)
-else:
- x = mean_p_X_given_z
+param_q_Z_given_x = model_q_Z_given_x(x)
+z = sample_gaussian(param_q_Z_given_x)
+param_p_X_given_z = model_p_X_given_z(z)
+x = sample_gaussian(param_p_X_given_z)
save_image(x, "output.png")
# Generate a bunch of images
-z = sample_gaussian(mean_p_Z.expand(x.size(0), -1), log_var_p_Z.expand(x.size(0), -1))
-mean_p_X_given_z, log_var_p_X_given_z = model_p_X_given_z(z)
-if args.no_hacks:
- x = sample_gaussian(mean_p_X_given_z, log_var_p_X_given_z)
-else:
- x = mean_p_X_given_z
+z = sample_gaussian(
+ param_p_Z[0].expand(x.size(0), -1), param_p_Z[1].expand(x.size(0), -1)
+)
+param_p_X_given_z = model_p_X_given_z(z)
+x = sample_gaussian(param_p_X_given_z)
save_image(x, "synth.png")
######################################################################