# Written by Francois Fleuret <francois@fleuret.org>
-import sys, os, argparse, time, math, itertools
+import sys, os, argparse, time, math
import torch, torchvision
######################################################################
-parser = argparse.ArgumentParser(description="Tiny LeNet-like auto-encoder.")
+parser = argparse.ArgumentParser(
+ description="Very simple implementation of a VAE for teaching."
+)
parser.add_argument("--nb_epochs", type=int, default=25)
+parser.add_argument("--learning_rate", type=float, default=1e-3)
+
parser.add_argument("--batch_size", type=int, default=100)
parser.add_argument("--data_dir", type=str, default="./data/")
parser.add_argument("--latent_dim", type=int, default=32)
-parser.add_argument("--nb_channels", type=int, default=128)
+parser.add_argument("--nb_channels", type=int, default=32)
parser.add_argument("--no_dkl", action="store_true")
+parser.add_argument("--beta", type=float, default=1.0)
+
args = parser.parse_args()
log_file = open(args.log_filename, "w")
def log_string(s):
- t = time.strftime("%Y-%m-%d_%H:%M:%S - ", time.localtime())
+ t = time.strftime("%Y-%m-%d_%H:%M:%S ", time.localtime())
if log_file is not None:
log_file.write(t + s + "\n")
######################################################################
-def sample_gaussian(mu, log_var):
+def sample_categorical(param):
+ dist = torch.distributions.Categorical(logits=param)
+ return (dist.sample().unsqueeze(1).float() - train_mu) / train_std
+
+
+def log_p_categorical(x, param):
+ x = (x.squeeze(1) * train_std + train_mu).long().clamp(min=0, max=255)
+ param = param.permute(0, 3, 1, 2)
+ return -F.cross_entropy(param, x, reduction="none").flatten(1).sum(dim=1)
+
+
+def sample_gaussian(param):
+ mean, log_var = param
std = log_var.mul(0.5).exp()
- return torch.randn(mu.size(), device=mu.device) * std + mu
+ return torch.randn(mean.size(), device=mean.device) * std + mean
-def log_p_gaussian(x, mu, log_var):
+def log_p_gaussian(x, param):
+ mean, log_var, x = param[0].flatten(1), param[1].flatten(1), x.flatten(1)
var = log_var.exp()
- return (
- (-0.5 * ((x - mu).pow(2) / var) - 0.5 * log_var - 0.5 * math.log(2 * math.pi))
- .flatten(1)
- .sum(1)
- )
+ return -0.5 * (((x - mean).pow(2) / var) + log_var + math.log(2 * math.pi)).sum(1)
-def dkl_gaussians(mu_a, log_var_a, mu_b, log_var_b):
- mu_a, log_var_a = mu_a.flatten(1), log_var_a.flatten(1)
- mu_b, log_var_b = mu_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 * (
- log_var_b - log_var_a - 1 + (mu_a - mu_b).pow(2) / var_b + var_a / var_b
+ log_var_b - log_var_a - 1 + (mean_a - mean_b).pow(2) / var_b + var_a / var_b
).sum(1)
+def dup_param(param, nb):
+ mean, log_var = param
+ s = (nb,) + (-1,) * (mean.dim() - 1)
+ return (mean.expand(s), log_var.expand(s))
+
+
######################################################################
-class LatentGivenImageNet(nn.Module):
+class VariationalAutoEncoder(nn.Module):
def __init__(self, nb_channels, latent_dim):
super().__init__()
- self.model = nn.Sequential(
+ self.encoder = nn.Sequential(
nn.Conv2d(1, nb_channels, kernel_size=1), # to 28x28
nn.ReLU(inplace=True),
nn.Conv2d(nb_channels, nb_channels, kernel_size=5), # to 24x24
nn.Conv2d(nb_channels, 2 * latent_dim, kernel_size=4),
)
- def forward(self, x):
- output = self.model(x).view(x.size(0), 2, -1)
- mu, log_var = output[:, 0], output[:, 1]
- return mu, log_var
-
-
-class ImageGivenLatentNet(nn.Module):
- def __init__(self, nb_channels, latent_dim):
- super().__init__()
-
- self.model = nn.Sequential(
+ self.decoder = nn.Sequential(
nn.ConvTranspose2d(latent_dim, nb_channels, kernel_size=4),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(
nn.ConvTranspose2d(nb_channels, 2, kernel_size=5), # from 24x24
)
- def forward(self, z):
- output = self.model(z.view(z.size(0), -1, 1, 1))
+ def encode(self, x):
+ output = self.encoder(x).view(x.size(0), 2, -1)
+ mu, log_var = output[:, 0], output[:, 1]
+ return mu, log_var
+
+ def decode(self, z):
+ # return self.decoder(z.view(z.size(0), -1, 1, 1)).permute(0, 2, 3, 1)
+ output = self.decoder(z.view(z.size(0), -1, 1, 1))
mu, log_var = output[:, 0:1], output[:, 1:2]
+ log_var.flatten(1)[...] = 1 # math.log(1e-1)
+ # log_var.flatten(1)[...] = log_var.flatten(1)[:, :1]
+ # log_var = log_var.clamp(min=2*math.log(1/256))
return mu, log_var
######################################################################
-model_q_Z_given_x = LatentGivenImageNet(
- nb_channels=args.nb_channels, latent_dim=args.latent_dim
-)
-model_p_X_given_z = ImageGivenLatentNet(
- nb_channels=args.nb_channels, latent_dim=args.latent_dim
-)
+def save_images(model, prefix=""):
+ def save_image(x, filename):
+ x = x * train_std + train_mu
+ x = x.clamp(min=0, max=255) / 255
+ torchvision.utils.save_image(1 - x, filename, nrow=12, pad_value=1.0)
+ log_string(f"wrote {filename}")
-optimizer = optim.Adam(
- itertools.chain(model_p_X_given_z.parameters(), model_q_Z_given_x.parameters()),
- lr=4e-4,
-)
+ # Save a bunch of train images
+
+ x = train_input[:36]
+ save_image(x, f"{prefix}train_input.png")
+
+ # Save the same images after encoding / decoding
-model_p_X_given_z.to(device)
-model_q_Z_given_x.to(device)
+ param_q_Z_given_x = model.encode(x)
+ z = sample_gaussian(param_q_Z_given_x)
+ param_p_X_given_z = model.decode(z)
+ x = sample_gaussian(param_p_X_given_z)
+ save_image(x, f"{prefix}train_output.png")
+ save_image(param_p_X_given_z[0], f"{prefix}train_output_mean.png")
+
+ # Save a bunch of test images
+
+ x = test_input[:36]
+ save_image(x, f"{prefix}input.png")
+
+ # Save the same images after encoding / decoding
+
+ param_q_Z_given_x = model.encode(x)
+ z = sample_gaussian(param_q_Z_given_x)
+ param_p_X_given_z = model.decode(z)
+ x = sample_gaussian(param_p_X_given_z)
+ save_image(x, f"{prefix}output.png")
+ save_image(param_p_X_given_z[0], f"{prefix}output_mean.png")
+
+ # Generate a bunch of images
+
+ z = sample_gaussian(dup_param(param_p_Z, x.size(0)))
+ param_p_X_given_z = model.decode(z)
+ x = sample_gaussian(param_p_X_given_z)
+ save_image(x, f"{prefix}synth.png")
+ save_image(param_p_X_given_z[0], f"{prefix}synth_mean.png")
+
+
+######################################################################
+
+model = VariationalAutoEncoder(nb_channels=args.nb_channels, latent_dim=args.latent_dim)
+
+model.to(device)
######################################################################
######################################################################
-mu_p_Z = train_input.new_zeros(1, args.latent_dim)
-log_var_p_Z = mu_p_Z
+zeros = train_input.new_zeros(1, args.latent_dim)
+
+param_p_Z = zeros, zeros
+
+for n_epoch in range(args.nb_epochs):
+ optimizer = optim.Adam(
+ model.parameters(),
+ lr=args.learning_rate,
+ )
-for epoch in range(args.nb_epochs):
acc_loss = 0
for x in train_input.split(args.batch_size):
- mu_q_Z_given_x, log_var_q_Z_given_x = model_q_Z_given_x(x)
- z = sample_gaussian(mu_q_Z_given_x, log_var_q_Z_given_x)
- mu_p_X_given_z, log_var_p_X_given_z = model_p_X_given_z(z)
+ param_q_Z_given_x = model.encode(x)
+ z = sample_gaussian(param_q_Z_given_x)
+ param_p_X_given_z = model.decode(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, mu_q_Z_given_x, log_var_q_Z_given_x)
- log_p_x_z = log_p_gaussian(
- x, mu_p_X_given_z, log_var_p_X_given_z
- ) + log_p_gaussian(z, mu_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_z
loss = -(log_p_x_z - log_q_z_given_x).mean()
else:
- log_p_x_given_z = log_p_gaussian(x, mu_p_X_given_z, log_var_p_X_given_z)
- dkl_q_Z_given_x_from_p_Z = dkl_gaussians(
- mu_q_Z_given_x, log_var_q_Z_given_x, mu_p_Z, log_var_p_Z
- )
- loss = (-log_p_x_given_z + dkl_q_Z_given_x_from_p_Z).mean()
+ dkl_q_Z_given_x_from_p_Z = dkl_gaussians(param_q_Z_given_x, param_p_Z)
+ loss = -(log_p_x_given_z - args.beta * dkl_q_Z_given_x_from_p_Z).mean()
optimizer.zero_grad()
loss.backward()
acc_loss += loss.item() * x.size(0)
- log_string(f"acc_loss {epoch} {acc_loss/train_input.size(0)}")
-
-######################################################################
-
-
-def save_image(x, filename):
- x = x * train_std + train_mu
- x = x.clamp(min=0, max=255) / 255
- torchvision.utils.save_image(1 - x, filename, nrow=16, pad_value=0.8)
-
-
-# Save a bunch of test images
-
-x = test_input[:256]
-save_image(x, "input.png")
-
-# Save the same images after encoding / decoding
-
-mu_q_Z_given_x, log_var_q_Z_given_x = model_q_Z_given_x(x)
-z = sample_gaussian(mu_q_Z_given_x, log_var_q_Z_given_x)
-mu_p_X_given_z, log_var_p_X_given_z = model_p_X_given_z(z)
-x = sample_gaussian(mu_p_X_given_z, log_var_p_X_given_z)
-save_image(x, "output.png")
-
-# Generate a bunch of images
+ log_string(f"acc_loss {n_epoch} {acc_loss/train_input.size(0)}")
-z = sample_gaussian(mu_p_Z.expand(x.size(0), -1), log_var_p_Z.expand(x.size(0), -1))
-mu_p_X_given_z, log_var_p_X_given_z = model_p_X_given_z(z)
-x = sample_gaussian(mu_p_X_given_z, log_var_p_X_given_z)
-save_image(x, "synth.png")
+ if (n_epoch + 1) % 25 == 0:
+ save_images(model, f"epoch_{n_epoch+1:04d}_")
######################################################################
projects / pytorch.git / blobdiff