From 0fdaaceb231d31d53d0c623848b8ac56964bedb5 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Wed, 6 Mar 2024 08:36:28 +0100 Subject: [PATCH] Update. --- tiny_vae.py | 112 ++++++++++++++++++++++++++-------------------------- 1 file changed, 57 insertions(+), 55 deletions(-) diff --git a/tiny_vae.py b/tiny_vae.py index 10ce19f..fa09831 100755 --- a/tiny_vae.py +++ b/tiny_vae.py @@ -11,7 +11,7 @@ # Written by Francois Fleuret -import sys, os, argparse, time, math, itertools +import sys, os, argparse, time, math import torch, torchvision @@ -28,9 +28,9 @@ parser = argparse.ArgumentParser( description="Very simple implementation of a VAE for teaching." ) -parser.add_argument("--nb_epochs", type=int, default=100) +parser.add_argument("--nb_epochs", type=int, default=25) -parser.add_argument("--learning_rate", type=float, default=1e-4) +parser.add_argument("--learning_rate", type=float, default=1e-3) parser.add_argument("--batch_size", type=int, default=100) @@ -40,7 +40,7 @@ parser.add_argument("--log_filename", type=str, default="train.log") parser.add_argument("--latent_dim", type=int, default=32) -parser.add_argument("--nb_channels", type=int, default=64) +parser.add_argument("--nb_channels", type=int, default=32) parser.add_argument("--no_dkl", action="store_true") @@ -54,7 +54,7 @@ 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") @@ -67,20 +67,27 @@ def log_string(s): ###################################################################### +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): - mu, log_var = 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, param): - mu, log_var = 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(param_a, param_b): @@ -93,14 +100,20 @@ def dkl_gaussians(param_a, param_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 @@ -114,17 +127,7 @@ class LatentGivenImageNet(nn.Module): 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( @@ -140,10 +143,18 @@ class ImageGivenLatentNet(nn.Module): 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 @@ -160,7 +171,7 @@ test_input = test_set.data.view(-1, 1, 28, 28).float() ###################################################################### -def save_images(model_q_Z_given_x, model_p_X_given_z, prefix=""): +def save_images(model, prefix=""): def save_image(x, filename): x = x * train_std + train_mu x = x.clamp(min=0, max=255) / 255 @@ -174,9 +185,9 @@ def save_images(model_q_Z_given_x, model_p_X_given_z, prefix=""): # Save the same images after encoding / decoding - param_q_Z_given_x = model_q_Z_given_x(x) + param_q_Z_given_x = model.encode(x) z = sample_gaussian(param_q_Z_given_x) - param_p_X_given_z = model_p_X_given_z(z) + 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") @@ -188,19 +199,17 @@ def save_images(model_q_Z_given_x, model_p_X_given_z, prefix=""): # Save the same images after encoding / decoding - param_q_Z_given_x = model_q_Z_given_x(x) + param_q_Z_given_x = model.encode(x) z = sample_gaussian(param_q_Z_given_x) - param_p_X_given_z = model_p_X_given_z(z) + 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( - (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) + 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") @@ -208,21 +217,9 @@ def save_images(model_q_Z_given_x, model_p_X_given_z, prefix=""): ###################################################################### -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 -) - -optimizer = optim.Adam( - itertools.chain(model_p_X_given_z.parameters(), model_q_Z_given_x.parameters()), - lr=args.learning_rate, -) +model = VariationalAutoEncoder(nb_channels=args.nb_channels, latent_dim=args.latent_dim) -model_p_X_given_z.to(device) -model_q_Z_given_x.to(device) +model.to(device) ###################################################################### @@ -239,18 +236,23 @@ 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, + ) + acc_loss = 0 for x in train_input.split(args.batch_size): - param_q_Z_given_x = model_q_Z_given_x(x) + param_q_Z_given_x = model.encode(x) z = sample_gaussian(param_q_Z_given_x) - param_p_X_given_z = model_p_X_given_z(z) + 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, 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 + log_p_x_z = log_p_x_given_z + log_p_z loss = -(log_p_x_z - log_q_z_given_x).mean() else: dkl_q_Z_given_x_from_p_Z = dkl_gaussians(param_q_Z_given_x, param_p_Z) @@ -265,6 +267,6 @@ for n_epoch in range(args.nb_epochs): log_string(f"acc_loss {n_epoch} {acc_loss/train_input.size(0)}") if (n_epoch + 1) % 25 == 0: - save_images(model_q_Z_given_x, model_p_X_given_z, f"epoch_{n_epoch+1:04d}_") + save_images(model, f"epoch_{n_epoch+1:04d}_") ###################################################################### -- 2.20.1