Spaces:
Sleeping
Sleeping
File size: 2,569 Bytes
b0b9200 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import gradio as gr
# Define the VAE model
class ConvVAE(nn.Module):
def __init__(self, input_channels=3, latent_dim=16):
super(ConvVAE, self).__init__()
self.latent_dim = latent_dim
self.enc_conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, stride=2, padding=1)
self.enc_conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.enc_conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.fc_mu = nn.Linear(5120, latent_dim)
self.fc_logvar = nn.Linear(5120, latent_dim)
self.fc_decode = nn.Linear(latent_dim, 5120)
self.dec_conv1 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=1, padding=1)
self.dec_conv2 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
self.dec_conv3 = nn.ConvTranspose2d(32, 3, kernel_size=3, stride=2, padding=1, output_padding=(0,1))
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self, x):
x = F.relu(self.enc_conv1(x))
x = F.relu(self.enc_conv2(x))
x = F.relu(self.enc_conv3(x))
x = x.view(x.size(0), -1)
mu = self.fc_mu(x)
logvar = self.fc_logvar(x)
z = self.reparameterize(mu, logvar)
return self.decode(z)
def decode(self, z):
x = F.relu(self.fc_decode(z))
x = x.view(x.size(0), 128, 4, 10)
x = F.relu(self.dec_conv1(x))
x = F.relu(self.dec_conv2(x))
x = self.dec_conv3(x)
return F.softmax(x, dim=1)
# Load model
model = ConvVAE()
model.load_state_dict(torch.load("vae_supertux.pth", map_location=torch.device("cpu")))
model.eval()
def generate_map(seed: int = None):
if seed:
torch.manual_seed(seed)
z = torch.randn(1, model.latent_dim)
with torch.no_grad():
output = model.decode(z) # Shape: (1, 3, 15, 40)
output = output.squeeze(0).argmax(dim=0)
grid = output.cpu().numpy()
padded_grid = np.vstack([np.zeros((5, grid.shape[1]), dtype=int), grid]) # Append 5 rows of zeros
return ["".join(map(str, row)) for row in padded_grid] # Convert each row to a string
gr.Interface(
fn=generate_map,
inputs=gr.Number(label="Seed"),
outputs=gr.JSON(label="Generated Map Grid"),
title="VAE Level Generator",
description="Returns a 20x40 grid as a list of strings where 0=air, 1=ground, 2=lava"
).launch() |