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demo_gan.py
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demo_gan.py
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from itertools import count
import torch
import time
import numpy as np
import sys
from rendering import MeshRenderer
from model.gan import Generator, LATENT_CODE_SIZE
from util import device, standard_normal_distribution
generator = Generator()
if "wgan" in sys.argv:
generator.filename = "wgan-generator.to"
generator.load()
generator.eval()
viewer = MeshRenderer()
STEPS = 20
TRANSITION_TIME = 0.4
WAIT_TIME = 0.8
def get_random():
return standard_normal_distribution.sample(sample_shape=(LATENT_CODE_SIZE,)).to(device)
previous_model = None
next_model = get_random()
for epoch in count():
try:
previous_model = next_model
next_model = get_random()
for step in range(STEPS + 1):
progress = step / STEPS
model = None
if step < STEPS:
model = previous_model * (1 - progress) + next_model * progress
else:
model = next_model
viewer.set_voxels(generator(model).squeeze().detach().cpu())
time.sleep(TRANSITION_TIME / STEPS)
time.sleep(WAIT_TIME)
except KeyboardInterrupt:
viewer.stop()
break