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generate.py
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generate.py
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import torch
from data import DiffSet
import pytorch_lightning as pl
from model import DiffusionModel
from torch.utils.data import DataLoader
from train import train_loader, val_loader, model, trainer, train_dataset
import imageio
import glob
import wandb
gif_shape = [3, 3]
sample_batch_size = gif_shape[0] * gif_shape[1]
n_hold_final = 10
# Generate samples from denoising process
gen_samples = []
x = torch.randn((sample_batch_size, train_dataset.depth, train_dataset.size, train_dataset.size))
sample_steps = torch.arange(model.t_range-1, 0, -1)
for t in sample_steps:
x = model.denoise_sample(x, t)
if t % 50 == 0:
gen_samples.append(x)
for _ in range(n_hold_final):
gen_samples.append(x)
gen_samples = torch.stack(gen_samples, dim=0).moveaxis(2, 4).squeeze(-1)
gen_samples = (gen_samples.clamp(-1, 1) + 1) / 2
# Process samples and save as gif
gen_samples = (gen_samples * 255).type(torch.uint8)
gen_samples = gen_samples.reshape(-1, gif_shape[0], gif_shape[1], train_dataset.size, train_dataset.size, train_dataset.depth)
def stack_samples(gen_samples, stack_dim):
gen_samples = list(torch.split(gen_samples, 1, dim=1))
for i in range(len(gen_samples)):
gen_samples[i] = gen_samples[i].squeeze(1)
return torch.cat(gen_samples, dim=stack_dim)
gen_samples = stack_samples(gen_samples, 2)
gen_samples = stack_samples(gen_samples, 2)
imageio.mimsave(
f"{trainer.logger.log_dir}/pred.gif",
list(gen_samples),
fps=5,
)