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sample_ldm_discrete_all.py
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sample_ldm_discrete_all.py
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from tools.fid_score import calculate_fid_given_paths
import ml_collections
import torch
from torch import multiprocessing as mp
import accelerate
import utils
import sde
import einops
from datasets import get_dataset
import tempfile
from dpm_solver_pp import NoiseScheduleVP, DPM_Solver
from absl import logging
import builtins
import libs.autoencoder
import torch.nn as nn
import numpy as np
import os
from tqdm import tqdm
from torchvision.utils import make_grid, save_image
from absl import logging
import pickle
def stable_diffusion_beta_schedule(linear_start=0.00085, linear_end=0.0120, n_timestep=1000):
_betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
)
return _betas.numpy()
def evaluate(config):
if config.get('benchmark', False):
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
mp.set_start_method('spawn')
accelerator = accelerate.Accelerator()
device = accelerator.device
accelerate.utils.set_seed(config.seed, device_specific=True)
logging.info(f'Process {accelerator.process_index} using device: {device}')
config.mixed_precision = accelerator.mixed_precision
config = ml_collections.FrozenConfigDict(config)
if accelerator.is_main_process:
utils.set_logger(log_level='info', fname=config.output_path)
else:
utils.set_logger(log_level='error')
builtins.print = lambda *args: None
dataset = get_dataset(**config.dataset)
nnet = utils.get_nnet(**config.nnet)
nnet = accelerator.prepare(nnet)
if config.nnet_path == '':
cluster_name = config.model_name + '-' + '-'.join(config.subset_path.split('/')).split('.txt')[0]
nnet_path = f'{config.dpm_path}/{cluster_name}/{config.resolution}/ckpts/{config.train.n_steps}.ckpt/nnet_ema.pth'
else:
nnet_path = config.nnet_path
logging.info(f'load nnet from {nnet_path}')
accelerator.unwrap_model(nnet).load_state_dict(torch.load(nnet_path, map_location='cpu'))
nnet.eval()
autoencoder = libs.autoencoder.get_model(config.autoencoder.pretrained_path)
autoencoder.to(device)
@torch.cuda.amp.autocast()
def encode(_batch):
return autoencoder.encode(_batch)
@torch.cuda.amp.autocast()
def decode(_batch):
return autoencoder.decode(_batch)
def decode_large_batch(_batch):
decode_mini_batch_size = 20 # use a small batch size since the decoder is large
xs = []
pt = 0
for _decode_mini_batch_size in utils.amortize(_batch.size(0), decode_mini_batch_size):
x = decode(_batch[pt: pt + _decode_mini_batch_size])
pt += _decode_mini_batch_size
xs.append(x)
xs = torch.concat(xs, dim=0)
assert xs.size(0) == _batch.size(0)
return xs
if 'cfg' in config.sample and config.sample.cfg and config.sample.scale > 0: # classifier free guidance
logging.info(f'Use classifier free guidance with scale={config.sample.scale}')
def cfg_nnet(x, timesteps, y):
_cond = nnet(x, timesteps, y=y)
_uncond = nnet(x, timesteps, y=torch.tensor([dataset.K] * x.size(0), device=device))
return _cond + config.sample.scale * (_cond - _uncond)
else:
def cfg_nnet(x, timesteps, y):
_cond = nnet(x, timesteps, y=y)
return _cond
logging.info(config.sample)
assert os.path.exists(dataset.fid_stat)
logging.info(f'sample: each class sample n_samples={config.augmentation_K}, mode={config.train.mode}, mixed_precision={config.mixed_precision}')
_betas = stable_diffusion_beta_schedule()
N = len(_betas)
def amortize(n_samples, batch_size):
k = n_samples // batch_size
r = n_samples % batch_size
return k * [batch_size] if r == 0 else k * [batch_size] + [r]
def sample2dir(accelerator, path, n_samples, mini_batch_size, sample_fn, unpreprocess_fn=None, class_num=0):
os.makedirs(path, exist_ok=True)
idx = 0
batch_size = mini_batch_size * accelerator.num_processes
for _batch_size in tqdm(amortize(n_samples, batch_size), disable=not accelerator.is_main_process, desc='sample2dir'):
samples = unpreprocess_fn(sample_fn(mini_batch_size, class_num))
samples = accelerator.gather(samples.contiguous())[:_batch_size]
if accelerator.local_process_index == 0:
for sample in samples:
save_image(sample, os.path.join(path, f"{idx}.png"))
idx += 1
def sample_z(_n_samples, _sample_steps, **kwargs):
_z_init = torch.randn(_n_samples, *config.z_shape, device=device)
if config.sample.algorithm == 'dpm_solver':
noise_schedule = NoiseScheduleVP(schedule='discrete', betas=torch.tensor(_betas, device=device).float())
def model_fn(x, t_continuous):
t = t_continuous * N
eps_pre = cfg_nnet(x, t, **kwargs)
return eps_pre
dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True, thresholding=False)
_z = dpm_solver.sample(_z_init, steps=_sample_steps, eps=1. / N, T=1.)
else:
raise NotImplementedError
return _z
def sample_fn(_n_samples, class_num):
if config.train.mode == 'uncond':
kwargs = dict()
elif config.train.mode == 'cond':
torch_arr = torch.ones(_n_samples // 10, device=device, dtype=int) * torch.tensor(int(class_num))
kwargs = dict(y=einops.repeat(torch_arr % dataset.K, 'nrow -> (nrow ncol)', ncol=10))
else:
raise NotImplementedError
_z = sample_z(_n_samples, _sample_steps=config.sample.sample_steps, **kwargs)
return decode_large_batch(_z)
f_read = open('idx_to_class.pkl', 'rb')
dict2 = pickle.load(f_read)
print(dict2)
for i in range(1000):
if config.sample.path == '':
aug_samples_path = f'{config.dpm_path}/{cluster_name}/{config.resolution}/samples_for_classifier/aug_{config.augmentation_K}_samples'
path = os.path.join(aug_samples_path, f'train/{dict2[i]}')
else:
path = os.path.join(config.sample.path, f'{dict2[i]}')
if accelerator.is_main_process:
os.makedirs(path, exist_ok=True)
sample2dir(accelerator, path, config.augmentation_K, config.sample.mini_batch_size, sample_fn, dataset.unpreprocess, class_num = i)
if config.sample.path != '' and accelerator.is_main_process:
os.system(f"cd {config.sample.path} && cd .. && tar -zcf samples.tar.gz samples")
from absl import flags
from absl import app
from ml_collections import config_flags
import os
import sys
from pathlib import Path
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file(
"config", None, "Training configuration.", lock_config=False)
flags.mark_flags_as_required(["config"])
flags.DEFINE_string("nnet_path", '', "The nnet to evaluate.")
flags.DEFINE_string("output_path", None, "The path to output log.")
def get_config_name():
argv = sys.argv
for i in range(1, len(argv)):
if argv[i].startswith('--config='):
return Path(argv[i].split('=')[-1]).stem
def get_hparams():
argv = sys.argv
lst = []
for i in range(1, len(argv)):
assert '=' in argv[i]
if argv[i].startswith('--config.'):
hparam, val = argv[i].split('=')
hparam = hparam.split('.')[-1]
if hparam.endswith('path'):
val = Path(val).stem
lst.append(f'{hparam}={val}')
hparams = '-'.join(lst)
if hparams == '':
hparams = 'default'
return hparams
def main(argv):
config = FLAGS.config
config_name = get_config_name()
hparams = get_hparams()
config.project = config_name
config.notes = hparams
config.nnet_path = FLAGS.nnet_path
config.output_path = FLAGS.output_path
evaluate(config)
if __name__ == "__main__":
app.run(main)