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image_sample.py
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image_sample.py
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"""
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
import argparse
import os
import random
import torch as th
import torch.distributed as dist
import torchvision as tv
import numpy as np
from guided_diffusion.image_datasets import load_data
from guided_diffusion import dist_util, logger
from guided_diffusion.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
add_dict_to_argparser,
args_to_dict,
)
def main():
args = create_argparser().parse_args()
logger.log("seed: "+str(args.seed))
deterministic = True
random.seed(args.seed)
np.random.seed(args.seed)
th.manual_seed(args.seed)
if deterministic:
th.backends.cudnn.deterministic = True
th.backends.cudnn.benchmark = False
dist_util.setup_dist(gpus_per_node=args.gpus_per_node)
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys())
)
model.load_state_dict(
dist_util.load_state_dict(args.model_path, map_location="cpu")
)
model.to(dist_util.dev())
logger.log("creating data loader...")
data = load_data(
dataset_mode=args.dataset_mode,
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
deterministic=True,
random_crop=False,
random_flip=False,
is_train=False
)
if args.use_fp16:
model.convert_to_fp16()
model.eval()
image_path = os.path.join(args.results_path, 'images')
os.makedirs(image_path, exist_ok=True)
label_path = os.path.join(args.results_path, 'labels')
os.makedirs(label_path, exist_ok=True)
sample_path = os.path.join(args.results_path, 'samples')
os.makedirs(sample_path, exist_ok=True)
logger.log("sampling...")
all_samples = []
for i, (batch, cond) in enumerate(data):
image = ((batch + 1.0) / 2.0).cuda()
label = (cond['label_ori'].float() / 255.0).cuda()
model_kwargs = preprocess_input(cond, num_classes=args.num_classes)
model_kwargs['results_path'] = args.results_path
if args.unlabeled_to_zero:
y = model_kwargs['y']
mask = th.nn.functional.one_hot(th.tensor([y.shape[1]-1]),num_classes=y.shape[1])
mask = th.ones_like(mask) - mask
mask = mask.unsqueeze(1).unsqueeze(2)
model_kwargs['y'] = (y.permute(0,2,3,1)*mask).permute(0,3,1,2)
if args.seed_align:
y = model_kwargs['y']
_ = th.rand((y.shape[0],y.shape[2],y.shape[3]),device=image.device)
# set hyperparameter
model_kwargs['s'] = args.s
model_kwargs['dynamic_threshold'] = args.dynamic_threshold
model_kwargs['extrapolation'] = args.extrapolation
sample_fn = (
diffusion.p_sample_loop
)
sample = sample_fn(
model,
(args.batch_size, 3, image.shape[2], image.shape[3]),
clip_denoised=args.clip_denoised,
model_kwargs=model_kwargs,
progress=True,
noise=None
)
sample = (sample + 1) / 2.0
gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_samples, sample) # gather not supported with NCCL
all_samples.extend([sample.cpu().numpy() for sample in gathered_samples])
for j in range(sample.shape[0]):
tv.utils.save_image(image[j], os.path.join(image_path, cond['path'][j].split('/')[-1].split('.')[0] + '.png'))
tv.utils.save_image(sample[j], os.path.join(sample_path, cond['path'][j].split('/')[-1].split('.')[0] + '.png'))
tv.utils.save_image(label[j], os.path.join(label_path, cond['path'][j].split('/')[-1].split('.')[0] + '.png'))
logger.log(f"created {len(all_samples) * args.batch_size} samples")
if len(all_samples) * args.batch_size >= args.num_samples:
break
dist.barrier()
logger.log("sampling complete")
def preprocess_input(data, num_classes):
# move to GPU and change data types
data['label'] = data['label'].long()
# create one-hot label map
label_map = data['label']
bs, _, h, w = label_map.size()
input_label = th.FloatTensor(bs, num_classes, h, w).zero_()
input_semantics = input_label.scatter_(1, label_map, 1.0)
# concatenate instance map if it exists
if 'instance' in data:
inst_map = data['instance']
instance_edge_map = get_edges(inst_map)
input_semantics = th.cat((input_semantics, instance_edge_map), dim=1)
return {'y': input_semantics}
def get_edges(t):
edge = th.ByteTensor(t.size()).zero_()
edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
return edge.float()
def create_argparser():
defaults = dict(
data_dir="",
dataset_mode="",
clip_denoised=True,
num_samples=10000,
batch_size=1,
model_path="",
results_path="",
is_train=False,
s=1.0,
gpus_per_node=4,
gpu=None,
unlabeled_to_zero = False,
dynamic_threshold = -1.0,
extrapolation = -1.0,
seed = 42,
seed_align = False,
)
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()