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import argparse
import os
import torch
from imaginaire.config import Config
from imaginaire.utils.cudnn import init_cudnn
from imaginaire.utils.dataset import get_test_dataloader
from imaginaire.utils.distributed import init_dist
from imaginaire.utils.gpu_affinity import set_affinity
from imaginaire.utils.io import get_checkpoint as get_checkpoint
from imaginaire.utils.logging import init_logging
from imaginaire.utils.trainer import \
(get_model_optimizer_and_scheduler, set_random_seed)
import imaginaire.config
def parse_args():
parser = argparse.ArgumentParser(description='Training')
parser.add_argument('--config', required=True,
help='Path to the training config file.')
parser.add_argument('--checkpoint', default='',
help='Checkpoint path.')
parser.add_argument('--output_dir', required=True,
help='Location to save the image outputs')
parser.add_argument('--logdir',
help='Dir for saving logs and models.')
parser.add_argument('--seed', type=int, default=0,
help='Random seed.')
parser.add_argument('--debug', action='store_true')
args = parser.parse_args()
return args
def main():
args = parse_args()
cfg = Config(args.config)
imaginaire.config.DEBUG = args.debug
if not hasattr(cfg, 'inference_args'):
cfg.inference_args = None
# Create log directory for storing training results.
cfg.date_uid, cfg.logdir = init_logging(args.config, args.logdir)
# Initialize cudnn.
init_cudnn(cfg.cudnn.deterministic, cfg.cudnn.benchmark)
# Initialize data loaders and models.
net_G = get_model_optimizer_and_scheduler(cfg, seed=args.seed, generator_only=True)
if args.checkpoint == '':
raise NotImplementedError("No checkpoint is provided for inference!")
# Load checkpoint.
# trainer.load_checkpoint(cfg, args.checkpoint)
checkpoint = torch.load(args.checkpoint, map_location='cpu')
net_G.load_state_dict(checkpoint['net_G'])
# Do inference.
net_G = net_G.module
net_G.eval()
for name, param in net_G.named_parameters():
param.requires_grad = False
torch.cuda.empty_cache()
device = torch.device('cuda')
rng_cuda = torch.Generator(device=device)
rng_cuda = rng_cuda.manual_seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
world_dir = os.path.join(args.output_dir)
os.makedirs(world_dir, exist_ok=True)
print('[PCGGenerator] Generating BEV scene representation...')
os.system('python terrain_generator.py --size {} --seed {} --outdir {}'.format(net_G.voxel.sample_size, args.seed, world_dir))
net_G.voxel.next_world(device, world_dir, checkpoint)
cam_mode = cfg.inference_args.camera_mode
current_outdir = os.path.join(world_dir, 'camera_{:02d}'.format(cam_mode))
os.makedirs(current_outdir, exist_ok=True)
os.makedirs(current_outdir, exist_ok=True)
z = torch.empty(1, net_G.style_dims, dtype=torch.float32, device=device)
z.normal_(generator=rng_cuda)
net_G.inference_givenstyle(z, current_outdir, **vars(cfg.inference_args))
if __name__ == "__main__":
main()
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