def main():
args = make_parser().parse_args()
ns = parse_cfg(os.getcwd() if not args.basedir else args.basedir, args.cfg)
net, m = viz.load_trained_net(ns.model_name, ns.savedir, ns.progress)
bw = (m.input_color == 'gray')
normalize_stats = vmdata.get_normalization_stats(ns.root, bw=bw)
assert bw, 'Illegal bw ({})'.format(bw)
transform = BWCAEPreprocess(trans.Normalize(*normalize_stats),
pool_scale=m.pool_scale,
downsample_scale=ns.downsample_scale)
logging.info('Complete initialization: config={}'.format(ns))
viz.visualize(ns.todir, ns.root, transform, normalize_stats,
ns.visualize_indices, net, m, temperature=ns.temperature,
bwth=ns.bwth, device=ns.device, batch_size=ns.batch_size)
import logging
logging.basicConfig(level=logging.INFO, format='%(name)s %(asctime)s -- %(message)s',
filename='main.{}.log'.format(_rid))
import sys
import torchvision.transforms as trans
import vmdata
import ezfirstae.loaddata as ld
import ezfirstae.train as train
max_epoch = 1
root = vmdata.dataset_root(9, (8, 0, 0))
normalize = trans.Normalize(*vmdata.get_normalization_stats(root, bw=True))
transform = ld.PreProcTransform(normalize, pool_scale=8, downsample_scale=3)
statdir = 'stat.{}'.format(_rid)
savedir = 'save.{}'.format(_rid)
device = 'cuda'
if __name__ == '__main__':
logger = logging.getLogger()
logger.info('Begin training: model=ezfirstae.models.pred9_f1to8(no-attention)')
with vmdata.VideoDataset(root, transform=transform, max_mmap=3, max_gzcache=100) as vdset:
trainset, testset = ld.contiguous_partition_dataset(range(len(vdset)), (5, 1))
try:
train.train_pred9_f1to8_no_attn(vdset, trainset, testset,
savedir, statdir, device, max_epoch)
except KeyboardInterrupt:
logger.warning('User interrupt')