def main():
args = make_parser().parse_args()
root = vmdata.dataset_root(args.camera, (8, 0, 0))
with vmdata.VideoDataset(root) as vdset:
_analyze_flow = partial(analyze_flow, vdset)
if args.debug:
print(args.fids)
return
for fid in args.fids:
print('Running on frame {}'.format(fid), file=sys.stderr)
_analyze_flow(fid)
def parse_cfg(basedir: str, filename: str) -> SimpleNamespace:
runid = int(re.findall(r'\b(\d+)\b', os.path.splitext(
os.path.basename(filename))[0])[0])
ini = ConfigParser()
ini.read(filename)
ns = SimpleNamespace()
camera_id = list(map(int, map(str.strip, ini['data']['camera'].split(','))))
ns.root = vmdata.dataset_root(camera_id[0], tuple(camera_id[1:4]))
ns.downsample_scale = int(ini['data']['downsample_scale'].strip())
ns.model_name = ini['model']['model_name'].strip()
run_name = ini['model']['run_name'].strip()
ns.savedir = os.path.join(basedir, '{}.{}'.format(run_name, runid), 'save')
ns.progress = tuple(map(int, map(str.strip, ini['model']['progress'].split(','))))
ns.visualize_indices = range(*tuple(map(int, map(
str.strip, ini['eval']['indices'].split(',')))))
try:
temperature = float(ini['eval']['temperature'].strip())
except (KeyError, ValueError):
temperature = 1.0
ns.temperature = temperature
try:
bwth = float(ini['eval']['bwth'].strip())
except (KeyError, ValueError):
bwth = None
ns.bwth = bwth
ns.todir = os.path.join(basedir,
'{}.{}.viz'.format(run_name, runid),
'{}_{}'.format(*ns.progress),
't{:.2f}'.format(ns.temperature))
try:
device = ini['eval']['device'].strip()
except KeyError:
device = 'cpu'
ns.device = device
try:
batch_size = int(ini['eval']['batch_size'].strip())
except KeyError:
batch_size = 8
ns.batch_size = batch_size
return ns
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: