def run_fthmc(
flow: nn.ModuleList,
config: TrainConfig,
lfconfig: lfConfig,
xi: torch.Tensor = None,
nprint: int = 50,
nplot: int = 10,
window: int = 1,
num_trajs: int = 1024,
**kwargs,
):
logger.rule(f'Running `ftHMC` using trained flow for {num_trajs} trajs')
if torch.cuda.is_available():
flow.to('cuda')
flow.eval()
ft = FieldTransformation(flow=flow, config=config, lfconfig=lfconfig)
logdir = config.logdir
ftstr = lfconfig.uniquestr()
fthmcdir = os.path.join(logdir, 'ftHMC', ftstr)
pdir = os.path.join(fthmcdir, 'plots')
sdir = os.path.join(fthmcdir, 'summaries')
writer = SummaryWriter(log_dir=sdir)
history = ft.run(x=xi, nprint=nprint, nplot=nplot, window=window,
num_trajs=num_trajs, writer=writer, plotdir=pdir,
**kwargs)
histfile = os.path.join(fthmcdir, 'history.z')
logger.log(f'Saving history to: {histfile}')
joblib.dump(history, histfile)
return {'field_transformation': ft, 'history': history}
def __init__(self, num_classes: int, base_net: nn.ModuleList, source_layer_indexes: List[int],
extras: nn.ModuleList, classification_headers: nn.ModuleList,
regression_headers: nn.ModuleList, is_test=False, config=None, device=None):
"""Compose a SSD model using the given components.
"""
super(SSD, self).__init__()
self.device = device
self.num_classes = num_classes
self.base_net = base_net.to(self.device)
self.source_layer_indexes = source_layer_indexes
self.extras = extras
self.classification_headers = classification_headers
self.regression_headers = regression_headers
self.is_test = is_test
self.config = config
self.source_layer_add_ons = nn.ModuleList([t[1] for t in source_layer_indexes
if isinstance(t, tuple) and not isinstance(t, GraphPath)])
if is_test:
self.config = config
self.priors = config.priors.to(self.device)