def evalloop(self):
self.tt.tick("testing")
tt = ticktock("-")
totaltestbats = len(self.dataloader)
self.model.eval()
outs = []
with torch.no_grad():
for i, batch in enumerate(self.dataloader):
batch = (batch, ) if not q.issequence(batch) else batch
batch = [batch_e.to(self._device) for batch_e in batch]
if self.transform_batch_inp is not None:
batch = self.transform_batch_inp(*batch)
batch_reset(self.model)
modelouts = self.model(*batch)
if self.transform_batch_out is not None:
modelouts = self.transform_batch_out(modelouts)
tt.live("eval - [{}/{}]".format(i + 1, totaltestbats))
outs.append(modelouts)
ttmsg = "eval done"
tt.stoplive()
tt.tock(ttmsg)
self.tt.tock("tested")
out = torch.cat(outs, 0)
return out
def __init__(self, model):
super(eval, self).__init__()
self.model = model
self._device = torch.device("cpu")
self.transform_batch_inp = None
self.transform_batch_out = None
self.dataloader = None
self.tt = ticktock("eval")
def testloop(self, epoch=None):
if epoch is None:
self.tt.tick("testing")
tt = ticktock("-")
self.model.eval()
self.do_callbacks(self.START_TEST)
self.losses.push_and_reset()
totalbats = len(self.dataloader)
for i, _batch in enumerate(self.dataloader):
self.do_callbacks(self.START_BATCH)
_batch = (_batch, ) if not q.issequence(_batch) else _batch
_batch = [batch_e.to(self._device) for batch_e in _batch]
if self.transform_batch_inp is not None:
batch = self.transform_batch_inp(*_batch)
else:
batch = _batch
if self.no_gold:
batch_in = batch
gold = None
else:
batch_in = batch[:-1]
gold = batch[-1]
batch_reset(self.model)
modelouts = self.model(*batch_in)
modelout2loss = modelouts
if self.transform_batch_out is not None:
modelout2loss = self.transform_batch_out(modelouts)
gold = batch[-1]
if self.transform_batch_gold is not None:
gold = self.transform_batch_gold(gold)
losses = self.losses(modelout2loss, gold)
epochmsg = ""
if epoch is not None:
curepoch, maxepoch = epoch
epochmsg = "Epoch {}/{} -".format(curepoch, maxepoch)
tt.live("{} - {}[{}/{}]: {}".format(self._name, epochmsg, i + 1,
totalbats, self.losses.pp()))
self.do_callbacks(self.END_BATCH)
# losses = self.losses.get_agg_errors()
tt.stoplive()
ttmsg = "{}: {}" \
.format(
self._name,
self.losses.pp()
)
self.do_callbacks(self.END_TEST)
if epoch is None:
tt.tock(ttmsg)
self.tt.tock("tested")
return ttmsg
def __init__(self, model, **kw):
super(tester, self).__init__(**kw)
self.model = model
self.losses = None
self.transform_batch_inp = None
self.transform_batch_out = None
self.transform_batch_gold = None
self.dataloader = None
self.tt = ticktock(self._name)
self._device = torch.device("cpu")
def trainloop(self):
if self.max_epochs == 0:
self.tt.msg("skipping training")
return
self.stop_training = False
self.tt.tick("training")
tt = ticktock("-")
while not self.stop_training:
tt.tick()
ttmsg = self.do_epoch(tt=tt)
tt.tock(ttmsg)
self.tt.tock("trained")
def __init__(self, model, **kw):
super(trainer, self).__init__(**kw)
self.model = model
self.losses = None
self.max_epochs = None
self.current_epoch = 0
self.stop_training = None
self._device = torch.device("cpu")
self.optim = None
self.transform_batch_inp = None
self.transform_batch_out = None
self.transform_batch_gold = None
self.dataloader = None
self.tt = ticktock("trainer")