def __call__(self, iter=None):
iter = self._iter if iter is None else iter
self.generator.eval()
with torch.no_grad():
# collect generated images
generated = []
self.tt.tick("running generator")
for i, batch in enumerate(self.gendata):
batch = (batch, ) if not q.issequence(batch) else batch
batch = [
torch.tensor(batch_e).to(self.device) for batch_e in batch
]
_gen = self.generator(*batch).detach().cpu()
_gen = _gen[0] if q.issequence(_gen) else _gen
generated.append(_gen)
self.tt.live("{}/{}".format(i, len(self.gendata)))
batsize = max(map(len, generated))
generated = torch.cat(generated, 0)
self.tt.tock("generated data")
gen_loaded = q.dataload(generated,
batch_size=batsize,
shuffle=False)
rets = [iter]
for scorer in self.scorers:
ret = scorer(gen_loaded)
if ret is not None:
rets.append(ret)
if self.logger is not None:
self.logger.liner_write("validator-{}.txt".format(self.name),
" ".join(map(str, rets)))
self._iter += 1
return " ".join(map(str, rets[1:]))
def forward(self, ldata, rdata):
ldata = ldata if q.issequence(ldata) else (ldata, )
rdata = rdata if q.issequence(rdata) else (rdata, )
# q.embed()
lvecs = self.lmodel(*ldata) # 2D
rvecs = self.rmodel(*rdata) # 2D
psim = self.sim(lvecs, rvecs) # 1D:(batsize,)
return psim
def __init__(self, scoremodel, eids, ldata, rdata, eid2rid_gold,
eid2rid_neg):
self.scoremodel = scoremodel
self.eids = eids
self.ldata = ldata if q.issequence(ldata) else (ldata,
) # already shuffled
self.rdata = rdata if q.issequence(rdata) else (
rdata, ) # indexed by eid space
self.eid2rid_neg = eid2rid_neg # indexed by eid space
self.eid2rid_gold = eid2rid_gold # indexed by eid space
def forward(self, ldata, posrdata, negrdata):
ldata = ldata if q.issequence(ldata) else (ldata, )
posrdata = posrdata if q.issequence(posrdata) else (posrdata, )
negrdata = negrdata if q.issequence(negrdata) else (negrdata, )
lvecs = self.lmodel(*ldata) # 2D
rvecs = self.rmodel(*posrdata) # 2D
nrvecs = self.rmodel(*negrdata)
psim = self.sim(lvecs, rvecs) # 1D:(batsize,)
nsim = self.sim(lvecs, nrvecs)
diffs = psim - nsim
zeros = q.var(torch.zeros_like(diffs.data)).cuda(diffs).v
losses = torch.max(zeros, self.margin - diffs)
return losses
def __init__(self, size_average=True, ignore_index=None, **kw):
super(DiscreteLoss, self).__init__(size_average=size_average, **kw)
if ignore_index is not None:
if not q.issequence(ignore_index):
self.ignore_indices = [ignore_index]
else:
self.ignore_indices = None
def dataset(*x):
if q.issequence(x):
assert(len(x) == 1)
x = x[0]
# if not issequence(x):
# x = (x,)
return tensor_dataset(*x)
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 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, size_average=True, ignore_index=0):
super(OldOldSeqAccuracy, self).__init__()
self.size_average = size_average
if ignore_index is not None:
if not q.issequence(ignore_index):
ignore_index = [ignore_index]
self.ignore_index = ignore_index
else:
self.ignore_index = None
self.EPS = 1e-6
def __call__(self, gendata):
ret = []
for batch in gendata:
if not q.issequence(batch):
batch = (batch, )
ret.append(batch)
ret = [[batch_e.cpu() for batch_e in batch] for batch in ret]
ret = [torch.cat(ret_i, 0).numpy() for ret_i in zip(*ret)]
tosave = dict(zip(map(str, range(len(ret))), ret))
if self.logger is not None:
np.savez(os.path.join(self.logger.p, self.p), **tosave)
def __call__(self, pred, gold, **kw):
l = self.loss(pred, gold, **kw)
numex = pred.size(0) if not q.issequence(pred) else pred[0].size(0)
if isinstance(l, tuple) and len(l) == 2: # loss returns numex too
numex = l[1]
l = l[0]
if isinstance(l, torch.Tensor):
lp = l.item()
else:
lp = l
self.update_agg(lp, numex)
return l
def __init__(self,
weight=None,
size_average=True,
time_average=True,
ignore_index=0):
if ignore_index is not None:
if not q.issequence(ignore_index):
ignore_index = [ignore_index]
else:
ignore_index = None
super(SeqNLLLoss, self).__init__(weight=weight,
size_average=size_average,
ignore_index=ignore_index)
self.time_average = time_average
def __call__(self, data):
"""
:param data: dataloader
:return:
"""
# 1. get a numpy array from dataloader
x = []
for i, batch in enumerate(data):
batch = (batch, ) if not q.issequence(batch) else batch
assert (len(batch) == 1)
x.append(batch[0])
x = np.concatenate(x, axis=0)
print(x.shape)
# 2. use tf code above to get the scores
means, vars = self.get_inception_score(x)
return means, vars
def __init__(self,
disc_trainer,
gen_trainer,
validators=None,
lr_decay=False):
"""
Creates a GAN trainer given a gen_trainer and disc_trainer.
both trainers already contain the model, optimizer and losses and implement updating and batching
Takes a validator or a list of validators (with different validinters).
"""
super(GANTrainer, self).__init__()
self.disc_trainer = disc_trainer
self.gen_trainer = gen_trainer
if not q.issequence(validators) and validators is not None:
validators = (validators, )
self.validators = validators
self.stop_training = False
self.lr_decay = lr_decay
def get_inception_outs(self, data): # dataloader
tt = q.ticktock("inception")
tt.tick("running data through network")
probses = []
activationses = []
for i, batch in enumerate(data):
batch = (batch, ) if not q.issequence(batch) else batch
batch = [
torch.tensor(batch_e).to(self.device) for batch_e in batch
]
probs, activations = self.inception(*batch)
probs = torch.nn.functional.softmax(probs)
probses.append(probs.detach())
activationses.append(activations.detach())
tt.live("{}/{}".format(i, len(data)))
tt.stoplive()
tt.tock("done")
probses = torch.cat(probses, 0)
activationses = torch.cat(activationses, 0)
return probses.cpu().detach().numpy(), activationses.cpu().detach(
).numpy()
def do_batch(self, _batch, i=-1):
"""
performs a single batch of SGD on the provided batch
with configured model, dataloader and optimizer
"""
self.do_callbacks(self.START_BATCH)
self.optim.zero_grad()
self.model.train()
# params = q.params_of(self.model)
_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)
if self.transform_batch_gold is not None:
gold = self.transform_batch_gold(gold)
trainlosses = self.losses(modelout2loss, gold)
# TODO: put in penalty mechanism
cost = trainlosses[0]
if torch.isnan(cost).any():
print("Cost is NaN!")
q.embed()
penalties = 0.
penalty_values = {}
for penalty_module in q.gather_penalties(self.model):
pen = penalty_module.get_penalty()
penalties = penalties + pen
if type(penalty_module) not in penalty_values:
penalty_values[type(penalty_module)] = 0.
penalty_values[type(penalty_module)] += pen.detach().item()
cost = cost + penalties
cost.backward()
self.do_callbacks(self.BEFORE_OPTIM_STEP)
self.optim.step()
self.do_callbacks(self.AFTER_OPTIM_STEP)
ttmsg = "train - Epoch {}/{} - [{}/{}]: {}".format(
self.current_epoch + 1,
self.max_epochs,
i + 1,
len(self.dataloader),
self.losses.pp(),
)
if len(penalty_values) > 0:
ttmsg += " " + " ".join([
"+{}={:.4f}".format(k.__pp_name__, v)
for k, v in penalty_values.items()
])
self.do_callbacks(self.END_BATCH)
return ttmsg
def forward(self, x, whereat, ctx=None, ctx_mask=None):
"""
:param x: (batsize, out_seqlen) integer ids of the words in the partial tree so far
:param whereat: (batsize,) integers, after which position index in x to insert predicted token
:param ctx: (batsize, in_seqlen, dim) the context to be used with attention
:return:
"""
if isinstance(self.out, q.AutoMaskedOut):
self.out.update(x, whereat)
mask = None
embs = self.emb(x)
if q.issequence(embs) and len(embs) == 2:
embs, mask = embs
if mask is None:
mask = torch.ones_like(x[:, :, 0])
y = self.encoder(x, mask=mask)
whereat_sel = whereat.view(whereat.size(0), 1,
1).repeat(1, 1,
y.size(2) // 2)
z_fwd = y[:, :, :y.size(2) // 2].gather(1, whereat_sel).squeeze(
1) # (batsize, dim) # not completely correct but fine
z_rev = y[:, :, y.size(2) // 2:].gather(1, whereat_sel + 1).squeeze(1)
z = torch.cat([z_fwd, z_rev], 1)
core_inp = embs
if self.att is not None:
assert (ctx is not None)
if self.feed_att:
if self._h_hat_tm1 is None:
assert (self.h_hat_0 is not None
) #"h_hat_0 must be set when feed_att=True"
self._h_hat_tm1 = self.h_hat_0
core_inp = torch.cat([core_inp, self._h_hat_tm1], 1)
core_out = self.core(core_inp)
alphas, summaries, scores = None, None, None
out_vec = core_out
if self.att is not None:
alphas, summaries, scores = self.att(core_out,
ctx,
ctx_mask=ctx_mask,
values=ctx)
out_vec = torch.cat([core_out, summaries], 1)
out_vec = self.merge(
out_vec) if self.merge is not None else out_vec
self._h_hat_tm1 = out_vec
ret = tuple()
if not self.return_outvecs:
out_vec = self.out(out_vec)
ret += (out_vec, )
if self.return_alphas:
ret += (alphas, )
if self.return_scores:
ret += (scores, )
if self.return_other:
ret += (embs, core_out, summaries)
return ret[0] if len(ret) == 1 else ret
def _forward(self, model_outs, gold, **kw):
if q.issequence(model_outs):
return model_outs[self.which], None
else:
assert (self.which == 0)
return model_outs, None
