def create_model(embdim=100,
hdim=100,
dropout=0.,
numlayers: int = 1,
sentence_encoder: SentenceEncoder = None,
query_encoder: SentenceEncoder = None,
smoothing: float = 0.,
feedatt=False):
inpemb = torch.nn.Embedding(sentence_encoder.vocab.number_of_ids(),
embdim,
padding_idx=0)
inpemb = TokenEmb(inpemb,
rare_token_ids=sentence_encoder.vocab.rare_ids,
rare_id=1)
encoder_dim = hdim
encoder = q.LSTMEncoder(embdim,
*([encoder_dim // 2] * numlayers),
bidir=True,
dropout_in=dropout)
# encoder = PytorchSeq2SeqWrapper(
# torch.nn.LSTM(embdim, hdim, num_layers=numlayers, bidirectional=True, batch_first=True,
# dropout=dropout))
decoder_emb = torch.nn.Embedding(query_encoder.vocab.number_of_ids(),
embdim,
padding_idx=0)
decoder_emb = TokenEmb(decoder_emb,
rare_token_ids=query_encoder.vocab.rare_ids,
rare_id=1)
dec_rnn_in_dim = embdim + (encoder_dim if feedatt else 0)
decoder_rnn = [torch.nn.LSTMCell(dec_rnn_in_dim, hdim)]
for i in range(numlayers - 1):
decoder_rnn.append(torch.nn.LSTMCell(hdim, hdim))
decoder_rnn = LSTMCellTransition(*decoder_rnn, dropout=dropout)
decoder_out = BasicGenOutput(hdim + encoder_dim, sentence_encoder.vocab,
query_encoder.vocab)
attention = q.Attention(q.MatMulDotAttComp(hdim, encoder_dim))
enctodec = torch.nn.Sequential(torch.nn.Linear(encoder_dim, hdim),
torch.nn.Tanh())
model = BasicPtrGenModel(inpemb,
encoder,
decoder_emb,
decoder_rnn,
decoder_out,
attention,
dropout=dropout,
enc_to_dec=enctodec,
feedatt=feedatt)
dec = TFTokenSeqDecoder(model, smoothing=smoothing)
return dec
def __init__(self,
qrydim=None,
ctxdim=None,
encdim=None,
dropout=0.,
numlayers=1,
**kw):
super(SigmoidLSTMAttComp, self).__init__(**kw)
encdims = [encdim] * numlayers
self.layers = q.LSTMEncoder(qrydim + ctxdim,
*encdims,
bidir=False,
dropout_in=dropout)
self.lin = torch.nn.Linear(encdim, 1)
self.act = torch.nn.Sigmoid()
def __init__(self,
embdim,
hdim,
numlayers: int = 1,
dropout=0.,
sentence_encoder: SequenceEncoder = None,
query_encoder: SequenceEncoder = None,
feedatt=False,
store_attn=True,
**kw):
super(BasicGenModel, self).__init__(**kw)
inpemb = torch.nn.Embedding(sentence_encoder.vocab.number_of_ids(),
300,
padding_idx=0)
inpemb = TokenEmb(inpemb,
adapt_dims=(300, embdim),
rare_token_ids=sentence_encoder.vocab.rare_ids,
rare_id=1)
_, covered_word_ids = load_pretrained_embeddings(
inpemb.emb,
sentence_encoder.vocab.D,
p="../../data/glove/glove300uncased"
) # load glove embeddings where possible into the inner embedding class
inpemb._do_rare(inpemb.rare_token_ids - covered_word_ids)
self.inp_emb = inpemb
encoder_dim = hdim
encoder = q.LSTMEncoder(embdim,
*([encoder_dim // 2] * numlayers),
bidir=True,
dropout_in=dropout)
self.inp_enc = encoder
decoder_emb = torch.nn.Embedding(query_encoder.vocab.number_of_ids(),
embdim,
padding_idx=0)
decoder_emb = TokenEmb(decoder_emb,
rare_token_ids=query_encoder.vocab.rare_ids,
rare_id=1)
self.out_emb = decoder_emb
dec_rnn_in_dim = embdim + (encoder_dim if feedatt else 0)
decoder_rnn = [torch.nn.LSTMCell(dec_rnn_in_dim, hdim)]
for i in range(numlayers - 1):
decoder_rnn.append(torch.nn.LSTMCell(hdim, hdim))
decoder_rnn = LSTMCellTransition(*decoder_rnn, dropout=dropout)
self.out_rnn = decoder_rnn
decoder_out = BasicGenOutput(hdim + encoder_dim,
vocab=query_encoder.vocab)
# decoder_out.build_copy_maps(inp_vocab=sentence_encoder.vocab)
self.out_lin = decoder_out
self.att = q.Attention(q.MatMulDotAttComp(hdim, encoder_dim))
self.enc_to_dec = torch.nn.Sequential(
torch.nn.Linear(encoder_dim, hdim), torch.nn.Tanh())
self.feedatt = feedatt
self.nocopy = True
self.store_attn = store_attn
def run_rerank(
lr=0.001,
batsize=20,
epochs=1,
embdim=301, # not used
encdim=200,
numlayers=1,
beamsize=5,
dropout=.2,
wreg=1e-10,
cuda=False,
gpu=0,
minfreq=2,
gradnorm=3.,
cosine_restarts=1.,
domain="restaurants",
gensavedp="overnight_basic/run{}",
genrunid=1,
):
localargs = locals().copy()
print(locals())
gensavedrunp = gensavedp.format(genrunid)
tt = q.ticktock("script")
device = torch.device("cpu") if not cuda else torch.device("cuda", gpu)
tt.tick("loading data")
ds = q.load_dataset(gensavedrunp)
# ds = OvernightDataset(domain=domain, sentence_encoder=SequenceEncoder(tokenizer=split_tokenizer), min_freq=minfreq)
print(
f"max lens: {ds.maxlen_input} (input) and {ds.maxlen_output} (output)")
tt.tock("data loaded")
do_rare_stats(ds)
# batch = next(iter(train_dl))
# print(batch)
# print("input graph")
# print(batch.batched_states)
genmodel, genargs = q.load_run(gensavedrunp)
# BasicGenModel(embdim=embdim, hdim=encdim, dropout=dropout, numlayers=numlayers,
# sentence_encoder=ds.sentence_encoder, query_encoder=ds.query_encoder, feedatt=True)
# sentence_rare_tokens = set([ds.sentence_encoder.vocab(i) for i in model.inp_emb.rare_token_ids])
# do_rare_stats(ds, sentence_rare_tokens=sentence_rare_tokens)
inpenc = q.LSTMEncoder(embdim,
*([encdim // 2] * numlayers),
bidir=True,
dropout_in=dropout)
outenc = q.LSTMEncoder(embdim,
*([encdim // 2] * numlayers),
bidir=True,
dropout_in=dropout)
scoremodel = SimpleScoreModel(genmodel.inp_emb, genmodel.out_emb,
LSTMEncoderWrapper(inpenc),
LSTMEncoderWrapper(outenc), DotSimilarity())
model = BeamReranker(genmodel, scoremodel, beamsize=beamsize, maxtime=50)
# todo: run over whole dataset to populate beam cache
testbatch = next(iter(ds.dataloader("train", batsize=2)))
model(testbatch)
sys.exit()
tfdecoder = SeqDecoder(TFTransition(model), [
CELoss(ignore_index=0, mode="logprobs"),
SeqAccuracies(),
TreeAccuracy(tensor2tree=partial(tensor2tree,
D=ds.query_encoder.vocab),
orderless={"op:and", "SW:concat"})
])
# beamdecoder = BeamActionSeqDecoder(tfdecoder.model, beamsize=beamsize, maxsteps=50)
freedecoder = BeamDecoder(
model,
maxtime=50,
beamsize=beamsize,
eval_beam=[
TreeAccuracy(tensor2tree=partial(tensor2tree,
D=ds.query_encoder.vocab),
orderless={"op:and", "SW:concat"})
])
# # test
# tt.tick("doing one epoch")
# for batch in iter(train_dl):
# batch = batch.to(device)
# ttt.tick("start batch")
# # with torch.no_grad():
# out = tfdecoder(batch)
# ttt.tock("end batch")
# tt.tock("done one epoch")
# print(out)
# sys.exit()
# beamdecoder(next(iter(train_dl)))
# print(dict(tfdecoder.named_parameters()).keys())
losses = make_array_of_metrics("loss", "seq_acc", "tree_acc")
vlosses = make_array_of_metrics("tree_acc", "tree_acc_at3",
"tree_acc_at_last")
trainable_params = tfdecoder.named_parameters()
exclude_params = {"model.model.inp_emb.emb.weight"
} # don't train input embeddings if doing glove
trainable_params = [
v for k, v in trainable_params if k not in exclude_params
]
# 4. define optim
optim = torch.optim.Adam(trainable_params, lr=lr, weight_decay=wreg)
# optim = torch.optim.SGD(tfdecoder.parameters(), lr=lr, weight_decay=wreg)
# lr schedule
if cosine_restarts >= 0:
# t_max = epochs * len(train_dl)
t_max = epochs
print(f"Total number of updates: {t_max}")
lr_schedule = q.WarmupCosineWithHardRestartsSchedule(
optim, 0, t_max, cycles=cosine_restarts)
reduce_lr = [lambda: lr_schedule.step()]
else:
reduce_lr = []
# 6. define training function
clipgradnorm = lambda: torch.nn.utils.clip_grad_norm_(
tfdecoder.parameters(), gradnorm)
trainbatch = partial(q.train_batch, on_before_optim_step=[clipgradnorm])
trainepoch = partial(q.train_epoch,
model=tfdecoder,
dataloader=ds.dataloader("train", batsize),
optim=optim,
losses=losses,
_train_batch=trainbatch,
device=device,
on_end=reduce_lr)
# 7. define validation function (using partial)
validepoch = partial(q.test_epoch,
model=freedecoder,
dataloader=ds.dataloader("valid", batsize),
losses=vlosses,
device=device)
# validepoch = partial(q.test_epoch, model=freedecoder, dataloader=valid_dl, losses=vlosses, device=device)
# p = q.save_run(freedecoder, localargs, filepath=__file__)
# q.save_dataset(ds, p)
# _freedecoder, _localargs = q.load_run(p)
# _ds = q.load_dataset(p)
# sys.exit()
# 7. run training
tt.tick("training")
q.run_training(run_train_epoch=trainepoch,
run_valid_epoch=validepoch,
max_epochs=epochs)
tt.tock("done training")
# testing
tt.tick("testing")
testresults = q.test_epoch(model=freedecoder,
dataloader=ds.dataloader("test", batsize),
losses=vlosses,
device=device)
print(testresults)
tt.tock("tested")
# save model?
tosave = input(
"Save this model? 'y(es)'=Yes, <int>=overwrite previous, otherwise=No) \n>"
)
if tosave.lower() == "y" or tosave.lower() == "yes" or re.match(
"\d+", tosave.lower()):
overwrite = int(tosave) if re.match("\d+", tosave) else None
p = q.save_run(model,
localargs,
filepath=__file__,
overwrite=overwrite)
q.save_dataset(ds, p)
_model, _localargs = q.load_run(p)
_ds = q.load_dataset(p)
_freedecoder = BeamDecoder(
_model,
maxtime=50,
beamsize=beamsize,
eval_beam=[
TreeAccuracy(tensor2tree=partial(tensor2tree,
D=ds.query_encoder.vocab),
orderless={"op:and", "SW:concat"})
])
# testing
tt.tick("testing reloaded")
_testresults = q.test_epoch(model=_freedecoder,
dataloader=_ds.dataloader("test", batsize),
losses=vlosses,
device=device)
print(_testresults)
assert (testresults == _testresults)
tt.tock("tested")
def run_gatedtree(
lr=0.01,
gradclip=5.,
batsize=20,
epochs=80,
embdim=200,
encdim=200,
numlayer=1,
cuda=False,
gpu=0,
wreg=1e-8,
dropout=0.5,
smoothing=0.4,
goldsmoothing=-0.1,
which="geo",
relatt=False,
):
tt = q.ticktock("script")
tt.msg("running gated tree decoder")
device = torch.device("cpu")
if cuda:
device = torch.device("cuda", gpu)
# region data
tt.tick("generating data")
# dss, D = gen_sort_data(seqlen=seqlen, numvoc=numvoc, numex=numex, prepend_inp=False)
dss, nlD, flD = gen_datasets(which=which)
tloader, vloader, xloader = [
torch.utils.data.DataLoader(ds, batch_size=batsize, shuffle=True)
for ds in dss
]
seqlen = len(dss[0][0][1])
id2pushpop = torch.zeros(len(flD), dtype=torch.long, device=device)
id2pushpop[flD["("]] = +1
id2pushpop[flD[")"]] = -1
tt.tock("data generated")
# endregion
# region model
tt.tick("building model")
# source side
inpemb = q.WordEmb(embdim, worddic=nlD)
encdims = [encdim] * numlayer
encoder = q.LSTMEncoder(embdim,
*encdims,
bidir=False,
dropout_in_shared=dropout)
# target side
decemb = q.WordEmb(embdim, worddic=flD)
decinpdim = embdim
decdims = [decinpdim] + [encdim] * numlayer
dec_core = \
[GatedTreeLSTMCell(decdims[i-1], decdims[i], dropout_in=dropout) for i in range(1, len(decdims))] ###
dec_core = TreeRNNDecoderCellCore(*dec_core)
if relatt:
att = ComboAbsRelAttention(ctxdim=encdim, vecdim=encdim)
else:
att = BasicAttention()
out = torch.nn.Sequential(q.WordLinout(encdim, worddic=flD),
# torch.nn.Softmax(-1)
)
merge = q.rnn.FwdDecCellMerge(decdims[-1], encdims[-1], outdim=encdim)
deccell = TreeRNNDecoderCell(emb=decemb,
core=dec_core,
att=att,
out=out,
merge=merge,
id2pushpop=id2pushpop)
train_dec = q.TFDecoder(deccell)
test_dec = q.FreeDecoder(deccell, maxtime=seqlen + 10)
train_encdec = EncDec(inpemb, encoder, train_dec)
test_encdec = Test_EncDec(inpemb, encoder, test_dec)
train_encdec.to(device)
test_encdec.to(device)
tt.tock("built model")
# endregion
# region training
# losses:
if smoothing == 0:
ce = q.loss.CELoss(mode="logits", ignore_index=0)
elif goldsmoothing < 0.:
ce = q.loss.SmoothedCELoss(mode="logits",
ignore_index=0,
smoothing=smoothing)
else:
ce = q.loss.DiffSmoothedCELoss(mode="logits",
ignore_index=0,
alpha=goldsmoothing,
beta=smoothing)
acc = q.loss.SeqAccuracy(ignore_index=0)
elemacc = q.loss.SeqElemAccuracy(ignore_index=0)
treeacc = TreeAccuracyLambdaDFPar(flD=flD)
# optim
optim = torch.optim.RMSprop(train_encdec.parameters(),
lr=lr,
alpha=0.95,
weight_decay=wreg)
clipgradnorm = lambda: torch.nn.utils.clip_grad_value_(
train_encdec.parameters(), clip_value=gradclip)
# lööps
batchloop = partial(q.train_batch, on_before_optim_step=[clipgradnorm])
trainloop = partial(
q.train_epoch,
model=train_encdec,
dataloader=tloader,
optim=optim,
device=device,
losses=[q.LossWrapper(ce),
q.LossWrapper(elemacc),
q.LossWrapper(acc)],
print_every_batch=False,
_train_batch=batchloop)
validloop = partial(q.test_epoch,
model=test_encdec,
dataloader=vloader,
device=device,
losses=[q.LossWrapper(treeacc)],
print_every_batch=False)
tt.tick("training")
q.run_training(trainloop, validloop, max_epochs=epochs)
tt.tock("trained")
tt.tick("testing")
test_results = validloop(model=test_encdec, dataloader=xloader)
print("Test results (freerunning): {}".format(test_results))
test_results = validloop(model=train_encdec, dataloader=xloader)
print("Test results (TF): {}".format(test_results))
tt.tock("tested")
# endregion
tt.msg("done")
def run_normal(lr=0.001,
gradclip=5.,
batsize=20,
epochs=150,
embdim=100,
encdim=200,
numlayer=1,
cuda=False,
gpu=0,
wreg=1e-8,
dropout=0.5,
smoothing=0.,
goldsmoothing=-0.1,
selfptr=False,
which="geo"):
tt = q.ticktock("script")
tt.msg("running normal att")
device = torch.device("cpu")
if cuda:
device = torch.device("cuda", gpu)
# region data
tt.tick("generating data")
# dss, D = gen_sort_data(seqlen=seqlen, numvoc=numvoc, numex=numex, prepend_inp=False)
dss, nlD, flD, rare_nl, rare_fl = gen_datasets(which=which)
tloader, vloader, xloader = [torch.utils.data.DataLoader(ds, batch_size=batsize, shuffle=True) for ds in dss]
seqlen = len(dss[0][0][1])
# merge nlD into flD and make mapper
nextflDid = max(flD.values()) + 1
sourcemap = torch.zeros(len(nlD), dtype=torch.long, device=device)
for k, v in nlD.items():
if k not in flD:
flD[k] = nextflDid
nextflDid += 1
sourcemap[v] = flD[k]
tt.tock("data generated")
# endregion
# region model
tt.tick("building model")
# source side
inpemb = q.UnkReplWordEmb(embdim, worddic=nlD, unk_tokens=rare_nl)
encdims = [encdim] * numlayer
encoder = q.LSTMEncoder(embdim, *encdims, bidir=True, dropout_in_shared=dropout)
# target side
decemb = q.UnkReplWordEmb(embdim, worddic=flD, unk_tokens=rare_fl)
decinpdim = embdim
decdims = [decinpdim] + [encdim] * numlayer
dec_core = torch.nn.Sequential(
*[q.rnn.LSTMCell(decdims[i-1], decdims[i], dropout_in=dropout) for i in range(1, len(decdims))]
)
att = attention.FwdAttention(decdims[-1], encdim * 2, decdims[-1])
out = torch.nn.Sequential(
q.UnkReplWordLinout(decdims[-1]+encdim*2, worddic=flD, unk_tokens=rare_fl),
# torch.nn.Softmax(-1)
)
if selfptr:
outgate = PointerGeneratorOutGate(decdims[-1] + encdim * 2, encdim, 3)
out = SelfPointerGeneratorOut(out, sourcemap=sourcemap, gate=outgate)
selfatt = attention.FwdAttention(decdims[-1], decdims[-1], decdims[-1])
deccell = SelfPointerGeneratorCell(emb=decemb, core=dec_core, att=att, selfatt=selfatt, out=out)
else:
outgate = PointerGeneratorOutGate(decdims[-1] + encdim * 2, encdim, 0)
out = PointerGeneratorOut(out, sourcemap=sourcemap, gate=outgate)
deccell = PointerGeneratorCell(emb=decemb, core=dec_core, att=att, out=out)
train_dec = q.TFDecoder(deccell)
test_dec = q.FreeDecoder(deccell, maxtime=seqlen+10)
train_encdec = EncDec(inpemb, encoder, train_dec)
test_encdec = Test_EncDec(inpemb, encoder, test_dec)
train_encdec.to(device)
test_encdec.to(device)
tt.tock("built model")
# endregion
# region training
# losses:
if smoothing == 0:
ce = q.loss.CELoss(mode="probs", ignore_index=0)
elif goldsmoothing < 0.:
ce = q.loss.SmoothedCELoss(mode="probs", ignore_index=0, smoothing=smoothing)
else:
ce = q.loss.DiffSmoothedCELoss(mode="probs", ignore_index=0, alpha=goldsmoothing, beta=smoothing)
acc = q.loss.SeqAccuracy(ignore_index=0)
elemacc = q.loss.SeqElemAccuracy(ignore_index=0)
trainmodel = TrainModel(train_encdec, [ce, elemacc, acc])
treeacc = TreeAccuracyPrologPar(flD=flD)
# optim
optim = torch.optim.Adam(train_encdec.parameters(), lr=lr, weight_decay=wreg)
clipgradnorm = lambda: torch.nn.utils.clip_grad_value_(train_encdec.parameters(), clip_value=gradclip)
# lööps
batchloop = partial(q.train_batch, on_before_optim_step=[clipgradnorm])
trainloop = partial(q.train_epoch, model=train_encdec, dataloader=tloader, optim=optim, device=device,
losses=[q.LossWrapper(ce), q.LossWrapper(elemacc), q.LossWrapper(acc)],
print_every_batch=False, _train_batch=batchloop)
validloop = partial(q.test_epoch, model=test_encdec, dataloader=vloader, device=device,
losses=[q.LossWrapper(treeacc)],
print_every_batch=False)
tt.tick("training")
q.run_training(trainloop, validloop, max_epochs=epochs)
tt.tock("trained")
tt.tick("testing")
test_results = validloop(model=test_encdec, dataloader=xloader)
print("Test results (freerunning): {}".format(test_results))
test_results = validloop(model=train_encdec, dataloader=xloader)
print("Test results (TF): {}".format(test_results))
tt.tock("tested")
# endregion
tt.msg("done")
def run_seq2seq_(
lr=0.001,
batsize=32,
evalbatsize=256,
epochs=100,
warmup=5,
embdim=50,
encdim=100,
numlayers=2,
dropout=.0,
wreg=1e-6,
cuda=False,
gpu=0,
):
settings = locals().copy()
device = torch.device("cpu") if not cuda else torch.device("cuda", gpu)
tt = q.ticktock("script")
tt.msg("running seq2seq on LC-QuAD")
tt.tick("loading data")
xsm, ysm, teststart, tok2act = load_data()
_tok2act = {ysm.RD[k]: v for k, v in tok2act.items()}
print("Some examples:")
for i in range(5):
print(
f"{xsm[i]}\n ->{ysm[i]}\n -> {Node.from_transitions(' '.join(ysm[i].split()[1:]), _tok2act)}"
)
print("Non-leaf tokens:")
print({ysm.RD[k]: v for k, v in tok2act.items() if v > 0})
devstart = teststart - 500
trainds = torch.utils.data.TensorDataset(
torch.tensor(xsm.matrix[:devstart]).long(),
torch.tensor(ysm.matrix[:devstart, :-1]).long(),
torch.tensor(ysm.matrix[:devstart, 1:]).long())
valds = torch.utils.data.TensorDataset(
torch.tensor(xsm.matrix[devstart:teststart]).long(),
torch.tensor(ysm.matrix[devstart:teststart, :-1]).long(),
torch.tensor(ysm.matrix[devstart:teststart, 1:]).long())
testds = torch.utils.data.TensorDataset(
torch.tensor(xsm.matrix[teststart:]).long(),
torch.tensor(ysm.matrix[teststart:, :-1]).long(),
torch.tensor(ysm.matrix[teststart:, 1:]).long())
tt.msg(
f"Data splits: train: {len(trainds)}, valid: {len(valds)}, test: {len(testds)}"
)
tloader = torch.utils.data.DataLoader(trainds,
batch_size=batsize,
shuffle=True)
vloader = torch.utils.data.DataLoader(valds,
batch_size=evalbatsize,
shuffle=False)
xloader = torch.utils.data.DataLoader(testds,
batch_size=evalbatsize,
shuffle=False)
tt.tock("data loaded")
# model
enclayers, declayers = numlayers, numlayers
decdim = encdim
xemb = q.WordEmb(embdim, worddic=xsm.D)
yemb = q.WordEmb(embdim, worddic=ysm.D)
encdims = [embdim] + [encdim // 2] * enclayers
xenc = q.LSTMEncoder(embdim,
*encdims[1:],
bidir=True,
dropout_in_shared=dropout)
decdims = [embdim] + [decdim] * declayers
dec_core = torch.nn.Sequential(*[
q.LSTMCell(decdims[i - 1],
decdims[i],
dropout_in=dropout,
dropout_rec=dropout) for i in range(1, len(decdims))
])
yout = q.WordLinout(encdim + decdim, worddic=ysm.D)
dec_cell = semparse.rnn.LuongCell(emb=yemb,
core=dec_core,
out=yout,
dropout=dropout)
decoder = q.TFDecoder(dec_cell)
testdecoder = q.FreeDecoder(dec_cell, maxtime=100)
m = Seq2Seq(xemb, xenc, decoder)
testm = Seq2Seq(xemb, xenc, testdecoder, test=True)
# test model
tt.tick("running a batch")
test_y = m(*iter(tloader).next()[:-1])
q.batch_reset(m)
test_y = testm(*iter(vloader).next()[:-1])
q.batch_reset(m)
tt.tock(f"ran a batch: {test_y.size()}")
optim = torch.optim.Adam(m.parameters(), lr=lr, weight_decay=wreg)
tlosses = [
q.CELoss(mode="logits", ignore_index=0),
q.Accuracy(ignore_index=0),
q.SeqAccuracy(ignore_index=0)
]
xlosses = [
q.CELoss(mode="logits", ignore_index=0),
q.Accuracy(ignore_index=0),
q.SeqAccuracy(ignore_index=0)
]
tlosses = [q.LossWrapper(l) for l in tlosses]
vlosses = [q.LossWrapper(l) for l in xlosses]
xlosses = [q.LossWrapper(l) for l in xlosses]
trainloop = partial(q.train_epoch,
model=m,
dataloader=tloader,
optim=optim,
losses=tlosses,
device=device)
devloop = partial(q.test_epoch,
model=testm,
dataloader=vloader,
losses=vlosses,
device=device)
testloop = partial(q.test_epoch,
model=testm,
dataloader=xloader,
losses=xlosses,
device=device)
lrplateau = q.util.ReduceLROnPlateau(optim,
mode="max",
factor=.1,
patience=3,
cooldown=1,
warmup=warmup,
threshold=0.,
verbose=True,
eps=1e-9)
on_after_valid = [lambda: lrplateau.step(vlosses[1].get_epoch_error())]
_devloop = partial(devloop, on_end=on_after_valid)
stoptrain = [lambda: all([pg["lr"] <= 1e-7 for pg in optim.param_groups])]
tt.tick("training")
q.run_training(trainloop,
_devloop,
max_epochs=epochs,
check_stop=stoptrain)
tt.tock("done training")
tt.tick("testing")
testres = testloop()
print(testres)
settings["testres"] = testres
tt.tock("tested")
devres = devloop()
print(devres, vlosses[0].get_epoch_error())
return vlosses[1].get_epoch_error()