def handle(h5f, vcbsf, vcbtf, rsfs, rsft):
td = h5py.File(h5f, "r")
ntest = td["ndata"][:].item()
nword = td["nwordi"][:].tolist()
nwordi = nword[0]
vcbs, nwords = ldvocab(vcbsf)
vcbs = reverse_dict(vcbs)
vcbt, nwordt = ldvocab(vcbtf)
vcbt = reverse_dict(vcbt)
src_grp, tgt_grp = td["src"], td["tgt"]
ens = "\n".encode("utf-8")
with open(rsfs, "wb") as fs:
with open(rsft, "wb") as ft:
for i in range(ntest):
curid = str(i)
curd = torch.from_numpy(src_grp[curid][:]).tolist()
md = []
for iu in curd:
md.append(" ".join([vcbs.get(i) for i in iu]))
fs.write("\n".join(md).encode("utf-8"))
fs.write(ens)
curd = torch.from_numpy(tgt_grp[curid][:]).tolist()
md = []
for tu in curd:
md.append(" ".join([vcbt.get(i) for i in tu]))
ft.write("\n".join(md).encode("utf-8"))
ft.write(ens)
td.close()
def handle(common, src, tgt, srcm, rsm, minfreq=False, vsize=False):
vcbc, nwordf = ldvocab(common,
minf=minfreq,
omit_vsize=vsize,
vanilla=False)
if src == common:
src_indices = None
else:
vcbw, nword = ldvocab(src,
minf=minfreq,
omit_vsize=vsize,
vanilla=False)
vcbw = reverse_dict(vcbw)
src_indices = torch.tensor(
[vcbc.get(vcbw[i], 0) for i in range(nword)], dtype=torch.long)
if tgt == common:
tgt_indices = None
else:
vcbw, nword = ldvocab(tgt,
minf=minfreq,
omit_vsize=vsize,
vanilla=False)
vcbw = reverse_dict(vcbw)
tgt_indices = torch.tensor(
[vcbc.get(vcbw[i], 0) for i in range(nword)], dtype=torch.long)
mymodel = NMT(cnfg.isize, nwordf, nwordf, cnfg.nlayer, cnfg.ff_hsize,
cnfg.drop, cnfg.attn_drop, cnfg.share_emb, cnfg.nhead,
cache_len_default, cnfg.attn_hsize, cnfg.norm_output,
cnfg.bindDecoderEmb, cnfg.forbidden_indexes)
mymodel = load_model_cpu(srcm, mymodel)
mymodel.update_vocab(src_indices=src_indices, tgt_indices=tgt_indices)
save_model(mymodel, rsm, sub_module=False, h5args=h5zipargs)
def handle(vcbf, embf, rsf):
vcb, nwd = ldvocab(vcbf)
emb = load_emb_txt(vcb, embf)
unkemb = emb.get("<unk>", torch.zeros(emb[list(emb.keys())[0]].size(0)))
vcb = reverse_dict(vcb)
rs = []
for i in range(nwd):
rs.append(emb.get(vcb[i], unkemb))
h5save(torch.stack(rs, 0), rsf)
def __init__(self, modelfs, fvocab_i, fvocab_t, cnfg, minbsize=1, expand_for_mulgpu=True, bsize=64, maxpad=16, maxpart=4, maxtoken=1536, minfreq = False, vsize = False): vcbi, nwordi = ldvocab(fvocab_i, minfreq, vsize) vcbt, nwordt = ldvocab(fvocab_t, minfreq, vsize) self.vcbi, self.vcbt = vcbi, reverse_dict(vcbt) if expand_for_mulgpu: self.bsize = bsize * minbsize self.maxtoken = maxtoken * minbsize else: self.bsize = bsize self.maxtoken = maxtoken self.maxpad = maxpad self.maxpart = maxpart self.minbsize = minbsize if isinstance(modelfs, (list, tuple)): models = [] for modelf in modelfs: tmp = NMT(cnfg.isize, nwordi, nwordt, cnfg.nlayer, cnfg.ff_hsize, cnfg.drop, cnfg.attn_drop, cnfg.share_emb, cnfg.nhead, cache_len_default, cnfg.attn_hsize, cnfg.norm_output, cnfg.bindDecoderEmb, cnfg.forbidden_indexes) tmp = load_model_cpu(modelf, tmp) tmp.apply(load_fixing) models.append(tmp) model = Ensemble(models) else: model = NMT(cnfg.isize, nwordi, nwordt, cnfg.nlayer, cnfg.ff_hsize, cnfg.drop, cnfg.attn_drop, cnfg.share_emb, cnfg.nhead, cache_len_default, cnfg.attn_hsize, cnfg.norm_output, cnfg.bindDecoderEmb, cnfg.forbidden_indexes) model = load_model_cpu(modelfs, model) model.apply(load_fixing) model.eval() self.use_cuda, self.cuda_device, cuda_devices, self.multi_gpu = parse_cuda_decode(cnfg.use_cuda, cnfg.gpuid, cnfg.multi_gpu_decoding) if self.use_cuda: model.to(self.cuda_device) if self.multi_gpu: model = DataParallelMT(model, device_ids=cuda_devices, output_device=self.cuda_device.index, host_replicate=True, gather_output=False) self.use_amp = cnfg.use_amp and self.use_cuda self.beam_size = cnfg.beam_size self.length_penalty = cnfg.length_penalty self.net = model
from utils.fmt.base import ldvocab, reverse_dict, init_vocab, sos_id, eos_id
from utils.fmt.base4torch import parse_cuda_decode
def load_fixing(module):
if hasattr(module, "fix_load"):
module.fix_load()
td = h5py.File(sys.argv[1], "r")
ntest = td["ndata"][:].item()
nwordi = td["nword"][:].tolist()[0]
vcbt, nwordt = ldvocab(sys.argv[3])
vcbt = reverse_dict(vcbt)
mymodel = NMT(cnfg.isize, nwordi, nwordt, cnfg.nlayer, cnfg.ff_hsize,
cnfg.drop, cnfg.attn_drop, cnfg.share_emb, cnfg.nhead,
cache_len_default, cnfg.attn_hsize, cnfg.norm_output,
cnfg.bindDecoderEmb, cnfg.forbidden_indexes, cnfg.num_layer_fwd)
mymodel = load_model_cpu(sys.argv[2], mymodel)
mymodel.apply(load_fixing)
mymodel.eval()
enc, trans, classifier = mymodel.enc, mymodel.dec.trans, mymodel.dec.classifier
use_cuda, cuda_device, cuda_devices, multi_gpu = parse_cuda_decode(
cnfg.use_cuda, cnfg.gpuid, cnfg.multi_gpu_decoding)
