Python onmt.ModelConstructor方法代碼示例

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def __init__(self, opt, dummy_opt={}):
        # Add in default model arguments, possibly added since training.
        self.opt = opt
        checkpoint = torch.load(opt.model,
                                map_location=lambda storage, loc: storage)
        self.fields = onmt.IO.ONMTDataset.load_fields(checkpoint['vocab'])

        model_opt = checkpoint['opt']
        for arg in dummy_opt:
            if arg not in model_opt:
                model_opt.__dict__[arg] = dummy_opt[arg]

        self._type = model_opt.encoder_type
        self.copy_attn = model_opt.copy_attn

        self.model = onmt.ModelConstructor.make_base_model(
                            model_opt, self.fields, use_gpu(opt), checkpoint)
        self.model.eval()
        self.model.generator.eval()

        # for debugging
        self.beam_accum = None 

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def main():
    dummy_parser = argparse.ArgumentParser(description='train.py')
    onmt.opts.model_opts(dummy_parser)
    dummy_opt = dummy_parser.parse_known_args([])[0]
    opt = parser.parse_args()
    opt.cuda = opt.gpu > -1
    if opt.cuda:
        torch.cuda.set_device(opt.gpu)

    # Add in default model arguments, possibly added since training.
    checkpoint = torch.load(opt.model,
                            map_location=lambda storage, loc: storage)
    model_opt = checkpoint['opt']
    src_dict = checkpoint['vocab'][1][1]
    tgt_dict = checkpoint['vocab'][0][1]

    fields = onmt.io.load_fields_from_vocab(checkpoint['vocab'])

    model_opt = checkpoint['opt']
    for arg in dummy_opt.__dict__:
        if arg not in model_opt:
            model_opt.__dict__[arg] = dummy_opt.__dict__[arg]

    model = onmt.ModelConstructor.make_base_model(
        model_opt, fields, use_gpu(opt), checkpoint)
    encoder = model.encoder
    decoder = model.decoder

    encoder_embeddings = encoder.embeddings.word_lut.weight.data.tolist()
    decoder_embeddings = decoder.embeddings.word_lut.weight.data.tolist()

    print("Writing source embeddings")
    write_embeddings(opt.output_dir + "/src_embeddings.txt", src_dict,
                     encoder_embeddings)

    print("Writing target embeddings")
    write_embeddings(opt.output_dir + "/tgt_embeddings.txt", tgt_dict,
                     decoder_embeddings)

    print('... done.')
    print('Converting model...') 

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def build_model(model_opt, opt, fields, checkpoint):
    print('Building model...')
    model = onmt.ModelConstructor.make_base_model(model_opt, fields,
                                                  use_gpu(opt), checkpoint)
    if len(opt.gpuid) > 1:
        print('Multi gpu training: ', opt.gpuid)
        model = nn.DataParallel(model, device_ids=opt.gpuid, dim=1)
    print(model)

    return model 

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def build_model(model_opt, opt, fields, checkpoint):
    print('Building model...')
    model = onmt.ModelConstructor.make_base_model(model_opt, fields, use_gpu(opt), checkpoint)
    if len(opt.gpuid) > 1:
        print('Multi gpu training: ', opt.gpuid)
        model = nn.DataParallel(model, device_ids=opt.gpuid, dim=1)
    print(model)
    return model 

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def __init__(self, model_loc, gpu=-1, beam_size=5, k=5):
        # Simulate all commandline args
        parser = argparse.ArgumentParser(
            description='translate.py',
            formatter_class=argparse.ArgumentDefaultsHelpFormatter)
        translate_opts(parser)
        self.opt = parser.parse_known_args()[0]
        self.opt.model = model_loc
        self.opt.beam_size = beam_size
        self.opt.batch_size = 1
        self.opt.n_best = k

        dummy_parser = argparse.ArgumentParser(description='train.py')
        model_opts(dummy_parser)
        self.dummy_opt = dummy_parser.parse_known_args([])[0]

        # Load the model.
        self.fields, self.model, self.model_opt = \
            onmt.ModelConstructor.load_test_model(
                self.opt, self.dummy_opt.__dict__)

        # Make GPU decoding possible
        self.opt.gpu = gpu
        self.opt.cuda = self.opt.gpu > -1
        if self.opt.cuda:
            torch.cuda.set_device(self.opt.gpu)

        # Translator
        self.scorer = onmt.translate.GNMTGlobalScorer(
            self.opt.alpha,
            self.opt.beta)
        self.translator = onmt.translate.Translator(
            self.model, self.fields,
            beam_size=self.opt.beam_size,
            n_best=self.opt.n_best,
            global_scorer=self.scorer,
            max_length=self.opt.max_sent_length,
            copy_attn=self.model_opt.copy_attn,
            cuda=self.opt.cuda,
            beam_trace=self.opt.dump_beam != "") 

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def main():
    dummy_parser = argparse.ArgumentParser(description='train.py')
    opts.model_opts(dummy_parser)
    dummy_opt = dummy_parser.parse_known_args([])[0]
    opt = parser.parse_args()
    opt.cuda = opt.gpu > -1
    if opt.cuda:
        torch.cuda.set_device(opt.gpu)

    # Add in default model arguments, possibly added since training.
    checkpoint = torch.load(opt.model,
                            map_location=lambda storage, loc: storage)
    model_opt = checkpoint['opt']
    src_dict = checkpoint['vocab'][1][1]
    tgt_dict = checkpoint['vocab'][0][1]

    fields = onmt.io.load_fields_from_vocab(checkpoint['vocab'])

    model_opt = checkpoint['opt']
    for arg in dummy_opt.__dict__:
        if arg not in model_opt:
            model_opt.__dict__[arg] = dummy_opt.__dict__[arg]

    model = onmt.ModelConstructor.make_base_model(
                            model_opt, fields, use_gpu(opt), checkpoint)
    encoder = model.encoder
    decoder = model.decoder

    encoder_embeddings = encoder.embeddings.word_lut.weight.data.tolist()
    decoder_embeddings = decoder.embeddings.word_lut.weight.data.tolist()

    print("Writing source embeddings")
    write_embeddings(opt.output_dir + "/src_embeddings.txt", src_dict,
                     encoder_embeddings)

    print("Writing target embeddings")
    write_embeddings(opt.output_dir + "/tgt_embeddings.txt", tgt_dict,
                     decoder_embeddings)

    print('... done.')
    print('Converting model...') 

# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import ModelConstructor [as 別名]
def create_model(fields, options=None):
    if options is None: options = copy.deepcopy(onmt.standard_options.stdOptions)
    if not isinstance(options, dict):
        options = mhf.convertToDictionary(options)
    options = handle_options(options)
    options = mhf.convertToNamedTuple(options)
    model = onmt.ModelConstructor.make_base_model(options, fields, USE_CUDA, checkpoint=None)
    if len(options.gpuid) > 1:
        model = nn.DataParallel(model, device_ids=options.gpuid, dim=1)

    return model