Hi, I am pretty new to seq2seq models and OpenNMT-py. I am using OpenNMT for a summarization problem and was able to train a basic model using the examples. However, I tried to visualize the attention weights using the code mentioned in this thread and I am getting the following error:
AttributeError: 'dict' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.
this is my codes：
import onmt
import onmt.inputters
import onmt.translate
import onmt.model_builder
from collections import namedtuple
# Load the model.
Opt = namedtuple('Opt', ['model', 'data_type', 'reuse_copy_attn', "gpu"])
opt = Opt("/Users/esalesky/projects/models/lstm_clean_acc_51.81_ppl_15.58_e13.pt", "text",False, 0)
fields, model, model_opt = onmt.model_builder.load_test_model(opt,{"reuse_copy_attn":False})
# Test data
data = onmt.inputters.build_dataset(fields, "text", None, use_filter_pred=False, src_path='/Users/esalesky/projects/data/fisher/test.es')
data_iter = onmt.inputters.OrderedIterator(
dataset=data, device='cuda',
batch_size=1, train=False, sort=False,
sort_within_batch=True, shuffle=False)
# Translator
translator = onmt.translate.Translator(model, fields,
beam_size=5,
n_best=1,
global_scorer=onmt.translate.GNMTGlobalScorer(0, 0, "none", "none"),
gpu=True)
builder = onmt.translate.TranslationBuilder(
data, translator.fields,
1, False, None)
for j, batch in enumerate(data_iter):
batch_data = translator.translate_batch(batch, data)
translations = builder.from_batch(batch_data)
print("src:", " ".join(translations[0].src_raw))
print("tgt:", " ".join(translations[0].pred_sents[0]))
print("idx:",str(j))
print("-----")