code stringlengths 3 6.57k |
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init_command_line(sys.argv[1:]) |
print ("Configure:") |
print (" w2v:",os.path.join(opts.w2v_path,opts.w2v) |
print (" test_file:",os.path.join(opts.corpus_path,opts.test_file) |
print (" max_context_size:",opts.max_context_size) |
print (" batch_size:",opts.batch_size) |
print (" enc_hidden_size:",opts.enc_hidden_size) |
print (" max_senten_len:",opts.max_senten_len) |
print (" dropout:",opts.dropout) |
print (" teach_forcing:",opts.teach_forcing) |
print (" print_every:",opts.print_every) |
print (" weights:",opts.weights) |
print ("") |
readingTestCorpus(test_file_path) |
print ("reading...") |
open(test_file_path,'r') |
line.strip('\n') |
list_pairs.append(tmp_pair) |
tmp_pair.append(line) |
test_file.close() |
len(pair) |
test_contexts.append(pair[0:-1]) |
test_replys.append(pair[-1]) |
max(len(pair[0:-1]) |
min(len(pair[0:-1]) |
print (max_con_size) |
print (min_con_size) |
preProcess(word2index,test_contexts,unk_char,ini_char,max_senten_len,max_context_size) |
print ("preprocessing...") |
filteringSenten(word2index,senten,unk_char,ini_char) |
filter_test_contexts.append(filter_context) |
len(context) |
len(context) |
len(context) |
len(context) |
paddingSenten(senten,max_senten_len) |
padded_test_pairs.append([padded_context,pad_list]) |
model.eval() |
buildingPairsBatch(test_pairs,batch_size,shuffle=False) |
print ("") |
print ("num of batch:",num_batches) |
getTensorsContextPairsBatch(word2index,pairs_batches,max_context_size) |
model.predict(contexts_tensor_batch,index2word,pad_matrix_batch,ini_idx,sep_char='\t') |
predict_sentences.extend(predict_batch) |
if (idx_batch+1) |
print ("{} batches finished".format(idx_batch+1) |
len(test_pairs) |
time.time() |
print ("loading word2vec...") |
W2vCharacterTable(os.path.join(opts.w2v_path,opts.w2v) |
print(" dict size:",ctable.getDictSize() |
print (" emb size:",ctable.getEmbSize() |
print (time.time() |
print ("") |
Seq2Seq(ctable.getDictSize() |
ctable.getEmbSize() |
cuda() |
print ("load model parameters...") |
seq2seq.load_state_dict(torch.load(opts.weights) |
print ("No model parameters!") |
exit() |
readingTestCorpus(os.path.join(opts.corpus_path,opts.test_file) |
print ("len(test_contexts) |
len(test_contexts) |
print ("len(test_replys) |
len(test_replys) |
ctable.getWord2Index() |
preProcess(word2index,test_contexts,unk_char,ini_char,opts.max_senten_len,opts.max_context_size) |
print ("len(test_pairs) |
len(test_pairs) |
print ("start predicting...") |
predictSentences(ctable.getIndex2Word() |
print ("writing...") |
os.path.exists('./result/') |
os.mkdir('./result/') |
open("./result/open_pred_res_hyb_t1_len2",'w') |
open("./result/open_pred_ans_hyb_t1_len2",'w') |
enumerate(predict_sentences) |
pred_res_file.write(test_post+'\n') |
pred_res_file.write(senten+'\n') |
pred_res_file.write(sub+'\n') |
senten.split('\t') |
pred_ans_file.write(' '.join(senten_l) |
pred_res_file.close() |
pred_ans_file.close() |
print ("end") |
setup_logger() |
logging.StreamHandler(sys.stdout) |
logging.basicConfig(handlers=handlers) |
logging.getLogger() |
root.setLevel(logging.INFO) |
setup_logger() |
logging.getLogger(__name__) |
dict(ns, deep=True) |
dic.items() |
isinstance(v, NS) |
NS.dict(v) |
from_dict(dic, deep=True) |
NS(dic) |
ns.__dict__.items() |
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