You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
95 lines
3.2 KiB
95 lines
3.2 KiB
import torch
|
|
import torch.nn as nn
|
|
|
|
import numpy as np
|
|
|
|
import models
|
|
|
|
import pickle
|
|
|
|
import time
|
|
start_time = time.time()
|
|
|
|
PAD = "<pad>"
|
|
BOS = "<bos>"
|
|
EOS = "<eos>"
|
|
|
|
word2idx = pickle.load(open("word2idx.pkl", "rb"))
|
|
wordvecs = pickle.load(open("wordvecs.pkl", "rb"))
|
|
slots = pickle.load(open("slots.pkl", "rb"))
|
|
intents = pickle.load(open("intents.pkl", "rb"))
|
|
num_words = len(word2idx)
|
|
num_intent = 7
|
|
num_slot = 72
|
|
filter_count = 300
|
|
dropout = 0
|
|
embedding_dim = 100
|
|
|
|
|
|
def pad_query(sequence):
|
|
sequence = [word2idx[BOS]] + sequence + [word2idx[EOS]]
|
|
sequence = sequence[:50]
|
|
sequence = np.pad(sequence, (0, 50 - len(sequence)), mode='constant', constant_values=(word2idx[PAD],))
|
|
return sequence
|
|
|
|
|
|
query = "What's the weather like in York PA right now?"
|
|
q = query.lower().replace("'", " ").replace("?", " ").strip()
|
|
# true_length = [len(q.split()), 0, 0, 0, 0, 0, 0 ,0]
|
|
true_length = [len(q.split())]
|
|
qq = torch.from_numpy(pad_query([word2idx[word] for word in q.split()]))
|
|
|
|
model = models.CNNJoint(num_words, embedding_dim, num_intent, num_slot, (filter_count,), 5, dropout, wordvecs)
|
|
model.eval()
|
|
|
|
model.load_state_dict(torch.load('snips_joint', map_location=torch.device('cpu')))
|
|
criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
|
|
|
|
pad_tensor = torch.from_numpy(pad_query([word2idx[w] for w in []]))
|
|
# batch = torch.stack([qq, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor])
|
|
batch = torch.stack([qq])
|
|
|
|
pred_intent, pred_slots = model(batch)
|
|
|
|
slt = [str(item) for batch_num, sublist in enumerate(pred_slots.max(1)[1].tolist()) for item in sublist[1:true_length[batch_num] + 1]]
|
|
out_slots = [slots[int(c)] for c in slt]
|
|
|
|
itnt = pred_intent.max(1)[1].tolist()[0]
|
|
out_intent = intents[itnt]
|
|
|
|
print("Input: {}\nIntent: {}\nSlots: {}".format(query, out_intent, out_slots))
|
|
|
|
print("--- %s seconds ---" % (time.time() - start_time))
|
|
|
|
# Write to output file
|
|
out = ""
|
|
collected_slots = {}
|
|
active_slot_words = []
|
|
active_slot_name = None
|
|
for words, slot_preds, intent_pred in zip([q.split()], [out_slots], [out_intent]):
|
|
line = ""
|
|
for word, pred in zip(words, slot_preds):
|
|
line = line + word + " "
|
|
if pred == 'O':
|
|
if active_slot_name:
|
|
collected_slots[active_slot_name] = " ".join(active_slot_words)
|
|
active_slot_words = []
|
|
active_slot_name = None
|
|
else:
|
|
# Naive BIO handling: treat B- and I- the same...
|
|
new_slot_name = pred[2:]
|
|
if active_slot_name is None:
|
|
active_slot_words.append(word)
|
|
active_slot_name = new_slot_name
|
|
elif new_slot_name == active_slot_name:
|
|
active_slot_words.append(word)
|
|
else:
|
|
collected_slots[active_slot_name] = " ".join(active_slot_words)
|
|
active_slot_words = [word]
|
|
active_slot_name = new_slot_name
|
|
out = line.strip()
|
|
if active_slot_name:
|
|
collected_slots[active_slot_name] = " ".join(active_slot_words)
|
|
|
|
print(collected_slots)
|
|
print("--- %s seconds ---" % (time.time() - start_time))
|