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60 lines
1.9 KiB
60 lines
1.9 KiB
import torch
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import torch.nn as nn
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import numpy as np
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import models
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import pickle
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import time
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start_time = time.time()
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PAD = "<pad>"
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BOS = "<bos>"
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EOS = "<eos>"
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word2idx = pickle.load(open("word2idx.pkl", "rb"))
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wordvecs = pickle.load(open("wordvecs.pkl", "rb"))
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slots = pickle.load(open("slots.pkl", "rb"))
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intents = pickle.load(open("intents.pkl", "rb"))
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num_words = len(word2idx)
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num_intent = 7
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num_slot = 72
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filter_count = 300
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dropout = 0
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embedding_dim = 100
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def pad_query(sequence):
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sequence = [word2idx[BOS]] + sequence + [word2idx[EOS]]
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sequence = sequence[:50]
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sequence = np.pad(sequence, (0, 50 - len(sequence)), mode='constant', constant_values=(word2idx[PAD],))
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return sequence
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query = "What's the weather like in Great Mills right now?"
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q = query.lower().replace("'", " ").replace("?", " ").strip()
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true_length = [len(q.split()), 0, 0, 0, 0, 0, 0 ,0]
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qq = torch.from_numpy(pad_query([word2idx[word] for word in q.split()]))
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model = models.CNNJoint(num_words, embedding_dim, num_intent, num_slot, (filter_count,), 5, dropout, wordvecs)
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model.eval()
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model.load_state_dict(torch.load('snips_joint', map_location=torch.device('cpu')))
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criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
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pad_tensor = torch.from_numpy(pad_query([word2idx[w] for w in []]))
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batch = torch.stack([qq, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor, pad_tensor])
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pred_intent, pred_slots = model(batch)
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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]]
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out_slots = [slots[int(c)] for c in slt]
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itnt = pred_intent.max(1)[1].tolist()[0]
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out_intent = intents[itnt]
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print("Input: {}\nIntent: {}\nSlots: {}".format(query, out_intent, out_slots))
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print("--- %s seconds ---" % (time.time() - start_time))
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