How do I test the model on custom input? about mime HOT 8 CLOSED

from model.trainer import Train_MIME
from utils.data_loader import prepare_data_seq
from utils import config
from model.common_layer import evaluate, count_parameters, make_infinite

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
import torch.utils.data as data
from tqdm import tqdm
import os
import time 
import numpy as np
import math
from collections import deque
from utils.beam_omt_mimic_model1 import Translator
DIALOG_SIZE = 3

class Dataset(data.Dataset):
    """Custom data.Dataset compatible with data.DataLoader."""
    def __init__(self, data, vocab):
        """Reads source and target sequences from txt files."""
        self.vocab = vocab
        self.data = data
    def __len__(self):
        return 1
    def __getitem__(self, index):
        # here we ignore index since we only have one input
        item = {}
        item["context_text"] = [x for x in self.data if x!="None"]
        X_dial = [config.CLS_idx]
        X_mask = [config.CLS_idx]
        for i, sentence in enumerate(item["context_text"]):
            X_dial += [self.vocab.word2index[word] if word in self.vocab.word2index else config.UNK_idx for word in sentence.split()]
            spk = self.vocab.word2index["USR"] if i % 2 == 0 else self.vocab.word2index["SYS"]
            X_mask += [spk for _ in range(len(sentence.split()))]
        assert len(X_dial) == len(X_mask)
        item["context"] = X_dial
        item["mask"] = X_mask
        item["len"] = len(X_dial)
        return item

def collate_fn(data):
    
    input_batch = torch.LongTensor([data[0]["context"]])
    input_mask = torch.LongTensor([data[0]["mask"]])
    if config.USE_CUDA:
        input_batch = input_batch.cuda()
        input_mask = input_mask.cuda()
    d = {}
    d["input_batch"] = input_batch
    d["input_lengths"] = torch.LongTensor([data[0]["len"]])
    d["mask_input"] = input_mask
    d["program_label"] = torch.LongTensor([9]) #fake label
    if config.USE_CUDA:
        d["program_label"] = d["program_label"].cuda()
    return d 

def make_batch(inp,vacab):
    d = Dataset(inp,vacab)
    loader = torch.utils.data.DataLoader(dataset=d, batch_size=1, shuffle=False, collate_fn=collate_fn)
    return iter(loader).next()

data_loader_tra, data_loader_val, data_loader_tst, vocab, program_number = prepare_data_seq(batch_size=config.batch_size)


model = Train_MIME(vocab, decoder_number=program_number, model_file_path=config.saved_model_path, is_eval=True)
if (config.USE_CUDA):
    model.cuda()
model = model.eval()

print('Start to chat')
context = deque(DIALOG_SIZE * ['None'], maxlen=DIALOG_SIZE)
t = Translator(model, model.vocab)

while(True):
    msg = input(">>> ")
    if(len(str(msg).rstrip().lstrip()) != 0):

        context.append(str(msg).rstrip().lstrip())
        print(context)
        batch = make_batch(context, vocab)
        # sent_g = model.decoder_greedy(batch,max_dec_step=30)
        # print(">>>",sent_g[0])
        # context.append(sent_g[0])
        
        # sent_b = t.beam_search(batch, max_dec_step=30)
        # print(">>>",sent_b[0])
        # context.append(sent_b[0])

        sent_t = model.decoder_topk(batch, max_dec_step=30)
        print(">>>",sent_t[0])
        context.append(sent_t[0])

runfile('/home/ayan/Data1/MIME/test.py', wdir='/home/ayan/Data1/MIME')
[nltk_data] Downloading package punkt to /home/ayan/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
Reloaded modules: utils.metric, utils.beam_omt_mimic_model1, model.common_layer, utils.data_reader, utils.data_loader, model.transformer_mulexpert, model.emotion_input_attention, model.complex_res_attention, model.complex_res_gate, model.decoder_context_v, model.VAE_noEmo_posterior, model.trainer
LOADING empathetic_dialogue
[nltk_data] Downloading package punkt to /home/ayan/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
04-03 13:47 Vocab  24645 
[situation]: i remember going to the fireworks with my best friend . there was a lot of people , but it only felt like us in the world .
[emotion]: sentimental
[context]: ['i remember going to see the fireworks with my best friend . it was the first time we ever spent time alone together . although there was a lot of people , we felt like the only people in the world .']
[target]: was this a friend you were in love with , or just a best friend ?
 
[situation]: i remember going to the fireworks with my best friend . there was a lot of people , but it only felt like us in the world .
[emotion]: sentimental
[context]: ['i remember going to see the fireworks with my best friend . it was the first time we ever spent time alone together . although there was a lot of people , we felt like the only people in the world .', 'was this a friend you were in love with , or just a best friend ?', 'this was a best friend . i miss her .']
[target]: where has she gone ?
 
[situation]: i remember going to the fireworks with my best friend . there was a lot of people , but it only felt like us in the world .
[emotion]: sentimental
[context]: ['i remember going to see the fireworks with my best friend . it was the first time we ever spent time alone together . although there was a lot of people , we felt like the only people in the world .', 'was this a friend you were in love with , or just a best friend ?', 'this was a best friend . i miss her .', 'where has she gone ?', 'we no longer talk .']
[target]: oh was this something that happened because of an argument ?
 
Embeddings: 24645 x 300
Loading embedding file: vectors/glove.6B.300d.txt
Pre-trained: 19481 (79.05%)
Start to chat

hi
deque(['None', 'None', 'hi'], maxlen=3)
Traceback (most recent call last):

  File "/home/ayan/Data1/MIME/test.py", line 92, in <module>
    sent_t = model.decoder_topk(batch, max_dec_step=30)

  File "/home/ayan/Data1/MIME/model/trainer.py", line 358, in decoder_topk
    context_emo = [self.positive_emotions[0] if d['compound'] > 0 else self.negative_emotions[0] for d in batch['context_emotion_scores']]

KeyError: 'context_emotion_scores'

Embeddings: 24645 x 300
Loading embedding file: vectors/glove.6B.300d.txt
Pre-trained: 19481 (79.05%)
loading weights
Start to chat

Me >>> hi how are you?
System >>> that is great . i hope you get a great care of yourself ! 

Me >>> I had an accident, feeling depressed right now
System >>> that is good , i am glad you have that a lot of courage to do that . 

emotion:lonely	vader-score: -0.4215	predicted_emotion: sad
Context:['i there , dont know what to do , jst broke up with my girlfirned , we were 8 years together']
Topk:oh no ! i hope you have to deal with that . 
Beam: i am sorry to hear that . what happened ? 
Greedy:that is a bummer . what happened ? 
Ref:sorry to hear ! do you have any idea about the break up ? did you think about it ?
----------------------------------------------------------------------
emotion:lonely	vader-score: -0.4215	predicted_emotion: lonely
Context:['i there , dont know what to do , jst broke up with my girlfirned , we were 8 years together', 'sorry to hear ! do you have any idea about the break up ? did you think about it ?', 'yes we decided together with our minds , and know i come home and feel so distant from the world']
Topk:that is so sad . i hope you find a good feeling better ! 
Beam: i am sorry to hear that . i hope you have a good time ! 
Greedy:that is a shame . i am sorry to hear that . i hope you get it . 
Ref:sorry again ! hope you will get relief from this sadness . please concentrate on your interests to divert your mind from this .

Me >>> i there , dont know what to do , jst broke up with my girlfirned , we were 8 years together
System beam_search >>> i am sorry to hear that . what happened ? 
predicted emotion : sad
System decoder_topk >>> that is so bad ! i hope you get it ! 

Me >>> yes we decided together with our minds , and know i come home and feel so distant from the world
System beam_search >>> i am sorry to hear that . i hope you have a good time ! 
predicted emotion : lonely
System decoder_topk >>> that is a good thing to do 

emotion:angry	vader-score: -0.9412	predicted_emotion: furious
Context:['i was very angry when a co worker lied about a car accident to get out of working july 4th . i covered his shift till midnight and he was lying about the accident']
Topk:i am glad you are ok . 
Beam: oh no , i am sorry to hear that . i am sorry to hear that . 
Greedy:that is terrible . i hope you get the car . 
Ref:that is not very nice .
----------------------------------------------------------------------
emotion:angry	vader-score: -0.9412	predicted_emotion: furious
Context:['i was very angry when a co worker lied about a car accident to get out of working july 4th . i covered his shift till midnight and he was lying about the accident', 'that is not very nice .', 'yeah he got caught in someones pics on facebook . never lie and then go out with people who like to take pictures .']
Topk:that is terrible . it happens to everyone in the world . i am sorry you have to deal with that . 
Beam: i am sorry to hear that . i hope you do not have to deal with that . 
Greedy:that is a shame . i hope you get it . 
Ref:yes . i do not have a facebook .

Me >>> i was very angry when a co worker lied about a car accident to get out of working july 4th . i covered his shift till midnight and he was lying about the accident
System beam_search >>> oh no , i am sorry to hear that . i am sorry to hear that . 
predicted emotion : furious
System decoder_topk >>> oh my goodness . i am sorry to hear that . 

Me >>> yeah he got caught in someones pics on facebook . never lie and then go out with people who like to take pictures .
System beam_search >>> i am sorry to hear that . i hope you do not have to deal with that . 
predicted emotion : furious
System decoder_topk >>> that is so sad . i have had to do that . it is always nice to have someone who can help ?