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emo_inference.py
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emo_inference.py
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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pickle as pkl
import copy
from tqdm import tqdm
import argparse
from copy import deepcopy
from model.binary_bert import BinaryBertClassifier
from transformers import BertTokenizer, AdamW
import random
from utils import nn_utils
from model.binary_lstm import BinaryLSTMClassifier
from utils.tokenizer import GloveTokenizer
parser = argparse.ArgumentParser(description='Options')
parser.add_argument('--batch_size', default=32, type=int, help="batch size")
parser.add_argument('--pad_len', default=50, type=int, help="batch size")
parser.add_argument('--postname', default='', type=str, help="post name")
parser.add_argument('--gamma', default=0.2, type=float, help="post name")
parser.add_argument('--folds', default=5, type=int, help="num of folds")
parser.add_argument('--en_lr', default=5e-4, type=float, help="encoder learning rate")
parser.add_argument('--de_lr', default=5e-4, type=float, help="decoder learning rate")
parser.add_argument('--loss', default='ce', type=str, help="loss function ce/focal")
parser.add_argument('--dataset', default='nlpcc', type=str, choices=['sem18', 'goemotions', 'bmet', 'nlpcc'])
parser.add_argument('--en_dim', default=800, type=int, help="dimension")
parser.add_argument('--de_dim', default=400, type=int, help="dimension")
parser.add_argument('--criterion', default='micro', type=str, choices=['jaccard', 'macro', 'micro', 'h_loss'])
parser.add_argument('--glove_path', default='data/glove.840B.300d.txt', type=str)
parser.add_argument('--attention', default='bert', type=str, choices=['bert', 'attentive', 'None'])
parser.add_argument('--dropout', default=0.3, type=float, help='dropout rate')
parser.add_argument('--encoder_dropout', default=0, type=float, help='dropout rate')
parser.add_argument('--decoder_dropout', default=0, type=float, help='dropout rate')
parser.add_argument('--attention_dropout', default=0.25, type=float, help='dropout rate')
parser.add_argument('--patience', default=10, type=int, help='dropout rate')
parser.add_argument('--download_elmo', action='store_true')
parser.add_argument('--scheduler', action='store_true')
parser.add_argument('--glorot_init', action='store_true')
parser.add_argument('--warmup_epoch', default=0, type=int, help='')
parser.add_argument('--stop_epoch', default=50, type=int, help='')
parser.add_argument('--max_epoch', default=100, type=int, help='')
parser.add_argument('--min_lr_ratio', default=0.1, type=float, help='')
parser.add_argument('--fix_emb', action='store_true')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--input_feeding', action='store_true')
parser.add_argument('--dev_split_seed', type=int, default=0)
parser.add_argument('--huang_init', action='store_true')
parser.add_argument('--normal_init', action='store_true')
parser.add_argument('--unify_decoder', action='store_true')
parser.add_argument('--eval_every', type=int, default=1)
parser.add_argument('--log_path', type=str, default=None)
parser.add_argument('--no_cross', action='store_true')
args = parser.parse_args(args=[])
SRC_EMB_DIM = 300
MAX_LEN_DATA = args.pad_len
PAD_LEN = MAX_LEN_DATA
MIN_LEN_DATA = 3
BATCH_SIZE = args.batch_size
CLIPS = 0.666
GAMMA = 0.5
SRC_HIDDEN_DIM = args.en_dim
TGT_HIDDEN_DIM = args.de_dim
VOCAB_SIZE = 60000
ENCODER_LEARNING_RATE = args.en_lr
DECODER_LEARNING_RATE = args.de_lr
ATTENTION = args.attention
PATIENCE = args.patience
WARMUP_EPOCH = args.warmup_epoch
STOP_EPOCH = args.stop_epoch
MAX_EPOCH = args.max_epoch
RANDOM_SEED = args.seed
print('loading lstm classifier...')
glove_tokenizer = GloveTokenizer(PAD_LEN)
EMOS = ['anger', 'disgust', 'happiness', 'like', 'sadness', 'none']
NUM_EMO = len(EMOS)
# Seed
RANDOM_SEED = args.seed
torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed_all(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)
def get_emotion(file, EMOS, EMOS_DIC):
text_list = []
label_list = []
with open(file, 'r', encoding='UTF-8') as f:
for line in f.readlines():
text, emotion = line.split('\t')
text_list.append(text)
label_list.append(int(EMOS_DIC[emotion.rstrip("\n")]))
return text_list, label_list
def load_NLPCC_data():
EMOS_DIC = {}
for idx, emo in enumerate(EMOS):
EMOS_DIC[emo] = idx
file1 = "/local/ssd_1/chengzhang/BiLSTM_emo_classifier/data/nlpcc/nlpcc2013_2014_adjust.txt"
X, y = get_emotion(file1, EMOS, EMOS_DIC)
X_train = X[:18000]
y_train = y[:18000]
X_dev = X[18000:19000]
y_dev = y[18000:19000]
X_test = X[19000:]
y_test = y[19000:]
X_train_dev = X_train + X_dev
y_train_dev = y_train + y_dev
# preprocess
return X_train_dev, y_train_dev, X_test, y_test, EMOS, EMOS_DIC, 'nlpcc'
class TestDataReader(Dataset):
def __init__(self, X, pad_len, max_size=None):
self.glove_ids = []
self.glove_ids_len = []
self.pad_len = pad_len
self.build_glove_ids(X)
def build_glove_ids(self, X):
for src in X:
glove_id, glove_id_len = glove_tokenizer.encode_ids_pad(src)
self.glove_ids.append(glove_id)
self.glove_ids_len.append(glove_id_len)
def __len__(self):
return len(self.glove_ids)
def __getitem__(self, idx):
return torch.LongTensor(self.glove_ids[idx]), \
torch.LongTensor([self.glove_ids_len[idx]])
class TrainDataReader(TestDataReader):
def __init__(self, X, y, pad_len, max_size=None):
super(TrainDataReader, self).__init__(X, pad_len, max_size)
self.y = []
self.read_target(y)
def read_target(self, y):
self.y = y
def __getitem__(self, idx):
return torch.LongTensor(self.glove_ids[idx]), \
torch.LongTensor([self.glove_ids_len[idx]]), \
torch.LongTensor([self.y[idx]])
X_train_dev, y_train_dev, X_test, y_test, EMOS, EMOS_DIC, data_set_name = \
load_NLPCC_data()
ALL_TRAINING = X_train_dev + X_test
glove_tokenizer.build_tokenizer(ALL_TRAINING, vocab_size=VOCAB_SIZE)
print('Done')
def inference_emotion(source, selected_emotion, batch_size):
model = BinaryLSTMClassifier(
emb_dim=SRC_EMB_DIM,
vocab_size=34177,
num_label=NUM_EMO,
hidden_dim=SRC_HIDDEN_DIM,
attention_mode=ATTENTION,
args=args
)
model.cuda()
data_set = TestDataReader(source, 20)
data_loader = DataLoader(data_set, batch_size=batch_size, shuffle=False)
pred_list = []
model.load_state_dict(torch.load("/local/ssd_1/chengzhang/SA_dialog/dialogue/saved_classifier/emotion_classifier1.pt"))
model.eval()
for _, (src, src_len) in enumerate(data_loader):
with torch.no_grad():
decoder_logit = model(src.cuda(), src_len.cuda())
softmax = nn.Softmax(dim=1)
prob = softmax(decoder_logit)
for i in range(batch_size):
pred_list.append(prob[i][int(selected_emotion)])
del decoder_logit
return np.asarray(pred_list)