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Evaluate.py
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Evaluate.py
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from __future__ import print_function
import sys
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
import os
import math
import argparse
import torch
from model import *
from tqdm import tqdm
from torch import optim
from Dataset import MyDataset
from torch.utils.data import DataLoader
from torch.nn import functional as F
from torch.nn.utils import clip_grad_norm
import torch.backends.cudnn as cudnn
from nltk.tokenize import wordpunct_tokenize
PAD_TOKEN = 0
SOS_token = 1
EOS_token = 2
mlength = 20
def eval_randomly(input_seq, test_dataset, encoder, decoder, max_length=mlength):
input_seq = wordpunct_tokenize(input_seq)
input_seqs = [test_dataset.indexes_from_sentence_char_to_word(input_seq)]
input_lengths = [len(s) for s in input_seqs]
input_batches = torch.LongTensor(input_seqs).transpose(0, 1)
max_length = max(max_length, input_lengths[0])
input_batches = input_batches.cuda()
# Set to not-training mode to disable dropout
encoder.eval()
decoder.eval()
with torch.no_grad():
# Run through encoder
encoder_outputs, encoder_hidden = encoder(input_batches, input_lengths, None)
# Create starting vectors for decoder
# decoder_input = torch.LongTensor([SOS_token]) # SOS
decoder_hidden = encoder_hidden[:decoder.n_layers] # Use last (forward) hidden state from encoder
# decoder_input = decoder_input.cuda()
# Store output words and attention states
decoded_words = [ ([SOS_token], 0, decoder_hidden) ]
# decoder_attentions = torch.zeros(max_length + 1, max_length + 1)
# Run through decoder
for di in range(5):
dlen = len(decoded_words)
for i in range(dlen):
decoder_hidden = decoded_words[i][2]
decoder_input = torch.LongTensor([decoded_words[i][0][-1]]).cuda()
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs
)
# decoder_attentions[di,:decoder_attention.size(2)] += decoder_attention.squeeze(0).squeeze(0).cpu().data
# Choose top word from output
topv, topi = decoder_output.data.topk(2)
for j in range(len(topi[0])):
ni = topi[0][j].item()
decoded_words.append( (decoded_words[i][0]+[ni], decoded_words[i][1]+topv[0][j].item(), decoder_hidden) )
decoded_words = decoded_words[dlen:]
decoded_words.sort(key=lambda item: -item[1])
decoded_words = decoded_words[:2]
decoded_words = decoded_words[0][0][1:]
final_res = []
for ni in decoded_words:
if ni == EOS_token:
final_res.append('<EOS>')
# break
else:
final_res.append(test_dataset.index2word[ni])
return (' '.join(final_res))
def parse_arguments():
parser = argparse.ArgumentParser(description='Correction classifier')
parser.add_argument('--train', '-t', required=True, type=str, help='file to train')
parser.add_argument('--test', type=str, help='file to test')
parser.add_argument('--load_en', type=str, help='encoder model to resume')
parser.add_argument('--load_de', type=str, help='decoder model to resume')
parser.add_argument('--max_length', type=int, default=20, help='max word length')
parser.add_argument('--dropout', type=float, default=0.2, help='dropout rate')
parser.add_argument('--n_layers', type=int, default=2, help="number of layers of GRU")
parser.add_argument('--hidden_size', type=int, default=300, help="hidden size")
parser.add_argument('--freq_threshold', type=int, default=0, help="word freq threshold")
parser.add_argument('--only_lowercase', type=int, default=0, help="only lower case")
global args
args = parser.parse_args()
return args
def setup():
eprint("loading...")
cudnn.benchmark = True
test_dataset = MyDataset(args.train, filter_pair=True, max_length = mlength, min_length = 3, \
max_word_length=args.max_length, freq_threshold=args.freq_threshold,\
onlylower=(args.only_lowercase>0), load_fprefix="dataset_freq2_")
voc_size = test_dataset.n_words
encoder = C2WEncoderRNN(args.hidden_size, args.n_layers, dropout=args.dropout)
decoder = BahdanauAttnDecoderRNN(voc_size, args.hidden_size, args.n_layers, dropout=args.dropout)
eprint(encoder)
eprint(decoder)
eprint ("vocab size", voc_size)
encoder.cuda()
decoder.cuda()
if args.load_en and args.load_de:
state_en = torch.load(args.load_en)
state_de = torch.load(args.load_de)
encoder.load_state_dict(state_en)
decoder.load_state_dict(state_de)
eprint('Loading parameters from {} {}'.format(args.load_en, args.load_de))
return encoder, decoder, test_dataset
def equal_pre(label, pre, lword):
labels = label.split()
pres = pre.split()
if labels[2].lower() != pres[2].lower() and labels[2].lower() != lword.lower():
return False
labels, pres = labels[:2]+labels[3:], pres[:2]+pres[3:]
cnt = 0
for i in range(len(labels)):
if labels[i].lower() == pres[i].lower():
cnt += 1
if cnt >= 2:
return True
return False
def collate(batch):
# Pad input with the PAD symbol
def pad_seq_word(seq, max_length):
seq += [[PAD_TOKEN for _ in range(args.max_length)] for _ in range(max_length - len(seq))]
return seq
# Pad target with the PAD symbol
def pad_seq(seq, max_length):
seq += [PAD_TOKEN for i in range(max_length - len(seq))]
return seq
batch = list(zip(*batch))
seq_pairs = sorted(zip(batch[0], batch[1]), key=lambda p: len(p[0]), reverse=True)
input_seqs, target_seqs = zip(*seq_pairs)
# For input and target sequences, get array of lengths and pad with 0s to max length
input_lengths = [len(s) for s in input_seqs]
input_padded = [pad_seq_word(s, max(input_lengths)) for s in input_seqs]
target_lengths = [len(s) for s in target_seqs]
target_padded = [pad_seq(s, max(target_lengths)) for s in target_seqs]
# Turn padded arrays into (batch_size x max_len) tensors, transpose into (max_len x batch_size)
input_var = torch.LongTensor(input_padded).transpose(0, 1)
target_var = torch.LongTensor(target_padded).transpose(0, 1)
return (input_var, input_lengths, target_var, target_lengths)
def collate_fn():
return lambda batch: collate(batch)
def correct(output, target, target_lengths):
target = target.transpose(0,1).float()
output = output.transpose(0,1)
acc = 0
for i in range(len(target_lengths)):
acc += target[i, :target_lengths[i]].eq(output[i, :target_lengths[i]]).float().mean()
return acc
def evaluate(test_data, encoder, decoder):
encoder.eval()
decoder.eval()
total_loss = 0
accuracy = 0
cnt = 0
with torch.no_grad():
for i, (src, len_src, trg, len_trg) in enumerate(tqdm(test_data, leave=False)):
src = src.cuda()
trg = trg.cuda()
batch_size = src.shape[1]
# Run words through encoder
encoder_outputs, encoder_hidden = encoder(src, len_src, None)
# Prepare input and output variables
decoder_input = torch.LongTensor([SOS_token] * batch_size)
decoder_input = decoder_input.cuda()
decoder_hidden = encoder_hidden[:decoder.n_layers] # Use last (forward) hidden state from encoder
max_target_length = max(len_trg)
all_decoder_outputs = torch.zeros(max_target_length, batch_size, decoder.output_size)
all_decoder_outputs = all_decoder_outputs.cuda()
top1s = torch.zeros(max_target_length, batch_size)
# Run through decoder one time step at a time
for t in range(max_target_length):
decoder_output, decoder_hidden, decoder_attn = decoder(
decoder_input, decoder_hidden, encoder_outputs
)
all_decoder_outputs[t] = decoder_output
top1 = decoder_output.max(1)[1]
top1s[t] = top1.cpu()
decoder_input = top1
loss = F.nll_loss(all_decoder_outputs.view(-1, decoder.output_size),
trg.contiguous().view(-1),
ignore_index=PAD_TOKEN)
cnt += int(src.shape[1])
total_loss += float(loss.item())
accuracy += correct(top1s, trg.cpu(), len_trg)
return total_loss / cnt, accuracy / cnt
def evalFileBatch():
args = parse_arguments()
encoder, decoder, dataset = setup()
test_data = MyDataset(args.train, filter_pair=True, max_length = mlength, min_length = 3, max_word_length=args.max_length, train=False)
test_data = DataLoader(test_data, batch_size=32, pin_memory=True,
shuffle=False, num_workers=2, collate_fn=collate_fn())
loss, acc = evaluate(test_data, encoder, decoder)
print ("acc:", acc, "loss", loss)
def evalFile(fname, encoder, decoder, dataset):
cnt = 0
right = 0
with open(fname) as f:
lines = f.readlines()
for i, line in enumerate(tqdm(lines, leave=False)):
if args.only_lowercase:
sent, label = line.lower().strip().split("\t")
else:
sent, label = line.strip().split("\t")
lword = sent.split()[-1]
# sent = sent[2:]
if len(sent.split()) <= 3:
continue
cnt += 1
pre = eval_randomly(sent, dataset, encoder, decoder)
if not equal_pre(label, pre, lword):
# print ("[wrong] %s\n[lb] %s\n[pr] %s" % (sent, label, pre))
pass
else:
print ("[right] %s\n[lb] %s\n[pr] %s" % (sent, label, pre))
right += 1
print ("acc :", right/cnt)
if __name__ == "__main__":
try:
args = parse_arguments()
encoder, decoder, dataset = setup()
# evalFile("/media/ray/My_Passport/ubuntu/codes/DragCorrect/DatasetProcessing/conll_valid5", encoder, decoder, dataset)
# evalFile("wiki.validate5", encoder, decoder, dataset)
# evalFile("test_exp_sub", encoder, decoder, dataset)
# evalFileBatch("testdata_eval")
# evalFile("exp.validate", encoder, decoder, dataset)
while True:
sent = input('Enter a sent: [no punc, cor at last]; "quit" to quit\n')
if sent == "quit":
break
print (eval_randomly(sent.strip(), dataset, encoder, decoder))
except KeyboardInterrupt as e:
eprint("[STOP]", e)