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test_accuracy_choi.py
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test_accuracy_choi.py
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import torch
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
from choiloader import ChoiDataset, collate_fn
from tqdm import tqdm
from argparse import ArgumentParser
from utils import maybe_cuda
import gensim
import utils
from tensorboard_logger import configure
import os
import sys
from pathlib2 import Path
import accuracy
import numpy as np
from termcolor import colored
torch.multiprocessing.set_sharing_strategy('file_system')
preds_stats = utils.predictions_analysis()
def softmax(x):
max_each_row = np.max(x, axis=1, keepdims=True)
exps = np.exp(x - max_each_row)
sums = np.sum(exps, axis=1, keepdims=True)
return exps / sums
def import_model(model_name):
module = __import__('models.' + model_name, fromlist=['models'])
return module.create()
class Accuracies(object):
def __init__(self):
self.thresholds = np.arange(0, 1, 0.05)
self.accuracies = {k: accuracy.Accuracy() for k in self.thresholds}
def update(self, output_np, targets_np):
current_idx = 0
for k, t in enumerate(targets_np):
document_sentence_count = len(t)
to_idx = int(current_idx + document_sentence_count)
for threshold in self.thresholds:
output = ((output_np[current_idx: to_idx, :])[:, 1] > threshold)
h = np.append(output, [1])
tt = np.append(t, [1])
self.accuracies[threshold].update(h, tt)
current_idx = to_idx
def calc_accuracy(self):
min_pk = np.inf
min_threshold = None
min_epoch_windiff = None
for threshold in self.thresholds:
epoch_pk, epoch_windiff = self.accuracies[threshold].calc_accuracy()
if epoch_pk < min_pk:
min_pk = epoch_pk
min_threshold = threshold
min_epoch_windiff = epoch_windiff
return min_pk, min_epoch_windiff, min_threshold
def validate(model, args, epoch, dataset, logger):
model.eval()
with tqdm(desc='Validatinging', total=len(dataset)) as pbar:
acc = Accuracies()
for i, (data, target, paths) in enumerate(dataset):
if True:
if i == args.stop_after:
break
pbar.update()
output = model(data)
output_softmax = F.softmax(output, 1)
targets_var = Variable(maybe_cuda(torch.cat(target, 0), args.cuda), requires_grad=False)
output_seg = output.data.cpu().numpy().argmax(axis=1)
target_seg = targets_var.data.cpu().numpy()
preds_stats.add(output_seg, target_seg)
acc.update(output_softmax.data.cpu().numpy(), target)
epoch_pk, epoch_windiff, threshold = acc.calc_accuracy()
logger.info('Validating Epoch: {}, accuracy: {:.4}, Pk: {:.4}, Windiff: {:.4}, F1: {:.4} . '.format(epoch + 1,
preds_stats.get_accuracy(),
epoch_pk,
epoch_windiff,
preds_stats.get_f1()))
preds_stats.reset()
return epoch_pk, threshold
def test(model, args, epoch, dataset, logger, test_threshold, test_acc):
model.eval()
with tqdm(desc='Testing', total=len(dataset)) as pbar:
for i, (data, target, paths) in enumerate(dataset):
if True:
if i == args.stop_after:
break
pbar.update()
output = model(data)
output_softmax = F.softmax(output, 1)
targets_var = Variable(maybe_cuda(torch.cat(target, 0), args.cuda), requires_grad=False)
output_seg = output.data.cpu().numpy().argmax(axis=1)
target_seg = targets_var.data.cpu().numpy()
preds_stats.add(output_seg, target_seg)
current_idx = 0
for k, t in enumerate(target):
document_sentence_count = len(t)
to_idx = int(current_idx + document_sentence_count)
output = ((output_softmax.data.cpu().numpy()[current_idx: to_idx, :])[:, 1] > test_threshold)
h = np.append(output, [1])
tt = np.append(t, [1])
test_acc.update(h, tt)
current_idx = to_idx
test_pk, epoch_windiff = test_acc.calc_accuracy()
logger.debug('Testing validation section: {}, accuracy: {:.4}, Pk: {:.4}, Windiff: {:.4}, F1: {:.4} . '.format(epoch + 1,
preds_stats.get_accuracy(),
test_pk,
epoch_windiff,
preds_stats.get_f1()))
preds_stats.reset()
return test_pk
def main(args):
sys.path.append(str(Path(__file__).parent))
logger = utils.setup_logger(__name__, 'cross_validate_choi.log')
utils.read_config_file(args.config)
utils.config.update(args.__dict__)
logger.debug('Running with config %s', utils.config)
configure(os.path.join('runs', args.expname))
if not args.test:
word2vec = gensim.models.KeyedVectors.load_word2vec_format(utils.config['word2vecfile'], binary=True)
else:
word2vec = None
dataset_path = Path(args.flat_choi)
with open(args.load_from, 'rb') as f:
model = torch.load(f)
model.eval()
model = maybe_cuda(model)
test_accuracy = accuracy.Accuracy()
for j in range(5):
validate_folder_numbers = range(5)
validate_folder_numbers.remove(j)
validate_folder_names = [dataset_path.joinpath(str(num)) for num in validate_folder_numbers]
dev_dataset = ChoiDataset(dataset_path , word2vec, folder=True, folders_paths=validate_folder_names)
test_dataset = ChoiDataset(dataset_path, word2vec, folder=True, folders_paths=[dataset_path.joinpath(str(j))])
dev_dl = DataLoader(dev_dataset, batch_size=args.test_bs, collate_fn=collate_fn, shuffle=False,
num_workers=args.num_workers)
test_dl = DataLoader(test_dataset, batch_size=args.test_bs, collate_fn=collate_fn, shuffle=False,
num_workers=args.num_workers)
_, threshold = validate(model, args, j, dev_dl, logger)
test_pk = test(model, args, j, test_dl, logger, threshold, test_accuracy)
logger.debug(colored('Cross validation section {} with p_k {} and threshold {}'.format(j, test_pk, threshold),'green'))
cross_validation_pk, _ = test_accuracy.calc_accuracy()
print ('Final cross validaiton Pk is: ' + str(cross_validation_pk))
logger.debug(
colored('Final cross validaiton Pk is: {}'.format(cross_validation_pk), 'green'))
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--cuda', help='Use cuda?', action='store_true')
parser.add_argument('--test', help='Test mode? (e.g fake word2vec)', action='store_true')
parser.add_argument('--bs', help='Batch size', type=int, default=8)
parser.add_argument('--test_bs', help='Batch size', type=int, default=5)
parser.add_argument('--load_from', help='Location of a .t7 model file to load. Training will continue')
parser.add_argument('--expname', help='Experiment name to appear on tensorboard', default='exp1')
parser.add_argument('--stop_after', help='Number of batches to stop after', default=None, type=int)
parser.add_argument('--config', help='Path to config.json', default='config.json')
parser.add_argument('--window_size', help='Window size to encode setence', type=int, default=1)
parser.add_argument('--num_workers', help='How many workers to use for data loading', type=int, default=0)
parser.add_argument('--flat_choi', help='Path to flat choi dataset')
main(parser.parse_args())