def evaluate_once(self):
session_manager = SessionManager(self.config)
global_step, sess = session_manager.restore(self.saver)
coord = tf.train.Coordinator()
try:
queue_runners = tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)
threads_per_qr = map(lambda qr: qr.create_threads(sess, coord=coord, daemon=True, start=True), queue_runners)
threads = reduce(list.__add__, threads_per_qr)
num_iter = math.ceil(self.config.dataset.set_sizes['test'] / self.config.training_params.batch_size)
step = 0
acc = []
full_logits = []
full_labels = []
while step < num_iter and not coord.should_stop():
logits, labels = sess.run([self.logits, self.labels])
full_logits += [logits]
full_labels += [labels]
step += 1
logits = np.vstack(full_logits)
labels = np.vstack(full_labels)
labels = np.array(labels, np.uint8).reshape((-1))
good_logits = logits[np.arange(logits.shape[0]), labels]
classirate = good_logits.sum() / logits.shape[0]
print ('Classification rate:', classirate)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
except Exception as e:
coord.request_stop(e)
def run(self):
files, labels, images = self.input_manager.get_inputs(type='submission', distorted = False, shuffle = False)
with tf.variable_scope("inference"):
logits = self.config.inference(images, testing=True)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(self.config.training_params.moving_average_decay)
variables_to_restore = variable_averages.variables_to_restore()
self.saver = tf.train.Saver(variables_to_restore)
session_manager = SessionManager(self.config)
global_step, sess = session_manager.restore(self.saver)
coord = tf.train.Coordinator()
try:
queue_runners = tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS)
threads_per_qr = map(lambda qr: qr.create_threads(sess, coord=coord, daemon=True, start=True), queue_runners)
threads = reduce(list.__add__, threads_per_qr)
num_iter = math.ceil(self.config.dataset.submission_size / self.config.training_params.batch_size)
step = 0
predictions_ids = []
predictions = []
while step < num_iter and not coord.should_stop():
submission_files, submission_logits = sess.run([files, logits])
submission_files = list(map(self.config.dataset.retrieve_file_id, submission_files))
predictions += [submission_logits]
predictions_ids += submission_files
step += 1
predictions = np.vstack(predictions)
predictions = np.float32(predictions)
predictions += 5e-2
row_sums = np.reshape(predictions.sum(axis=1), [-1, 1])
predictions /= row_sums
df = pd.DataFrame(data=predictions)
cols = list(map(lambda c: 'c' + str(c), range(10)))
df.columns = cols
df['img'] = predictions_ids
df = df[['img'] + cols]
df = df.drop_duplicates(subset = ['img'], keep='first')
df.to_csv('submission.csv', index=False)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
except Exception as e:
coord.request_stop(e)
