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evaler.py
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evaler.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from pprint import pprint
import h5py
import numpy as np
from six.moves import xrange
import tensorflow as tf
from input_ops import create_input_ops, check_data_id
from util import log
from config import argparser
class EvalManager(object):
def __init__(self):
# collection of batches (not flattened)
self._ids = []
self._predictions = []
self._groundtruths = []
def add_batch(self, id, prediction, groundtruth):
# for now, store them all (as a list of minibatch chunks)
self._ids.append(id)
self._predictions.append(prediction)
self._groundtruths.append(groundtruth)
def compute_accuracy(self, pred, gt):
correct_prediction = np.sum(np.argmax(pred[:, :-1], axis=1) == np.argmax(gt, axis=1))
return float(correct_prediction)/pred.shape[0]
def report(self):
# report L2 loss
log.info("Computing scores...")
score = {}
score = []
for id, pred, gt in zip(self._ids, self._predictions, self._groundtruths):
score.append(self.compute_accuracy(pred, gt))
avg = np.average(score)
log.infov("Average accuracy : %.4f", avg*100)
class Evaler(object):
def __init__(self, config, model, dataset):
self.config = config
self.model = model
self.train_dir = config.train_dir
log.info("self.train_dir = %s", self.train_dir)
# --- input ops ---
self.batch_size = config.batch_size
self.dataset = dataset
check_data_id(dataset, config.data_id)
_, self.batch = create_input_ops(dataset, self.batch_size,
data_id=config.data_id,
is_training=False,
shuffle=False)
self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)
self.step_op = tf.no_op(name='step_no_op')
tf.set_random_seed(1234)
session_config = tf.ConfigProto(
allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True),
device_count={'GPU': 1},
)
self.session = tf.Session(config=session_config)
# --- checkpoint and monitoring ---
self.saver = tf.train.Saver(max_to_keep=100)
self.checkpoint = config.checkpoint
if self.checkpoint is None and self.train_dir:
self.checkpoint = tf.train.latest_checkpoint(self.train_dir)
if self.checkpoint is None:
log.warn("No checkpoint is given. Just random initialization :-)")
self.session.run(tf.global_variables_initializer())
else:
log.info("Checkpoint path : %s", self.checkpoint)
def eval_run(self):
# load checkpoint
if self.checkpoint:
self.saver.restore(self.session, self.checkpoint)
log.info("Loaded from checkpoint!")
log.infov("Start 1-epoch Inference and Evaluation")
log.info("# of examples = %d", len(self.dataset))
length_dataset = len(self.dataset)
max_steps = int(length_dataset / self.batch_size) + 1
log.info("max_steps = %d", max_steps)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(self.session,
coord=coord, start=True)
evaler = EvalManager()
try:
for s in xrange(max_steps):
step, loss, step_time, batch_chunk, prediction_pred, prediction_gt = \
self.run_single_step(self.batch)
self.log_step_message(s, loss, step_time)
evaler.add_batch(batch_chunk['id'], prediction_pred, prediction_gt)
except Exception as e:
coord.request_stop(e)
coord.request_stop()
try:
coord.join(threads, stop_grace_period_secs=3)
except RuntimeError as e:
log.warn(str(e))
evaler.report()
log.infov("Evaluation complete.")
def run_single_step(self, batch, step=None, is_train=True):
_start_time = time.time()
batch_chunk = self.session.run(batch)
print("9"*50)
print(batch_chunk["id"])
print("9"*50)
[step, accuracy, all_preds, all_targets, _] = self.session.run(
[self.global_step, self.model.accuracy, self.model.all_preds, self.model.all_targets, self.step_op],
feed_dict=self.model.get_feed_dict(batch_chunk)
)
_end_time = time.time()
return step, accuracy, (_end_time - _start_time), batch_chunk, all_preds, all_targets
def log_step_message(self, step, accuracy, step_time, is_train=False):
if step_time == 0: step_time = 0.001
log_fn = (is_train and log.info or log.infov)
log_fn((" [{split_mode:5s} step {step:4d}] " +
"batch total-accuracy (test): {test_accuracy:.2f}% " +
"({sec_per_batch:.3f} sec/batch, {instance_per_sec:.3f} instances/sec) "
).format(split_mode=(is_train and 'train' or 'val'),
step=step,
test_accuracy=accuracy*100,
sec_per_batch=step_time,
instance_per_sec=self.batch_size / step_time,
)
)
def main():
config, model, dataset_train, _, dataset_test = argparser(is_train=False)
evaler = Evaler(config, model, dataset_test)
log.warning("dataset: %s", config.dataset)
evaler.eval_run()
if __name__ == '__main__':
main()