eval.py
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from __future__ import division, print_function
import tensorflow as tf
import numpy as np
import argparse
from tqdm import trange
import os
from data_utils import get_batch_data
from misc_utils import parse_anchors, read_class_names, AverageMeter
from eval_utils import evaluate_on_cpu, evaluate_on_gpu, get_preds_gpu, voc_eval, parse_gt_rec
from nms_utils import gpu_nms
from model import yolov3
### ArgumentParser
parser = argparse.ArgumentParser(description="YOLO-V3 eval procedure.")
# paths
parser.add_argument("--eval_file", type=str, default="../../data/test.tfrecord",
help="The path of the validation or test txt file.")
parser.add_argument("--restore_path", type=str, default="../../data/darknet_weights/yolov3.ckpt",
help="The path of the weights to restore.")
parser.add_argument("--anchor_path", type=str, default="../../data/yolo_anchors.txt",
help="The path of the anchor txt file.")
parser.add_argument("--class_name_path", type=str, default="../../data/classes.txt",
help="The path of the class names.")
# some numbers
parser.add_argument("--img_size", nargs='*', type=int, default=[416, 416],
help="Resize the input image to `img_size`, size format: [width, height]")
parser.add_argument("--letterbox_resize", type=lambda x: (str(x).lower() == 'true'), default=False,
help="Whether to use the letterbox resize, i.e., keep the original image aspect ratio.")
parser.add_argument("--num_threads", type=int, default=10,
help="Number of threads for image processing used in tf.data pipeline.")
parser.add_argument("--prefetech_buffer", type=int, default=5,
help="Prefetech_buffer used in tf.data pipeline.")
parser.add_argument("--nms_threshold", type=float, default=0.45,
help="IOU threshold in nms operation.")
parser.add_argument("--score_threshold", type=float, default=0.01,
help="Threshold of the probability of the classes in nms operation.")
parser.add_argument("--nms_topk", type=int, default=400,
help="Keep at most nms_topk outputs after nms.")
parser.add_argument("--use_voc_07_metric", type=lambda x: (str(x).lower() == 'true'), default=False,
help="Whether to use the voc 2007 mAP metrics.")
args = parser.parse_args()
# args params
args.anchors = parse_anchors(args.anchor_path)
args.classes = read_class_names(args.class_name_path)
args.class_num = len(args.classes)
args.img_cnt = len(open(args.eval_file, 'r').readlines())
# setting placeholders
is_training = tf.placeholder(dtype=tf.bool, name="phase_train")
handle_flag = tf.placeholder(tf.string, [], name='iterator_handle_flag')
pred_boxes_flag = tf.placeholder(tf.float32, [1, None, None])
pred_scores_flag = tf.placeholder(tf.float32, [1, None, None])
gpu_nms_op = gpu_nms(pred_boxes_flag, pred_scores_flag, args.class_num, args.nms_topk, args.score_threshold, args.nms_threshold)
### tf.data pipeline
val_dataset = tf.data.TFRecordDataset(filenames=args.eval_file, compression_type='GZIP')
val_dataset = val_dataset.batch(1)
val_dataset = val_dataset.map(
lambda x: tf.py_func(get_batch_data, [x, args.class_num, args.img_size, args.anchors, False, False, False, args.letterbox_resize], [tf.int64, tf.float32, tf.float32, tf.float32, tf.float32]),
num_parallel_calls=args.num_threads
)
val_dataset.prefetch(args.prefetech_buffer)
iterator = val_dataset.make_one_shot_iterator()
image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()
image_ids.set_shape([None])
y_true = [y_true_13, y_true_26, y_true_52]
image.set_shape([None, args.img_size[1], args.img_size[0], 3])
for y in y_true:
y.set_shape([None, None, None, None, None])
### Model definition
yolo_model = yolov3(args.class_num, args.anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(image, is_training=is_training)
loss = yolo_model.compute_loss(pred_feature_maps, y_true)
y_pred = yolo_model.predict(pred_feature_maps)
saver_to_restore = tf.train.Saver()
with tf.Session() as sess:
sess.run([tf.global_variables_initializer()])
if os.path.exists(args.restore_path):
saver_to_restore.restore(sess, args.restore_path)
print('\nStart evaluation...\n')
val_loss_total, val_loss_xy, val_loss_wh, val_loss_conf, val_loss_class = \
AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
val_preds = []
for j in trange(args.img_cnt):
__image_ids, __y_pred, __loss = sess.run([image_ids, y_pred, loss], feed_dict={is_training: False})
pred_content = get_preds_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, __image_ids, __y_pred)
val_preds.extend(pred_content)
val_loss_total.update(__loss[0])
val_loss_xy.update(__loss[1])
val_loss_wh.update(__loss[2])
val_loss_conf.update(__loss[3])
val_loss_class.update(__loss[4])
rec_total, prec_total, ap_total = AverageMeter(), AverageMeter(), AverageMeter()
gt_dict = parse_gt_rec(args.eval_file, 'GZIP', args.img_size, args.letterbox_resize)
print('mAP eval:')
for ii in range(args.class_num):
npos, nd, rec, prec, ap = voc_eval(gt_dict, val_preds, ii, iou_thres=0.5, use_07_metric=args.use_voc_07_metric)
rec_total.update(rec, npos)
prec_total.update(prec, nd)
ap_total.update(ap, 1)
print('Class {}: Recall: {:.4f}, Precision: {:.4f}, AP: {:.4f}'.format(ii, rec, prec, ap))
mAP = ap_total.average
print('final mAP: {:.4f}'.format(mAP))
print("recall: {:.3f}, precision: {:.3f}".format(rec_total.average, prec_total.average))
print("total_loss: {:.3f}, loss_xy: {:.3f}, loss_wh: {:.3f}, loss_conf: {:.3f}, loss_class: {:.3f}".format(
val_loss_total.average, val_loss_xy.average, val_loss_wh.average, val_loss_conf.average, val_loss_class.average
))