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test.py
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test.py
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import os
import cv2
import numpy as np
import pickle
import argparse
from darknet import Darknet19
import utils.yolo as yolo_utils
import utils.network as net_utils
from utils.timer import Timer
from datasets.pascal_voc import VOCDataset
import cfgs.config as cfg
parser = argparse.ArgumentParser(description='PyTorch Yolo')
parser.add_argument('--image_size_index', type=int, default=0,
metavar='image_size_index',
help='setting images size index 0:320, 1:352, 2:384, 3:416, 4:448, 5:480, 6:512, 7:544, 8:576')
args = parser.parse_args()
# hyper-parameters
# ------------
imdb_name = cfg.imdb_test
# trained_model = cfg.trained_model
trained_model = os.path.join(cfg.train_output_dir,
'darknet19_voc07trainval_exp3_73.h5')
output_dir = cfg.test_output_dir
max_per_image = 300
thresh = 0.01
vis = False
# ------------
def test_net(net, imdb, max_per_image=300, thresh=0.5, vis=False):
num_images = imdb.num_images
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in range(num_images)]
for _ in range(imdb.num_classes)]
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
det_file = os.path.join(output_dir, 'detections.pkl')
size_index = args.image_size_index
for i in range(num_images):
batch = imdb.next_batch(size_index=size_index)
ori_im = batch['origin_im'][0]
im_data = net_utils.np_to_variable(batch['images'], is_cuda=True,
volatile=True).permute(0, 3, 1, 2)
_t['im_detect'].tic()
bbox_pred, iou_pred, prob_pred = net(im_data)
# to numpy
bbox_pred = bbox_pred.data.cpu().numpy()
iou_pred = iou_pred.data.cpu().numpy()
prob_pred = prob_pred.data.cpu().numpy()
bboxes, scores, cls_inds = yolo_utils.postprocess(bbox_pred,
iou_pred,
prob_pred,
ori_im.shape,
cfg,
thresh,
size_index
)
detect_time = _t['im_detect'].toc()
_t['misc'].tic()
for j in range(imdb.num_classes):
inds = np.where(cls_inds == j)[0]
if len(inds) == 0:
all_boxes[j][i] = np.empty([0, 5], dtype=np.float32)
continue
c_bboxes = bboxes[inds]
c_scores = scores[inds]
c_dets = np.hstack((c_bboxes,
c_scores[:, np.newaxis])).astype(np.float32,
copy=False)
all_boxes[j][i] = c_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in range(imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in range(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
nms_time = _t['misc'].toc()
if i % 20 == 0:
print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s'.format(i + 1, num_images, detect_time, nms_time)) # noqa
_t['im_detect'].clear()
_t['misc'].clear()
if vis:
im2show = yolo_utils.draw_detection(ori_im,
bboxes,
scores,
cls_inds,
cfg,
thr=0.1)
if im2show.shape[0] > 1100:
im2show = cv2.resize(im2show,
(int(1000. * float(im2show.shape[1]) / im2show.shape[0]), 1000)) # noqa
cv2.imshow('test', im2show)
cv2.waitKey(0)
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
imdb.evaluate_detections(all_boxes, output_dir)
if __name__ == '__main__':
# data loader
imdb = VOCDataset(imdb_name, cfg.DATA_DIR, cfg.batch_size,
yolo_utils.preprocess_test,
processes=1, shuffle=False, dst_size=cfg.multi_scale_inp_size)
net = Darknet19()
net_utils.load_net(trained_model, net)
net.cuda()
net.eval()
test_net(net, imdb, max_per_image, thresh, vis)
imdb.close()