def dump_n_eval(self, outputs, action):
"""Dump the model's outputs to files and evaluate."""
if not is_primary_worker('global'):
return
if action == 'init':
if os.path.exists(FLAGS.outputs_dump_dir):
shutil.rmtree(FLAGS.outputs_dump_dir)
os.mkdir(FLAGS.outputs_dump_dir)
elif action == 'dump':
filename = outputs['predictions']['filename'][0].decode('utf8')[:-4]
shape = outputs['predictions']['shape'][0]
for idx_cls in range(1, FLAGS.nb_classes):
with open(os.path.join(FLAGS.outputs_dump_dir, 'results_%d.txt' % idx_cls), 'a') as o_file:
scores = outputs['predictions']['scores_%d' % idx_cls][0]
bboxes = outputs['predictions']['bboxes_%d' % idx_cls][0]
bboxes[:, 0] = (bboxes[:, 0] * shape[0]).astype(np.int32, copy=False) + 1
bboxes[:, 1] = (bboxes[:, 1] * shape[1]).astype(np.int32, copy=False) + 1
bboxes[:, 2] = (bboxes[:, 2] * shape[0]).astype(np.int32, copy=False) + 1
bboxes[:, 3] = (bboxes[:, 3] * shape[1]).astype(np.int32, copy=False) + 1
for idx_bbox in range(bboxes.shape[0]):
bbox = bboxes[idx_bbox][:]
if bbox[2] > bbox[0] and bbox[3] > bbox[1]:
o_file.write('%s %.3f %.1f %.1f %.1f %.1f\n'
% (filename, scores[idx_bbox], bbox[1], bbox[0], bbox[3], bbox[2]))
elif action == 'eval':
do_python_eval(os.path.join(self.dataset_eval.data_dir, 'test'), FLAGS.outputs_dump_dir)
else:
raise ValueError('unrecognized action in dump_n_eval(): ' + action)
def dump_n_eval(self, outputs, action):
"""Dump the model's outputs to files and evaluate."""
if not is_primary_worker('global'):
return
if action == 'init':
if os.path.exists(FLAGS.outputs_dump_dir):
shutil.rmtree(FLAGS.outputs_dump_dir)
os.mkdir(FLAGS.outputs_dump_dir)
elif action == 'dump':
filename = outputs['predictions']['filename'][0].decode('utf8')[:-4]
raw_shape = outputs['predictions']['shape'][0]
resized_shape= outputs['predictions']['resized_shape']
detected_boxes = outputs['predictions']['detected_boxes']
detected_scores = outputs['predictions']['detected_scores']
detected_categories = outputs['predictions']['detected_categories']
raw_h, raw_w = raw_shape[0], raw_shape[1]
resized_h, resized_w = resized_shape[1], resized_shape[2]
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
dets = np.hstack((detected_categories.reshape(-1, 1),
detected_scores.reshape(-1, 1),
boxes))
for cls_id in range(1, FLAGS.nb_classes):
with open(os.path.join(FLAGS.outputs_dump_dir, 'results_%d.txt' % cls_id), 'a') as o_file:
this_cls_detections = dets[dets[:, 0] == cls_id]
if this_cls_detections.shape[0] == 0:
continue # this cls has none detections in this img
for a_det in this_cls_detections:
o_file.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(filename, a_det[1],
a_det[2], a_det[3],
a_det[4], a_det[5])) # that is [img_name, score, xmin, ymin, xmax, ymax]
elif action == 'eval':
do_python_eval(os.path.join(self.dataset_eval.data_dir, 'test'), FLAGS.outputs_dump_dir)
else:
raise ValueError('unrecognized action in dump_n_eval(): ' + action)