def _init_metrics(self):
if self.mode == 'eval':
if self.cfg.metric == 'COCO':
mask_resolution = self.model.mask_post_process.mask_resolution if hasattr(
self.model, 'mask_post_process') else None
self._metrics = [
COCOMetric(
anno_file=self.dataset.get_anno(),
with_background=self.cfg.with_background,
mask_resolution=mask_resolution)
]
elif self.cfg.metric == 'VOC':
self._metrics = [
VOCMetric(
anno_file=self.dataset.get_anno(),
with_background=self.cfg.with_background,
class_num=self.cfg.num_classes,
map_type=self.cfg.map_type)
]
else:
logger.warn("Metric not support for metric type {}".format(
self.cfg.metric))
self._metrics = []
else:
self._metrics = []
def eval():
dataset = reader_cfg['EvalDataset']
val_loader = create('TestReader')(dataset,
reader_cfg['worker_num'],
return_list=True)
place = paddle.CUDAPlace(0) if args.devices == 'gpu' else paddle.CPUPlace()
exe = paddle.static.Executor(place)
val_program, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
args.model_dir,
exe,
model_filename=args.model_filename,
params_filename=args.params_filename)
clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
anno_file = dataset.get_anno()
metric = COCOMetric(anno_file=anno_file,
clsid2catid=clsid2catid,
bias=0,
IouType='bbox')
for batch_id, data in enumerate(val_loader):
data_new = {k: np.array(v) for k, v in data.items()}
outs = exe.run(val_program,
feed={
'image': data['image'],
'im_shape': data['im_shape'],
'scale_factor': data['scale_factor']
},
fetch_list=fetch_targets,
return_numpy=False)
res = {}
for out in outs:
v = np.array(out)
if len(v.shape) > 1:
res['bbox'] = v
else:
res['bbox_num'] = v
metric.update(data_new, res)
if batch_id % 100 == 0:
print('Eval iter:', batch_id)
metric.accumulate()
metric.log()
metric.reset()
def _init_metrics(self, validate=False):
if self.mode == 'test' or (self.mode == 'train' and not validate):
self._metrics = []
return
classwise = self.cfg['classwise'] if 'classwise' in self.cfg else False
if self.cfg.metric == 'COCO':
# TODO: bias should be unified
bias = self.cfg['bias'] if 'bias' in self.cfg else 0
output_eval = self.cfg['output_eval'] \
if 'output_eval' in self.cfg else None
save_prediction_only = self.cfg['save_prediction_only'] \
if 'save_prediction_only' in self.cfg else False
# pass clsid2catid info to metric instance to avoid multiple loading
# annotation file
clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} \
if self.mode == 'eval' else None
# when do validation in train, annotation file should be get from
# EvalReader instead of self.dataset(which is TrainReader)
anno_file = self.dataset.get_anno()
if self.mode == 'train' and validate:
eval_dataset = self.cfg['EvalDataset']
eval_dataset.check_or_download_dataset()
anno_file = eval_dataset.get_anno()
self._metrics = [
COCOMetric(anno_file=anno_file,
clsid2catid=clsid2catid,
classwise=classwise,
output_eval=output_eval,
bias=bias,
save_prediction_only=save_prediction_only)
]
elif self.cfg.metric == 'VOC':
self._metrics = [
VOCMetric(label_list=self.dataset.get_label_list(),
class_num=self.cfg.num_classes,
map_type=self.cfg.map_type,
classwise=classwise)
]
elif self.cfg.metric == 'WiderFace':
multi_scale = self.cfg.multi_scale_eval if 'multi_scale_eval' in self.cfg else True
self._metrics = [
WiderFaceMetric(image_dir=os.path.join(
self.dataset.dataset_dir, self.dataset.image_dir),
anno_file=self.dataset.get_anno(),
multi_scale=multi_scale)
]
else:
logger.warn("Metric not support for metric type {}".format(
self.cfg.metric))
self._metrics = []
def _init_metrics(self):
if self.cfg.metric == 'COCO':
self._metrics = [COCOMetric(anno_file=self.dataset.get_anno())]
elif self.cfg.metric == 'VOC':
self._metrics = [
VOCMetric(
anno_file=self.dataset.get_anno(),
class_num=self.cfg.num_classes,
map_type=self.cfg.map_type)
]
else:
logger.warn("Metric not support for metric type {}".format(
self.cfg.metric))
self._metrics = []
def _init_metrics(self):
if self.mode == 'test':
self._metrics = []
return
if self.cfg.metric == 'COCO':
# TODO: bias should be unified
bias = 1 if 'bias' in self.cfg else 0
self._metrics = [
COCOMetric(anno_file=self.dataset.get_anno(), bias=bias)
]
elif self.cfg.metric == 'VOC':
self._metrics = [
VOCMetric(anno_file=self.dataset.get_anno(),
class_num=self.cfg.num_classes,
map_type=self.cfg.map_type)
]
else:
logger.warn("Metric not support for metric type {}".format(
self.cfg.metric))
self._metrics = []
def eval_function(exe, compiled_test_program, test_feed_names,
test_fetch_list):
clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
anno_file = dataset.get_anno()
metric = COCOMetric(anno_file=anno_file,
clsid2catid=clsid2catid,
bias=1,
IouType='bbox')
for batch_id, data in enumerate(val_loader):
data_new = {k: np.array(v) for k, v in data.items()}
outs = exe.run(compiled_test_program,
feed={
'image': data['image'],
'im_shape': data['im_shape'],
'scale_factor': data['scale_factor']
},
fetch_list=test_fetch_list,
return_numpy=False)
res = {}
for out in outs:
v = np.array(out)
if len(v.shape) > 1:
res['bbox'] = v
else:
res['bbox_num'] = v
metric.update(data_new, res)
if batch_id % 100 == 0:
print('Eval iter:', batch_id)
metric.accumulate()
metric.log()
map_res = metric.get_results()
metric.reset()
return map_res['bbox'][0]