def get_symbol(network, data_shape, **kwargs):
"""Wrapper for get symbol for test
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol for more details
"""
if network.startswith('legacy'):
logging.warn('Using legacy model.')
return symbol_builder.import_module(network).get_symbol(**kwargs)
config = get_config(network, data_shape, **kwargs).copy()
config.update(kwargs)
return symbol_builder.get_symbol(**config)
def get_symbol(network, data_shape, **kwargs):
"""Wrapper for get symbol for test
Parameters
----------
network : str
name for the base network symbol
data_shape : int
input shape
kwargs : dict
see symbol_builder.get_symbol for more details
"""
if network.startswith('legacy'):
logging.warn('Using legacy model.')
return symbol_builder.import_module(network).get_symbol(**kwargs)
config = get_config(network, data_shape, **kwargs).copy()
config.update(kwargs)
return symbol_builder.get_symbol(**config)
def evaluate_net(net,
path_imgrec,
num_classes,
mean_pixels,
data_shape,
model_prefix,
epoch,
ctx=mx.cpu(),
batch_size=1,
path_imglist="",
nms_thresh=0.45,
force_nms=False,
ovp_thresh=0.5,
use_difficult=False,
class_names=None,
voc07_metric=False):
"""
evalute network given validation record file
Parameters:
----------
net : str or None
Network name or use None to load from json without modifying
path_imgrec : str
path to the record validation file
path_imglist : str
path to the list file to replace labels in record file, optional
num_classes : int
number of classes, not including background
mean_pixels : tuple
(mean_r, mean_g, mean_b)
data_shape : tuple or int
(3, height, width) or height/width
model_prefix : str
model prefix of saved checkpoint
epoch : int
load model epoch
ctx : mx.ctx
mx.gpu() or mx.cpu()
batch_size : int
validation batch size
nms_thresh : float
non-maximum suppression threshold
force_nms : boolean
whether suppress different class objects
ovp_thresh : float
AP overlap threshold for true/false postives
use_difficult : boolean
whether to use difficult objects in evaluation if applicable
class_names : comma separated str
class names in string, must correspond to num_classes if set
voc07_metric : boolean
whether to use 11-point evluation as in VOC07 competition
"""
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# args
if isinstance(data_shape, int):
data_shape = (3, data_shape, data_shape)
assert len(data_shape) == 3 and data_shape[0] == 3
# model_prefix += '_' + str(data_shape[1])
# iterator
eval_iter = DetRecordIter(path_imgrec,
batch_size,
data_shape,
path_imglist=path_imglist,
**cfg.valid)
# model params
load_net, args, auxs = mx.model.load_checkpoint(model_prefix, epoch)
# network
if net is None:
net = load_net
else:
sys.path.append(os.path.join(cfg.ROOT_DIR, 'symbol'))
net = get_symbol('symbol_' + net, num_classes, nms_thresh, force_nms)
# net = importlib.import_module("symbol_" + net) \
# .get_symbol(num_classes, nms_thresh, force_nms)
if not 'yolo_output_label' in net.list_arguments():
label = mx.sym.Variable(name='yolo_output_label')
net = mx.sym.Group([net, label])
# init module
mod = mx.mod.Module(net,
label_names=('yolo_output_label', ),
logger=logger,
context=ctx,
fixed_param_names=net.list_arguments())
mod.bind(data_shapes=eval_iter.provide_data,
label_shapes=eval_iter.provide_label)
mod.set_params(args, auxs, allow_missing=False, force_init=True)
# run evaluation
if voc07_metric:
metric = VOC07MApMetric(ovp_thresh, use_difficult, class_names)
else:
metric = MApMetric(ovp_thresh, use_difficult, class_names)
results = mod.score(eval_iter, metric, num_batch=None)
for k, v in results:
print("{}: {}".format(k, v))
