def get_output_threshold(self):
output_info = ffi.new('bmnet_output_info_t *')
lib.bmnet_get_output_info(self.net[0], output_info)
output_threshold = []
for i in range(output_info.output_num):
output_threshold.append(output_info.threshold_array[i])
return output_threshold
def get_output_fmt(self):
output_info = ffi.new('bmnet_output_info_t *')
lib.bmnet_get_output_info(self.net[0], output_info)
output_fmt = []
for i in range(output_info.output_num):
output_fmt.append(output_info.fmt_array[i])
return output_fmt
def get_output_shape(self):
output_info = ffi.new('bmnet_output_info_t *')
lib.bmnet_get_output_info(self.net[0], output_info)
output_shape = []
for i in range(output_info.output_num):
shape = output_info.shape_array[i]
if shape.dim == 1:
output_shape.append([shape.n])
elif shape.dim == 2:
output_shape.append([shape.n, shape.c])
elif shape.dim == 3:
output_shape.append([shape.n, shape.c, shape.h])
elif shape.dim == 4:
output_shape.append([shape.n, shape.c, shape.h, shape.w])
return output_shape
def get_output_size(self):
output_info = ffi.new('bmnet_output_info_t *')
lib.bmnet_get_output_info(self.net[0], output_info)
return output_info.output_size
def inference(self, input, output):
output_info = ffi.new('bmnet_output_info_t *')
lib.bmnet_get_output_info(self.net[0], output_info)
lib.bmnet_inference(self.net[0], ffi.from_buffer(input),
ffi.from_buffer(output))