def net_inference_wrap(self, request):
start = time.time()
try:
response = self.net_inference(request)
except ErrorCV2ImageRead as err:
xl.warn("net_inference", extra={'reqid': request.reqid})
response = pb.InferenceResponse(code=err.code, message=err.message)
except ErrorBase as err:
xl.warn("net_inference",
exc_info=err,
extra={'reqid': request.reqid})
response = pb.InferenceResponse(code=err.code, message=err.message)
except schema.SchemaError as err:
xl.warn("net_inference",
exc_info=err,
extra={'reqid': request.reqid})
response = pb.InferenceResponse(
code=400, message='bad api param: {}'.format(err))
except Exception as err: # pylint: disable=broad-except
xl.exception("net_inference",
exc_info=err,
extra={'reqid': request.reqid})
response = pb.InferenceResponse(
code=599,
message='app {} net_inference unknow error: {}'.format(
self.app_name, err))
self.monitor_push.send(
pb.MonitorMetric(kind="eval_time",
pid=str(self.pid),
code=str(response.code),
value=time.time() - start).SerializeToString())
return response
def net_inference(self, request):
assert isinstance(request, pb.InferenceRequest)
img = load_imagev2(request.data.body)
assert img.ndim == 3 # TODO
img_height, img_width, _ = img.shape
img_cls = cls_preProcessImage(img)
img_det = det_preProcessImage(img)
forward_req = {'img_cls': img_cls, 'img_det': img_det}
msg = pb.ForwardMsg(network_input_buf=cPickle.dumps(
forward_req, protocol=cPickle.HIGHEST_PROTOCOL),
reqid=request.reqid)
msg_out = self.inference_req.inference_msg(msg)
output = cPickle.loads(msg_out.network_output_buf)
image_index = json.loads(
msg_out.meta['data'].decode('utf8'))['image_index']
cls_result = cls_post_eval(output['output_fine'],
output['output_coarse'], image_index,
self.cls_model)
det_result = det_post_eval(img_height, img_width, output['output_det'],
self.det_label_dict, image_index)
cls_result = cls_merge_det(cls_result, det_result, self.cls_model,
self.det_model)
cls_result = merge_confidences(cls_result, self.cls_model)
resp = postProcess(cls_result, det_result, self.det_model)
return pb.InferenceResponse(code=200, result=json.dumps(resp))
def net_inference(self, request):
# pylint: disable=too-many-locals
assert isinstance(request, pb.InferenceRequest)
img = _load_image(request.data.body, self.image_width,
self.image_height, self.mean_value, self.std_value)
msg = pb.ForwardMsg(network_input_buf=img.tobytes(),
reqid=request.reqid)
msg_out = self.inference_req.inference_msg(msg)
output = cPickle.loads(msg_out.network_output_buf)
return pb.InferenceResponse(code=200,
result=json.dumps(
_build_result(output, self.labels)))
def net_inference(self, request):
classes_dict = self.labels['class']
threshold_dict = self.labels['minMt'] # minModelThreshold
assert isinstance(request, pb.InferenceRequest)
img = load_imagev2(request.data.body)
assert img.ndim == 3 # TODO
nms = py_nms_wrapper(config.TEST.NMS)
if img.shape[0] > img.shape[1]:
long_side, short_side = img.shape[0], img.shape[1]
else:
long_side, short_side = img.shape[1], img.shape[0]
if short_side > 0 and float(long_side) / float(short_side) > 50.0:
raise ErrorBase(
400,
'aspect ration is too large, long_size:short_side should not larger than 50.0'
)
batch = generate_batch(img)
msg = pb.ForwardMsg()
msg.network_input_buf = cPickle.dumps(
batch, protocol=cPickle.HIGHEST_PROTOCOL)
msg_out = self.inference_req.inference_msg(msg)
scores = []
boxes = []
r = cPickle.loads(msg_out.network_output_buf)
scores.append(r['scores'])
boxes.append(r['boxes'])
det_ret = []
for cls_index in sorted(classes_dict.keys()):
cls_ind = cls_index
cls_name = classes_dict.get(cls_ind)
cls_boxes = boxes[0][:, 4:8] if config.CLASS_AGNOSTIC else boxes[
0][:, 4 * cls_ind:4 * 4 * (cls_ind + 1)]
cls_scores = scores[0][:, cls_ind, np.newaxis]
threshold = float(threshold_dict[cls_ind])
keep = np.where(cls_scores > threshold)[0]
dets = np.hstack(
(cls_boxes, cls_scores)).astype(np.float32)[keep, :]
keep = nms(dets)
det_ret.extend(
_build_result(det, cls_name, cls_ind, self.labels)
for det in dets[keep, :])
return pb.InferenceResponse(code=200,
result=json.dumps({'detections': det_ret}))
def net_inference(self, request): # pylint: disable=no-self-use
assert isinstance(request, pb.InferenceRequest)
return pb.InferenceResponse(code=200,
result=json.dumps({'hello': 'world'}))