def get_fake_request(model_name, data_shape, input_blob, version=None): request = predict_pb2.PredictRequest() request.model_spec.name = model_name if version is not None: request.model_spec.version.value = version data = np.ones(shape=data_shape) request.inputs[input_blob].CopyFrom( make_tensor_proto(data, shape=data.shape)) return request
def infer_batch(batch_input, input_tensor, grpc_stub, model_spec_name, model_spec_version, output_tensors): request = predict_pb2.PredictRequest() request.model_spec.name = model_spec_name if model_spec_version is not None: request.model_spec.version.value = model_spec_version print("input shape", list(batch_input.shape)) request.inputs[input_tensor].CopyFrom( make_tensor_proto(batch_input, shape=list(batch_input.shape))) result = grpc_stub.Predict(request, 10.0) data = {} for output_tensor in output_tensors: data[output_tensor] = make_ndarray(result.outputs[output_tensor]) return data
def infer(imgs, slice_number, input_tensor, grpc_stub, model_spec_name, model_spec_version, output_tensors): request = predict_pb2.PredictRequest() request.model_spec.name = model_spec_name if model_spec_version is not None: request.model_spec.version.value = model_spec_version img = imgs[slice_number, ...] print("input shape", list((1, ) + img.shape)) request.inputs[input_tensor].CopyFrom( make_tensor_proto(img, shape=list((1, ) + img.shape))) result = grpc_stub.Predict(request, 10.0) data = {} for output_tensor in output_tensors: data[output_tensor] = make_ndarray(result.outputs[output_tensor]) return data