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
