def create_input()

in dlrm_s_caffe2.py [0:0]

• 24 lines of code
• 14 McCabe index (conditional complexity)

    def create_input(self, X, S_lengths, S_indices, T):
        # feed input data to blobs
        self.FeedBlobWrapper(self.tdin, X, split=True)
        # save the blob shapes for latter (only needed if onnx is requested)
        if self.save_onnx:
            self.onnx_tsd[self.tdin] = (onnx.TensorProto.FLOAT, X.shape)

        for i in range(len(self.emb_l)):
            # select device
            if self.ndevices > 1:
                d = i % self.ndevices
            else:
                d = -1
            # create tags
            on_device = "" if self.ndevices <= 1 else "gpu_" + str(d) + "/"
            len_s = on_device + self.temb + ":::" + "sls" + str(i) + "_l"
            ind_s = on_device + self.temb + ":::" + "sls" + str(i) + "_i"
            self.FeedBlobWrapper(len_s, np.array(S_lengths[i]), False, device_id=d)
            self.FeedBlobWrapper(ind_s, np.array(S_indices[i]), False, device_id=d)
            # save the blob shapes for latter (only needed if onnx is requested)
            if self.save_onnx:
                lshape = (len(S_lengths[i]),)  # =args.mini_batch_size
                ishape = (len(S_indices[i]),)
                self.onnx_tsd[len_s] = (onnx.TensorProto.INT32, lshape)
                self.onnx_tsd[ind_s] = (onnx.TensorProto.INT32, ishape)

        # feed target data to blobs
        if T is not None:
            zeros_fp32 = np.zeros(T.shape).astype(np.float32)
            self.FeedBlobWrapper(self.ttar, zeros_fp32, split=True)
            # save the blob shapes for latter (only needed if onnx is requested)
            if self.save_onnx:
                self.onnx_tsd[self.ttar] = (onnx.TensorProto.FLOAT, T.shape)