Lines Matching refs:i
35 for (uint32_t i = 0; i < io_num.n_input; i++)
37 dyn_range[i].index = i;
38 … ret = rknn_query(ctx, RKNN_QUERY_INPUT_DYNAMIC_RANGE, &dyn_range[i], sizeof(rknn_input_range));
44 dump_input_dynamic_range(&dyn_range[i]);
56 for (int i = 0; i < io_num.n_input; i++)
58 for (int j = 0; j < input_attrs[i].n_dims; ++j)
60 input_attrs[i].dims[j] = shape_range[i].dyn_range[s][j];
63 ret = rknn_set_input_shape(ctx, &input_attrs[i]);
72 其中，shape_num是支持的形状个数，shape_range[i]是第i个输入的rknn_input_range结构体，input_attrs[i]是第i个输入的rknn_tensor_attr…
79 for (uint32_t i = 0; i < io_num.n_input; i++)
81 cur_input_attrs[i].index = i;
82 …ret = rknn_query(ctx, RKNN_QUERY_CURRENT_INPUT_ATTR, &(cur_input_attrs[i]), sizeof(rknn_tensor_att…
88 dump_tensor_attr(&cur_input_attrs[i]);
93 for (uint32_t i = 0; i < io_num.n_output; i++)
95 cur_output_attrs[i].index = i;
96 …ret = rknn_query(ctx, RKNN_QUERY_CURRENT_OUTPUT_ATTR, &(cur_output_attrs[i]), sizeof(rknn_tensor_a…
102 dump_tensor_attr(&cur_output_attrs[i]);
116 for (int i = 0; i < io_num.n_input; i++)
118 …int height = cur_input_attrs[i].fmt == RKNN_TENSOR_NHWC ? cur_input_attrs[i].dims[1] : cur_input_a…
119 …int width = cur_input_attrs[i].fmt == RKNN_TENSOR_NHWC ? cur_input_attrs[i].dims[2] : cur_input_at…
120 cv::resize(imgs[i], imgs[i], cv::Size(width, height));
121 inputs[i].index = i;
122 inputs[i].pass_through = 0;
123 inputs[i].type = RKNN_TENSOR_UINT8;
124 inputs[i].fmt = RKNN_TENSOR_NHWC;
125 inputs[i].buf = imgs[i].data;
126 inputs[i].size = imgs[i].total() * imgs[i].channels();
140 for (int i = 0; i < loop_count; ++i)
151 …printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_…
158 for (uint32_t i = 0; i < io_num.n_output; ++i)
160 outputs[i].want_float = 1;
161 outputs[i].index = i;
162 outputs[i].is_prealloc = 0;