| 25 | } |
| 26 | |
| 27 | static TinyNNStatus execute(Instruction* inst, VM* vm) { |
| 28 | Tensor *input = inst->workload.reshape.inputs[0], |
| 29 | *shape_tensor = inst->workload.reshape.inputs[1], |
| 30 | *output = inst->workload.reshape.output; |
| 31 | output->layout = input->layout; |
| 32 | output->layout.nr_dim = shape_tensor->layout.dims[0]; |
| 33 | uint32_t nr_elem = 1; |
| 34 | for (int i = 0; i < input->layout.nr_dim; ++i) { |
| 35 | nr_elem *= input->layout.dims[i]; |
| 36 | } |
| 37 | int* tshape = shape_tensor->ptr; |
| 38 | int neg_axis = -1; |
| 39 | for (int i = 0; i < shape_tensor->layout.dims[0]; ++i) { |
| 40 | if (tshape[i] > 0) { |
| 41 | output->layout.dims[i] = tshape[i]; |
| 42 | nr_elem = nr_elem / output->layout.dims[i]; |
| 43 | } else { |
| 44 | TINYNN_ASSERT(tshape[i] == -1 && neg_axis == -1); |
| 45 | neg_axis = i; |
| 46 | } |
| 47 | } |
| 48 | if (neg_axis >= 0) { |
| 49 | output->layout.dims[neg_axis] = nr_elem; |
| 50 | } |
| 51 | force_layout_contiguous(&(output->layout)); |
| 52 | output->ptr = input->ptr; |
| 53 | output->size = tensor_length_in_byte(output); |
| 54 | return TinyNN_SUCCESS; |
| 55 | } |
| 56 | |
| 57 | static TinyNNStatus destruct(VM* vm, Instruction* inst) { |
| 58 | FREE(inst->workload.reshape.inputs); |
nothing calls this directly
no test coverage detected