| 26 | namespace { |
| 27 | |
| 28 | void ReferenceImpl(const quint8* inp, float inp_min, float inp_max, |
| 29 | const TensorShape& shape, float var_eps, float* out) { |
| 30 | int N = shape.dim_size(0); |
| 31 | int H = shape.dim_size(1); |
| 32 | int W = shape.dim_size(2); |
| 33 | int C = shape.dim_size(3); |
| 34 | |
| 35 | int total = N * H * W * C; |
| 36 | float inp_scale = (inp_max - inp_min) / 255.0f; |
| 37 | std::unique_ptr<float[]> dequantized(new float[total]); |
| 38 | |
| 39 | for (int i = 0; i < total; ++i) { |
| 40 | dequantized[i] = inp_min + inp_scale * static_cast<float>(inp[i]); |
| 41 | } |
| 42 | |
| 43 | std::unique_ptr<float[]> inp_mean(new float[N * C]); |
| 44 | std::unique_ptr<float[]> inp_var(new float[N * C]); |
| 45 | |
| 46 | float img_size = static_cast<float>(H) * static_cast<float>(W); |
| 47 | |
| 48 | // Compute mean |
| 49 | for (int n = 0; n < N; ++n) { |
| 50 | for (int c = 0; c < C; ++c) { |
| 51 | float sum = 0.0; |
| 52 | for (int i = 0; i < H * W; ++i) { |
| 53 | sum += dequantized[n * H * W * C + i * C + c]; |
| 54 | } |
| 55 | inp_mean[n * C + c] = sum / img_size; |
| 56 | } |
| 57 | } |
| 58 | |
| 59 | // Compute var |
| 60 | for (int n = 0; n < N; ++n) { |
| 61 | for (int c = 0; c < C; ++c) { |
| 62 | float sum = 0.0; |
| 63 | for (int i = 0; i < H * W; ++i) { |
| 64 | float tmp = |
| 65 | dequantized[n * H * W * C + i * C + c] - inp_mean[n * C + c]; |
| 66 | sum += tmp * tmp; |
| 67 | } |
| 68 | inp_var[n * C + c] = sum / img_size; |
| 69 | } |
| 70 | } |
| 71 | |
| 72 | for (int n = 0; n < N; ++n) { |
| 73 | for (int c = 0; c < C; ++c) { |
| 74 | for (int i = 0; i < H * W; ++i) { |
| 75 | out[n * H * W * C + i * C + c] = |
| 76 | (dequantized[n * H * W * C + i * C + c] - inp_mean[n * C + c]) / |
| 77 | std::sqrt(inp_var[n * C + c] + var_eps); |
| 78 | } |
| 79 | } |
| 80 | } |
| 81 | } |
| 82 | |
| 83 | void Expect(const Tensor& input, float x_min, float x_max, |
| 84 | bool output_range_given, float give_y_min, float given_y_max) { |