Make all fp16 routines work with fp32 as input instead of fp64, since that is what...

[movit] / resample_effect.cpp

diff --git a/resample_effect.cpp b/resample_effect.cpp
index a7315689532e85a98a9c2b7884ec52b85f2ed7db..9c8caf3bb77ed58bf2d143dcc2b3b837b81a001c 100644 (file)
--- a/resample_effect.cpp
+++ b/resample_effect.cpp
@@ -192,13 +192,13 @@ template<class T>
 void normalize_sum(Tap<T>* vals, unsigned num)
 {
        for (int normalize_pass = 0; normalize_pass < 2; ++normalize_pass) {
-               double sum = 0.0;
+               float sum = 0.0;
                for (unsigned i = 0; i < num; ++i) {
-                       sum += to_fp64(vals[i].weight);
+                       sum += to_fp32(vals[i].weight);
                }
-               double inv_sum = 1.0 / sum;
+               float inv_sum = 1.0 / sum;
                for (unsigned i = 0; i < num; ++i) {
-                       vals[i].weight = from_fp64<T>(to_fp64(vals[i].weight) * inv_sum);
+                       vals[i].weight = from_fp32<T>(to_fp32(vals[i].weight) * inv_sum);
                }
        }
 }
@@ -255,8 +255,8 @@ double compute_sum_sq_error(const Tap<float>* weights, unsigned num_weights,
        // Find the effective range of the bilinear-optimized kernel.
        // Due to rounding of the positions, this is not necessarily the same
        // as the intended range (ie., the range of the original weights).
-       int lower_pos = int(floor(to_fp64(bilinear_weights[0].pos) * size - 0.5));
-       int upper_pos = int(ceil(to_fp64(bilinear_weights[num_bilinear_weights - 1].pos) * size - 0.5)) + 2;
+       int lower_pos = int(floor(to_fp32(bilinear_weights[0].pos) * size - 0.5));
+       int upper_pos = int(ceil(to_fp32(bilinear_weights[num_bilinear_weights - 1].pos) * size - 0.5)) + 2;
        lower_pos = min<int>(lower_pos, lrintf(weights[0].pos * size - 0.5));
        upper_pos = max<int>(upper_pos, lrintf(weights[num_weights - 1].pos * size - 0.5) + 1);
 
@@ -267,7 +267,7 @@ double compute_sum_sq_error(const Tap<float>* weights, unsigned num_weights,
 
        // Now find the effective weights that result from this sampling.
        for (unsigned i = 0; i < num_bilinear_weights; ++i) {
-               const float pixel_pos = to_fp64(bilinear_weights[i].pos) * size - 0.5f;
+               const float pixel_pos = to_fp32(bilinear_weights[i].pos) * size - 0.5f;
                const int x0 = int(floor(pixel_pos)) - lower_pos;
                const int x1 = x0 + 1;
                const float f = lrintf((pixel_pos - (x0 + lower_pos)) / movit_texel_subpixel_precision) * movit_texel_subpixel_precision;
@@ -277,8 +277,8 @@ double compute_sum_sq_error(const Tap<float>* weights, unsigned num_weights,
                assert(x0 < upper_pos - lower_pos);
                assert(x1 < upper_pos - lower_pos);
 
-               effective_weights[x0] += to_fp64(bilinear_weights[i].weight) * (1.0 - f);
-               effective_weights[x1] += to_fp64(bilinear_weights[i].weight) * f;
+               effective_weights[x0] += to_fp32(bilinear_weights[i].weight) * (1.0 - f);
+               effective_weights[x1] += to_fp32(bilinear_weights[i].weight) * f;
        }
 
        // Subtract the desired weights to get the error.