Use only absolute prior boxes explicit sizes. Remove scales attributes. (#10874)

* Use only absolute prior boxes explicit sizes. Remove scales attributes.

* Simplified PriorBox layer forward pass

modules/dnn/src/opencl/prior_box.cl

@@ -45,14 +45,12 @@
 __kernel void prior_box(const int nthreads,
                         const Dtype stepX,
                         const Dtype stepY,
-                        const Dtype _minSize,
-                        const Dtype _maxSize,
                         __global const Dtype* _offsetsX,
                         __global const Dtype* _offsetsY,
                         const int offsetsX_size,
-                        __global const Dtype* _aspectRatios,
-                        const int aspectRatios_size,
-                        __global const Dtype* scales,
+                        __global const Dtype* _widths,
+                        __global const Dtype* _heights,
+                        const int widths_size,
                         __global Dtype* dst,
                         const int _layerHeight,
                         const int _layerWidth,
@@ -64,57 +62,19 @@ __kernel void prior_box(const int nthreads,
         int w = index % _layerWidth;
         int h = index / _layerWidth;
         __global Dtype* outputPtr;
-        int aspect_count = (_maxSize > 0) ? 1 : 0;
-        outputPtr = dst + index * 4 * offsetsX_size * (1 + aspect_count + aspectRatios_size);
+
+        outputPtr = dst + index * 4 * offsetsX_size * widths_size;
 
         Dtype _boxWidth, _boxHeight;
         Dtype4 vec;
-        _boxWidth = _boxHeight = _minSize * scales[0];
-        for (int i = 0; i < offsetsX_size; ++i)
-        {
-            float center_x = (w + _offsetsX[i]) * stepX;
-            float center_y = (h + _offsetsY[i]) * stepY;
-
-            vec.x = (center_x - _boxWidth * 0.5f) / imgWidth;    // xmin
-            vec.y = (center_y - _boxHeight * 0.5f) / imgHeight;  // ymin
-            vec.z = (center_x + _boxWidth * 0.5f) / imgWidth;    // xmax
-            vec.w = (center_y + _boxHeight * 0.5f) / imgHeight;  // ymax
-            vstore4(vec, 0, outputPtr);
-
-            outputPtr += 4;
-        }
-
-        if (_maxSize > 0)
-        {
-            _boxWidth = _boxHeight = native_sqrt(_minSize * _maxSize) * scales[1];
-
-            for (int i = 0; i < offsetsX_size; ++i)
-            {
-                float center_x = (w + _offsetsX[i]) * stepX;
-                float center_y = (h + _offsetsY[i]) * stepY;
-
-                vec.x = (center_x - _boxWidth * 0.5f) / imgWidth;    // xmin
-                vec.y = (center_y - _boxHeight * 0.5f) / imgHeight;  // ymin
-                vec.z = (center_x + _boxWidth * 0.5f) / imgWidth;    // xmax
-                vec.w = (center_y + _boxHeight * 0.5f) / imgHeight;  // ymax
-                vstore4(vec, 0, outputPtr);
-
-                outputPtr += 4;
-            }
-        }
-
-        for (int r = 0; r < aspectRatios_size; ++r)
+        for (int i = 0; i < widths_size; ++i)
         {
-            float ar = native_sqrt(_aspectRatios[r]);
-            float scale = scales[(_maxSize > 0 ? 2 : 1) + r];
-
-            _boxWidth = _minSize * ar * scale;
-            _boxHeight = _minSize / ar * scale;
-
-            for (int i = 0; i < offsetsX_size; ++i)
+            _boxWidth = _widths[i];
+            _boxHeight = _heights[i];
+            for (int j = 0; j < offsetsX_size; ++j)
             {
-                float center_x = (w + _offsetsX[i]) * stepX;
-                float center_y = (h + _offsetsY[i]) * stepY;
+                float center_x = (w + _offsetsX[j]) * stepX;
+                float center_y = (h + _offsetsY[j]) * stepY;
 
                 vec.x = (center_x - _boxWidth * 0.5f) / imgWidth;    // xmin
                 vec.y = (center_y - _boxHeight * 0.5f) / imgHeight;  // ymin

samples/dnn/tf_text_graph_ssd.py

@@ -26,6 +26,8 @@ parser.add_argument('--max_scale', default=0.95, type=float, help='Hyper-paramet
 parser.add_argument('--num_layers', default=6, type=int, help='Hyper-parameter of ssd_anchor_generator from config file.')
 parser.add_argument('--aspect_ratios', default=[1.0, 2.0, 0.5, 3.0, 0.333], type=float, nargs='+',
                     help='Hyper-parameter of ssd_anchor_generator from config file.')
+parser.add_argument('--image_width', default=300, type=int, help='Training images width.')
+parser.add_argument('--image_height', default=300, type=int, help='Training images height.')
 args = parser.parse_args()
 
 # Nodes that should be kept.
@@ -192,7 +194,6 @@ for i in range(args.num_layers):
 
     text_format.Merge('b: false', priorBox.attr["flip"])
     text_format.Merge('b: false', priorBox.attr["clip"])
-    text_format.Merge('b: true', priorBox.attr["normalized_bbox"])
 
     if i == 0:
         widths = [args.min_scale * 0.5, args.min_scale * sqrt(2.0), args.min_scale * sqrt(0.5)]
@@ -203,6 +204,8 @@ for i in range(args.num_layers):
 
         widths += [sqrt(scales[i] * scales[i + 1])]
         heights += [sqrt(scales[i] * scales[i + 1])]
+    widths = [w * args.image_width for w in widths]
+    heights = [h * args.image_height for h in heights]
     text_format.Merge(tensorMsg(widths), priorBox.attr["width"])
     text_format.Merge(tensorMsg(heights), priorBox.attr["height"])
     text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])