fix confusion results if ratios are set differently

src/operator/contrib/multibox_prior.cc

@@ -44,11 +44,12 @@ inline void MultiBoxPriorForward(const Tensor<cpu, 2, DType> &out,
  float center_y = (r + offsets[0]) * step_y;
  for (int c = 0; c < in_width; ++c) {
  float center_x = (c + offsets[1]) * step_x;
- // ratio = 1, various sizes
+ // ratio = first ratio, various sizes
+ float ratio = num_ratios > 0? sqrtf(ratios[0]) : 1.f;
  for (int i = 0; i < num_sizes; ++i) {
  float size = sizes[i];
- float w = size * in_height / in_width / 2;
- float h = size / 2;
+ float w = size * in_height / in_width * ratio / 2;
+ float h = size / ratio / 2;
  out[count][0] = center_x - w; // xmin
  out[count][1] = center_y - h; // ymin
  out[count][2] = center_x + w; // xmax

src/operator/contrib/multibox_prior.cu

@@ -83,10 +83,11 @@ inline void MultiBoxPriorForward(const Tensor<gpu, 2, DType> &out,
 
  const int stride = 4 * (num_sizes + num_ratios - 1);
  int offset = 0;
- // ratio = 1, various sizes
+ // ratio = first ratio, various sizes
+ float ratio = num_ratios > 0? sqrtf(ratios[0]) : 1.f;
  for (int i = 0; i < num_sizes; ++i) {
  cuda::AssignPriors<DType><<<dimGrid, dimBlock, 0, stream>>>(out_ptr,
- sizes[i], 1.f, in_width, in_height, step_x, step_y, offset_y, offset_x, stride, offset);
+ sizes[i], ratio, in_width, in_height, step_x, step_y, offset_y, offset_x, stride, offset);
  ++offset;
  }
  MULTIBOXPRIOR_CUDA_CHECK(cudaPeekAtLastError());

tests/python/unittest/test_contrib_operator.py

@@ -270,7 +270,7 @@ def f(x, a, b, c):
  b = np.random.random_sample()
  c = np.random.random_sample()
  m = np.random.random_sample() - 0.5
- 
+
  data = mx.symbol.Variable('data')
  quad_sym = mx.sym.contrib.quadratic(data=data, a=a, b=b, c=c)
  gr_q_sym = mx.sym.contrib.gradientmultiplier(quad_sym, scalar=m)
@@ -297,6 +297,18 @@ def f(x, a, b, c):
  [backward_expected],
  rtol=1e-2 if dtype is np.float16 else 1e-5,
  atol=1e-2 if dtype is np.float16 else 1e-5)
+def test_multibox_prior_op():
+ h = 561
+ w = 728
+ X = mx.nd.random.uniform(shape=(1, 3, h, w))
+ Y = mx.contrib.nd.MultiBoxPrior(X, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5])
+ assert_array_equal(Y.shape, np.array((1, 2042040, 4)))
+ boxes = Y.reshape((h, w, 5, 4))
+ assert_allclose(boxes.asnumpy()[250, 250, 0, :], np.array([0.055117, 0.071524, 0.63307 , 0.821524]), atol=1e-5, rtol=1e-5)
+ # relax first ratio if user insists
+ Y = mx.contrib.nd.MultiBoxPrior(X, sizes=[0.75, 0.5, 0.25], ratios=[20, 2, 0.5])
+ boxes = Y.reshape((h, w, 5, 4))
+ assert_allclose(boxes.asnumpy()[250, 250, 0, :], np.array([-0.948249, 0.362671, 1.636436, 0.530377]), atol=1e-5, rtol=1e-5)
 
 if __name__ == '__main__':
  import nose