[GitHub] anirudhacharya commented on a change in pull request #13609: [MXNET-1258]fix unittest for ROIAlign Operator

[GitBox <gi...@apache.org>]

anirudhacharya commented on a change in pull request #13609: [MXNET-1258]fix unittest for ROIAlign Operator
URL: https://github.com/apache/incubator-mxnet/pull/13609#discussion_r242773831
 
 

 ##########
 File path: tests/python/unittest/test_operator.py
 ##########
 @@ -6891,54 +6894,70 @@ def bilinear_interpolate(bottom, height, width, y, x):
         w3 = ly * hx
         w4 = ly * lx
 
+        assert w1.dtype == T
+        assert w2.dtype == T
+        assert w3.dtype == T
+        assert w4.dtype == T
+
         val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+        assert val.dtype == T
         grad = [(y_low, x_low, w1), (y_low, x_high, w2),
                 (y_high, x_low, w3), (y_high, x_high, w4)
-               ]
+                ]
         return val, grad
 
+
     def roialign_forward_backward(data, rois, pooled_size, spatial_scale, sampling_ratio, dy):
         N, C, H, W = data.shape
         R = rois.shape[0]
         PH, PW = pooled_size
         assert len(rois.shape) == 2
         assert rois.shape[1] == 5
+        assert data.dtype == T
+        assert rois.dtype == T
 
-        out = np.zeros((R, C, PH, PW))
+        out = np.zeros((R, C, PH, PW), dtype=T)
         dx = np.zeros_like(data)
         drois = np.zeros_like(rois)
 
         for r in range(R):
             batch_ind = int(rois[r, 0])
-            sw, sh, ew, eh = rois[r, 1:5] * spatial_scale
-            roi_w = max(ew - sw, 1.0)
-            roi_h = max(eh - sh, 1.0)
-            bin_h = roi_h * 1.0 / PH
-            bin_w = roi_w * 1.0 / PW
+            sw, sh, ew, eh = rois[r, 1:5] * T(spatial_scale)
+            roi_w = T(max(ew - sw, 1.0))
+            roi_h = T(max(eh - sh, 1.0))
+            bin_h = roi_h / T(PH)
+            bin_w = roi_w / T(PW)
             bdata = data[batch_ind]
             if sampling_ratio > 0:
                 roi_bin_grid_h = roi_bin_grid_w = sampling_ratio
             else:
-                roi_bin_grid_h = int(np.ceil(roi_h * 1.0 / PH))
-                roi_bin_grid_w = int(np.ceil(roi_w * 1.0 / PW))
-            count = roi_bin_grid_h * roi_bin_grid_w
+                roi_bin_grid_h = int(np.ceil(roi_h / T(PH)))
+                roi_bin_grid_w = int(np.ceil(roi_w / T(PW)))
+            count = T(roi_bin_grid_h * roi_bin_grid_w)
             for c in range(C):
                 for ph in range(PH):
                     for pw in range(PW):
-                        val = 0.0
+                        val = T(0.0)
                         for iy in range(roi_bin_grid_h):
-                            y = sh + ph * bin_h + (iy + 0.5) * bin_h / roi_bin_grid_h
+                            y = sh + T(ph) * bin_h + (T(iy) + T(0.5)) * \
+                                bin_h / T(roi_bin_grid_h)
                             for ix in range(roi_bin_grid_w):
-                                x = sw + pw * bin_w + (ix + 0.5) * bin_w / roi_bin_grid_w
+                                x = sw + T(pw) * bin_w + (T(ix) + T(0.5)) * \
+                                    bin_w / T(roi_bin_grid_w)
                                 v, g = bilinear_interpolate(bdata[c], H, W, y, x)
+                                assert v.dtype == T
                                 val += v
                                 # compute grad
                                 for qy, qx, qw in g:
-                                    dx[batch_ind, c, qy, qx] += dy[r, c, ph, pw] * qw * 1.0 / count
+                                    assert qw.dtype == T
+                                    dx[batch_ind, c, qy, qx] += dy[r,
+                                                                   c, ph, pw] * qw / count
 
-                        out[r, c, ph, pw] = val * 1.0 / count
+                        out[r, c, ph, pw] = val / count
+        assert out.dtype == T, out.dtype
 
 Review comment:
   can you add a more descriptive error message

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