[TOPI] Fix resize nearest with fractional scaling (#3244)

nnvm/tests/python/compiler/test_top_level2.py

@@ -305,7 +305,7 @@ def test_upsampling_nearest_neighbor():
         data = tvm.nd.array(a_np)
         m.run(x=data)
         out = m.get_output(0, tvm.nd.empty(oshape, dtype))
-        b_np = topi.testing.upsampling_python(a_np, scale, "NCHW")
+        b_np = topi.testing.upsampling_python(a_np, (scale, scale), "NCHW")
         tvm.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
 
 def test_upsampling_bilinear():

nnvm/tests/python/frontend/coreml/test_forward.py

@@ -195,7 +195,7 @@ def verify_UpsampleLayerParams(input_dim, scale, mode):
 
     a_np = np.full(input_dim, 1, dtype=dtype)
     if mode == 'NN':
-        b_np = topi.testing.upsampling_python(a_np, scale)
+        b_np = topi.testing.upsampling_python(a_np, (scale, scale))
     else:
         new_h = input_dim[2] * scale
         new_w = input_dim[3] * scale

nnvm/tests/python/frontend/onnx/test_forward.py

@@ -405,7 +405,7 @@ def _test_upsample_nearest():
     y = helper.make_node("Upsample", ['in'], ['out'], mode='nearest', scales=[1.0, 1.0, 2.0, 2.0])
 
     in_array = np.random.uniform(size=in_shape).astype(np.float32)
-    out_array = topi.testing.upsampling_python(in_array, scale, "NCHW")
+    out_array = topi.testing.upsampling_python(in_array, (scale, scale), "NCHW")
 
     graph = helper.make_graph([y],
                               'upsample_nearest_test',

tests/python/frontend/coreml/test_forward.py

@@ -179,7 +179,7 @@ def verify_UpsampleLayerParams(input_dim, scale, mode):
 
     a_np = np.full(input_dim, 1, dtype=dtype)
     if mode == 'NN':
-        b_np = topi.testing.upsampling_python(a_np, scale)
+        b_np = topi.testing.upsampling_python(a_np, (scale, scale))
     else:
         new_h = input_dim[2] * scale
         new_w = input_dim[3] * scale

tests/python/frontend/onnx/test_forward.py

@@ -417,7 +417,7 @@ def _test_upsample_nearest():
     y = helper.make_node("Upsample", ['in'], ['out'], mode='nearest', scales=[1.0, 1.0, 2.0, 2.0])
 
     in_array = np.random.uniform(size=in_shape).astype(np.float32)
-    out_array = topi.testing.upsampling_python(in_array, scale, "NCHW")
+    out_array = topi.testing.upsampling_python(in_array, (scale, scale), "NCHW")
 
     graph = helper.make_graph([y],
                               'upsample_nearest_test',

tests/python/relay/test_op_level2.py

@@ -485,7 +485,7 @@ def _test_upsampling(layout, method):
     func = relay.Function([x], y)
     data = np.random.uniform(size=dshape).astype(dtype)
     if method == "NEAREST_NEIGHBOR":
-        ref = topi.testing.upsampling_python(data, scale, layout)
+        ref = topi.testing.upsampling_python(data, (scale, scale), layout)
     else:
         ref = topi.testing.bilinear_resize_python(data, (h*scale, w*scale), layout)
     for target, ctx in ctx_list():

tests/python/relay/test_op_level5.py

@@ -48,7 +48,7 @@ def test_resize():
         if method == "BILINEAR":
             ref_res = topi.testing.bilinear_resize_python(x_data, size, layout)
         else:
-            ref_res = topi.testing.upsampling_python(x_data, scale, layout)
+            ref_res = topi.testing.upsampling_python(x_data, (scale, scale), layout)
         x = relay.var("x", relay.TensorType(dshape, "float32"))
         z = relay.image.resize(x, size, layout, method, False)
         assert "size=" in z.astext()

topi/include/topi/image/resize.h

@@ -101,15 +101,12 @@ inline Tensor resize_nearest_neighbor_nhwc(const Tensor& input,
   out_shape.push_back(shape[1]);
   out_shape.push_back(input->shape[3]);
 
-  Expr h_ratio = shape[0] / input->shape[1];
-  Expr w_ratio = shape[1] / input->shape[2];
-
   return compute(
     out_shape, [&](const Array<Var>& indices) {
     Array<Expr> idx;
     idx.push_back(indices[0]);
-    idx.push_back(indices[1] / h_ratio);
-    idx.push_back(indices[2] / w_ratio);
+    idx.push_back(indices[1] * input->shape[1] / shape[0]);
+    idx.push_back(indices[2] * input->shape[2] / shape[1]);
     idx.push_back(indices[3]);
 
     return input(idx);
@@ -138,16 +135,13 @@ inline Tensor resize_nearest_neighbor_nchw(const Tensor& input,
   out_shape.push_back(shape[0]);
   out_shape.push_back(shape[1]);
 
-  Expr h_ratio = shape[0] / input->shape[2];
-  Expr w_ratio = shape[1] / input->shape[3];
-
   return compute(
     out_shape, [&](const Array<Var>& indices) {
     Array<Expr> idx;
     idx.push_back(indices[0]);
     idx.push_back(indices[1]);
-    idx.push_back(indices[2] / h_ratio);
-    idx.push_back(indices[3] / w_ratio);
+    idx.push_back(indices[2] * input->shape[2] / shape[0]);
+    idx.push_back(indices[3] * input->shape[3] / shape[1]);
 
     return input(idx);
     }, name, tag);
@@ -176,16 +170,13 @@ inline Tensor resize_nearest_neighbor_nchwc(const Tensor& input,
   out_shape.push_back(shape[1]);
   out_shape.push_back(input->shape[4]);
 
-  Expr h_ratio = shape[0] / input->shape[2];
-  Expr w_ratio = shape[1] / input->shape[3];
-
   return compute(
     out_shape, [&](const Array<Var>& indices) {
     Array<Expr> idx;
     idx.push_back(indices[0]);
     idx.push_back(indices[1]);
-    idx.push_back(indices[2] / h_ratio);
-    idx.push_back(indices[3] / w_ratio);
+    idx.push_back(indices[2] * input->shape[2] / shape[0]);
+    idx.push_back(indices[3] * input->shape[3] / shape[1]);
     idx.push_back(indices[4]);
 
      return input(idx);

topi/python/topi/nn/upsampling.py

@@ -53,5 +53,4 @@ def upsampling(data, scale, layout="NCHW", method='NEAREST_NEIGHBOR'):
         out_shape = (simplify(data.shape[1] * scale), simplify(data.shape[2] * scale))
     else:
         raise ValueError("not support this layout {} yet".format(layout))
-
     return topi.cpp.nn.upsampling(data, out_shape, layout, method)

topi/python/topi/testing/upsampling_python.py

@@ -16,25 +16,35 @@
 # under the License.
 # pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
 """Upsampling in python"""
+import math
 import numpy as np
 
 def upsample_nearest(arr, scale):
     """ Populate the array by scale factor"""
-    return arr.repeat(scale, axis=0).repeat(scale, axis=1)
+    h, w = arr.shape
+    out_h = math.floor(h * scale[0])
+    out_w = math.floor(w * scale[1])
+    out = np.empty((out_h, out_w))
+    for y in range(out_h):
+        for x in range(out_w):
+            in_y = math.floor(y / scale[0])
+            in_x = math.floor(x / scale[1])
+            out[y, x] = arr[in_y, in_x]
+    return out
 
 def upsampling_python(data, scale, layout='NCHW'):
     """ Python version of scaling using nearest neighbour """
 
     ishape = data.shape
     if layout == 'NCHW':
-        oshape = (ishape[0], ishape[1], ishape[2]*scale, ishape[3]*scale)
+        oshape = (ishape[0], ishape[1], math.floor(ishape[2]*scale[0]), math.floor(ishape[3]*scale[1]))
         output_np = np.zeros(oshape, dtype=data.dtype)
         for b in range(oshape[0]):
             for c in range(oshape[1]):
                 output_np[b, c, :, :] = upsample_nearest(data[b, c, :, :], scale)
         return output_np
     if layout == 'NHWC':
-        oshape = (ishape[0], ishape[1]*scale, ishape[1]*scale, ishape[3])
+        oshape = (ishape[0], math.floor(ishape[1]*scale[0]), math.floor(ishape[1]*scale[1]), ishape[3])
         output_np = np.zeros(oshape, dtype=data.dtype)
         for b in range(oshape[0]):
             for c in range(oshape[3]):

topi/tests/python/test_topi_resize.py

@@ -23,8 +23,7 @@ import math
 
 from common import get_all_backend
 
-def verify_bilinear_scale(batch, in_channel, in_height, in_width, out_height, out_width, layout='NCHW', align_corners=False):
-
+def verify_resize(batch, in_channel, in_height, in_width, out_height, out_width, layout='NCHW', align_corners=False, method="BILINEAR"):
     if layout == 'NCHW':
         A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='float32')
         dtype = A.dtype
@@ -39,9 +38,14 @@ def verify_bilinear_scale(batch, in_channel, in_height, in_width, out_height, ou
         raise NotImplementedError(
             'Layout not supported {} '.format(layout))
 
-    B = topi.image.resize(A, (out_height, out_width), layout=layout, align_corners=align_corners)
+    B = topi.image.resize(A, (out_height, out_width), layout=layout, align_corners=align_corners, method=method)
 
-    b_np = topi.testing.bilinear_resize_python(a_np, (out_height, out_width), layout, align_corners)
+    if method == "BILINEAR":
+        b_np = topi.testing.bilinear_resize_python(a_np, (out_height, out_width), layout, align_corners)
+    else:
+        scale_h = out_height / in_height
+        scale_w = out_width / in_width
+        b_np = topi.testing.upsampling_python(a_np, (scale_h, scale_w), layout)
 
     def check_device(device):
         ctx = tvm.context(device, 0)
@@ -61,15 +65,19 @@ def verify_bilinear_scale(batch, in_channel, in_height, in_width, out_height, ou
     for device in get_all_backend():
         check_device(device)
 
+
 def test_resize():
     # Scale NCHW
-    verify_bilinear_scale(4, 16, 32, 32, 50, 50, 'NCHW')
+    verify_resize(4, 16, 32, 32, 50, 50, 'NCHW')
     # Scale NCHW + Align Corners
-    verify_bilinear_scale(6, 32, 64, 64, 20, 20, 'NCHW', True)
+    verify_resize(6, 32, 64, 64, 20, 20, 'NCHW', True)
     # Scale NHWC
-    verify_bilinear_scale(4, 16, 32, 32, 50, 50, "NHWC")
+    verify_resize(4, 16, 32, 32, 50, 50, "NHWC")
     # Scale NHWC + Align Corners
-    verify_bilinear_scale(6, 32, 64, 64, 20, 20, "NHWC", True)
+    verify_resize(6, 32, 64, 64, 20, 20, "NHWC", True)
+    # Nearest + Fractional
+    verify_resize(4, 16, 32, 32, 50, 50, 'NCHW', method="NEAREST_NEIGHBOR")
+    verify_resize(4, 16, 32, 32, 50, 50, 'NHWC', method="NEAREST_NEIGHBOR")
 
 if __name__ == "__main__":
     test_resize()

topi/tests/python/test_topi_upsampling.py

@@ -46,7 +46,7 @@ def verify_upsampling(batch, in_channel, in_height, in_width, scale, layout='NCH
         out_size = (in_height*scale, in_width*scale)
         b_np = topi.testing.bilinear_resize_python(a_np, out_size, layout)
     else:
-        b_np = topi.testing.upsampling_python(a_np, scale, layout)
+        b_np = topi.testing.upsampling_python(a_np, (scale, scale), layout)
 
     def check_device(device):
         ctx = tvm.context(device, 0)