Addressed review comments

apache · Feb 19, 2019 · 05cc7ea · 05cc7ea
1 parent 50d43d4
commit 05cc7ea
Show file tree

Hide file tree

Showing 3 changed files with 21 additions and 14 deletions.
diff --git a/src/operator/nn/upsampling-inl.h b/src/operator/nn/upsampling-inl.h
@@ -59,7 +59,9 @@ struct UpSamplingParam : public dmlc::Parameter<UpSamplingParam> {
     .set_range(1, 1000)
     .describe("Up sampling scale");
     DMLC_DECLARE_FIELD(num_filter)
-    .describe("Input filter. Only used by bilinear sample_type.")
+    .describe("Input filter. Only used by bilinear sample_type."
+              "Since bilinear upsampling uses deconvolution, num_filters "
+              "is set to the number of channels.")
     .set_default(0);
     DMLC_DECLARE_FIELD(sample_type)
     .add_enum("nearest", up_enum::kNearest)

diff --git a/src/operator/nn/upsampling.cc b/src/operator/nn/upsampling.cc
@@ -121,7 +121,9 @@ struct UpSamplingGrad {
 DMLC_REGISTER_PARAMETER(UpSamplingParam);
 
 NNVM_REGISTER_OP(UpSampling)
-.describe("Performs nearest neighbor/bilinear up sampling to inputs.")
+.describe("Performs nearest neighbor/bilinear up sampling to inputs. "
+          "Bilinear upsampling makes use of deconvolution. Therefore, "
+          "provide 2 inputs - data and weight. ")
 .set_num_inputs([](const NodeAttrs& attrs) {
   const UpSamplingParam& params = nnvm::get<UpSamplingParam>(attrs.parsed);
   return params.sample_type == up_enum::kNearest ? params.num_args : 2;
@@ -149,7 +151,8 @@ NNVM_REGISTER_OP(UpSampling)
 .set_attr<FCompute>("FCompute<cpu>", UpSamplingCompute<cpu>)
 .set_attr<nnvm::FGradient>("FGradient", UpSamplingGrad{"_backward_UpSampling"})
 .set_attr<std::string>("key_var_num_args", "num_args")
-.add_argument("data", "NDArray-or-Symbol[]", "Array of tensors to upsample")
+.add_argument("data", "NDArray-or-Symbol[]",
+              "Array of tensors to upsample. For bilinear upsampling, there should be 2 inputs - 1 data and 1 weight.")
 .add_arguments(UpSamplingParam::__FIELDS__())
 .set_attr<nnvm::FSetInputVarAttrOnCompose>("FSetInputVarAttrOnCompose",
     [](const nnvm::NodeAttrs& attrs, nnvm::NodePtr var, const int index) {

diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
@@ -1530,17 +1530,19 @@ def test_nearest_upsampling():
 
 @with_seed()
 def test_bilinear_upsampling():
-    for root_scale in [2,3]:
-        for scale in [1,2,3]:
-            for num_filter in [1,2,3]:
-                for base in [1,2,3]:
-                    # bilinear upsampling takes only 1 data and 1 weight
-                    # multi input mode is not applicable
-                    dimension = base*root_scale*scale
-                    kernel = 2 * root_scale - root_scale % 2
-                    data_shape = (1, num_filter, dimension, dimension)
-                    weight_shape = (num_filter, 1, kernel, kernel)
-                    check_bilinear_upsampling_with_shape(data_shape, weight_shape, scale, root_scale, num_filter)
+    rootscale = [2,3]
+    scales = [1,2,3]
+    filters = [1,2,3]
+    bases = [1,2,3]
+    for params in itertools.product(rootscale, scales, filters, bases):
+        root_scale, scale, num_filter, base = params
+        # bilinear upsampling takes only 1 data and 1 weight
+        # multi input mode is not applicable
+        dimension = base*root_scale*scale
+        kernel = 2 * root_scale - root_scale % 2
+        data_shape = (1, num_filter, dimension, dimension)
+        weight_shape = (1, num_filter, kernel, kernel)
+        check_bilinear_upsampling_with_shape(data_shape, weight_shape, scale, root_scale, num_filter)
 
 @with_seed()
 def test_batchnorm_training():