Upsampling op support (#298)

* add nnvm upsampling symbol

* add upsampling mxnet frontend

* add doc for upsampling op

* cleanup upsampling test

* minor fix

* use schedule_injective for upsampling

* upgrade tvm

nnvm/include/nnvm/top/nn.h

@@ -257,6 +257,15 @@ struct GlobalPool2DParam : public dmlc::Parameter<GlobalPool2DParam> {
   }
 };
 
+struct UpSamplingParam : public dmlc::Parameter<UpSamplingParam> {
+  int scale;
+
+  DMLC_DECLARE_PARAMETER(UpSamplingParam) {
+    DMLC_DECLARE_FIELD(scale)
+      .describe("upsampling scaling factor");
+  }
+};
+
 }  // namespace top
 }  // namespace nnvm
 

nnvm/python/nnvm/frontend/mxnet.py

@@ -190,6 +190,12 @@ def _softmax_output(inputs, attrs):
         new_attrs['axis'] = 1
     return _get_nnvm_op(op_name)(inputs[0], **new_attrs)
 
+def _upsampling(inputs, attrs):
+    scale = attrs.get('scale')
+    new_attrs = {'scale':int(scale)}
+    return _get_nnvm_op('upsampling')(inputs[0], **new_attrs)
+
+
 _identity_list = ['__add_scalar__', '__add_symbol__', '__div_scalar__',
                   '__div_symbol__', '__mul_scalar__', '__mul_symbol__',
                   '__pow_scalar__', '__rdiv_scalar__', '__rpow_scalar__',
@@ -231,6 +237,7 @@ _convert_map = {
     'min_axis'      : _rename('min'),
     'reshape'       : _reshape,
     'sum_axis'      : _rename('sum'),
+    'UpSampling'    : _upsampling
 }
 
 def _convert_symbol(op_name, inputs, attrs,

nnvm/python/nnvm/top/nn.py

@@ -250,3 +250,18 @@ def schedule_global_avg_pool2d(_, outs, target):
         return topi.generic.schedule_global_pool(outs)
 
 reg.register_pattern("global_avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
+@reg.register_compute("upsampling")
+def compute_upsampling(attrs, inputs, _):
+    """Compute definition of upsampling"""
+    scale = attrs.get_int("scale")
+    return topi.nn.upsampling(inputs[0], scale)
+
+@reg.register_schedule("upsampling")
+def schedule_upsampling(_, outs, target):
+    """Compute definition of upsampling"""
+    with tvm.target.create(target):
+        return topi.generic.schedule_injective(outs)
+
+reg.register_pattern("upsampling", OpPattern.OUT_ELEMWISE_FUSABLE)

nnvm/src/top/nn/upsampling.cc

+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file pooling.cc
+ * \brief Property def of pooling operators.
+ */
+#include <nnvm/op.h>
+#include <nnvm/node.h>
+#include <nnvm/op_attr_types.h>
+#include <nnvm/top/nn.h>
+#include "./nn_common.h"
+#include "../op_common.h"
+#include "../elemwise_op_common.h"
+
+namespace nnvm {
+namespace top {
+
+DMLC_REGISTER_PARAMETER(UpSamplingParam);
+
+inline bool UpSamplingInferShape(const nnvm::NodeAttrs& attrs,
+                                   std::vector<TShape>* in_shape,
+                                   std::vector<TShape>* out_shape) {
+  const UpSamplingParam& param = nnvm::get<UpSamplingParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 1U);
+  CHECK_EQ(out_shape->size(), 1U);
+  TShape dshape = (*in_shape)[0];
+  if (dshape.ndim() ==  0) return false;
+  TShape oshape = dshape;
+  oshape[2] = oshape[2] * param.scale;
+  oshape[3] = oshape[3] * param.scale;
+  NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, oshape);
+  return true;
+}
+
+NNVM_REGISTER_OP(upsampling)
+.describe(R"(Perform nearest neighbor upsampling to input array.
+
+- **data**: Input is 4D array of shape (batch_size, channels, in_height, in_width).
+- **out**: Output is 4D array of shape (batch_size, channels, in_height*scale, in_width*scale).
+
+)" NNVM_ADD_FILELINE)
+.add_argument("data", "4D Tensor", "Input data.")
+.add_arguments(UpSamplingParam::__FIELDS__())
+.set_attr_parser(ParamParser<UpSamplingParam>)
+.set_attr<FGetAttrDict>("FGetAttrDict", ParamGetAttrDict<UpSamplingParam>)
+.set_attr<FInferShape>("FInferShape", UpSamplingInferShape)
+.set_attr<FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_num_outputs(1)
+.set_num_inputs(1)
+.set_support_level(2);
+
+}  // namespace top
+}  // namespace nnvm

nnvm/tests/python/compiler/test_top_level2.py

@@ -148,6 +148,25 @@ def test_global_avg_pool2d():
         np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
 
 
+def test_upsampling():
+    x = sym.Variable("x")
+    scale = 2
+    y = sym.upsampling(x, scale=scale, name="y")
+    dtype = "float32"
+    dshape = (1, 16, 32, 32)
+    oshape = (1, 16, 32*scale, 32*scale)
+    shape_dict = {"x": dshape}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
+        m = graph_runtime.create(graph, lib, ctx)
+        a_np = np.random.uniform(size=dshape).astype(dtype)
+        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)
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
+
+
 if __name__ == "__main__":
     test_conv2d()
     test_grouped_conv2d()
@@ -156,3 +175,4 @@ if __name__ == "__main__":
     test_avg_pool2d()
     test_global_max_pool2d()
     test_global_avg_pool2d()
+    test_upsampling()