Merge branch 'master' into arange

include/tvm/relay/attrs/vision.h

@@ -74,6 +74,30 @@ struct NMSAttrs : public tvm::AttrsNode<NMSAttrs>{
   }
 };
 
+/*! \brief Attributes used in roi_align operators */
+struct ROIAlignAttrs : public tvm::AttrsNode<ROIAlignAttrs> {
+  Array<IndexExpr> pooled_size;
+  double spatial_scale;
+  int sample_ratio;
+  std::string layout;
+  TVM_DECLARE_ATTRS(ROIAlignAttrs, "relay.attrs.ROIAlignAttrs") {
+    TVM_ATTR_FIELD(pooled_size).describe("Output size of roi align.");
+    TVM_ATTR_FIELD(spatial_scale)
+        .describe(
+            "Ratio of input feature map height (or w) to raw image height (or w). "
+            "Equals the reciprocal of total stride in convolutional layers, which should be "
+            "in range (0.0, 1.0]");
+    TVM_ATTR_FIELD(sample_ratio)
+        .set_default(-1)
+        .describe("Optional sampling ratio of ROI align, using adaptive size by default.");
+    TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
+        "Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
+        "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+        "dimensions respectively. Convolution is applied on the 'H' and"
+        "'W' dimensions.");
+  }
+};
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_VISION_H_

python/tvm/relay/frontend/mxnet.py

@@ -272,11 +272,21 @@ def _mx_arange(inputs, attrs):
     assert len(inputs) == 0
     if attrs.get_int("repeat", 1) != 1:
         raise RuntimeError("arange doesn't support repeat")
-    start = attrs.get_float("start", 0)
-    stop = attrs.get_float("stop")
-    step = attrs.get_float("step", 1)
-    dtype = attrs.get_str("dtype", "float32")
-    return _op.arange(start, stop, step, dtype)
+    new_attrs = {}
+    new_attrs["start"] = attrs.get_float("start", 0)
+    new_attrs["stop"] = attrs.get_float("stop")
+    new_attrs["step"] = attrs.get_float("step", 1)
+    new_attrs["dtype"] = attrs.get_str("dtype", "float32")
+    return _op.arange(**new_attrs)
+
+
+def _mx_roi_align(inputs, attrs):
+    new_attrs = {}
+    new_attrs["pooled_size"] = attrs.get_int_tuple("pooled_size")
+    new_attrs["spatial_scale"] = attrs.get_float("spatial_scale")
+    new_attrs["sample_ratio"] = attrs.get_int("sample_ratio", -1)
+    new_attrs["layout"] = "NCHW"
+    return _op.vision.roi_align(inputs[0], inputs[1], **new_attrs)
 
 
 # Note: due to attribute conversion constraint
@@ -369,6 +379,7 @@ def _mx_arange(inputs, attrs):
     # vision
     "_contrib_MultiBoxPrior" : _mx_multibox_prior,
     "_contrib_MultiBoxDetection" : _mx_multibox_detection,
+    "_contrib_ROIAlign" : _mx_roi_align,
     # List of missing operators that are present in NNVMv1
     # TODO(tvm-tvm): support all operators.
     #

python/tvm/relay/op/vision/__init__.py

@@ -4,4 +4,6 @@
 
 from .multibox import *
 from .nms import *
+from .rcnn import *
 from . import _multibox
+from . import _rcnn

python/tvm/relay/op/vision/_rcnn.py

@@ -0,0 +1,23 @@
+# pylint: disable=invalid-name, unused-argument
+"""Faster R-CNN and Mask R-CNN operations."""
+import topi
+from topi.util import get_const_tuple
+from .. import op as reg
+from ..op import OpPattern
+
+
+@reg.register_compute("vision.roi_align")
+def compute_roi_align(attrs, inputs, _, target):
+    """Compute definition of roi_align"""
+    assert attrs.layout == "NCHW"
+    return [topi.vision.rcnn.roi_align_nchw(
+        inputs[0], inputs[1], pooled_size=get_const_tuple(attrs.pooled_size),
+        spatial_scale=attrs.spatial_scale, sample_ratio=attrs.sample_ratio)]
+
+@reg.register_schedule("vision.roi_align")
+def schedule_roi_align(_, outs, target):
+    """Schedule definition of roi_align"""
+    with target:
+        return topi.generic.vision.schedule_roi_align(outs)
+
+reg.register_pattern("vision.roi_align", OpPattern.OUT_ELEMWISE_FUSABLE)

python/tvm/relay/op/vision/rcnn.py

@@ -0,0 +1,32 @@
+"""Faster R-CNN and Mask R-CNN operations."""
+from . import _make
+
+
+def roi_align(data, rois, pooled_size, spatial_scale, sample_ratio=-1, layout='NCHW'):
+    """ROI align operator.
+
+    Parameters
+    ----------
+    data : relay.Expr
+        4-D tensor with shape [batch, channel, height, width]
+
+    rois : relay.Expr
+        2-D tensor with shape [num_roi, 5]. The last dimension should be in format of
+        [batch_index, w_start, h_start, w_end, h_end]
+
+    pooled_size : list/tuple of two ints
+        output size
+
+    spatial_scale : float
+        Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal
+        of total stride in convolutional layers, which should be in range (0.0, 1.0]
+
+    sample_ratio : int
+        Optional sampling ratio of ROI align, using adaptive size by default.
+
+    Returns
+    -------
+    output : relay.Expr
+        4-D tensor with shape [num_roi, channel, pooled_size, pooled_size]
+    """
+    return _make.roi_align(data, rois, pooled_size, spatial_scale, sample_ratio, layout)

src/relay/op/vision/rcnn_op.cc

@@ -0,0 +1,67 @@
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file rcnn_op.cc
+ * \brief Faster RCNN and Mask RCNN operators
+ */
+#include <tvm/relay/op.h>
+#include <tvm/relay/op_attr_types.h>
+#include <tvm/relay/attrs/vision.h>
+
+namespace tvm {
+namespace relay {
+
+TVM_REGISTER_NODE_TYPE(ROIAlignAttrs);
+
+bool ROIAlignRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                 const TypeReporter& reporter) {
+  auto roi_align_attrs = attrs.as<ROIAlignAttrs>();
+  CHECK_EQ(types.size(), 3);
+  const auto* data = types[0].as<TensorTypeNode>();
+  const auto* rois = types[1].as<TensorTypeNode>();
+  const auto& dshape = data->shape;
+  const auto& rshape = rois->shape;
+  CHECK(roi_align_attrs);
+  CHECK_EQ(dshape.size(), 4) << "Input data should be 4-D.";
+  CHECK_EQ(rshape.size(), 2) << "Input rois should be 2-D.";
+  CHECK_EQ(roi_align_attrs->layout, "NCHW") << "ROI Align only supports NCHW layout";
+  // assign output type
+  std::vector<IndexExpr> oshape(
+      {rshape[0], dshape[1], roi_align_attrs->pooled_size[0], roi_align_attrs->pooled_size[1]});
+  reporter->Assign(types[2], TensorTypeNode::make(oshape, data->dtype));
+  return true;
+}
+
+Expr MakeROIAlign(Expr data, Expr rois, Array<IndexExpr> pooled_size, double spatial_scale,
+                  int sample_ratio, std::string layout) {
+  auto attrs = make_node<ROIAlignAttrs>();
+  attrs->pooled_size = pooled_size;
+  attrs->spatial_scale = spatial_scale;
+  attrs->sample_ratio = sample_ratio;
+  attrs->layout = layout;
+  static const Op& op = Op::Get("vision.roi_align");
+  return CallNode::make(op, {data, rois}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_API("relay.op.vision._make.roi_align")
+.set_body([](const TVMArgs& args, TVMRetValue* rv) {
+    runtime::detail::unpack_call<Expr, 6>(MakeROIAlign, args, rv);
+  });
+
+RELAY_REGISTER_OP("vision.roi_align")
+    .describe(R"doc(ROI Align operator.
+
+ - **data**: This depends on the `layout` parameter. Input is 4D array of shape
+             (batch_size, channels, height, width) if `layout` is `NCHW`.
+ - **rois**: 2D array of shape (num_roi, 5). The last dimension should be in format of
+             [batch_index, w_start, h_start, w_end, h_end].
+ - **out**: This depends on the `layout` parameter. Output is 4D array of shape
+            (num_roi, channels, pooled_height, pooled_width) if `layout` is `NCHW`.
+ )doc" TVM_ADD_FILELINE)
+.set_num_inputs(2)
+.add_argument("data", "Tensor", "The input tensor.")
+.add_argument("rois", "Tensor", "The input rois")
+.set_support_level(5)
+.add_type_rel("ROIAlign", ROIAlignRel);
+
+}  // namespace relay
+}  // namespace tvm

tests/python/relay/test_op_level5.py

@@ -273,9 +273,44 @@ def test_threshold():
     test_threshold()
 
 
+def test_roi_align():
+    def verify_roi_align(data_shape, rois_shape, pooled_size, spatial_scale, sample_ratio):
+        data = relay.var("data", relay.ty.TensorType(data_shape, "float32"))
+        rois = relay.var("rois", relay.ty.TensorType(rois_shape, "float32"))
+        z = relay.vision.roi_align(data, rois, pooled_size=(pooled_size, pooled_size),
+                                   spatial_scale=spatial_scale, sample_ratio=sample_ratio,
+                                   layout="NCHW")
+        zz = relay.ir_pass.infer_type(z)
+
+        batch, channel, in_size, _ = data_shape
+        num_roi = rois_shape[0]
+        assert zz.checked_type == relay.ty.TensorType(
+                (num_roi, channel, pooled_size, pooled_size), "float32")
+
+        func = relay.Function([data, rois], z)
+        func = relay.ir_pass.infer_type(func)
+        np_data = np.random.uniform(size=data_shape).astype("float32")
+        np_rois = np.random.uniform(size=rois_shape).astype('float32') * in_size
+        np_rois[:, 0] = np.random.randint(low = 0, high = batch, size = num_roi)
+        ref_res = topi.testing.roi_align_nchw_python(np_data, np_rois, pooled_size=pooled_size,
+                                                     spatial_scale=spatial_scale,
+                                                     sample_ratio=sample_ratio)
+        for target, ctx in ctx_list():
+            intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
+            op_res1 = intrp1.evaluate(func)(np_data, np_rois)
+            tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-4)
+            intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
+            op_res2 = intrp2.evaluate(func)(np_data, np_rois)
+            tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-4)
+
+    verify_roi_align((1, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=1.0, sample_ratio=-1)
+    verify_roi_align((4, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2)
+
+
 if __name__ == "__main__":
     test_resize_infer_type()
     test_resize()
     test_multibox_prior()
     test_multibox_transform_loc()
     test_nms()
+    test_roi_align()

topi/python/topi/vision/rcnn/roi_align.py

@@ -68,8 +68,8 @@ def _sample(i, c, ph, pw):
         if sample_ratio > 0:
             roi_bin_grid_h = roi_bin_grid_w = tvm.const(sample_ratio, 'int32')
         else:
-            roi_bin_grid_h = tvm.ceil(roi_h / pooled_size).astype('int32')
-            roi_bin_grid_w = tvm.ceil(roi_w / pooled_size).astype('int32')
+            roi_bin_grid_h = tvm.ceil(roi_h / pooled_size_h).astype('int32')
+            roi_bin_grid_w = tvm.ceil(roi_w / pooled_size_w).astype('int32')
 
         count = roi_bin_grid_h * roi_bin_grid_w
         rh = tvm.reduce_axis((0, roi_bin_grid_h))