Removed obsolete argument correct_transform_coords in bbox_transform op. (pytorch#16723)

Summary:
Pull Request resolved: pytorch#16723

Removed obsolete argument correct_transform_coords in bbox_transform op.
* It was only for backward compatibility. We should not have models using it now.

Differential Revision: D13937430

fbshipit-source-id: 504bb066137ce408c12dc9dcc2e0a513bad9b7ee

Author: newstzpz

caffe2/operators/bbox_transform_op.cc

@@ -21,12 +21,6 @@ Transform proposal bounding boxes to target bounding box using bounding box
         " after applying the bbox deltas."
         "Set to false to match the detectron code, set to true for keypoint"
         " models and for backward compatibility")
-    .Arg(
-        "correct_transform_coords",
-        "bool (default false), Correct bounding box transform coordates,"
-        " see bbox_transform() in boxes.py "
-        "Set to true to match the detectron code, set to false for backward"
-        " compatibility")
     .Arg(
         "rotated",
         "bool (default false). If true, then boxes (rois and deltas) include "
@@ -160,7 +154,6 @@ bool BBoxTransformOp<float, CPUContext>::RunOnDevice() {
           cur_deltas,
           weights_,
           utils::BBOX_XFORM_CLIP_DEFAULT,
-          correct_transform_coords_,
           angle_bound_on_,
           angle_bound_lo_,
           angle_bound_hi_);
@@ -188,7 +181,8 @@ bool BBoxTransformOp<float, CPUContext>::RunOnDevice() {
 
 } // namespace caffe2
 
-using BBoxTransformOpFloatCPU = caffe2::BBoxTransformOp<float, caffe2::CPUContext>;
+using BBoxTransformOpFloatCPU =
+    caffe2::BBoxTransformOp<float, caffe2::CPUContext>;
 
 C10_REGISTER_CAFFE2_OPERATOR_CPU(
     BBoxTransform,
@@ -198,7 +192,6 @@ C10_REGISTER_CAFFE2_OPERATOR_CPU(
         c10::Argument("im_info"),
         c10::Argument("weights", ListType::create(FloatType::get())),
         c10::Argument("apply_scale", BoolType::get()),
-        c10::Argument("correct_transform_coords", BoolType::get()),
         c10::Argument("rotated", BoolType::get()),
         c10::Argument("angle_bound_on", BoolType::get()),
         c10::Argument("angle_bound_lo", IntType::get()),
@@ -209,5 +202,4 @@ C10_REGISTER_CAFFE2_OPERATOR_CPU(
         c10::Argument("output_0"),
         c10::Argument("output_1"),
     }),
-    BBoxTransformOpFloatCPU
-);
+    BBoxTransformOpFloatCPU);

caffe2/operators/bbox_transform_op.h

@@ -23,9 +23,6 @@ class BBoxTransformOp final : public Operator<Context> {
             vector<T>{1.0f, 1.0f, 1.0f, 1.0f})),
         apply_scale_(
             this->template GetSingleArgument<bool>("apply_scale", true)),
-        correct_transform_coords_(this->template GetSingleArgument<bool>(
-            "correct_transform_coords",
-            false)),
         rotated_(this->template GetSingleArgument<bool>("rotated", false)),
         angle_bound_on_(
             this->template GetSingleArgument<bool>("angle_bound_on", true)),
@@ -52,10 +49,6 @@ class BBoxTransformOp final : public Operator<Context> {
   // Set to false to match the detectron code, set to true for the keypoint
   //   model and for backward compatibility
   bool apply_scale_{true};
-  // Correct bounding box transform coordates, see bbox_transform() in boxes.py
-  // Set to true to match the detectron code, set to false for backward
-  //   compatibility
-  bool correct_transform_coords_{false};
   // Set for RRPN case to handle rotated boxes. Inputs should be in format
   // [ctr_x, ctr_y, width, height, angle (in degrees)].
   bool rotated_{false};

caffe2/operators/generate_proposals_op.cc

@@ -228,7 +228,6 @@ void GenerateProposalsOp<CPUContext>::ProposalsForOneImage(
       bbox_deltas_sorted,
       bbox_weights,
       utils::BBOX_XFORM_CLIP_DEFAULT,
-      correct_transform_coords_,
       angle_bound_on_,
       angle_bound_lo_,
       angle_bound_hi_);
@@ -363,12 +362,6 @@ non-maximum suppression is applied to generate the final bounding boxes.
     .Arg("post_nms_topN", "(int) RPN_POST_NMS_TOP_N")
     .Arg("nms_thresh", "(float) RPN_NMS_THRESH")
     .Arg("min_size", "(float) RPN_MIN_SIZE")
-    .Arg(
-        "correct_transform_coords",
-        "bool (default false), Correct bounding box transform coordates,"
-        " see bbox_transform() in boxes.py "
-        "Set to true to match the detectron code, set to false for backward"
-        " compatibility")
     .Arg(
         "angle_bound_on",
         "bool (default true). If set, for rotated boxes, angle is "
@@ -425,7 +418,6 @@ C10_REGISTER_CAFFE2_OPERATOR_CPU(
         c10::Argument("post_nms_topN", IntType::get()),
         c10::Argument("nms_thresh", FloatType::get()),
         c10::Argument("min_size", FloatType::get()),
-        c10::Argument("correct_transform_coords", BoolType::get()),
         c10::Argument("angle_bound_on", BoolType::get()),
         c10::Argument("angle_bound_lo", IntType::get()),
         c10::Argument("angle_bound_hi", IntType::get()),
@@ -435,5 +427,4 @@ C10_REGISTER_CAFFE2_OPERATOR_CPU(
         c10::Argument("output_0"),
         c10::Argument("output_1"),
     }),
-    caffe2::GenerateProposalsOp<caffe2::CPUContext>
-);
+    caffe2::GenerateProposalsOp<caffe2::CPUContext>);

caffe2/operators/generate_proposals_op.cu

@@ -23,7 +23,6 @@ __global__ void GeneratePreNMSUprightBoxesKernel(
     const float* d_img_info_vec,
     const int num_images,
     const float bbox_xform_clip,
-    const bool correct_transform,
     float4* d_out_boxes,
     const int prenms_nboxes, // leading dimension of out_boxes
     float* d_inout_scores,
@@ -87,19 +86,14 @@ __global__ void GeneratePreNMSUprightBoxesKernel(
     const float pred_ctr_x = ctr_x + width * dx; // TODO fuse madd
     const float pred_w = width * expf(dw);
     x1 = pred_ctr_x - 0.5f * pred_w;
-    x2 = pred_ctr_x + 0.5f * pred_w;
+    x2 = pred_ctr_x + 0.5f * pred_w - 1.0f;
 
     float height = y2 - y1 + 1.0f;
     const float ctr_y = y1 + 0.5f * height;
     const float pred_ctr_y = ctr_y + height * dy;
     const float pred_h = height * expf(dh);
     y1 = pred_ctr_y - 0.5f * pred_h;
-    y2 = pred_ctr_y + 0.5f * pred_h;
-
-    if (correct_transform) {
-      x2 -= 1.0f;
-      y2 -= 1.0f;
-    }
+    y2 = pred_ctr_y + 0.5f * pred_h - 1.0f;
 
     // Clipping box to image
     const float img_height = d_img_info_vec[3 * image_index + 0];
@@ -491,7 +485,6 @@ bool GenerateProposalsOp<CUDAContext>::RunOnDevice() {
         d_im_info_vec,
         num_images,
         utils::BBOX_XFORM_CLIP_DEFAULT,
-        correct_transform_coords_,
         reinterpret_cast<float4*>(d_boxes),
         nboxes_to_generate,
         d_sorted_scores,

caffe2/operators/generate_proposals_op.h

@@ -91,9 +91,6 @@ class GenerateProposalsOp final : public Operator<Context> {
         rpn_nms_thresh_(
             this->template GetSingleArgument<float>("nms_thresh", 0.7f)),
         rpn_min_size_(this->template GetSingleArgument<float>("min_size", 16)),
-        correct_transform_coords_(this->template GetSingleArgument<bool>(
-            "correct_transform_coords",
-            false)),
         angle_bound_on_(
             this->template GetSingleArgument<bool>("angle_bound_on", true)),
         angle_bound_lo_(
@@ -135,10 +132,6 @@ class GenerateProposalsOp final : public Operator<Context> {
   float rpn_nms_thresh_{0.7};
   // RPN_MIN_SIZE
   float rpn_min_size_{16};
-  // Correct bounding box transform coordates, see bbox_transform() in boxes.py
-  // Set to true to match the detectron code, set to false for backward
-  // compatibility
-  bool correct_transform_coords_{false};
   // If set, for rotated boxes in RRPN, output angles are normalized to be
   // within [angle_bound_lo, angle_bound_hi].
   bool angle_bound_on_{true};

caffe2/operators/generate_proposals_op_util_boxes.h

@@ -39,8 +39,7 @@ EArrXXt<typename Derived1::Scalar> bbox_transform_upright(
     const Eigen::ArrayBase<Derived2>& deltas,
     const std::vector<typename Derived2::Scalar>& weights =
         std::vector<typename Derived2::Scalar>{1.0, 1.0, 1.0, 1.0},
-    const float bbox_xform_clip = BBOX_XFORM_CLIP_DEFAULT,
-    const bool correct_transform_coords = false) {
+    const float bbox_xform_clip = BBOX_XFORM_CLIP_DEFAULT) {
   using T = typename Derived1::Scalar;
   using EArrXX = EArrXXt<T>;
   using EArrX = EArrXt<T>;
@@ -70,17 +69,15 @@ EArrXXt<typename Derived1::Scalar> bbox_transform_upright(
   EArrX pred_w = dw.exp() * widths;
   EArrX pred_h = dh.exp() * heights;
 
-  T offset(correct_transform_coords ? 1.0 : 0.0);
-
   EArrXX pred_boxes = EArrXX::Zero(deltas.rows(), deltas.cols());
   // x1
   pred_boxes.col(0) = pred_ctr_x - T(0.5) * pred_w;
   // y1
   pred_boxes.col(1) = pred_ctr_y - T(0.5) * pred_h;
   // x2
-  pred_boxes.col(2) = pred_ctr_x + T(0.5) * pred_w - offset;
+  pred_boxes.col(2) = pred_ctr_x + T(0.5) * pred_w - T(1.0);
   // y2
-  pred_boxes.col(3) = pred_ctr_y + T(0.5) * pred_h - offset;
+  pred_boxes.col(3) = pred_ctr_y + T(0.5) * pred_h - T(1.0);
 
   return pred_boxes;
 }
@@ -169,15 +166,13 @@ EArrXXt<typename Derived1::Scalar> bbox_transform(
     const std::vector<typename Derived2::Scalar>& weights =
         std::vector<typename Derived2::Scalar>{1.0, 1.0, 1.0, 1.0},
     const float bbox_xform_clip = BBOX_XFORM_CLIP_DEFAULT,
-    const bool correct_transform_coords = false,
     const bool angle_bound_on = true,
     const int angle_bound_lo = -90,
     const int angle_bound_hi = 90) {
   CAFFE_ENFORCE(boxes.cols() == 4 || boxes.cols() == 5);
   if (boxes.cols() == 4) {
     // Upright boxes
-    return bbox_transform_upright(
-        boxes, deltas, weights, bbox_xform_clip, correct_transform_coords);
+    return bbox_transform_upright(boxes, deltas, weights, bbox_xform_clip);
   } else {
     // Rotated boxes with angle info
     return bbox_transform_rotated(

caffe2/operators/generate_proposals_op_util_boxes_test.cc

@@ -31,8 +31,7 @@ TEST(UtilsBoxesTest, TestBboxTransformRandom) {
       bbox.array(),
       deltas.array(),
       std::vector<float>{1.0, 1.0, 1.0, 1.0},
-      BBOX_XFORM_CLIP,
-      true);
+      BBOX_XFORM_CLIP);
   EXPECT_NEAR((result.matrix() - result_gt).norm(), 0.0, 1e-4);
 }
 
@@ -65,7 +64,6 @@ TEST(UtilsBoxesTest, TestBboxTransformRotated) {
       deltas.array(),
       std::vector<float>{1.0, 1.0, 1.0, 1.0},
       BBOX_XFORM_CLIP,
-      true, /* correct_transform_coords */
       false /* angle_bound_on */);
   EXPECT_NEAR((result.matrix() - result_gt).norm(), 0.0, 1e-2);
 }
@@ -98,7 +96,6 @@ TEST(UtilsBoxesTest, TestBboxTransformRotatedNormalized) {
       deltas.array(),
       std::vector<float>{1.0, 1.0, 1.0, 1.0},
       BBOX_XFORM_CLIP,
-      true, /* correct_transform_coords */
       true, /* angle_bound_on */
       -90, /* angle_bound_lo */
       90 /* angle_bound_hi */);

caffe2/python/operator_test/torch_integration_test.py

@@ -10,6 +10,7 @@
 import hypothesis.strategies as st
 import numpy as np
 
+
 def generate_rois(roi_counts, im_dims):
     assert len(roi_counts) == len(im_dims)
     all_rois = []
@@ -36,6 +37,7 @@ def generate_rois(roi_counts, im_dims):
         return np.vstack(all_rois)
     return np.empty((0, 5)).astype(np.float32)
 
+
 def generate_rois_rotated(roi_counts, im_dims):
     rois = generate_rois(roi_counts, im_dims)
     # [batch_id, ctr_x, ctr_y, w, h, angle]
@@ -48,6 +50,7 @@ def generate_rois_rotated(roi_counts, im_dims):
     rotated_rois[:, 5] = np.random.uniform(-90.0, 90.0)  # angle in degrees
     return rotated_rois
 
+
 class TorchIntegration(hu.HypothesisTestCase):
 
     @given(
@@ -58,15 +61,16 @@ class TorchIntegration(hu.HypothesisTestCase):
         clip_angle_thresh=st.sampled_from([-1.0, 1.0]),
         **hu.gcs_cpu_only
     )
-    def test_bbox_transform(self,
+    def test_bbox_transform(
+        self,
         roi_counts,
         num_classes,
         rotated,
         angle_bound_on,
         clip_angle_thresh,
         gc,
         dc,
-            ):
+    ):
         """
         Test with rois for multiple images in a batch
         """
@@ -91,7 +95,6 @@ def bbox_transform_ref():
                 ["rois", "deltas", "im_info"],
                 ["box_out"],
                 apply_scale=False,
-                correct_transform_coords=True,
                 rotated=rotated,
                 angle_bound_on=angle_bound_on,
                 clip_angle_thresh=clip_angle_thresh,
@@ -107,7 +110,7 @@ def bbox_transform_ref():
                 torch.tensor(rois), torch.tensor(deltas),
                 torch.tensor(im_info),
                 [1.0, 1.0, 1.0, 1.0],
-                False, True, rotated, angle_bound_on,
+                False, rotated, angle_bound_on,
                 -90, 90, clip_angle_thresh)
 
         torch.testing.assert_allclose(box_out, a)
@@ -117,7 +120,7 @@ def bbox_transform_ref():
         H=st.integers(min_value=10, max_value=10),
         W=st.integers(min_value=8, max_value=8),
         img_count=st.integers(min_value=3, max_value=3),
-        )
+    )
     def test_generate_proposals(self, A, H, W, img_count):
         scores = np.ones((img_count, A, H, W)).astype(np.float32)
         bbox_deltas = np.linspace(0, 10, num=img_count*4*A*H*W).reshape(
@@ -145,6 +148,6 @@ def generate_proposals_ref():
         a, b = torch.ops._caffe2.GenerateProposals(
                 torch.tensor(scores), torch.tensor(bbox_deltas),
                 torch.tensor(im_info), torch.tensor(anchors),
-                2.0, 6000, 300, 0.7, 16, False, True, -90, 90, 1.0)
+                2.0, 6000, 300, 0.7, 16, True, -90, 90, 1.0)
         torch.testing.assert_allclose(rois, a)
         torch.testing.assert_allclose(rois_probs, b)