diff --git a/paddle/fluid/operators/iou_similarity_op.h b/paddle/fluid/operators/iou_similarity_op.h index c76448c736847..9f193ebc59b7b 100644 --- a/paddle/fluid/operators/iou_similarity_op.h +++ b/paddle/fluid/operators/iou_similarity_op.h @@ -41,22 +41,24 @@ struct IOUSimilarityFunctor { IOUSimilarityFunctor(const T* x, const T* y, T* z, int cols) : x_(x), y_(y), z_(z), cols_(static_cast(cols)) {} - inline HOSTDEVICE void operator()(size_t row_id) const { + inline HOSTDEVICE void operator()(size_t tid) const { + size_t row_id = tid / cols_; + size_t col_id = tid % cols_; + T x_min1 = x_[row_id * 4]; T y_min1 = x_[row_id * 4 + 1]; T x_max1 = x_[row_id * 4 + 2]; T y_max1 = x_[row_id * 4 + 3]; - for (size_t i = 0; i < cols_; ++i) { - T x_min2 = y_[i * 4]; - T y_min2 = y_[i * 4 + 1]; - T x_max2 = y_[i * 4 + 2]; - T y_max2 = y_[i * 4 + 3]; - T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2, - x_max2, y_max2); + T x_min2 = y_[col_id * 4]; + T y_min2 = y_[col_id * 4 + 1]; + T x_max2 = y_[col_id * 4 + 2]; + T y_max2 = y_[col_id * 4 + 3]; + + T sim = IOUSimilarity(x_min1, y_min1, x_max1, y_max1, x_min2, y_min2, + x_max2, y_max2); - z_[row_id * cols_ + i] = sim; - } + z_[row_id * cols_ + col_id] = sim; } const T* x_; const T* y_; @@ -81,7 +83,7 @@ class IOUSimilarityKernel : public framework::OpKernel { out->mutable_data(ctx.GetPlace()), y_n); platform::ForRange for_range( - static_cast(ctx.device_context()), x_n); + static_cast(ctx.device_context()), x_n * y_n); for_range(functor); } }; // namespace operators diff --git a/python/paddle/fluid/tests/unittests/test_iou_similarity_op.py b/python/paddle/fluid/tests/unittests/test_iou_similarity_op.py index e33436b63c0e0..8f62ac20a5c13 100644 --- a/python/paddle/fluid/tests/unittests/test_iou_similarity_op.py +++ b/python/paddle/fluid/tests/unittests/test_iou_similarity_op.py @@ -14,6 +14,7 @@ import unittest import numpy as np +import numpy.random as random import sys import math from op_test import OpTest @@ -25,14 +26,27 @@ def test_check_output(self): def setUp(self): self.op_type = "iou_similarity" - self.boxes1 = np.array( - [[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]]).astype('float32') - self.boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0], - [0.0, 0.0, 20.0, 20.0]]).astype('float32') - self.output = np.array( - [[2.0 / 16.0, 0, 6.0 / 400.0], - [1.0 / 16.0, 0.0, 5.0 / 400.0]]).astype('float32') - + self.boxes1 = random.rand(2, 4).astype('float32') + self.boxes2 = random.rand(3, 4).astype('float32') + self.output = random.rand(2, 3).astype('float32') + for row in range(self.boxes1.shape[0]): + for col in range(self.boxes2.shape[0]): + xmin1, ymin1, xmax1, ymax1 = self.boxes1[row] + xmin2, ymin2, xmax2, ymax2 = self.boxes2[col] + area1 = (ymax1 - ymin1) * (xmax1 - xmin1) + area2 = (ymax2 - ymin2) * (xmax2 - xmin2) + inter_xmax = min(xmax1, xmax2) + inter_ymax = min(ymax1, ymax2) + inter_xmin = max(xmin1, xmin2) + inter_ymin = max(ymin1, ymin2) + inter_height = inter_ymax - inter_ymin + inter_width = inter_xmax - inter_xmin + inter_height = max(inter_height, 0) + inter_width = max(inter_width, 0) + inter_area = inter_width * inter_height + union_area = area1 + area2 - inter_area + sim_score = inter_area / union_area + self.output[row, col] = sim_score self.inputs = {'X': self.boxes1, 'Y': self.boxes2} self.outputs = {'Out': self.output}