chore: import upstream snapshot with attribution

test/legacy_test/test_box_coder_op.py

@@ -0,0 +1,511 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from op_test import OpTest, get_places
+
+import paddle
+
+
+def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
+    pb_w = p_box[:, 2] - p_box[:, 0] + (not norm)
+    pb_h = p_box[:, 3] - p_box[:, 1] + (not norm)
+    pb_x = pb_w * 0.5 + p_box[:, 0]
+    pb_y = pb_h * 0.5 + p_box[:, 1]
+    shape = (1, p_box.shape[0]) if axis == 0 else (p_box.shape[0], 1)
+
+    pb_w = pb_w.reshape(shape)
+    pb_h = pb_h.reshape(shape)
+    pb_x = pb_x.reshape(shape)
+    pb_y = pb_y.reshape(shape)
+
+    if pb_v.ndim == 2:
+        var_shape = (
+            (1, pb_v.shape[0], pb_v.shape[1])
+            if axis == 0
+            else (pb_v.shape[0], 1, pb_v.shape[1])
+        )
+        pb_v = pb_v.reshape(var_shape)
+    if pb_v.ndim == 1:
+        tb_x = pb_v[0] * t_box[:, :, 0] * pb_w + pb_x
+        tb_y = pb_v[1] * t_box[:, :, 1] * pb_h + pb_y
+        tb_w = np.exp(pb_v[2] * t_box[:, :, 2]) * pb_w
+        tb_h = np.exp(pb_v[3] * t_box[:, :, 3]) * pb_h
+    else:
+        tb_x = pb_v[:, :, 0] * t_box[:, :, 0] * pb_w + pb_x
+        tb_y = pb_v[:, :, 1] * t_box[:, :, 1] * pb_h + pb_y
+        tb_w = np.exp(pb_v[:, :, 2] * t_box[:, :, 2]) * pb_w
+        tb_h = np.exp(pb_v[:, :, 3] * t_box[:, :, 3]) * pb_h
+    output_box[:, :, 0] = tb_x - tb_w / 2
+    output_box[:, :, 1] = tb_y - tb_h / 2
+    output_box[:, :, 2] = tb_x + tb_w / 2 - (not norm)
+    output_box[:, :, 3] = tb_y + tb_h / 2 - (not norm)
+
+
+def box_encoder(t_box, p_box, pb_v, output_box, norm):
+    pb_w = p_box[:, 2] - p_box[:, 0] + (not norm)
+    pb_h = p_box[:, 3] - p_box[:, 1] + (not norm)
+    pb_x = pb_w * 0.5 + p_box[:, 0]
+    pb_y = pb_h * 0.5 + p_box[:, 1]
+    shape = (1, p_box.shape[0])
+
+    pb_w = pb_w.reshape(shape)
+    pb_h = pb_h.reshape(shape)
+    pb_x = pb_x.reshape(shape)
+    pb_y = pb_y.reshape(shape)
+
+    if pb_v.ndim == 2:
+        pb_v = pb_v.reshape(1, pb_v.shape[0], pb_v.shape[1])
+    tb_x = ((t_box[:, 2] + t_box[:, 0]) / 2).reshape(t_box.shape[0], 1)
+    tb_y = ((t_box[:, 3] + t_box[:, 1]) / 2).reshape(t_box.shape[0], 1)
+    tb_w = (t_box[:, 2] - t_box[:, 0]).reshape(t_box.shape[0], 1) + (not norm)
+    tb_h = (t_box[:, 3] - t_box[:, 1]).reshape(t_box.shape[0], 1) + (not norm)
+    if pb_v.ndim == 1:
+        output_box[:, :, 0] = (tb_x - pb_x) / pb_w / pb_v[0]
+        output_box[:, :, 1] = (tb_y - pb_y) / pb_h / pb_v[1]
+        output_box[:, :, 2] = np.log(np.fabs(tb_w / pb_w)) / pb_v[2]
+        output_box[:, :, 3] = np.log(np.fabs(tb_h / pb_h)) / pb_v[3]
+    else:
+        output_box[:, :, 0] = (tb_x - pb_x) / pb_w / pb_v[:, :, 0]
+        output_box[:, :, 1] = (tb_y - pb_y) / pb_h / pb_v[:, :, 1]
+        output_box[:, :, 2] = np.log(np.fabs(tb_w / pb_w)) / pb_v[:, :, 2]
+        output_box[:, :, 3] = np.log(np.fabs(tb_h / pb_h)) / pb_v[:, :, 3]
+
+
+def batch_box_coder(p_box, pb_v, t_box, lod, code_type, norm, axis=0):
+    n = t_box.shape[0]
+    m = p_box.shape[0]
+    if code_type == "DecodeCenterSize":
+        m = t_box.shape[1]
+    output_box = np.zeros((n, m, 4), dtype=np.float32)
+    cur_offset = 0
+    for i in range(len(lod)):
+        if code_type == "EncodeCenterSize":
+            box_encoder(
+                t_box[cur_offset : (cur_offset + lod[i]), :],
+                p_box,
+                pb_v,
+                output_box[cur_offset : (cur_offset + lod[i]), :, :],
+                norm,
+            )
+        elif code_type == "DecodeCenterSize":
+            box_decoder(t_box, p_box, pb_v, output_box, norm, axis)
+        cur_offset += lod[i]
+    return output_box
+
+
+class TestBoxCoderOp(OpTest):
+    def test_check_output(self):
+        self.check_output(check_pir=True)
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        self.python_api = paddle.vision.ops.box_coder
+        lod = [[1, 1, 1, 1, 1]]
+        prior_box = np.random.random((81, 4)).astype('float32')
+        prior_box_var = np.random.random((81, 4)).astype('float32')
+        target_box = np.random.random((20, 81, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        output_box = batch_box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            lod[0],
+            code_type,
+            box_normalized,
+        )
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False,
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
+class TestBoxCoderOpWithoutBoxVar(OpTest):
+    def test_check_output(self):
+        self.check_output(check_pir=True)
+
+    def setUp(self):
+        self.python_api = paddle.vision.ops.box_coder
+        self.op_type = "box_coder"
+        lod = [[0, 1, 2, 3, 4, 5]]
+        prior_box = np.random.random((81, 4)).astype('float32')
+        prior_box_var = np.ones((81, 4)).astype('float32')
+        target_box = np.random.random((20, 81, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        output_box = batch_box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            lod[0],
+            code_type,
+            box_normalized,
+        )
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False,
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
+class TestBoxCoderOpWithLoD(OpTest):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.python_api = paddle.vision.ops.box_coder
+        self.op_type = "box_coder"
+        lod = [[10, 20, 20]]
+        prior_box = np.random.random((20, 4)).astype('float32')
+        prior_box_var = np.random.random((20, 4)).astype('float32')
+        target_box = np.random.random((50, 4)).astype('float32')
+        code_type = "EncodeCenterSize"
+        box_normalized = True
+        output_box = batch_box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            lod[0],
+            code_type,
+            box_normalized,
+        )
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': (target_box, lod),
+        }
+        self.attrs = {'code_type': 'encode_center_size', 'box_normalized': True}
+        self.outputs = {'OutputBox': output_box}
+
+
+class TestBoxCoderOpWithAxis(OpTest):
+    def test_check_output(self):
+        self.check_output(check_pir=True)
+
+    def setUp(self):
+        self.python_api = paddle.vision.ops.box_coder
+        self.op_type = "box_coder"
+        lod = [[1, 1, 1, 1, 1]]
+        prior_box = np.random.random((30, 4)).astype('float32')
+        prior_box_var = np.random.random((30, 4)).astype('float32')
+        target_box = np.random.random((30, 81, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        axis = 1
+        output_box = batch_box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            lod[0],
+            code_type,
+            box_normalized,
+            axis,
+        )
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False,
+            'axis': axis,
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
+def wrapper_box_coder(
+    prior_box,
+    prior_box_var=None,
+    target_box=None,
+    code_type="encode_center_size",
+    box_normalized=True,
+    axis=0,
+    variance=[],
+):
+    if isinstance(prior_box_var, paddle.Tensor):
+        output_box = paddle._C_ops.box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            code_type,
+            box_normalized,
+            axis,
+            [],
+        )
+    elif isinstance(prior_box_var, list):
+        output_box = paddle._C_ops.box_coder(
+            prior_box,
+            None,
+            target_box,
+            code_type,
+            box_normalized,
+            axis,
+            prior_box_var,
+        )
+    else:
+        output_box = paddle._C_ops.box_coder(
+            prior_box,
+            None,
+            target_box,
+            code_type,
+            box_normalized,
+            axis,
+            variance,
+        )
+    return output_box
+
+
+class TestBoxCoderOpWithVariance(OpTest):
+    def test_check_output(self):
+        self.check_output(check_pir=True)
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        self.python_api = wrapper_box_coder
+        lod = [[1, 1, 1, 1, 1]]
+        prior_box = np.random.random((30, 4)).astype('float32')
+        prior_box_var = np.random.random(4).astype('float32')
+        target_box = np.random.random((30, 81, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        axis = 1
+        output_box = batch_box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            lod[0],
+            code_type,
+            box_normalized,
+            axis,
+        )
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False,
+            'variance': prior_box_var.astype(np.float64).flatten(),
+            'axis': axis,
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
+class TestBoxCoderOpWithVarianceDygraphAPI(unittest.TestCase):
+    def setUp(self):
+        self.lod = [[1, 1, 1, 1, 1]]
+        self.prior_box = np.random.random((30, 4)).astype('float32')
+        self.prior_box_var = np.random.random(4).astype('float32')
+        self.target_box = np.random.random((30, 81, 4)).astype('float32')
+        self.code_type = "DecodeCenterSize"
+        self.box_normalized = False
+        self.axis = 1
+        self.output_ref = batch_box_coder(
+            self.prior_box,
+            self.prior_box_var,
+            self.target_box,
+            self.lod[0],
+            self.code_type,
+            self.box_normalized,
+            self.axis,
+        )
+        self.place = get_places()
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            output_box = paddle.vision.ops.box_coder(
+                paddle.to_tensor(self.prior_box),
+                self.prior_box_var.tolist(),
+                paddle.to_tensor(self.target_box),
+                "decode_center_size",
+                self.box_normalized,
+                axis=self.axis,
+            )
+            np.testing.assert_allclose(
+                np.sum(self.output_ref), np.sum(output_box.numpy()), rtol=1e-05
+            )
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
+class TestBoxCoderAPI(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(678)
+        self.prior_box_np = np.random.random((80, 4)).astype('float32')
+        self.prior_box_var_np = np.random.random((80, 4)).astype('float32')
+        self.target_box_np = np.random.random((20, 80, 4)).astype('float32')
+
+    def test_dygraph_with_static(self):
+        paddle.enable_static()
+        exe = paddle.static.Executor()
+        main = paddle.static.Program()
+        startup = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            prior_box = paddle.static.data(
+                name='prior_box', shape=[80, 4], dtype='float32'
+            )
+            prior_box_var = paddle.static.data(
+                name='prior_box_var', shape=[80, 4], dtype='float32'
+            )
+            target_box = paddle.static.data(
+                name='target_box', shape=[20, 80, 4], dtype='float32'
+            )
+
+            boxes = paddle.vision.ops.box_coder(
+                prior_box=prior_box,
+                prior_box_var=prior_box_var,
+                target_box=target_box,
+                code_type="decode_center_size",
+                box_normalized=False,
+            )
+
+        boxes_np = exe.run(
+            main,
+            feed={
+                'prior_box': self.prior_box_np,
+                'prior_box_var': self.prior_box_var_np,
+                'target_box': self.target_box_np,
+            },
+            fetch_list=[boxes],
+        )
+
+        paddle.disable_static()
+        prior_box_dy = paddle.to_tensor(self.prior_box_np)
+        prior_box_var_dy = paddle.to_tensor(self.prior_box_var_np)
+        target_box_dy = paddle.to_tensor(self.target_box_np)
+
+        boxes_dy = paddle.vision.ops.box_coder(
+            prior_box=prior_box_dy,
+            prior_box_var=prior_box_var_dy,
+            target_box=target_box_dy,
+            code_type="decode_center_size",
+            box_normalized=False,
+        )
+        boxes_dy_np = boxes_dy.numpy()
+
+        np.testing.assert_allclose(boxes_np[0], boxes_dy_np)
+        paddle.enable_static()
+
+
+class TestBoxCoderSupporttuple(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(678)
+        self.prior_box_np = np.random.random((80, 4)).astype('float32')
+        self.target_box_np = np.random.random((20, 80, 4)).astype('float32')
+
+    def test_support_tuple(self):
+        paddle.enable_static()
+        exe = paddle.static.Executor()
+        main = paddle.static.Program()
+        startup = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            prior_box = paddle.static.data(
+                name='prior_box', shape=[80, 4], dtype='float32'
+            )
+            target_box = paddle.static.data(
+                name='target_box', shape=[20, 80, 4], dtype='float32'
+            )
+
+            boxes = paddle.vision.ops.box_coder(
+                prior_box=prior_box,
+                prior_box_var=(1, 2, 3, 4),
+                target_box=target_box,
+                code_type="decode_center_size",
+                box_normalized=False,
+            )
+
+        boxes_np = exe.run(
+            main,
+            feed={
+                'prior_box': self.prior_box_np,
+                'target_box': self.target_box_np,
+            },
+            fetch_list=[boxes],
+        )[0]
+
+        paddle.disable_static()
+        prior_box_dy = paddle.to_tensor(self.prior_box_np)
+        target_box_dy = paddle.to_tensor(self.target_box_np)
+
+        boxes_dy = paddle.vision.ops.box_coder(
+            prior_box=prior_box_dy,
+            prior_box_var=(1, 2, 3, 4),
+            target_box=target_box_dy,
+            code_type="decode_center_size",
+            box_normalized=False,
+        )
+        boxes_dy_np = boxes_dy.numpy()
+
+        np.testing.assert_allclose(boxes_np, boxes_dy_np)
+        paddle.enable_static()
+
+
+class TestBoxCoderOp_ZeroSize(OpTest):
+    def test_check_output(self):
+        self.check_output(check_pir=True)
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        self.python_api = paddle.vision.ops.box_coder
+        lod = [[1, 1, 1, 1, 1]]
+        prior_box = np.random.random((81, 4)).astype('float32')
+        prior_box_var = np.random.random((81, 4)).astype('float32')
+        target_box = np.random.random((0, 81, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        output_box = batch_box_coder(
+            prior_box,
+            prior_box_var,
+            target_box,
+            lod[0],
+            code_type,
+            box_normalized,
+        )
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False,
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()