[Hackathon No.26]

python/paddle/fluid/tests/unittests/test_triplet_margin_loss.py

@@ -0,0 +1,395 @@
+# Copyright (c) 2022 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 paddle
+import numpy as np
+import unittest
+
+
+def call_TripletMarginLoss_layer(
+    input,
+    positive,
+    negative,
+    p=2,
+    margin=0.3,
+    swap=False,
+    eps=1e-6,
+    reduction='mean',
+):
+    triplet_margin_loss = paddle.nn.TripletMarginLoss(p=p,
+                                                      epsilon=eps,
+                                                      margin=margin,
+                                                      swap=swap,
+                                                      reduction=reduction)
+    res = triplet_margin_loss(
+        input=input,
+        positive=positive,
+        negative=negative,
+    )
+    return res
+
+
+def call_TripletMarginLoss_functional(
+    input,
+    positive,
+    negative,
+    p=2,
+    margin=0.3,
+    swap=False,
+    eps=1e-6,
+    reduction='mean',
+):
+    res = paddle.nn.functional.triplet_margin_loss(input=input,
+                                                   positive=positive,
+                                                   negative=negative,
+                                                   p=p,
+                                                   epsilon=eps,
+                                                   margin=margin,
+                                                   swap=swap,
+                                                   reduction=reduction)
+    return res
+
+
+def test_static(place,
+                input_np,
+                positive_np,
+                negative_np,
+                p=2,
+                margin=0.3,
+                swap=False,
+                eps=1e-6,
+                reduction='mean',
+                functional=False):
+    prog = paddle.static.Program()
+    startup_prog = paddle.static.Program()
+    with paddle.static.program_guard(prog, startup_prog):
+        input = paddle.static.data(name='input',
+                                   shape=input_np.shape,
+                                   dtype='float64')
+        positive = paddle.static.data(name='positive',
+                                      shape=positive_np.shape,
+                                      dtype='float64')
+        negative = paddle.static.data(name='negative',
+                                      shape=negative_np.shape,
+                                      dtype='float64')
+        feed_dict = {
+            "input": input_np,
+            "positive": positive_np,
+            "negative": negative_np
+        }
+
+        if functional:
+            res = call_TripletMarginLoss_functional(input=input,
+                                                    positive=positive,
+                                                    negative=negative,
+                                                    p=p,
+                                                    eps=eps,
+                                                    margin=margin,
+                                                    swap=swap,
+                                                    reduction=reduction)
+        else:
+            res = call_TripletMarginLoss_layer(input=input,
+                                               positive=positive,
+                                               negative=negative,
+                                               p=p,
+                                               eps=eps,
+                                               margin=margin,
+                                               swap=swap,
+                                               reduction=reduction)
+
+        exe = paddle.static.Executor(place)
+        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
+    return static_result
+
+
+def test_dygraph(place,
+                 input,
+                 positive,
+                 negative,
+                 p=2,
+                 margin=0.3,
+                 swap=False,
+                 eps=1e-6,
+                 reduction='mean',
+                 functional=False):
+    paddle.disable_static()
+    input = paddle.to_tensor(input)
+    positive = paddle.to_tensor(positive)
+    negative = paddle.to_tensor(negative)
+
+    if functional:
+        dy_res = call_TripletMarginLoss_functional(input=input,
+                                                   positive=positive,
+                                                   negative=negative,
+                                                   p=p,
+                                                   eps=eps,
+                                                   margin=margin,
+                                                   swap=swap,
+                                                   reduction=reduction)
+    else:
+        dy_res = call_TripletMarginLoss_layer(input=input,
+                                              positive=positive,
+                                              negative=negative,
+                                              p=p,
+                                              eps=eps,
+                                              margin=margin,
+                                              swap=swap,
+                                              reduction=reduction)
+    dy_result = dy_res.numpy()
+    paddle.enable_static()
+    return dy_result
+
+
+def calc_triplet_margin_loss(
+    input,
+    positive,
+    negative,
+    p=2,
+    margin=0.3,
+    swap=False,
+    reduction='mean',
+):
+    positive_dist = np.linalg.norm((input - positive), p, axis=1)
+    negative_dist = np.linalg.norm((input - negative), p, axis=1)
+
+    if swap:
+        swap_dist = np.linalg.norm((positive - negative), p, axis=1)
+        negative_dist = np.minimum(negative_dist, swap_dist)
+    expected = np.maximum(positive_dist - negative_dist + margin, 0)
+
+    if reduction == 'mean':
+        expected = np.mean(expected)
+    elif reduction == 'sum':
+        expected = np.sum(expected)
+    else:
+        expected = expected
+
+    return expected
+
+
+class TestTripletMarginLoss(unittest.TestCase):
+
+    def test_TripletMarginLoss(self):
+        shape = (2, 2)
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        positive = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        negative = np.random.uniform(0, 2, size=shape).astype(np.float64)
+
+        places = [paddle.CPUPlace()]
+        if paddle.device.is_compiled_with_cuda():
+            places.append(paddle.CUDAPlace(0))
+        reductions = ['sum', 'mean', 'none']
+        for place in places:
+            for reduction in reductions:
+                expected = calc_triplet_margin_loss(input=input,
+                                                    positive=positive,
+                                                    negative=negative,
+                                                    reduction=reduction)
+
+                dy_result = test_dygraph(
+                    place=place,
+                    input=input,
+                    positive=positive,
+                    negative=negative,
+                    reduction=reduction,
+                )
+
+                static_result = test_static(
+                    place=place,
+                    input_np=input,
+                    positive_np=positive,
+                    negative_np=negative,
+                    reduction=reduction,
+                )
+                self.assertTrue(np.allclose(static_result, expected))
+                self.assertTrue(np.allclose(static_result, dy_result))
+                self.assertTrue(np.allclose(dy_result, expected))
+                static_functional = test_static(place=place,
+                                                input_np=input,
+                                                positive_np=positive,
+                                                negative_np=negative,
+                                                reduction=reduction,
+                                                functional=True)
+                dy_functional = test_dygraph(place=place,
+                                             input=input,
+                                             positive=positive,
+                                             negative=negative,
+                                             reduction=reduction,
+                                             functional=True)
+                self.assertTrue(np.allclose(static_functional, expected))
+                self.assertTrue(np.allclose(static_functional, dy_functional))
+                self.assertTrue(np.allclose(dy_functional, expected))
+
+    def test_TripletMarginLoss_error(self):
+        paddle.disable_static()
+        self.assertRaises(ValueError,
+                          paddle.nn.loss.TripletMarginLoss,
+                          reduction="unsupport reduction")
+        input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        positive = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        negative = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        self.assertRaises(ValueError,
+                          paddle.nn.functional.triplet_margin_loss,
+                          input=input,
+                          positive=positive,
+                          negative=negative,
+                          reduction="unsupport reduction")
+        paddle.enable_static()
+
+    def test_TripletMarginLoss_dimension(self):
+        paddle.disable_static()
+
+        input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
+        positive = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        negative = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        self.assertRaises(
+            ValueError,
+            paddle.nn.functional.triplet_margin_loss,
+            input=input,
+            positive=positive,
+            negative=negative,
+        )
+        TMLoss = paddle.nn.loss.TripletMarginLoss()
+        self.assertRaises(
+            ValueError,
+            TMLoss,
+            input=input,
+            positive=positive,
+            negative=negative,
+        )
+        paddle.enable_static()
+
+    def test_TripletMarginLoss_swap(self):
+        reduction = 'mean'
+        place = paddle.CPUPlace()
+        shape = (2, 2)
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        positive = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        negative = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        expected = calc_triplet_margin_loss(input=input,
+                                            swap=True,
+                                            positive=positive,
+                                            negative=negative,
+                                            reduction=reduction)
+
+        dy_result = test_dygraph(
+            place=place,
+            swap=True,
+            input=input,
+            positive=positive,
+            negative=negative,
+            reduction=reduction,
+        )
+
+        static_result = test_static(
+            place=place,
+            swap=True,
+            input_np=input,
+            positive_np=positive,
+            negative_np=negative,
+            reduction=reduction,
+        )
+        self.assertTrue(np.allclose(static_result, expected))
+        self.assertTrue(np.allclose(static_result, dy_result))
+        self.assertTrue(np.allclose(dy_result, expected))
+        static_functional = test_static(place=place,
+                                        swap=True,
+                                        input_np=input,
+                                        positive_np=positive,
+                                        negative_np=negative,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     swap=True,
+                                     input=input,
+                                     positive=positive,
+                                     negative=negative,
+                                     reduction=reduction,
+                                     functional=True)
+        self.assertTrue(np.allclose(static_functional, expected))
+        self.assertTrue(np.allclose(static_functional, dy_functional))
+        self.assertTrue(np.allclose(dy_functional, expected))
+
+    def test_TripletMarginLoss_margin(self):
+        paddle.disable_static()
+
+        input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        positive = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        negative = paddle.to_tensor([[0.2, 0.1]], dtype='float32')
+        margin = -0.5
+        self.assertRaises(
+            ValueError,
+            paddle.nn.functional.triplet_margin_loss,
+            margin=margin,
+            input=input,
+            positive=positive,
+            negative=negative,
+        )
+        paddle.enable_static()
+
+    def test_TripletMarginLoss_p(self):
+        p = 3
+        shape = (2, 2)
+        reduction = 'mean'
+        place = paddle.CPUPlace()
+        input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
+        positive = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        negative = np.random.uniform(0, 2, size=shape).astype(np.float64)
+        expected = calc_triplet_margin_loss(input=input,
+                                            p=p,
+                                            positive=positive,
+                                            negative=negative,
+                                            reduction=reduction)
+
+        dy_result = test_dygraph(
+            place=place,
+            p=p,
+            input=input,
+            positive=positive,
+            negative=negative,
+            reduction=reduction,
+        )
+
+        static_result = test_static(
+            place=place,
+            p=p,
+            input_np=input,
+            positive_np=positive,
+            negative_np=negative,
+            reduction=reduction,
+        )
+        self.assertTrue(np.allclose(static_result, expected))
+        self.assertTrue(np.allclose(static_result, dy_result))
+        self.assertTrue(np.allclose(dy_result, expected))
+        static_functional = test_static(place=place,
+                                        p=p,
+                                        input_np=input,
+                                        positive_np=positive,
+                                        negative_np=negative,
+                                        reduction=reduction,
+                                        functional=True)
+        dy_functional = test_dygraph(place=place,
+                                     p=p,
+                                     input=input,
+                                     positive=positive,
+                                     negative=negative,
+                                     reduction=reduction,
+                                     functional=True)
+        self.assertTrue(np.allclose(static_functional, expected))
+        self.assertTrue(np.allclose(static_functional, dy_functional))
+        self.assertTrue(np.allclose(dy_functional, expected))
+
+
+if __name__ == "__main__":
+    unittest.main()