add binary cross entropy with logit loss

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

@@ -0,0 +1,260 @@
+# Copyright (c) 2020 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 paddle.fluid as fluid
+import numpy as np
+import unittest
+from op_test import OpTest
+
+
+def call_bce_layer(logit, label, weight=None, reduction='mean',
+                   pos_weight=None):
+    bce_logit_loss = paddle.nn.loss.BCEWithLogitsLoss(
+        weight=weight, reduction=reduction, pos_weight=pos_weight)
+    res = bce_logit_loss(logit, label)
+    return res
+
+
+def call_bce_functional(logit,
+                        label,
+                        weight=None,
+                        reduction='mean',
+                        pos_weight=None):
+    res = paddle.nn.functional.binary_cross_entropy_with_logits(
+        logit, label, weight=weight, reduction=reduction, pos_weight=pos_weight)
+    return res
+
+
+def test_static(place,
+                logit_np,
+                label_np,
+                weight_np=None,
+                reduction='mean',
+                pos_weight_np=None,
+                functional=False):
+    paddle.enable_static()
+    prog = paddle.static.Program()
+    startup_prog = paddle.static.Program()
+    with paddle.static.program_guard(prog, startup_prog):
+        logit = paddle.data(name='logit', shape=logit_np.shape, dtype='float64')
+        label = paddle.data(name='label', shape=label_np.shape, dtype='float64')
+        feed_dict = {"logit": logit_np, "label": label_np}
+
+        pos_weight = None
+        weight = None
+        if pos_weight_np is not None:
+            pos_weight = paddle.data(
+                name='pos_weight', shape=pos_weight_np.shape, dtype='float64')
+            feed_dict["pos_weight"] = pos_weight_np
+        if weight_np is not None:
+            weight = paddle.data(
+                name='weight', shape=weight_np.shape, dtype='float64')
+            feed_dict["weight"] = weight_np
+        if functional:
+            res = call_bce_functional(logit, label, weight, reduction,
+                                      pos_weight)
+        else:
+            res = call_bce_layer(logit, label, weight, reduction, pos_weight)
+        exe = paddle.static.Executor(place)
+        static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
+    return static_result
+
+
+def test_dygraph(place,
+                 logit_np,
+                 label_np,
+                 weight_np=None,
+                 reduction='mean',
+                 pos_weight_np=None,
+                 functional=False):
+    paddle.disable_static()
+    logit = paddle.to_tensor(logit_np)
+    label = paddle.to_tensor(label_np)
+    weight = None
+    pos_weight = None
+    if weight_np is not None:
+        weight = paddle.to_tensor(weight_np)
+    if pos_weight_np is not None:
+        pos_weight = paddle.to_tensor(pos_weight_np)
+    if functional:
+        dy_res = call_bce_functional(logit, label, weight, reduction,
+                                     pos_weight)
+    else:
+        dy_res = call_bce_layer(logit, label, weight, reduction, pos_weight)
+    dy_result = dy_res.numpy()
+    paddle.enable_static()
+    return dy_result
+
+
+def calc_bce_with_logits_loss(logit_np,
+                              label_np,
+                              reduction='mean',
+                              weight_np=None,
+                              pos_weight=None):
+    expected = np.maximum(
+        logit_np,
+        0) - logit_np * label_np + np.log(1 + np.exp(-np.abs(logit_np)))
+    if pos_weight is not None:
+        expected = expected * ((pos_weight - 1) * label_np + 1)
+    if weight_np is not None:
+        expected = weight_np * expected
+
+    if reduction == 'mean':
+        expected = np.mean(expected)
+    elif reduction == 'sum':
+        expected = np.sum(expected)
+    else:
+        expected = expected
+
+    return expected
+
+
+class TestBCEWithLogitsLoss(unittest.TestCase):
+    def test_BCEWithLogitsLoss(self):
+        logit_np = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
+        label_np = np.random.randint(0, 2, size=(20, 30)).astype(np.float64)
+        places = [fluid.CPUPlace()]
+        if fluid.core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        reductions = ['sum', 'mean', 'none']
+        for place in places:
+            for reduction in reductions:
+                static_result = test_static(
+                    place, logit_np, label_np, reduction=reduction)
+                dy_result = test_dygraph(
+                    place, logit_np, label_np, reduction=reduction)
+                expected = calc_bce_with_logits_loss(logit_np, label_np,
+                                                     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,
+                    logit_np,
+                    label_np,
+                    reduction=reduction,
+                    functional=True)
+                dy_functional = test_dygraph(
+                    place,
+                    logit_np,
+                    label_np,
+                    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_BCEWithLogitsLoss_weight(self):
+        logit_np = np.random.uniform(
+            0.1, 0.8, size=(2, 3, 4, 10)).astype(np.float64)
+        label_np = np.random.randint(
+            0, 2, size=(2, 3, 4, 10)).astype(np.float64)
+        weight_np = np.random.random(size=(2, 3, 4, 10)).astype(np.float64)
+        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        for reduction in ['sum', 'mean', 'none']:
+            static_result = test_static(
+                place,
+                logit_np,
+                label_np,
+                weight_np=weight_np,
+                reduction=reduction)
+            dy_result = test_dygraph(
+                place,
+                logit_np,
+                label_np,
+                weight_np=weight_np,
+                reduction=reduction)
+            expected = calc_bce_with_logits_loss(
+                logit_np, label_np, reduction, weight_np=weight_np)
+            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,
+                logit_np,
+                label_np,
+                weight_np=weight_np,
+                reduction=reduction,
+                functional=True)
+            dy_functional = test_dygraph(
+                place,
+                logit_np,
+                label_np,
+                weight_np=weight_np,
+                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_BCEWithLogitsLoss_pos_weight(self):
+        logit_np = np.random.uniform(
+            0.1, 0.8, size=(2, 3, 4, 10)).astype(np.float64)
+        label_np = np.random.randint(
+            0, 2, size=(2, 3, 4, 10)).astype(np.float64)
+        pos_weight_np = np.random.random(size=(3, 4, 10)).astype(np.float64)
+        weight_np = np.random.random(size=(2, 3, 4, 10)).astype(np.float64)
+        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        reduction = "mean"
+        static_result = test_static(place, logit_np, label_np, weight_np,
+                                    reduction, pos_weight_np)
+        dy_result = test_dygraph(place, logit_np, label_np, weight_np,
+                                 reduction, pos_weight_np)
+        expected = calc_bce_with_logits_loss(logit_np, label_np, reduction,
+                                             weight_np, pos_weight_np)
+        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,
+            logit_np,
+            label_np,
+            weight_np,
+            reduction,
+            pos_weight_np,
+            functional=True)
+        dy_functional = test_dygraph(
+            place,
+            logit_np,
+            label_np,
+            weight_np,
+            reduction,
+            pos_weight_np,
+            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_BCEWithLogitsLoss_error(self):
+        paddle.disable_static()
+        self.assertRaises(
+            ValueError,
+            paddle.nn.BCEWithLogitsLoss,
+            reduction="unsupport reduction")
+        logit = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
+        label = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
+        self.assertRaises(
+            ValueError,
+            paddle.nn.functional.binary_cross_entropy_with_logits,
+            logit=logit,
+            label=label,
+            reduction="unsupport reduction")
+        paddle.enable_static()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/nn/__init__.py

@@ -104,6 +104,7 @@
 # from .layer.learning_rate import PiecewiseDecay        #DEFINE_ALIAS
 # from .layer.learning_rate import PolynomialDecay        #DEFINE_ALIAS
 # from .layer.loss import NCELoss        #DEFINE_ALIAS
+from .layer.loss import BCEWithLogitsLoss  #DEFINE_ALIAS
 from .layer.loss import CrossEntropyLoss  #DEFINE_ALIAS
 from .layer.loss import MSELoss  #DEFINE_ALIAS
 from .layer.loss import L1Loss  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -124,6 +124,7 @@
 # from .lod import dynamic_lstm        #DEFINE_ALIAS
 # from .lod import dynamic_lstmp        #DEFINE_ALIAS
 from .loss import binary_cross_entropy  #DEFINE_ALIAS
+from .loss import binary_cross_entropy_with_logits  #DEFINE_ALIAS
 from .loss import bpr_loss  #DEFINE_ALIAS
 from .loss import center_loss  #DEFINE_ALIAS
 from .loss import cross_entropy  #DEFINE_ALIAS