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

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+#   Copyright (c) 2021 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.
+
+from __future__ import print_function
+
+import copy
+import unittest
+import numpy as np
+from op_test import OpTest, skip_check_grad_ci
+
+
+def rrelu_np(x: np.array,
+             lower_bound: float=0.125,
+             upper_bound: float=0.3333,
+             is_test: bool=False):
+    """
+
+    """
+    x = x.astype(np.float32)
+    if is_test:
+        middle_value = (lower_bound + upper_bound) / 2.0
+        mask = copy.deepcopy(x)
+        mask[x >= 0.0] = 1.0
+        mask[x < 0.0] = middle_value
+    else:
+        x_shape = x.shape
+        x = x.reshape(-1)
+        mask = copy.deepcopy(x)
+        for i in range(x.shape[0]):
+            if x[i].item() >= 0.0:
+                mask[i] = 1.0
+            else:
+                mask[i] = np.random.uniform(lower_bound, upper_bound)
+        x = x.reshape(x_shape)
+        mask = mask.reshape(x_shape)
+
+    out = x * mask
+    return out, mask
+
+
+class TestRReLUOp(OpTest):
+    def setUp(self):
+        self.op_type = "rrelu"
+        X = np.random.uniform(low=-100, high=10, size=(32, )).astype("float32")
+        lower_bound = 0.0
+        upper_bound = 0.5
+        fix_seed = True
+        seed = 100
+        is_test = False
+        self.inputs = {'X': X}
+        self.attrs = {
+            'lower_bound': lower_bound,
+            'upper_bound': upper_bound,
+            'fix_seed': fix_seed,
+            'seed': seed,
+            'is_test': is_test
+        }
+        np.random.seed(seed)
+        Out, Mask = rrelu_np(
+            x=X,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+            is_test=is_test)
+        self.outputs = {'Out': Out, 'Mask': Mask}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestRReLUOp2(TestRReLUOp):
+    def setUp(self):
+        self.op_type = "rrelu"
+        X = np.random.uniform(low=-100, high=10, size=(8, 16)).astype("float32")
+        lower_bound = 0.4
+        upper_bound = 0.99
+        fix_seed = True
+        seed = 3
+        is_test = False
+        self.inputs = {'X': X}
+        self.attrs = {
+            'lower_bound': lower_bound,
+            'upper_bound': upper_bound,
+            'fix_seed': fix_seed,
+            'seed': seed,
+            'is_test': is_test
+        }
+        np.random.seed(seed)
+        Out, Mask = rrelu_np(
+            x=X,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+            is_test=is_test)
+        self.outputs = {'Out': Out, 'Mask': Mask}
+
+
+class TestRReLUOp3(TestRReLUOp):
+    def setUp(self):
+        self.op_type = "rrelu"
+        X = np.random.uniform(
+            low=-100, high=10, size=(8, 16, 32)).astype("float32")
+        lower_bound = 0.5
+        upper_bound = 0.51
+        fix_seed = True
+        seed = 5
+        is_test = False
+        self.inputs = {'X': X}
+        self.attrs = {
+            'lower_bound': lower_bound,
+            'upper_bound': upper_bound,
+            'fix_seed': fix_seed,
+            'seed': seed,
+            'is_test': is_test
+        }
+        np.random.seed(seed)
+        Out, Mask = rrelu_np(
+            x=X,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+            is_test=is_test)
+        self.outputs = {'Out': Out, 'Mask': Mask}
+
+
+@skip_check_grad_ci(reason="For inference, check_grad is not required.")
+class TestRReLUOp4(OpTest):
+    def setUp(self):
+        self.op_type = "rrelu"
+        X = np.random.uniform(low=-100, high=10, size=(32, )).astype("float32")
+        lower_bound = 0.0
+        upper_bound = 0.3
+        fix_seed = True
+        seed = 11
+        is_test = True
+        self.inputs = {'X': X}
+        self.attrs = {
+            'lower_bound': lower_bound,
+            'upper_bound': upper_bound,
+            'fix_seed': fix_seed,
+            'seed': seed,
+            'is_test': is_test
+        }
+        Out, Mask = rrelu_np(
+            x=X,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+            is_test=is_test)
+        self.outputs = {'Out': Out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+@skip_check_grad_ci(reason="For inference, check_grad is not required.")
+class TestRReLUOp5(OpTest):
+    def setUp(self):
+        self.op_type = "rrelu"
+        X = np.random.uniform(
+            low=-100, high=10, size=(32, 16, 8)).astype("float32")
+        lower_bound = 0.0
+        upper_bound = 0.3
+        is_test = True
+        self.inputs = {'X': X}
+        self.attrs = {
+            'lower_bound': lower_bound,
+            'upper_bound': upper_bound,
+            'is_test': is_test
+        }
+        Out, Mask = rrelu_np(
+            x=X,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+            is_test=is_test)
+        self.outputs = {'Out': Out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestRReLUOpWithSeed(OpTest):
+    def setUp(self):
+        self.op_type = "rrelu"
+        X = np.random.uniform(
+            low=-100, high=10, size=(32, 16)).astype("float32")
+        Seed = np.asarray([125], dtype="int32")
+        lower_bound = 0.0
+        upper_bound = 0.3
+        is_test = False
+        self.inputs = {'X': X, 'Seed': Seed}
+        self.attrs = {
+            'lower_bound': lower_bound,
+            'upper_bound': upper_bound,
+            'is_test': is_test
+        }
+        np.random.seed(125)
+        Out, Mask = rrelu_np(
+            x=X,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+            is_test=is_test)
+        self.outputs = {'Out': Out, 'Mask': Mask}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out', max_relative_error=0.05)
+
+
+if __name__ == "__main__":
+    unittest.main()