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804 lines
23 KiB
804 lines
23 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from op_test import OpTest
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from scipy.special import expit, erf
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class TestActivation(OpTest):
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def setUp(self):
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self.op_type = "exp"
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self.dtype = np.float32
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self.init_dtype()
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self.init_kernel_type()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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out = np.exp(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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def init_dtype(self):
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self.dtype = np.float32
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def init_kernel_type(self):
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pass
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class TestSigmoid(TestActivation):
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def setUp(self):
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self.op_type = "sigmoid"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
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out = 1 / (1 + np.exp(-x))
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.01)
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class TestLogSigmoid(TestActivation):
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def setUp(self):
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self.op_type = "logsigmoid"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
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out = np.log(1 / (1 + np.exp(-x)))
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.008)
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class TestTanh(TestActivation):
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def setUp(self):
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self.op_type = "tanh"
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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out = np.tanh(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestAtan(TestActivation):
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def setUp(self):
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self.op_type = "atan"
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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out = np.arctan(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestTanhShrink(TestActivation):
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def setUp(self):
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self.op_type = "tanh_shrink"
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [10, 17]).astype(self.dtype)
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out = x - np.tanh(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.008)
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class TestHardShrink(TestActivation):
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def setUp(self):
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self.op_type = "hard_shrink"
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self.init_dtype()
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threshold = 0.5
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.copy(x)
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out[(out >= -threshold) & (out <= threshold)] = 0
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self.attrs = {'lambda': threshold}
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.005)
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class TestSoftShrink(TestActivation):
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def setUp(self):
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self.op_type = "softshrink"
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self.init_dtype()
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lambda_val = 0.1
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x = np.random.uniform(0.25, 10, [4, 4]).astype(self.dtype)
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out = np.copy(x)
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out = (out < -lambda_val) * (out + lambda_val) + (out > lambda_val) * (
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out - lambda_val)
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self.attrs = {'lambda': lambda_val}
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestSqrt(TestActivation):
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def setUp(self):
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self.op_type = "sqrt"
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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out = np.sqrt(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestRsqrt(TestActivation):
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def setUp(self):
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self.op_type = "rsqrt"
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [2, 3]).astype(self.dtype)
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out = 1.0 / np.sqrt(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.0005)
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class TestAbs(TestActivation):
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def setUp(self):
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self.op_type = "abs"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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# Because we set delta = 0.005 in calculating numeric gradient,
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# if x is too small, such as 0.002, x_neg will be -0.003
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# x_pos will be 0.007, so the numeric gradient is inaccurate.
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# we should avoid this
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x[np.abs(x) < 0.005] = 0.02
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out = np.abs(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestCeil(TestActivation):
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def setUp(self):
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self.op_type = "ceil"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.ceil(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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# The same reason with TestFloor
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def test_check_grad(self):
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pass
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class TestFloor(TestActivation):
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def setUp(self):
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self.op_type = "floor"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.floor(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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# the gradient on floor, ceil, round is undefined.
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# we return zero as gradient, but the numpy return nan
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# The same reason with TestFloor
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def test_check_grad(self):
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pass
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class TestCos(TestActivation):
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def setUp(self):
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self.op_type = "cos"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.cos(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestAcos(TestActivation):
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def setUp(self):
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self.op_type = "acos"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.arccos(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestSin(TestActivation):
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def setUp(self):
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self.op_type = "sin"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.sin(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestAsin(TestActivation):
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def setUp(self):
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self.op_type = "asin"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.arcsin(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestRound(TestActivation):
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def setUp(self):
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self.op_type = "round"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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out = np.round(x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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pass
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class TestRelu(TestActivation):
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def setUp(self):
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self.op_type = "relu"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
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# The same reason with TestAbs
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x[np.abs(x) < 0.005] = 0.02
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out = np.maximum(x, 0)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestLeakyRelu(TestActivation):
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def setUp(self):
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self.op_type = "leaky_relu"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
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# The same reason with TestAbs
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x[np.abs(x) < 0.005] = 0.02
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out = np.maximum(x, 0.02 * x)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestGelu(TestActivation):
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def setUp(self):
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self.op_type = "gelu"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
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out = 0.5 * x * (1.0 + erf(x / np.sqrt(2.0)))
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.007)
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class TestBRelu(TestActivation):
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def setUp(self):
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self.op_type = "brelu"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 4]).astype(self.dtype)
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t_min = 1.0
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t_max = 4.0
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# The same with TestAbs
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x[np.abs(x - t_min) < 0.005] = t_min + 0.02
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x[np.abs(x - t_max) < 0.005] = t_max + 0.02
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t = np.copy(x)
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t[t < t_min] = t_min
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t[t > t_max] = t_max
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {'t_min': t_min, 't_max': t_max}
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self.outputs = {'Out': t}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.02)
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class TestRelu6(TestActivation):
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def setUp(self):
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self.op_type = "relu6"
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self.init_dtype()
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x = np.random.uniform(-1, 1, [4, 10]).astype(self.dtype)
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threshold = 6.0
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# The same with TestAbs
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x[np.abs(x) < 0.005] = 0.02
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x[np.abs(x - threshold) < 0.005] = threshold + 0.02
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out = np.minimum(np.maximum(x, 0), threshold)
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {'threshold': threshold}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.02)
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class TestHardSwish(TestActivation):
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def setUp(self):
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self.op_type = 'hard_swish'
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self.init_dtype()
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x = np.random.uniform(-6, 6, [4, 4]).astype(self.dtype)
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threshold = 6.0
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scale = 6.0
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offset = 3.0
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#the same with TestAbs
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x[np.abs(x + offset) < 0.005] = 0.02
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x[np.abs(x - threshold + offset) < 0.005] = threshold - offset + 0.02
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out = x * np.minimum(np.maximum(x + offset, 0), threshold) / scale
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {'threshold': threshold, 'scale': scale, 'offset': offset}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.02)
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class TestSoftRelu(TestActivation):
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def setUp(self):
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self.op_type = "soft_relu"
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self.init_dtype()
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x = np.random.uniform(-3, 3, [4, 4]).astype(self.dtype)
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threshold = 2.0
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# The same reason with TestAbs
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x[np.abs(x - threshold) < 0.005] = threshold + 0.02
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x[np.abs(x + threshold) < 0.005] = -threshold + 0.02
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t = np.copy(x)
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t[t < -threshold] = -threshold
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t[t > threshold] = threshold
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out = np.log((np.exp(t) + 1))
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
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self.attrs = {'threshold': threshold}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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self.check_grad(['X'], 'Out', max_relative_error=0.02)
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class TestELU(TestActivation):
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def setUp(self):
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self.op_type = "elu"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(-3, 3, [4, 4]).astype(self.dtype)
|
|
alpha = 1.
|
|
out = np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1))
|
|
# Note: unlike other Relu extensions, point 0 on standard ELU function (i.e. alpha = 1)
|
|
# is differentiable, so we can skip modifications like x[np.abs(x) < 0.005] = 0.02 here
|
|
self.inputs = {'X': x}
|
|
self.attrs = {'alpha': alpha}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.02)
|
|
|
|
|
|
class TestReciprocal(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "reciprocal"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
|
|
out = np.reciprocal(x)
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.01)
|
|
|
|
|
|
class TestLog(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "log"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
|
|
out = np.log(x)
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.007)
|
|
|
|
|
|
class TestSquare(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "square"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
|
|
out = np.square(x)
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.007)
|
|
|
|
|
|
class TestPow(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "pow"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
|
|
out = np.power(x, 3)
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.attrs = {'factor': 3.0}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.02)
|
|
|
|
|
|
class TestSTanh(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "stanh"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
|
|
scale_a = 2.0 / 3.0
|
|
scale_b = 1.7159
|
|
out = scale_b * np.tanh(x * scale_a)
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.attrs = {'scale_a': scale_a, 'scale_b': scale_b}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.007)
|
|
|
|
|
|
class TestSoftplus(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "softplus"
|
|
self.init_dtype()
|
|
self.dtype = np.float64
|
|
|
|
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
|
|
out = np.log(1 + np.exp(x))
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.007)
|
|
|
|
|
|
class TestSoftsign(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "softsign"
|
|
self.init_dtype()
|
|
|
|
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
|
|
out = np.divide(x, 1 + np.abs(x))
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.007)
|
|
|
|
|
|
class TestThresholdedRelu(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "thresholded_relu"
|
|
self.init_dtype()
|
|
|
|
threshold = 0.25
|
|
self.relative_error = 0.005
|
|
X = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
|
|
|
|
# Same reason as TestAbs
|
|
X[np.abs(X - threshold) < self.relative_error] = threshold + 0.2
|
|
out = (X > threshold) * X
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(X)}
|
|
self.attrs = {'threshold': threshold}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=self.relative_error)
|
|
|
|
|
|
class TestHardSigmoid(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "hard_sigmoid"
|
|
self.init_dtype()
|
|
|
|
self.relative_error = 0.002
|
|
|
|
X = np.random.uniform(-5, 5, [2, 2]).astype("float32")
|
|
slope = 0.2
|
|
offset = 0.5
|
|
lower_threshold = -offset / slope
|
|
upper_threshold = (1 - offset) / slope
|
|
|
|
# Same reason as TestAbs
|
|
X[np.abs(X - lower_threshold) < self.relative_error] = \
|
|
lower_threshold + 0.2
|
|
X[np.abs(X - upper_threshold) < self.relative_error] = \
|
|
upper_threshold - 0.2
|
|
|
|
temp = X * slope + offset
|
|
out = np.maximum(0.0, np.minimum(1.0, temp))
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(X)}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.002)
|
|
|
|
|
|
class TestSwish(TestActivation):
|
|
def setUp(self):
|
|
self.op_type = "swish"
|
|
self.init_dtype()
|
|
|
|
X = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
|
|
beta = 2.3
|
|
out = X * expit(beta * X)
|
|
|
|
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(X)}
|
|
self.attrs = {'beta': beta}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
self.check_grad(['X'], 'Out', max_relative_error=0.008)
|
|
|
|
|
|
#------------------ Test Cudnn Activation----------------------
|
|
def create_test_act_cudnn_class(parent, atol=1e-3, grad_atol=1e-3):
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestActCudnn(parent):
|
|
def init_kernel_type(self):
|
|
self.attrs = {"use_cudnn": True}
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "cudnn")
|
|
TestActCudnn.__name__ = cls_name
|
|
globals()[cls_name] = TestActCudnn
|
|
|
|
|
|
create_test_act_cudnn_class(TestRelu)
|
|
create_test_act_cudnn_class(TestRelu6)
|
|
create_test_act_cudnn_class(TestSigmoid)
|
|
create_test_act_cudnn_class(TestTanh)
|
|
|
|
|
|
#------------------ Test Fp16 ----------------------
|
|
def create_test_act_fp16_class(parent,
|
|
atol=1e-3,
|
|
grad_check=True,
|
|
grad_atol=0.80):
|
|
@unittest.skipIf(not core.is_compiled_with_cuda(),
|
|
"core is not compiled with CUDA")
|
|
class TestActFp16(parent):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
place = core.CUDAPlace(0)
|
|
support_fp16 = core.is_float16_supported(place)
|
|
if support_fp16:
|
|
self.check_output_with_place(place, atol=atol)
|
|
|
|
def test_check_grad(self):
|
|
place = core.CUDAPlace(0)
|
|
support_fp16 = core.is_float16_supported(place)
|
|
if support_fp16 and grad_check:
|
|
self.check_grad_with_place(
|
|
place, ['X'], 'Out', max_relative_error=grad_atol)
|
|
|
|
cls_name = "{0}_{1}".format(parent.__name__, "fp16")
|
|
TestActFp16.__name__ = cls_name
|
|
globals()[cls_name] = TestActFp16
|
|
|
|
|
|
create_test_act_fp16_class(TestActivation)
|
|
create_test_act_fp16_class(TestSigmoid)
|
|
create_test_act_fp16_class(TestLogSigmoid)
|
|
create_test_act_fp16_class(TestTanh)
|
|
create_test_act_fp16_class(TestTanhShrink)
|
|
create_test_act_fp16_class(TestHardShrink)
|
|
create_test_act_fp16_class(TestSoftShrink)
|
|
create_test_act_fp16_class(TestSqrt)
|
|
create_test_act_fp16_class(TestAbs)
|
|
create_test_act_fp16_class(TestCeil, grad_check=False)
|
|
create_test_act_fp16_class(TestFloor, grad_check=False)
|
|
create_test_act_fp16_class(TestCos, grad_atol=0.85)
|
|
create_test_act_fp16_class(TestAcos, grad_atol=0.85)
|
|
create_test_act_fp16_class(TestSin)
|
|
create_test_act_fp16_class(TestAsin)
|
|
create_test_act_fp16_class(TestAtan)
|
|
create_test_act_fp16_class(TestRound, grad_check=False)
|
|
create_test_act_fp16_class(TestRelu)
|
|
create_test_act_fp16_class(TestGelu)
|
|
create_test_act_fp16_class(TestBRelu)
|
|
create_test_act_fp16_class(TestRelu6)
|
|
create_test_act_fp16_class(TestSoftRelu)
|
|
create_test_act_fp16_class(TestELU)
|
|
create_test_act_fp16_class(TestReciprocal)
|
|
create_test_act_fp16_class(TestLog)
|
|
create_test_act_fp16_class(TestSquare)
|
|
create_test_act_fp16_class(TestPow, atol=5e-2)
|
|
create_test_act_fp16_class(TestSTanh, grad_atol=0.9)
|
|
create_test_act_fp16_class(TestSoftplus)
|
|
create_test_act_fp16_class(TestSoftsign)
|
|
create_test_act_fp16_class(TestThresholdedRelu)
|
|
create_test_act_fp16_class(TestHardSigmoid)
|
|
create_test_act_fp16_class(TestSwish)
|
|
create_test_act_fp16_class(TestHardSwish)
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|