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304 lines
10 KiB
304 lines
10 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 as fluid
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import six
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import paddle.fluid.core as core
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from paddle.fluid import Program, program_guard
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from op_test import OpTest, skip_check_grad_ci
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import paddle
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import paddle.nn.functional as F
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def ref_prelu(x, weight):
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x_t = x.copy()
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weight = weight.reshape(1, -1, 1, 1)
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neg_indices = x <= 0
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assert x.shape == neg_indices.shape
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x_t[neg_indices] = (x_t * weight)[neg_indices]
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return (x_t, )
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def ref_prelu_nn(x, num_parameters, init):
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weight_np = np.full((num_parameters), init)
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return ref_prelu(x, weight_np)
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class TestFunctionalPReluAPI(unittest.TestCase):
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def setUp(self):
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self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else paddle.CPUPlace()
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self.x_np = np.random.uniform(-1., 1., [1, 2, 3, 4]).astype('float32')
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self.weight_np_0 = np.random.randn(1).astype('float32')
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self.weight_np_1 = np.random.randn(self.x_np.shape[1]).astype('float32')
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def static_check(self, weight_np):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.data('X', self.x_np.shape, 'float32')
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weight = paddle.data('Alpha', weight_np.shape, 'float32')
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out = F.prelu(x, weight)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x_np,
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'Alpha': weight_np},
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fetch_list=[out])
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out_ref = ref_prelu(self.x_np, weight_np)
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self.assertEqual(np.allclose(out_ref, res[0]), True)
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def dygraph_check(self, weight_np):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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weight = paddle.to_tensor(weight_np)
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out = F.prelu(x, weight)
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out_ref = ref_prelu(self.x_np, weight_np)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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paddle.enable_static()
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def test_static_api(self):
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self.static_check(self.weight_np_0)
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self.static_check(self.weight_np_1)
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def test_dygraph_api(self):
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self.dygraph_check(self.weight_np_0)
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self.dygraph_check(self.weight_np_1)
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def test_error(self):
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with paddle.static.program_guard(paddle.static.Program()):
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weight_fp32 = paddle.data(
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name='weight_fp32', shape=[1], dtype='float32')
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# The input type must be Variable.
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self.assertRaises(TypeError, F.prelu, x=1, weight=weight_fp32)
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# The input dtype must be float16, float32, float64.
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x_int32 = paddle.data(name='x_int32', shape=[2, 3], dtype='int32')
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self.assertRaises(TypeError, F.prelu, x=x_int32, weight=weight_fp32)
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# support the input dtype is float16
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x_fp16 = paddle.data(name='x_fp16', shape=[2, 3], dtype='float16')
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F.prelu(x=x_fp16, weight=weight_fp32)
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class TestNNPReluAPI(unittest.TestCase):
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def setUp(self):
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self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else paddle.CPUPlace()
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self.x_np = np.ones([1, 2, 3, 4]).astype('float32')
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def test_static_api(self):
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startup_program = paddle.static.Program()
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train_program = paddle.static.Program()
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with paddle.static.program_guard(train_program, startup_program):
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x = paddle.data(name='X', shape=self.x_np.shape, dtype='float32')
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m = paddle.nn.PReLU()
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out = m(x)
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exe = paddle.static.Executor(self.place)
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exe.run(startup_program)
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res = exe.run(train_program,
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feed={'X': self.x_np},
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fetch_list=[out])
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out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
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self.assertEqual(np.allclose(out_ref, res[0]), True)
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def test_dygraph_api(self):
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paddle.disable_static(self.place)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU()
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(num_parameters=self.x_np.shape[1])
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, self.x_np.shape[1], 0.25)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(init=0.5)
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.5)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(weight_attr=fluid.ParamAttr(name="weight"))
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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x = paddle.to_tensor(self.x_np)
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m = paddle.nn.PReLU(weight_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(0.5)))
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out = m(x)
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out_ref = ref_prelu_nn(self.x_np, 1, 0.5)
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self.assertEqual(np.allclose(out_ref, out.numpy()), True)
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paddle.enable_static()
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class PReluTest(OpTest):
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def setUp(self):
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self.init_input_shape()
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self.init_attr()
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self.op_type = "prelu"
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x_np = np.random.uniform(-1, 1, self.x_shape)
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# Since zero point in prelu is not differentiable, avoid randomize
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# zero.
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x_np[np.abs(x_np) < 0.005] = 0.02
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if self.attrs == {'mode': "all"}:
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alpha_np = np.random.uniform(-1, -0.5, (1))
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elif self.attrs == {'mode': "channel"}:
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alpha_np = np.random.uniform(-1, -0.5, [1, self.x_shape[1], 1, 1])
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else:
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alpha_np = np.random.uniform(-1, -0.5, [1] + self.x_shape[1:])
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self.inputs = {'X': x_np, 'Alpha': alpha_np}
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# NOTE(zhiqu): reshape inputs['Alpha'] from [1, 100, 1, 1] to [1, 100] + [1]*len(x.shape[2:])
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# since np operands could not be broadcast together with shapes (1,100,2,2,2,3) (1,100,1,1)
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reshaped_alpha = self.inputs['Alpha']
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if self.attrs == {'mode': "channel"}:
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reshaped_alpha = np.reshape(
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self.inputs['Alpha'],
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[1, self.x_shape[1]] + [1] * len(self.x_shape[2:]))
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out_np = np.maximum(self.inputs['X'], 0.)
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out_np = out_np + np.minimum(self.inputs['X'], 0.) * reshaped_alpha
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assert out_np is not self.inputs['X']
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self.outputs = {'Out': out_np}
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def init_input_shape(self):
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self.x_shape = [2, 100, 3, 4]
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def init_attr(self):
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self.attrs = {'mode': "channel"}
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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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self.check_grad(['X', 'Alpha'], 'Out')
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAll(PReluTest):
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def init_input_shape(self):
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self.x_shape = [2, 3, 4, 5]
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def init_attr(self):
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self.attrs = {'mode': "all"}
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class TestModeElt(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 2, 5, 10]
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def init_attr(self):
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self.attrs = {'mode': "element"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllRank3(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 200, 3]
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def init_attr(self):
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self.attrs = {'mode': "all"}
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@skip_check_grad_ci(
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reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
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)
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class TestModeAllRank6(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 2, 3, 4, 5, 6]
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def init_attr(self):
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self.attrs = {'mode': "all"}
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class TestModeChannelRank3(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 200, 3]
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def init_attr(self):
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self.attrs = {'mode': "channel"}
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class TestModeChannelRank6(PReluTest):
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def init_input_shape(self):
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self.x_shape = [1, 100, 2, 2, 2, 2]
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def init_attr(self):
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self.attrs = {'mode': "channel"}
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class TestModeElementRank3(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 10, 10]
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def init_attr(self):
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self.attrs = {'mode': "element"}
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class TestModeElementRank6(PReluTest):
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def init_input_shape(self):
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self.x_shape = [3, 2, 2, 4, 5, 2]
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def init_attr(self):
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self.attrs = {'mode': "element"}
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def prelu_t(x, mode, param_attr=None, name=None):
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helper = fluid.layer_helper.LayerHelper('prelu', **locals())
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alpha_shape = [1, x.shape[1], 1, 1]
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dtype = helper.input_dtype(input_param_name='x')
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alpha = helper.create_parameter(
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attr=helper.param_attr,
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shape=alpha_shape,
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dtype='float32',
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is_bias=False,
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default_initializer=fluid.initializer.ConstantInitializer(0.25))
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out = helper.create_variable_for_type_inference(dtype)
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helper.append_op(
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type="prelu",
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inputs={"X": x,
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'Alpha': alpha},
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attrs={"mode": mode},
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outputs={"Out": out})
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return out
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# error message test if mode is not one of 'all', 'channel', 'element'
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class TestModeError(unittest.TestCase):
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def test_mode_error(self):
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main_program = Program()
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with fluid.program_guard(main_program, Program()):
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x = fluid.data(name='x', shape=[2, 3, 4, 5])
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try:
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y = prelu_t(x, 'any')
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except Exception as e:
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assert (e.args[0].find('InvalidArgumentError') != -1)
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if __name__ == "__main__":
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unittest.main()
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