wehub-resource-sync
318 lines
10 KiB
Python
318 lines
10 KiB
Python
# 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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import copy
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core, dygraph
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def ref_selu(
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x,
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scale=1.0507009873554804934193349852946,
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alpha=1.6732632423543772848170429916717,
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):
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out = np.copy(x)
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out_flat = out.flatten()
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for i in range(out_flat.size):
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if out_flat[i] < 0:
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out_flat[i] = alpha * np.exp(out_flat[i]) - alpha
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out_flat[i] = scale * out_flat[i]
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out = out_flat.reshape(x.shape)
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return out
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class SeluTest(OpTest):
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def setUp(self):
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self.op_type = "selu"
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self.python_api = paddle.nn.functional.selu
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self.init_x_shape()
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self.init_dtype()
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alpha = 1.6732632423543772848170429916717
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scale = 1.0507009873554804934193349852946
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if self.dtype == np.uint16:
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x = np.random.normal(size=self.x_shape).astype(np.float32)
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else:
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x = np.random.normal(size=self.x_shape).astype(self.dtype)
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# Since zero point in selu is not differentiable, avoid randomize
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# zero.
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x[np.abs(x) < 0.005] = 0.02
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out = ref_selu(x, scale, alpha)
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if self.dtype == np.uint16:
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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else:
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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self.attrs = {
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'alpha': alpha,
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'scale': scale,
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}
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def init_x_shape(self):
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self.x_shape = [3, 5, 5, 10]
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def init_dtype(self):
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self.dtype = np.float64
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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class SeluTestFP16OP(SeluTest):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class SeluTestBF16OP(SeluTest):
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def init_dtype(self):
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self.dtype = np.uint16
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def test_check_output(self):
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self.check_output_with_place(get_device_place(), check_pir=True)
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def test_check_grad(self):
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self.check_grad_with_place(
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get_device_place(), ['X'], 'Out', check_pir=True
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)
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class SeluTestZeroSize1(SeluTest):
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def init_x_shape(self):
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self.x_shape = [9, 0]
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class SeluTestZeroSize2(SeluTest):
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def init_x_shape(self):
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self.x_shape = [0, 0]
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class SeluTestZeroSize3(SeluTest):
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def init_x_shape(self):
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self.x_shape = [5, 0, 8]
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class TestSeluAPI(unittest.TestCase):
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# test paddle.nn.SELU, paddle.nn.functional.selu
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def setUp(self):
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self.scale = 1.5
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self.alpha = 2.0
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self.x_np = np.random.normal(size=[3, 5, 5, 10]).astype(np.float64)
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# Since zero point in selu is not differentiable, avoid randomize
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# zero.
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self.x_np[np.abs(self.x_np) < 0.005] = 0.02
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self.place = get_device_place()
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def test_static_api(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.x_np.shape, self.x_np.dtype)
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out1 = F.selu(x, self.scale, self.alpha)
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selu = paddle.nn.SELU(self.scale, self.alpha)
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out2 = selu(x)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
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out_ref = ref_selu(self.x_np, self.scale, self.alpha)
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for r in res:
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np.testing.assert_allclose(out_ref, r, rtol=1e-05)
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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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out1 = F.selu(x, self.scale, self.alpha)
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selu = paddle.nn.SELU(self.scale, self.alpha)
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out2 = selu(x)
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out_ref = ref_selu(self.x_np, self.scale, self.alpha)
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for r in [out1, out2]:
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np.testing.assert_allclose(out_ref, r.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_base_api(self):
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with base.program_guard(base.Program()):
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x = paddle.static.data('X', self.x_np.shape, self.x_np.dtype)
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out = F.selu(x, self.scale, self.alpha)
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exe = base.Executor(self.place)
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res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
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out_ref = ref_selu(self.x_np, self.scale, self.alpha)
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np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
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def test_errors(self):
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with paddle.static.program_guard(paddle.static.Program()):
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# The input type must be Variable.
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self.assertRaises(TypeError, F.selu, 1)
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# The input dtype must be float16, float32, float64.
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x_int32 = paddle.static.data(
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name='x_int32', shape=[12, 10], dtype='int32'
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)
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self.assertRaises(TypeError, F.selu, x_int32)
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# The scale must be greater than 1.0
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x_fp32 = paddle.static.data(
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name='x_fp32', shape=[12, 10], dtype='float32'
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)
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self.assertRaises(ValueError, F.selu, x_fp32, -1.0)
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# The alpha must be no less than 0
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self.assertRaises(ValueError, F.selu, x_fp32, 1.6, -1.0)
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# support the input dtype is float16
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if paddle.is_compiled_with_cuda() or is_custom_device():
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x_fp16 = paddle.static.data(
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name='x_fp16', shape=[12, 10], dtype='float16'
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)
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F.selu(x_fp16)
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class TestSELUOpClass_Inplace(unittest.TestCase):
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def _test_case1_cpu(self):
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x_np = np.random.normal(size=[3, 5, 5, 10]).astype(np.float32)
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alpha = 2.0
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scale = 1.5
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y_ref = ref_selu(x_np, alpha, scale)
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place = base.CPUPlace()
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with dygraph.guard(place) as g:
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x_var1 = paddle.to_tensor(x_np)
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x_var2 = paddle.to_tensor(x_np)
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y_var1 = F.selu(x_var1, alpha, scale, True)
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y_test1 = y_var1.numpy()
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func = paddle.nn.SELU(alpha, scale, True)
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y_var2 = func(x_var2)
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y_test2 = y_var2.numpy()
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np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(
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y_ref, x_var1.numpy(), rtol=1e-05, atol=1e-08
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)
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np.testing.assert_allclose(
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y_ref, x_var2.numpy(), rtol=1e-05, atol=1e-08
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)
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def _test_case1_gpu(self):
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x = np.random.normal(size=[3, 5, 5, 10]).astype(np.float32)
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x[np.abs(x) < 0.005] = 0.02
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alpha = 1.6732632423543772848170429916717
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scale = 1.0507009873554804934193349852946
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y_ref = ref_selu(x, alpha, scale)
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place = get_device_place()
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with dygraph.guard(place) as g:
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x_var1 = paddle.to_tensor(x)
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x_var2 = paddle.to_tensor(x)
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y_var1 = F.selu(x_var1, alpha, scale, True)
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y_test1 = y_var1.numpy()
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func = paddle.nn.SELU(alpha, scale, True)
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y_var2 = func(x_var2)
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y_test2 = y_var2.numpy()
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np.testing.assert_allclose(y_ref, y_test1, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(y_ref, y_test2, rtol=1e-05, atol=1e-08)
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np.testing.assert_allclose(
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y_ref, x_var1.numpy(), rtol=1e-05, atol=1e-08
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)
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np.testing.assert_allclose(
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y_ref, x_var2.numpy(), rtol=1e-05, atol=1e-08
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)
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def test_cases(self):
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self._test_case1_cpu()
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if base.is_compiled_with_cuda() or is_custom_device():
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self._test_case1_gpu()
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class TestSELUAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(0)
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self.shape = [3, 5, 5, 10]
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self.x_np = np.random.normal(size=self.shape).astype(np.float32)
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self.x_np[np.abs(self.x_np) < 0.005] = 0.02
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self.alpha = 1.6732632423543772848170429916717
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self.scale = 1.0507009873554804934193349852946
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self.place = [get_device_place()]
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self.x_feed = copy.deepcopy(self.x_np)
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def test_api_static(self):
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paddle.enable_static()
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def run(place, inplace):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.shape)
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out = F.selu(x, self.alpha, self.scale, inplace)
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exe = paddle.static.Executor(place)
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res = exe.run(
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feed={
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'X': self.x_feed,
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},
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fetch_list=[out],
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)
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target = copy.deepcopy(self.x_np)
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out_ref = ref_selu(target, self.alpha, self.scale)
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for out in res:
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np.testing.assert_allclose(out, out_ref, rtol=0.001)
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for place in self.place:
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run(place, True)
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run(place, False)
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def test_api_dygraph(self):
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def run(place, inplace):
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paddle.disable_static(place)
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x_tensor = paddle.to_tensor(self.x_np)
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out = F.selu(x_tensor, self.alpha, self.scale, inplace)
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target = copy.deepcopy(self.x_np)
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out_ref = ref_selu(target, self.alpha, self.scale)
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np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.001)
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paddle.enable_static()
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for place in self.place:
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run(place, True)
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run(place, False)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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