wehub-resource-sync
542 lines
20 KiB
Python
542 lines
20 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 unittest
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import numpy as np
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from op_test import get_device_place, get_places, is_custom_device
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import paddle
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from paddle import base
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# rename this function, or `pytest` will treat it as a fixture
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class TestAlphaDropoutFunctionAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_static_result(self, place):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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input = paddle.static.data(
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name="input", shape=[40, 40], dtype="float32"
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)
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res1 = paddle.nn.functional.alpha_dropout(x=input, p=0.0)
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res2 = paddle.nn.functional.alpha_dropout(
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x=input, p=0.0, training=False
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)
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res3 = paddle.nn.functional.alpha_dropout(x=input, p=1.0)
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in_np = np.random.random([40, 40]).astype("float32")
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res_np = in_np
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res_np3 = np.zeros_like(in_np)
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exe = base.Executor(place)
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fetches = exe.run(
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main_prog,
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feed={"input": in_np},
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fetch_list=[res1, res2, res3],
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)
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np.testing.assert_allclose(fetches[0], res_np, rtol=1e-05)
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np.testing.assert_allclose(fetches[1], res_np, rtol=1e-05)
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np.testing.assert_allclose(fetches[2], res_np3, rtol=1e-05)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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in_np = np.random.random([40, 40]).astype("float32")
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res_np = in_np
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res_np3 = np.zeros_like(in_np)
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input = paddle.to_tensor(in_np)
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input.stop_gradient = False
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res1 = paddle.nn.functional.alpha_dropout(x=input, p=0.0)
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res2 = paddle.nn.functional.alpha_dropout(
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x=input, p=0.0, training=False
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)
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res3 = paddle.nn.functional.alpha_dropout(x=input, p=1.0)
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res_list = [res1, res2]
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for res in res_list:
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np.testing.assert_allclose(res.numpy(), res_np, rtol=1e-05)
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np.testing.assert_allclose(res3.numpy(), res_np3, rtol=1e-05)
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# test backward
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res1.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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def test_dygraph_bfp16(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with base.dygraph.guard(place):
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in_np = np.random.random([40, 40]).astype("uint16")
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res_np = in_np
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res_np3 = np.zeros_like(in_np)
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input = paddle.to_tensor(in_np).astype("bfloat16")
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input.stop_gradient = False
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res1 = paddle.nn.functional.alpha_dropout(x=input, p=0.0)
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res2 = paddle.nn.functional.alpha_dropout(
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x=input, p=0.0, training=False
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)
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res3 = paddle.nn.functional.alpha_dropout(x=input, p=1.0)
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res_list = [res1, res2]
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for res in res_list:
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np.testing.assert_allclose(res.numpy(), res_np, rtol=1e-05)
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np.testing.assert_allclose(res3.numpy(), res_np3, rtol=1e-05)
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# test backward
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res1.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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class TestAlphaDropoutFunctionAPIError(unittest.TestCase):
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def test_input_type_errors(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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def test_Variable():
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# the input of dropout must be Variable.
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x1 = base.create_lod_tensor(
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np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], base.CPUPlace()
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)
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paddle.nn.functional.alpha_dropout(x1, p=0.5)
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self.assertRaises(TypeError, test_Variable)
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def test_input_dtype_errors(self):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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def test_dtype():
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# the input dtype of dropout must be float32 or float64
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xr = paddle.static.data(
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name='xr', shape=[3, 4, 5, 6], dtype="int32"
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)
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paddle.nn.functional.alpha_dropout(xr)
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self.assertRaises(TypeError, test_dtype)
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def test_pdtype():
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# p should be int or float
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x2 = paddle.static.data(
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name='x2', shape=[3, 4, 5, 6], dtype="float32"
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)
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paddle.nn.functional.alpha_dropout(x2, p='0.5')
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self.assertRaises(TypeError, test_pdtype)
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def test_pvalue():
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# p should be 0.<=p<=1.
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x2 = paddle.static.data(
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name='x2', shape=[3, 4, 5, 6], dtype="float32"
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)
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paddle.nn.functional.alpha_dropout(x2, p=1.2)
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self.assertRaises(ValueError, test_pvalue)
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class TestAlphaDropoutClassAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([40, 40]).astype("float32")
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result_np = input_np
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input = paddle.to_tensor(input_np)
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input.stop_gradient = False
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m = paddle.nn.AlphaDropout(p=0.0)
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m.eval()
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result = m(input)
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np.testing.assert_allclose(
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result.numpy(), result_np, rtol=1e-05
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)
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# test backward
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result.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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def test_dygraph_bfp16(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with base.dygraph.guard(place):
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input_np = np.random.random([40, 40]).astype("uint16")
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result_np = input_np
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input = paddle.to_tensor(input_np).astype("bfloat16")
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input.stop_gradient = False
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m = paddle.nn.AlphaDropout(p=0.0)
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m.eval()
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result = m(input)
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np.testing.assert_allclose(
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result.numpy(), result_np, rtol=1e-05
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)
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# test backward
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result.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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def test_static_fp16_gpu(self):
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paddle.enable_static()
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input = np.random.random([2, 3]).astype("float16")
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x = paddle.static.data(name="x", shape=[2, 3], dtype="float16")
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m = paddle.nn.AlphaDropout(p=0.0)
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y = m(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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np.testing.assert_allclose(res[0], input, rtol=1e-05)
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def test_static_bfp16_gpu(self):
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paddle.enable_static()
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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input = np.random.random([2, 3]).astype("uint16")
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x = paddle.static.data(name="x", shape=[2, 3], dtype="bfloat16")
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m = paddle.nn.AlphaDropout(p=0.0)
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y = m(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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np.testing.assert_allclose(res[0], input, rtol=1e-05)
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class TestFeatureAlphaDropoutFunctionAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_static_result(self, place):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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input = paddle.static.data(
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name="input", shape=[40, 40], dtype="float32"
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)
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res1 = paddle.nn.functional.feature_alpha_dropout(x=input, p=0.0)
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res2 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=0.0, training=False
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)
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res3 = paddle.nn.functional.feature_alpha_dropout(x=input, p=1.0)
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in_np = np.random.random([40, 40]).astype("float32")
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res_np = in_np
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res_np3 = np.zeros_like(in_np)
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exe = base.Executor(place)
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fetches = exe.run(
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main_prog,
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feed={"input": in_np},
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fetch_list=[res1, res2, res3],
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)
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np.testing.assert_allclose(fetches[0], res_np, rtol=1e-05)
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np.testing.assert_allclose(fetches[1], res_np, rtol=1e-05)
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np.testing.assert_allclose(fetches[2], res_np3, rtol=1e-05)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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in_np = np.random.random([40, 40]).astype("float32")
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res_np = in_np
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res_np3 = np.zeros_like(in_np)
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input = paddle.to_tensor(in_np)
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input.stop_gradient = False
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res1 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=0.0
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)
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res2 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=0.0, training=False
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)
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res3 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=1.0
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)
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res_list = [res1, res2]
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for res in res_list:
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np.testing.assert_allclose(res.numpy(), res_np, rtol=1e-05)
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np.testing.assert_allclose(res3.numpy(), res_np3, rtol=1e-05)
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# test backward
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res1.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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def test_dygraph_bfp16(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with base.dygraph.guard(place):
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in_np = np.random.random([40, 40]).astype("uint16")
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res_np = in_np
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res_np3 = np.zeros_like(in_np)
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input = paddle.to_tensor(in_np).astype("bfloat16")
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input.stop_gradient = False
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res1 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=0.0
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)
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res2 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=0.0, training=False
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)
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res3 = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=1.0
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)
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res_list = [res1, res2]
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for res in res_list:
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np.testing.assert_allclose(res.numpy(), res_np, rtol=1e-05)
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np.testing.assert_allclose(res3.numpy(), res_np3, rtol=1e-05)
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# test backward
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res1.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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class TestFeatureAlphaDropoutFunctionAPIError(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def test_input_ndim_errors(self):
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for place in self.places:
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with base.dygraph.guard(place):
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in_np = np.random.random(
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[
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40,
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]
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).astype("float32")
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input = paddle.to_tensor(in_np)
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with self.assertRaises(ValueError):
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_ = paddle.nn.functional.feature_alpha_dropout(
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x=input, p=0.0
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)
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def test_input_type_errors(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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def test_Variable():
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# the input of dropout must be Variable.
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x1 = base.create_lod_tensor(
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np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], base.CPUPlace()
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)
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paddle.nn.functional.feature_alpha_dropout(x1, p=0.5)
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self.assertRaises(TypeError, test_Variable)
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def test_input_dtype_errors(self):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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def test_dtype():
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# the input dtype of dropout must be float32 or float64
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xr = paddle.static.data(
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name='xr', shape=[3, 4, 5, 6], dtype="int32"
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)
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paddle.nn.functional.feature_alpha_dropout(xr)
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self.assertRaises(TypeError, test_dtype)
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def test_pdtype():
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# p should be int or float
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x2 = paddle.static.data(
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name='x2', shape=[3, 4, 5, 6], dtype="float32"
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)
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paddle.nn.functional.feature_alpha_dropout(x2, p='0.5')
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self.assertRaises(TypeError, test_pdtype)
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def test_pvalue():
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# p should be 0.<=p<=1.
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x2 = paddle.static.data(
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name='x2', shape=[3, 4, 5, 6], dtype="float32"
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)
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paddle.nn.functional.feature_alpha_dropout(x2, p=1.2)
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self.assertRaises(ValueError, test_pvalue)
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class TestFeatureAlphaDropoutClassAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([40, 40]).astype("float32")
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result_np = input_np
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input = paddle.to_tensor(input_np)
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input.stop_gradient = False
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m = paddle.nn.FeatureAlphaDropout(p=0.0)
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m.eval()
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result = m(input)
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np.testing.assert_allclose(
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result.numpy(), result_np, rtol=1e-05
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)
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# test backward
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result.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
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def test_dygraph_bfp16(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with base.dygraph.guard(place):
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input_np = np.random.random([40, 40]).astype("uint16")
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result_np = input_np
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input = paddle.to_tensor(input_np).astype("bfloat16")
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input.stop_gradient = False
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m = paddle.nn.FeatureAlphaDropout(p=0.0)
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m.eval()
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result = m(input)
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np.testing.assert_allclose(
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result.numpy(), result_np, rtol=1e-05
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)
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# test backward
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result.backward()
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grad = input.grad
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self.assertTrue(grad.dtype == input.dtype)
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self.assertTrue(grad.shape == input.shape)
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self.assertTrue((grad == 1).all())
|
|
|
|
def test_static_fp16_gpu(self):
|
|
if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input = np.random.random([2, 3]).astype("float16")
|
|
|
|
x = paddle.static.data(name="x", shape=[2, 3], dtype="float16")
|
|
|
|
m = paddle.nn.FeatureAlphaDropout(p=0.0)
|
|
y = m(x)
|
|
|
|
exe = paddle.static.Executor(place)
|
|
res = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={
|
|
"x": input,
|
|
},
|
|
fetch_list=[y],
|
|
)
|
|
|
|
np.testing.assert_allclose(res[0], input, rtol=1e-05)
|
|
|
|
def test_static_bfp16_gpu(self):
|
|
if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
input = np.random.random([2, 3]).astype("uint16")
|
|
|
|
x = paddle.static.data(name="x", shape=[2, 3], dtype="bfloat16")
|
|
|
|
m = paddle.nn.FeatureAlphaDropout(p=0.0)
|
|
y = m(x)
|
|
|
|
exe = paddle.static.Executor(place)
|
|
res = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={
|
|
"x": input,
|
|
},
|
|
fetch_list=[y],
|
|
)
|
|
|
|
np.testing.assert_allclose(res[0], input, rtol=1e-05)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|