wehub-resource-sync
222 lines
6.5 KiB
Python
222 lines
6.5 KiB
Python
# Copyright (c) 2020 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 paddle
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paddle.framework.set_default_dtype("float64")
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import unittest
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import numpy as np
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from convert import convert_params_for_cell
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from rnn_numpy import GRUCell, LSTMCell, SimpleRNNCell
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class TestSimpleRNNCell(unittest.TestCase):
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def __init__(self, weight=True, bias=True, place="cpu"):
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super().__init__(methodName="runTest")
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self.weight = weight
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self.bias = bias
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self.place = (
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paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
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)
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def setUp(self):
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paddle.disable_static(self.place)
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rnn1 = SimpleRNNCell(16, 32, weight=self.weight, bias=self.bias)
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rnn2 = paddle.nn.SimpleRNNCell(
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16,
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32,
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weight_ih_attr=self.weight,
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weight_hh_attr=self.weight,
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bias_ih_attr=self.bias,
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bias_hh_attr=self.bias,
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)
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convert_params_for_cell(rnn1, rnn2)
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self.rnn1 = rnn1
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self.rnn2 = rnn2
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def test_with_initial_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(4, 16)
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prev_h = np.random.randn(4, 32)
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y1, h1 = rnn1(x, prev_h)
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y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_zero_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(4, 16)
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y1, h1 = rnn1(x)
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y2, h2 = rnn2(paddle.to_tensor(x))
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_errors(self):
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def test_zero_hidden_size():
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cell = paddle.nn.SimpleRNNCell(-1, 0)
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self.assertRaises(ValueError, test_zero_hidden_size)
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def runTest(self):
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self.test_with_initial_state()
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self.test_with_zero_state()
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self.test_errors()
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class TestGRUCell(unittest.TestCase):
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def __init__(self, weight=True, bias=True, place="cpu"):
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super().__init__(methodName="runTest")
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self.weight = weight
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self.bias = bias
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self.place = (
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paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
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)
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def setUp(self):
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paddle.disable_static(self.place)
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rnn1 = GRUCell(16, 32, weight=self.weight, bias=self.bias)
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rnn2 = paddle.nn.GRUCell(
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16,
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32,
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weight_ih_attr=self.weight,
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weight_hh_attr=self.weight,
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bias_ih_attr=self.bias,
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bias_hh_attr=self.bias,
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)
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convert_params_for_cell(rnn1, rnn2)
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self.rnn1 = rnn1
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self.rnn2 = rnn2
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def test_with_initial_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(4, 16)
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prev_h = np.random.randn(4, 32)
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y1, h1 = rnn1(x, prev_h)
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y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_zero_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(4, 16)
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y1, h1 = rnn1(x)
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y2, h2 = rnn2(paddle.to_tensor(x))
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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def test_errors(self):
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def test_zero_hidden_size():
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cell = paddle.nn.GRUCell(-1, 0)
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self.assertRaises(ValueError, test_zero_hidden_size)
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def runTest(self):
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self.test_with_initial_state()
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self.test_with_zero_state()
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self.test_errors()
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class TestLSTMCell(unittest.TestCase):
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def __init__(self, weight=True, bias=True, place="cpu"):
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super().__init__(methodName="runTest")
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self.weight = weight
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self.bias = bias
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self.place = (
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paddle.CPUPlace() if place == "cpu" else paddle.CUDAPlace(0)
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)
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def setUp(self):
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rnn1 = LSTMCell(16, 32, weight=self.weight, bias=self.bias)
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rnn2 = paddle.nn.LSTMCell(
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16,
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32,
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weight_ih_attr=self.weight,
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weight_hh_attr=self.weight,
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bias_ih_attr=self.bias,
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bias_hh_attr=self.bias,
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)
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convert_params_for_cell(rnn1, rnn2)
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self.rnn1 = rnn1
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self.rnn2 = rnn2
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def test_with_initial_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(4, 16)
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prev_h = np.random.randn(4, 32)
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prev_c = np.random.randn(4, 32)
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y1, (h1, c1) = rnn1(x, (prev_h, prev_c))
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y2, (h2, c2) = rnn2(
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paddle.to_tensor(x),
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(paddle.to_tensor(prev_h), paddle.to_tensor(prev_c)),
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)
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
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def test_with_zero_state(self):
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rnn1 = self.rnn1
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rnn2 = self.rnn2
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x = np.random.randn(4, 16)
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y1, (h1, c1) = rnn1(x)
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y2, (h2, c2) = rnn2(paddle.to_tensor(x))
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np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)
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def test_errors(self):
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def test_zero_hidden_size():
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cell = paddle.nn.LSTMCell(-1, 0)
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self.assertRaises(ValueError, test_zero_hidden_size)
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def runTest(self):
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self.test_with_initial_state()
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self.test_with_zero_state()
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self.test_errors()
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def load_tests(loader, tests, pattern):
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suite = unittest.TestSuite()
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devices = ["cpu", "gpu"] if paddle.base.is_compiled_with_cuda() else ["cpu"]
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for weight in [True, False]:
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for bias in [True, False]:
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for device in devices:
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for test_class in [
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TestSimpleRNNCell,
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TestGRUCell,
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TestLSTMCell,
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]:
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suite.addTest(test_class(weight, bias, device))
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return suite
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if __name__ == "__main__":
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unittest.main()
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