wehub-resource-sync
148 lines
5.2 KiB
Python
148 lines
5.2 KiB
Python
# Copyright (c) 2019 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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import paddle
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from paddle import base
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from paddle.nn import Linear
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class TestImperativeContainerSequential(unittest.TestCase):
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def test_sequential(self):
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data = np.random.uniform(-1, 1, [5, 10]).astype('float32')
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with base.dygraph.guard():
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data = paddle.to_tensor(data)
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model1 = paddle.nn.Sequential(Linear(10, 1), Linear(1, 2))
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res1 = model1(data)
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self.assertListEqual(res1.shape, [5, 2])
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model1[1] = Linear(1, 3)
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res1 = model1(data)
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self.assertListEqual(res1.shape, [5, 3])
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loss1 = paddle.mean(res1)
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loss1.backward()
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l1 = Linear(10, 1)
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l2 = Linear(1, 3)
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model2 = paddle.nn.Sequential(('l1', l1), ('l2', l2))
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self.assertEqual(len(model2), 2)
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res2 = model2(data)
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self.assertTrue(l1 is model2.l1)
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self.assertListEqual(res2.shape, res1.shape)
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self.assertEqual(len(model1.parameters()), len(model2.parameters()))
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del model2['l2']
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self.assertEqual(len(model2), 1)
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res2 = model2(data)
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self.assertListEqual(res2.shape, [5, 1])
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model2.add_sublayer('l3', Linear(1, 3))
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model2.add_sublayer('l4', Linear(3, 4))
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self.assertEqual(len(model2), 3)
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res2 = model2(data)
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self.assertListEqual(res2.shape, [5, 4])
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loss2 = paddle.mean(res2)
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loss2.backward()
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def test_append_insert_extend(self):
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data = np.random.uniform(-1, 1, [5, 10]).astype('float32')
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with base.dygraph.guard():
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data = paddle.to_tensor(data)
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model1 = paddle.nn.Sequential()
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# test append
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model1.append(Linear(10, 1))
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model1.append(Linear(1, 2))
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res1 = model1(data)
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self.assertListEqual(res1.shape, [5, 2])
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# test insert
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model1.insert(0, Linear(10, 10))
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res1 = model1(data)
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# test insert type error(non nn.Layer type)
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model2 = paddle.nn.Sequential()
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self.assertRaises(AssertionError, model2.insert, 0, 1)
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# test insert index error(1)
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model2 = paddle.nn.Sequential()
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self.assertRaises(IndexError, model2.insert, 1, Linear(10, 10))
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# test insert at negative index -1
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model2 = paddle.nn.Sequential()
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model2.insert(0, Linear(10, 10))
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self.assertEqual(len(model2), 1)
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# res1 = model1(data)
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# test extend
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model1.extend([Linear(2, 3), Linear(3, 4)])
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res1 = model1(data)
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self.assertListEqual(res1.shape, [5, 4])
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loss1 = paddle.mean(res1)
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loss1.backward()
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# test __iter__
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model3 = paddle.nn.Sequential(
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Linear(10, 1),
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Linear(1, 2),
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)
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output1 = model3(data)
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output2 = data
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for layer in model3:
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output2 = layer(output2)
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np.testing.assert_allclose(
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output1.numpy(), output2.numpy(), equal_nan=True
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)
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def test_sequential_list_params(self):
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data = np.random.uniform(-1, 1, [5, 10]).astype('float32')
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with base.dygraph.guard():
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data = paddle.to_tensor(data)
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model1 = paddle.nn.Sequential(Linear(10, 1), Linear(1, 2))
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res1 = model1(data)
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self.assertListEqual(res1.shape, [5, 2])
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model1[1] = Linear(1, 3)
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res1 = model1(data)
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self.assertListEqual(res1.shape, [5, 3])
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loss1 = paddle.mean(res1)
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loss1.backward()
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l1 = Linear(10, 1)
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l2 = Linear(1, 3)
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model2 = paddle.nn.Sequential(['l1', l1], ['l2', l2])
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self.assertEqual(len(model2), 2)
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res2 = model2(data)
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self.assertTrue(l1 is model2.l1)
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self.assertListEqual(res2.shape, res1.shape)
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self.assertEqual(len(model1.parameters()), len(model2.parameters()))
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del model2['l2']
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self.assertEqual(len(model2), 1)
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res2 = model2(data)
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self.assertListEqual(res2.shape, [5, 1])
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model2.add_sublayer('l3', Linear(1, 3))
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model2.add_sublayer('l4', Linear(3, 4))
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self.assertEqual(len(model2), 3)
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res2 = model2(data)
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self.assertListEqual(res2.shape, [5, 4])
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loss2 = paddle.mean(res2)
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loss2.backward()
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if __name__ == '__main__':
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unittest.main()
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