wehub-resource-sync
111 lines
3.5 KiB
Python
111 lines
3.5 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 unittest
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import numpy as np
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import paddle
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from paddle import base, nn
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class LeNetDygraph(paddle.nn.Layer):
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def __init__(self, num_classes=10, classifier_activation='softmax'):
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super().__init__()
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self.num_classes = num_classes
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self.features = nn.Sequential(
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nn.Conv2D(1, 6, 3, stride=1, padding=1),
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nn.ReLU(),
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paddle.nn.MaxPool2D(2, 2),
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nn.Conv2D(6, 16, 5, stride=1, padding=0),
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nn.ReLU(),
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paddle.nn.MaxPool2D(2, 2),
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)
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if num_classes > 0:
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self.fc = nn.Sequential(
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nn.Linear(400, 120),
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nn.Linear(120, 84),
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nn.Linear(84, 10),
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nn.Softmax(),
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) # Todo: accept any activation
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def forward(self, inputs):
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x = self.features(inputs)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1, -1)
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x = self.fc(x)
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return x
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def init_weights(layer):
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if type(layer) == nn.Linear:
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new_weight = paddle.tensor.fill_constant(
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layer.weight.shape, layer.weight.dtype, value=0.9
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)
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layer.weight.set_value(new_weight)
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new_bias = paddle.tensor.fill_constant(
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layer.bias.shape, layer.bias.dtype, value=-0.1
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)
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layer.bias.set_value(new_bias)
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elif type(layer) == nn.Conv2D:
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new_weight = paddle.tensor.fill_constant(
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layer.weight.shape, layer.weight.dtype, value=0.7
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)
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layer.weight.set_value(new_weight)
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new_bias = paddle.tensor.fill_constant(
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layer.bias.shape, layer.bias.dtype, value=-0.2
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)
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layer.bias.set_value(new_bias)
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class TestLayerApply(unittest.TestCase):
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def test_apply_init_weight(self):
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with base.dygraph.guard():
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net = LeNetDygraph()
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net.apply(init_weights)
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for layer in net.sublayers():
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if type(layer) == nn.Linear:
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np.testing.assert_allclose(layer.weight.numpy(), 0.9)
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np.testing.assert_allclose(layer.bias.numpy(), -0.1)
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elif type(layer) == nn.Conv2D:
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np.testing.assert_allclose(layer.weight.numpy(), 0.7)
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np.testing.assert_allclose(layer.bias.numpy(), -0.2)
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def test_apply_order_and_return_self(self):
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class ApplyOrderNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.child1 = paddle.nn.Layer()
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self.child2 = paddle.nn.Sequential(paddle.nn.Layer())
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with base.dygraph.guard():
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net = ApplyOrderNet()
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visited_layers = []
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result = net.apply(lambda layer: visited_layers.append(layer))
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self.assertIs(result, net)
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self.assertEqual(
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visited_layers,
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[net.child1, net.child2[0], net.child2, net],
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)
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if __name__ == '__main__':
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unittest.main()
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