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127 lines
3.9 KiB
127 lines
3.9 KiB
# 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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from __future__ import division
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from __future__ import print_function
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import unittest
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import numpy as np
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import shutil
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import tempfile
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from paddle import fluid
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from paddle.nn import Conv2D, Pool2D, Linear, ReLU, Sequential
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from paddle.incubate.hapi.utils import uncombined_weight_to_state_dict
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class LeNetDygraph(fluid.dygraph.Layer):
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def __init__(self, num_classes=10, classifier_activation='softmax'):
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super(LeNetDygraph, self).__init__()
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self.num_classes = num_classes
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self.features = Sequential(
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Conv2D(
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1, 6, 3, stride=1, padding=1),
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ReLU(),
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Pool2D(2, 'max', 2),
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Conv2D(
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6, 16, 5, stride=1, padding=0),
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ReLU(),
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Pool2D(2, 'max', 2))
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if num_classes > 0:
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self.fc = Sequential(
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Linear(400, 120),
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Linear(120, 84),
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Linear(
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84, 10, act=classifier_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 = fluid.layers.flatten(x, 1)
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x = self.fc(x)
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return x
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class TestUncombinedWeight2StateDict(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.save_dir = tempfile.mkdtemp()
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.save_dir)
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def test_infer(self):
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start_prog = fluid.Program()
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train_prog = fluid.Program()
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x = fluid.data(name='x', shape=[None, 1, 28, 28], dtype='float32')
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with fluid.program_guard(train_prog, start_prog):
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with fluid.unique_name.guard():
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x = fluid.data(
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name='x', shape=[None, 1, 28, 28], dtype='float32')
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model = LeNetDygraph()
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output = model.forward(x)
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excutor = fluid.Executor()
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excutor.run(start_prog)
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test_prog = train_prog.clone(for_test=True)
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fluid.io.save_params(excutor, self.save_dir, test_prog)
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rand_x = np.random.rand(1, 1, 28, 28).astype('float32')
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out = excutor.run(program=test_prog,
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feed={'x': rand_x},
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fetch_list=[output.name],
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return_numpy=True)
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state_dict = uncombined_weight_to_state_dict(self.save_dir)
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key2key_dict = {
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'features.0.weight': 'conv2d_0.w_0',
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'features.0.bias': 'conv2d_0.b_0',
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'features.3.weight': 'conv2d_1.w_0',
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'features.3.bias': 'conv2d_1.b_0',
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'fc.0.weight': 'linear_0.w_0',
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'fc.0.bias': 'linear_0.b_0',
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'fc.1.weight': 'linear_1.w_0',
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'fc.1.bias': 'linear_1.b_0',
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'fc.2.weight': 'linear_2.w_0',
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'fc.2.bias': 'linear_2.b_0'
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}
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fluid.enable_imperative()
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dygraph_model = LeNetDygraph()
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converted_state_dict = dygraph_model.state_dict()
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for k1, k2 in key2key_dict.items():
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converted_state_dict[k1] = state_dict[k2]
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dygraph_model.set_dict(converted_state_dict)
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dygraph_model.eval()
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dy_out = dygraph_model(fluid.dygraph.to_variable(rand_x))
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np.testing.assert_allclose(dy_out.numpy(), out[0], atol=1e-5)
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if __name__ == '__main__':
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unittest.main()
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