mirror of
https://github.com/zebrajr/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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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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##############################################################################
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# This a large test that goes through the translation of the bvlc caffenet
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# model, runs an example through the whole model, and verifies numerically
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# that all the results look right. In default, it is disabled unless you
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# explicitly want to run it.
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from caffe.proto import caffe_pb2
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from google.protobuf import text_format
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import numpy as np
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import os
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from caffe2.python import caffe_translator, utils, workspace, test_util
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import sys
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import unittest
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@unittest.skipIf(not os.path.exists('data/testdata/caffe_translator'),
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'No testdata existing for the caffe translator test. Exiting.')
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def setUpModule():
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# We will do all the computation stuff in the global space.
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caffenet = caffe_pb2.NetParameter()
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caffenet_pretrained = caffe_pb2.NetParameter()
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text_format.Merge(
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open('data/testdata/caffe_translator/deploy.prototxt').read(), caffenet
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)
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caffenet_pretrained.ParseFromString(
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open(
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'data/testdata/caffe_translator/bvlc_reference_caffenet.caffemodel')
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.read()
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)
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for remove_legacy_pad in [True, False]:
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net, pretrained_params = caffe_translator.TranslateModel(
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caffenet, caffenet_pretrained, is_test=True,
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remove_legacy_pad=remove_legacy_pad
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)
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with open('data/testdata/caffe_translator/'
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'bvlc_reference_caffenet.translatedmodel',
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'w') as fid:
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fid.write(str(net))
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for param in pretrained_params.protos:
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workspace.FeedBlob(param.name, utils.Caffe2TensorToNumpyArray(param))
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# Let's also feed in the data from the Caffe test code.
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data = np.load('data/testdata/caffe_translator/data_dump.npy').astype(
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np.float32)
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workspace.FeedBlob('data', data)
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# Actually running the test.
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workspace.RunNetOnce(net.SerializeToString())
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class TestNumericalEquivalence(test_util.TestCase):
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def testBlobs(self):
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names = [
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"conv1", "pool1", "norm1", "conv2", "pool2", "norm2", "conv3",
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"conv4", "conv5", "pool5", "fc6", "fc7", "fc8", "prob"
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]
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for name in names:
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print('Verifying {}'.format(name))
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caffe2_result = workspace.FetchBlob(name)
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reference = np.load(
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'data/testdata/caffe_translator/' + name + '_dump.npy'
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)
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self.assertEqual(caffe2_result.shape, reference.shape)
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scale = np.max(caffe2_result)
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np.testing.assert_almost_equal(
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caffe2_result / scale,
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reference / scale,
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decimal=5
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)
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if __name__ == '__main__':
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if len(sys.argv) == 1:
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print(
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'If you do not explicitly ask to run this test, I will not run it. '
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'Pass in any argument to have the test run for you.'
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)
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sys.exit(0)
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unittest.main()
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