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https://github.com/zebrajr/pytorch.git
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014d1fe5c4
Summary: These are all essentially no-op changes which allow for nose-style (or pytest-style) test discovery. With this patch, you can use any of these methods to discover and run tests under `caffe2/python`: ``` python -m unittest discover -p '*test*.py' caffe2/python/ python -m nose caffe2/python/ python -m pytest caffe2/python/ ``` Future work: * Get all of the tests to pass * Some seem to be testing operations which don't have GPU implementations * I get a segfault unless I set `CUDA_VISIBLE_DEVICES=0` * Some tests are flaky * Allow test discovery throughout the whole project (e.g. the `experiments/` dir) Closes https://github.com/caffe2/caffe2/pull/199 Reviewed By: pietern Differential Revision: D4704504 Pulled By: Yangqing fbshipit-source-id: 8f5687ec9c8aa873dfaff30dbf44272bc38a206b
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
# 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 caffe2.proto import caffe2_pb2
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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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net, pretrained_params = caffe_translator.TranslateModel(
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caffenet, caffenet_pretrained, is_test=True
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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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