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https://github.com/zebrajr/pytorch.git
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40109b16d0
Summary: This was used as a convenient way for us to convert c1 models. Now that conversion is more or less done, we should probably require any users who need to convert c1 models to explicitly install c1. This PR removes the explicit c1 proto (which was copied from c1) in favor of explicit installation. Note that caffe_translator would still work properly, only difference is that now users need to install c1 separately. Pull Request resolved: https://github.com/pytorch/pytorch/pull/10380 Differential Revision: D9267981 Pulled By: Yangqing fbshipit-source-id: a6ce5d9463e6567976da83f2d08b2c3d94d14390
94 lines
3.4 KiB
Python
94 lines
3.4 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 google.protobuf import text_format
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import numpy as np
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import os
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import sys
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CAFFE_FOUND = False
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try:
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from caffe.proto import caffe_pb2
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from caffe2.python import caffe_translator
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CAFFE_FOUND = True
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except Exception as e:
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# Safeguard so that we only catch the caffe module not found exception.
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if ("'caffe'" in str(e)):
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print(
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"PyTorch/Caffe2 now requires a separate installation of caffe. "
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"Right now, this is not found, so we will skip the caffe "
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"translator test.")
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from caffe2.python import utils, workspace, test_util
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import unittest
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def setUpModule():
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# Do nothing is caffe is not found.
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if not CAFFE_FOUND:
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return
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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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@unittest.skipIf(not CAFFE_FOUND,
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'No Caffe installation found.')
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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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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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