mirror of
https://github.com/pytorch/pytorch.git
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71c6e24373
Summary: Hello, This is a patch to fix `ResourceWarning: unclosed file`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/15746 Differential Revision: D13587286 Pulled By: soumith fbshipit-source-id: 08ac34c5b51d9334867f65a2927bff11511553f3
91 lines
3.5 KiB
Python
91 lines
3.5 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 if caffe and test data is not found
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if not (CAFFE_FOUND and os.path.exists('data/testdata/caffe_translator')):
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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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with open('data/testdata/caffe_translator/deploy.prototxt') as f:
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text_format.Merge(f.read(), caffenet)
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with open('data/testdata/caffe_translator/'
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'bvlc_reference_caffenet.caffemodel') as f:
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caffenet_pretrained.ParseFromString(f.read())
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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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