1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
|
# This a large test that goes through the translation of the bvlc caffenet
# model, runs an example through the whole model, and verifies numerically
# that all the results look right. In default, it is disabled unless you
# explicitly want to run it.
from google.protobuf import text_format
import numpy as np
import os
import sys
CAFFE_FOUND = False
try:
from caffe.proto import caffe_pb2
from caffe2.python import caffe_translator
CAFFE_FOUND = True
except Exception as e:
# Safeguard so that we only catch the caffe module not found exception.
if ("'caffe'" in str(e)):
print(
"PyTorch/Caffe2 now requires a separate installation of caffe. "
"Right now, this is not found, so we will skip the caffe "
"translator test.")
from caffe2.python import utils, workspace, test_util
import unittest
def setUpModule():
# Do nothing if caffe and test data is not found
if not (CAFFE_FOUND and os.path.exists('data/testdata/caffe_translator')):
return
# We will do all the computation stuff in the global space.
caffenet = caffe_pb2.NetParameter()
caffenet_pretrained = caffe_pb2.NetParameter()
with open('data/testdata/caffe_translator/deploy.prototxt') as f:
text_format.Merge(f.read(), caffenet)
with open('data/testdata/caffe_translator/'
'bvlc_reference_caffenet.caffemodel') as f:
caffenet_pretrained.ParseFromString(f.read())
for remove_legacy_pad in [True, False]:
net, pretrained_params = caffe_translator.TranslateModel(
caffenet, caffenet_pretrained, is_test=True,
remove_legacy_pad=remove_legacy_pad
)
with open('data/testdata/caffe_translator/'
'bvlc_reference_caffenet.translatedmodel',
'w') as fid:
fid.write(str(net))
for param in pretrained_params.protos:
workspace.FeedBlob(param.name, utils.Caffe2TensorToNumpyArray(param))
# Let's also feed in the data from the Caffe test code.
data = np.load('data/testdata/caffe_translator/data_dump.npy').astype(
np.float32)
workspace.FeedBlob('data', data)
# Actually running the test.
workspace.RunNetOnce(net.SerializeToString())
@unittest.skipIf(not CAFFE_FOUND,
'No Caffe installation found.')
@unittest.skipIf(not os.path.exists('data/testdata/caffe_translator'),
'No testdata existing for the caffe translator test. Exiting.')
class TestNumericalEquivalence(test_util.TestCase):
def testBlobs(self):
names = [
"conv1", "pool1", "norm1", "conv2", "pool2", "norm2", "conv3",
"conv4", "conv5", "pool5", "fc6", "fc7", "fc8", "prob"
]
for name in names:
print('Verifying {}'.format(name))
caffe2_result = workspace.FetchBlob(name)
reference = np.load(
'data/testdata/caffe_translator/' + name + '_dump.npy'
)
self.assertEqual(caffe2_result.shape, reference.shape)
scale = np.max(caffe2_result)
np.testing.assert_almost_equal(
caffe2_result / scale,
reference / scale,
decimal=5
)
if __name__ == '__main__':
if len(sys.argv) == 1:
print(
'If you do not explicitly ask to run this test, I will not run it. '
'Pass in any argument to have the test run for you.'
)
sys.exit(0)
unittest.main()
|
