def setUpModule():
# We will do all the computation stuff in the global space.
caffenet = caffe_pb2.NetParameter()
caffenet_pretrained = caffe_pb2.NetParameter()
text_format.Merge(
open('data/testdata/caffe_translator/deploy.prototxt').read(),
caffenet)
caffenet_pretrained.ParseFromString(
open(
'data/testdata/caffe_translator/bvlc_reference_caffenet.caffemodel'
).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())
def load_and_convert_caffe_model(prototxt_file_name, caffemodel_file_name):
caffenet = caffe_pb2.NetParameter()
caffenet_weights = caffe_pb2.NetParameter()
text_format.Merge(open(prototxt_file_name).read(), caffenet)
caffenet_weights.ParseFromString(open(caffemodel_file_name).read())
# C2 conv layers current require biases, but they are optional in C1
# Add zeros as biases is they are missing
add_missing_biases(caffenet_weights)
# We only care about getting parameters, so remove layers w/o parameters
remove_layers_without_parameters(caffenet, caffenet_weights)
# BatchNorm is not implemented in the translator *and* we need to fold Scale
# layers into the new C2 SpatialBN op, hence we remove the batch norm layers
# and apply custom translations code
bn_weights = remove_spatial_bn_layers(caffenet, caffenet_weights)
# Set num, channel, height and width for blobs that use shape.dim instead
normalize_shape(caffenet_weights)
# Translate the rest of the model
net, pretrained_weights = caffe_translator.TranslateModel(
caffenet, caffenet_weights)
pretrained_weights.protos.extend(bn_weights)
return net, pretrained_weights
'Pass in any argument to have the test run for you.')
sys.exit(0)
if not os.path.exists('data/testdata/caffe_translator'):
print('No testdata existing for the caffe translator test. Exiting.')
sys.exit(0)
# We will do all the computation stuff in the global space.
caffenet = caffe_pb2.NetParameter()
caffenet_pretrained = caffe_pb2.NetParameter()
text_format.Merge(
open('data/testdata/caffe_translator/deploy.prototxt').read(),
caffenet)
caffenet_pretrained.ParseFromString(
open(
'data/testdata/caffe_translator/bvlc_reference_caffenet.caffemodel'
).read())
net, pretrained_params = caffe_translator.TranslateModel(
caffenet, caffenet_pretrained, is_test=True)
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.main()
'Pass in any argument to have the test run for you.')
sys.exit(0)
if not os.path.exists('data/testdata/caffe_translator'):
print 'No testdata existing for the caffe translator test. Exiting.'
sys.exit(0)
# We will do all the computation stuff in the global space.
caffenet = caffe_pb2.NetParameter()
caffenet_pretrained = caffe_pb2.NetParameter()
text_format.Merge(
open('data/testdata/caffe_translator/deploy.prototxt').read(),
caffenet)
caffenet_pretrained.ParseFromString(
open(
'data/testdata/caffe_translator/bvlc_reference_caffenet.caffemodel'
).read())
net, pretrained_params = caffe_translator.TranslateModel(
caffenet, caffenet_pretrained)
caffe_translator.DeleteDropout(net)
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.main()