def load_caffe_proto_model(caffe_pb2, proto_path: str, model_path: [str, None] = None):
# 1. python protobuf is used
if api_implementation._implementation_type == 'python':
message = 'Please expect that Model Optimizer conversion might be slow. ' \
'You are currently using Python protobuf library implementation. \n'
try:
from google.protobuf.pyext import cpp_message
# Check os windows and env variable PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION
if os.name == 'nt' and os.environ.get('PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION', default='') != 'cpp':
# 2. cpp implementation is available but not used
message += 'However, cpp implementation is available, you can boost ' \
'model conversion by setting PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION env variable to cpp. \n' \
'Run: set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp \n'
except ImportError:
# 3. cpp implementation is not available
message += 'However you can use the C++ protobuf implementation that is supplied with the OpenVINO toolkit' \
'or build protobuf library from sources. \n' \
'Navigate to "install_prerequisites" folder and run: ' \
'python -m easy_install protobuf-3.5.1-py($your_python_version)-win-amd64.egg \n' \
'set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp'
print(message + '\n\n' + refer_to_faq_msg(80))
# Read proto layers
try:
proto = caffe_pb2.NetParameter()
with open(proto_path, "r") as file:
text_format.Merge(str(file.read()), proto)
except Exception as e:
log.error('Exception message: {}\n\n'.format(e) +
' Possible reasons:\n' +
' 1. {} does not exist\n'.format(proto_path) +
' 2. {} does not have a valid structure, for example, it was downloaded as html\n'.format(
proto_path) +
' 3. {} contains custom layers or attributes that are not supported\n'.format(proto_path) +
' in Model Optimizer by default.\n\n' +
' After you made sure that {} has a valid structure and still see this issue, then\n'.format(
proto_path) +
' you need to generate a python parser for caffe.proto that was used when the model\n' +
' was created.\n' +
' Run "python3 generate_caffe_pb2.py --input_proto ${PATH_TO_CAFFE}/src/caffe/proto/caffe.proto"' +
refer_to_faq_msg(1) + '\n\n', extra={'framework_error': True})
raise FrameworkError('Model Optimizer is not able to parse {}'.format(proto_path)) from e
# Read model layer if exists
model = None
try:
if model_path:
model = caffe_pb2.NetParameter()
with open(model_path, "rb") as infile:
map = mmap.mmap(infile.fileno(), 0, access=mmap.ACCESS_READ)
model.MergeFromString(map)
except Exception as e:
log.error('Exception message: {}\n\n'.format(e) +
' Possible reasons:\n' +
' 1. {} does not exist\n'.format(model_path) +
' 2. {} does not have a valid structure\n'.format(model_path), extra={'framework_error': True})
raise FrameworkError('Model Optimizer is not able to parse {}'.format(model_path)) from e
return proto, model
def test_caffe_same_name_layer(self):
proto = caffe_pb2.NetParameter()
text_format.Merge(proto_str_multi_input + proto_same_name_layers,
proto)
graph, input_shapes = caffe_pb_to_nx(proto, None)
# 6 nodes because: 2 inputs + 2 convolutions
np.testing.assert_equal(len(graph.nodes()), 4)
def test_caffe_same_name_layer(self):
proto = caffe_pb2.NetParameter()
text_format.Merge(proto_str_multi_input + proto_same_name_layers,
proto)
graph = Graph()
caffe_pb_to_nx(graph, proto, None)
# 6 nodes because: 2 inputs + 2 convolutions + 2 identity nodes used as fake outputs
np.testing.assert_equal(len(graph.nodes()), 6)
def test_caffe_pb_to_nx_one_input(self):
proto = caffe_pb2.NetParameter()
text_format.Merge(proto_str_one_input, proto)
input_shapes = caffe_pb_to_nx(Graph(), proto, None)
expected_input_shapes = {'Input0': np.array([1, 3, 224, 224])}
for i in expected_input_shapes:
np.testing.assert_array_equal(input_shapes[i],
expected_input_shapes[i])
def test_caffe_pb_to_standart_input(self):
proto = caffe_pb2.NetParameter()
text_format.Merge(proto_str_input + layer_proto_str, proto)
graph, input_shapes = caffe_pb_to_nx(proto, None)
expected_input_shapes = {'data': np.array([1, 3, 224, 224])}
for i in expected_input_shapes:
np.testing.assert_array_equal(input_shapes[i],
expected_input_shapes[i])
def test_caffe_pb_to_nx_old_styled_multi_input(self):
proto = caffe_pb2.NetParameter()
text_format.Merge(proto_str_old_styled_multi_input + layer_proto_str,
proto)
self.assertRaises(Error, caffe_pb_to_nx, Graph(), proto, None)
def get_layers(proto):
if len(proto.layer):
return proto.layer
elif len(proto.layers):
return proto.layers
else:
print(
'Invalid proto file: there is neither "layer" nor "layers" top-level messages. '
)
#the model is downloaded from http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
caffemodel_filename = './vgg16/VGG_ILSVRC_16_layers.caffemodel'
model = caffe_pb2.NetParameter()
f = open(caffemodel_filename, 'rb')
model.ParseFromString(f.read())
f.close()
# print(str(model))
proto_layers = get_layers(model)
for i, layer in enumerate(proto_layers):
print('----------- i=' + str(i))
# print(str(layer))
print(layer.name)
print(layer.type)
for j, node in enumerate(layer.blobs):
print(' ----------- j=' + str(j))
print(node.num)
print(node.channels)
