def prepare(cls, model, device='CPU', **kwargs):
'''
For Onnx Caffe2Backend, we require that init_graph don't initialize the actual input of the predict_graph,
for example, if "img" is the input blob for the predict_net, we require that in init_graph and in
initializer of the predict_graph, "img" is not initalized. We don't have a check for this, since
there is no way we can know which blob is the input of the predict_graph.
'''
super(Caffe2Backend, cls).prepare(model, device, **kwargs)
opset_version = None
for imp in model.opset_import:
if not imp.HasField("domain") or imp.domain == "":
opset_version = imp.version
if imp.version > cls._known_opset_version:
warnings.warn(
"This version of onnx-caffe2 targets ONNX operator set version {}, but the model we are trying to import uses version {}. We will try to import it anyway, but if the model uses operators which had BC-breaking changes in the intervening versions, import will fail."
.format(cls._known_opset_version, imp.version))
else:
warnings.warn("Unrecognized operator set {}".format(
imp.domain))
if opset_version is None:
if model.ir_version >= 0x00000003:
raise RuntimeError(
"Model with IR version >= 3 did not specify ONNX operator set version (onnx-caffe2 requires it)"
)
else:
opset_version = 1
ws = Workspace()
device_option = get_device_option(Device(device))
# Directly load initializer data into blobs in workspace
cls._direct_initialize_parameters(
model.graph.initializer,
ws,
device_option,
)
initialized = {init.name for init in model.graph.initializer}
cls._direct_initialize_inputs(
model.graph.input,
initialized,
ws,
device_option,
)
uninitialized = [
value_info.name for value_info in model.graph.input
if value_info.name not in initialized
]
init_net, predict_net = cls._onnx_model_to_caffe2_net(
model, device, opset_version, False)
retval = Caffe2Rep(init_net, predict_net, ws, uninitialized)
return retval
def prepare(cls, model, device='CPU', raw_values_dict=None, **kwargs):
'''
For Onnx Caffe2Backend, we require that init_graph don't initialize the actual input of the predict_graph,
for example, if "img" is the input blob for the predict_net, we require that in init_graph and in
initializer of the predict_graph, "img" is not initalized. We don't have a check for this, since
there is no way we can know which blob is the input of the predict_graph.
'''
if not kwargs.pop('no_check_UNSAFE', False):
super(Caffe2Backend, cls).prepare(model, device, **kwargs)
opset_version = None
for imp in model.opset_import:
if not imp.HasField("domain") or imp.domain == "":
opset_version = imp.version
if imp.version > cls._known_opset_version:
warnings.warn(
"This version of onnx-caffe2 targets ONNX operator set version {}, but the model we are trying to import uses version {}. We will try to import it anyway, but if the model uses operators which had BC-breaking changes in the intervening versions, import will fail."
.format(cls._known_opset_version, imp.version))
else:
warnings.warn("Unrecognized operator set {}".format(
imp.domain))
if opset_version is None:
if model.ir_version >= 0x00000003:
raise RuntimeError(
"Model with IR version >= 3 did not specify ONNX operator set version (onnx-caffe2 requires it)"
)
else:
opset_version = 1
model = onnx.shape_inference.infer_shapes(model)
# Check whether we have RNN related ops
pred_model = cls.optimize_onnx(model, predict=True)
rnn_nodes = []
for node in pred_model.graph.node:
if node.op_type in {'LSTM', 'GRU', 'RNN'}:
rnn_nodes.append(node)
# Build the C++ backend
# TODO: build a predictor that supports GPU
# And for RNN nets, we need to avoid adding init_net
use_cpp_backend = device == 'CPU' and not rnn_nodes
# use python backend for now
use_cpp_backend = False
if use_cpp_backend:
c2_rnn_ops = []
if rnn_nodes:
init_model = cls.optimize_onnx(model, init=True)
for node in rnn_nodes:
c2ops = cls._onnx_node_to_caffe2_op(
init_model, pred_model, node, opset_version)
init_ops = [x.SerializeToString() for x in c2ops.init_ops]
ops = [x.SerializeToString() for x in c2ops.ops]
external_inputs = c2ops.interface_blobs
c2_rnn_ops.append(
C.Caffe2Ops(init_ops, ops, external_inputs))
del init_model
cbackend = C.Caffe2Backend(cls._dummy_name)
if raw_values_dict:
cls._external_value_resolution_pass(model, raw_values_dict)
rep = cbackend.prepare(model.SerializeToString(), device,
c2_rnn_ops)
# For testing
# Dump the net descriptions to file for comparison with the Python ones
if "ONNX_CAFFE2_DEBUG" in os.environ:
pred_net_str = rep.pred_net()
pn = caffe2_pb2.NetDef()
pn.ParseFromString(pred_net_str)
init_net_str = rep.init_net()
inn = caffe2_pb2.NetDef()
inn.ParseFromString(init_net_str)
with open("cpp.txt", "w") as f:
f.write("pred_net: \n{}".format(pn))
rep_wrapper = Caffe2CppRep(rep)
return rep_wrapper
else:
ws = Workspace()
device_option = get_device_option(Device(device))
init_net, predict_net = cls._onnx_model_to_caffe2_net(
model, device, opset_version, False)
if raw_values_dict:
cls._external_value_resolution_pass(model, raw_values_dict)
# Directly load initializer data into blobs in workspace
cls._direct_initialize_parameters(
model.graph.initializer,
ws,
device_option,
)
initialized = {init.name for init in model.graph.initializer}
cls._direct_initialize_inputs(
model.graph.input,
initialized,
ws,
device_option,
)
uninitialized = [
value_info.name for value_info in model.graph.input
if value_info.name not in initialized
]
if "ONNX_CAFFE2_DEBUG" in os.environ:
with open("python.txt", "w") as f:
f.write("pred_net: \n{}".format(predict_net))
retval = Caffe2Rep(init_net, predict_net, ws, uninitialized)
return retval
def prepare(cls, model, device='CPU', raw_values_dict=None, **kwargs):
'''
For Onnx Caffe2Backend, we require that init_graph don't initialize the actual input of the predict_graph,
for example, if "img" is the input blob for the predict_net, we require that in init_graph and in
initializer of the predict_graph, "img" is not initalized. We don't have a check for this, since
there is no way we can know which blob is the input of the predict_graph.
'''
if not kwargs.pop('no_check_UNSAFE', False):
super(Caffe2Backend, cls).prepare(model, device, **kwargs)
opset_version = None
for imp in model.opset_import:
if not imp.HasField("domain") or imp.domain == "":
opset_version = imp.version
if imp.version > cls._known_opset_version:
warnings.warn(
"This version of onnx-caffe2 targets ONNX operator set version {}, but the model we are trying to import uses version {}. We will try to import it anyway, but if the model uses operators which had BC-breaking changes in the intervening versions, import will fail."
.format(cls._known_opset_version, imp.version))
else:
warnings.warn("Unrecognized operator set {}".format(
imp.domain))
if opset_version is None:
if model.ir_version >= 0x00000003:
raise RuntimeError(
"Model with IR version >= 3 did not specify ONNX operator set version (onnx-caffe2 requires it)"
)
else:
opset_version = 1
# Prior to onnx version update to onnx-1.8.0, errors caused by failures in
# in the onnx shape inference call were being supressed. Hence a try-catch block
# is added around the infer_shapes call to avoid these failures and preserve status
try:
model = onnx.shape_inference.infer_shapes(model)
except RuntimeError:
warnings.warn(
"ShapeInferenceWarning: Inferred shape and existing shape differ in rank"
)
ws = Workspace()
device_option = get_device_option(Device(device))
init_net, predict_net = cls._onnx_model_to_caffe2_net(
model, device, opset_version, False)
if raw_values_dict:
cls._external_value_resolution_pass(model, raw_values_dict)
# Directly load initializer data into blobs in workspace
cls._direct_initialize_parameters(
model.graph.initializer,
ws,
device_option,
)
initialized = {init.name for init in model.graph.initializer}
cls._direct_initialize_inputs(
model.graph.input,
initialized,
ws,
device_option,
)
uninitialized = [
value_info.name for value_info in model.graph.input
if value_info.name not in initialized
]
retval = Caffe2Rep(init_net, predict_net, ws, uninitialized)
return retval
