## @package onnx # Module caffe2.python.onnx.backend """Backend for running ONNX on Caffe2 To run this, you will need to have Caffe2 installed as well. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os import collections from subprocess import Popen, PIPE import zipfile import itertools # When onnx is built against a version of protobuf that is older than # that which is vendored with caffe2, onnx will crash if caffe2's # vendored protobuf is loaded first. We can work around this by # importing onnx first, which will cause it to go out and pick up the # system protobuf. import onnx.backend import caffe2 from caffe2.python import core, workspace, rnn_cell, gru_cell from caffe2.python.compatibility import container_abcs from caffe2.python.model_helper import ModelHelper from caffe2.proto import caffe2_pb2 import caffe2.python.utils import numpy as np import onnx from onnx import checker, GraphProto, TensorProto, AttributeProto, ModelProto import onnx.numpy_helper import onnx.defs import onnx.optimizer import onnx.shape_inference import onnx.utils from onnx.backend.base import Backend, Device, DeviceType, namedtupledict from caffe2.python.onnx.workspace import Workspace from caffe2.python.onnx.backend_rep import Caffe2Rep from caffe2.python.onnx.backend_cpp_rep import Caffe2CppRep import caffe2.python._import_c_extension as C import warnings def force_unicode(s): try: return s.decode('utf-8') except AttributeError: return s def get_device_option(device): m = { DeviceType.CPU: caffe2_pb2.CPU, DeviceType.CUDA: workspace.GpuDeviceType } return core.DeviceOption(m[device.type], device.device_id) class OnnxAttributes(dict): """ This is a more convenient way to work with ONNX/Caffe2 attributes that is not the protobuf representation. """ @staticmethod def from_onnx(args): d = OnnxAttributes() for arg in args: d[arg.name] = convertAttributeProto(arg) return d def caffe2(self, kmap=lambda k: k): for k, v in self.items(): if kmap(k) != '': yield caffe2.python.utils.MakeArgument(kmap(k), v) # TODO: Move this into ONNX main library def convertAttributeProto(onnx_arg): """ Convert an ONNX AttributeProto into an appropriate Python object for the type. NB: Tensor attribute gets returned as the straight proto. """ if onnx_arg.HasField('f'): return onnx_arg.f elif onnx_arg.HasField('i'): return onnx_arg.i elif onnx_arg.HasField('s'): return onnx_arg.s elif onnx_arg.HasField('t'): return onnx_arg.t # this is a proto! elif onnx_arg.HasField('g'): return Caffe2Backend._graph_to_net(onnx_arg.g, Caffe2Backend._known_opset_version) elif len(onnx_arg.floats): return list(onnx_arg.floats) elif len(onnx_arg.ints): return list(onnx_arg.ints) elif len(onnx_arg.strings): return list(onnx_arg.strings) elif len(onnx_arg.graphs): retval = [] # TODO: this doesn't work with RNN ops for g in onnx_arg.graphs: retval.append( Caffe2Backend._graph_to_net(g, Caffe2Backend._known_opset_version)) return retval else: raise ValueError("Unsupported ONNX attribute: {}".format(onnx_arg)) # TODO: Move this into ONNX main library class OnnxNode(object): """ Reimplementation of NodeProto from ONNX, but in a form more convenient to work with from Python. We may temporarily edit these nodes to get them into Caffe2 form, before actually translating into the Caffe2 protobuf, since this is easier than decomposing everything, and putting it back together when we're ready. """ def __init__(self, node): self.name = str(node.name) self.op_type = str(node.op_type) self.attrs = OnnxAttributes.from_onnx(node.attribute) self.inputs = list(node.input) self.outputs = list(node.output) Caffe2Ops = collections.namedtuple('Caffe2Ops', ['ops', 'init_ops', 'interface_blobs']) class Caffe2Backend(Backend): # The greatest version of the ONNX operator set which we are aware of. # Models whose version is larger than this will cause us to emit a warning # that we are attempting to translate on a "best effort" basis. # # If you increase this, make SURE you cross-reference all BC-breaking # changes from one version to the next, and any that you did not # implement, mark as broken in _broken_operators _known_opset_version = 9 # This dictionary will record operators which are KNOWN to be # broken, so we give a good error message rather than do something # bogus and then fail. _broken_operators = { # 'BrokenOp': version_it_was_broken_in } # Operators that are different between Caffe2 and # ONNX but only in their name. # In most cases, this should be empty - as the effort of ONNX is # to unify the operator definitions. _renamed_operators = { 'GlobalMaxPool': 'MaxPool', 'GlobalAveragePool': 'AveragePool', 'Pad': 'PadImage', 'Neg': 'Negative', 'BatchNormalization': 'SpatialBN', 'InstanceNormalization': 'InstanceNorm', 'MatMul': 'BatchMatMul', 'Upsample': 'ResizeNearest', 'Identity': 'Copy', 'InstanceNormalization': 'InstanceNorm', 'Equal': 'EQ', 'Less': 'LT', 'Greater': 'GT', 'Unsqueeze': 'ExpandDims', 'Loop': 'ONNXWhile', 'Tile': 'NumpyTile', 'RandomNormal': 'GaussianFill', 'RandomUniform': 'UniformFill', } _global_renamed_attrs = {'kernel_shape': 'kernels'} _per_op_renamed_attrs = { 'Squeeze': { 'axes': 'dims' }, 'Unsqueeze': { 'axes': 'dims' }, 'Transpose': { 'perm': 'axes' }, 'Upsample': { 'mode': '', 'scales': '' }, 'ConvTranspose': { 'output_padding': 'adjs' }, 'Selu': { 'gamma': 'scale' }, 'If': { 'then_branch': 'then_net', 'else_branch': 'else_net' }, 'RandomUniform': { 'low': 'min', 'high': 'max' } } # operators whose behavior is different beyond renaming # the value is an attribute of this class that is a # function from ToffeIR node_def to caffe2 op_def _special_operators = { 'LSTM': '_create_rnn_variant', 'GRU': '_create_rnn_variant', 'RNN': '_create_rnn_variant', 'Loop': '_create_loop', 'If': '_create_if', 'Upsample': '_create_upsample', 'RandomNormal': '_create_gaussian_fill' } # Dummy name generator _dummy_name = C.DummyName() @classmethod def dummy_name(cls): return cls._dummy_name.new_dummy_name() # NB: By default, you will use the LATEST definition of the operator, # so this interface MAY make BC-breaking changes. Specify an # opset_version if you don't want this to version. @classmethod def run_node(cls, node, inputs, device='CPU', opset_version=_known_opset_version, outputs_info=None): super(Caffe2Backend, cls).run_node(node, inputs, device=device, outputs_info=outputs_info, opset_version=opset_version) value_infos = [] device_option = get_device_option(Device(device)) ws = Workspace() with core.DeviceScope(device_option): # temporary! if isinstance(inputs, dict): for key, value in inputs.items(): ws.FeedBlob(key, value) value_infos.append( onnx.helper.make_tensor_value_info( name=key, elem_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[ value.dtype], shape=value.shape).SerializeToString()) else: assert len(node.input) == len( inputs), "{}: expected {} but got {}".format( node.op_type, len(node.input), len(inputs)) for key, value in zip(node.input, inputs): ws.FeedBlob(key, value) value_infos.append( onnx.helper.make_tensor_value_info( name=key, elem_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[ value.dtype], shape=value.shape).SerializeToString()) ops = [] cbackend = C.Caffe2Backend(cls._dummy_name) ops_str = cbackend.convert_node(node.SerializeToString(), value_infos, opset_version) for s in ops_str[0] + ops_str[1]: op = caffe2_pb2.OperatorDef() op.ParseFromString(s) op.device_option.CopyFrom(device_option) ops.append(op) ws.RunOperatorsOnce(ops) output_values = [ws.FetchBlob(name) for name in node.output] return namedtupledict('Outputs', node.output)(*output_values) @classmethod def _create_tensor_filling_op(cls, onnx_tensor, name=None): """ Given an Onnx TensorProto, translate it into a Caffe2 operator which produces the given tensor filling op. """ assert name or onnx_tensor.name name = name or onnx_tensor.name c2_op = caffe2_pb2.OperatorDef() c2_values = c2_op.arg.add() c2_values.name = "values" def tensor2list(onnx_tensor): # Use the onnx.numpy_helper because the data may be raw return onnx.numpy_helper.to_array(onnx_tensor).flatten().tolist() if onnx_tensor.data_type in [TensorProto.FLOAT]: c2_op.type = 'GivenTensorFill' c2_values.floats.extend(tensor2list(onnx_tensor)) elif onnx_tensor.data_type in [TensorProto.DOUBLE]: c2_op.type = 'GivenTensorDoubleFill' c2_values.floats.extend(tensor2list(onnx_tensor)) elif onnx_tensor.data_type in [TensorProto.INT64, TensorProto.UINT32]: c2_op.type = 'GivenTensorInt64Fill' c2_values.ints.extend(tensor2list(onnx_tensor)) elif onnx_tensor.data_type in [ TensorProto.UINT8, TensorProto.INT8, TensorProto.UINT16, TensorProto.INT16, TensorProto.INT32 ]: c2_op.type = 'GivenTensorIntFill' c2_values.ints.extend(tensor2list(onnx_tensor)) elif onnx_tensor.data_type == TensorProto.BOOL: c2_op.type = 'GivenTensorBoolFill' c2_values.ints.extend(tensor2list(onnx_tensor)) elif onnx_tensor.data_type == TensorProto.STRING: c2_op.type = 'GivenTensorStringFill' c2_values.strings.extend(onnx_tensor.string_data) else: raise RuntimeError("unrecognized tensor type {}".format( onnx_tensor.data_type)) c2_shape = c2_op.arg.add() c2_shape.name = "shape" c2_shape.ints.extend(onnx_tensor.dims) c2_op.output.append(name) return c2_op @classmethod def _rnn_reform_weights(cls, reforms, name, hidden_size, init_net, gates, reorder_indices): for name_from, name_to, do_concat, extra_dims in reforms: gate_blobs = [ '%s/%s_%s' % (name, prefix, name_to) for prefix in gates ] for i, x in enumerate(gate_blobs): dim0 = i * hidden_size, (i + 1) * hidden_size starts, ends = zip(dim0, *extra_dims) init_net.Slice(name_from, x, starts=starts, ends=ends) if do_concat: reordered_gate_blobs = [gate_blobs[i] for i in reorder_indices] init_net.Concat(reordered_gate_blobs, ['%s/%s' % (name, name_to), cls.dummy_name()], axis=0) @classmethod def _make_rnn_direction(cls, input_blob, B, W, R, initial_states_and_names, sequence_lens, pred_mh, init_net, input_size, hidden_size, num_gates, direction_offset, Bi, Br, W_, R_, reform, make_cell, keep_outputs): name = cls.dummy_name() # input and recurrence biases are squashed together in onnx # but not in caffe2 gates_hidden_size = num_gates * hidden_size bias_offset = 2 * direction_offset * gates_hidden_size weight_offset = direction_offset * gates_hidden_size Bi = init_net.Slice(B, name + Bi, starts=[bias_offset + 0 * gates_hidden_size], ends=[bias_offset + 1 * gates_hidden_size]) Br = init_net.Slice(B, name + Br, starts=[bias_offset + 1 * gates_hidden_size], ends=[bias_offset + 2 * gates_hidden_size]) W_ = init_net.Slice(W, name + W_, starts=[weight_offset + 0 * gates_hidden_size, 0], ends=[weight_offset + 1 * gates_hidden_size, -1]) R_ = init_net.Slice(R, name + R_, starts=[weight_offset + 0 * gates_hidden_size, 0], ends=[weight_offset + 1 * gates_hidden_size, -1]) initial_states_sliced = [] for initial_state, name_suffix in initial_states_and_names: initial_states_sliced.append( pred_mh.net.Slice(initial_state, name + name_suffix, starts=[direction_offset + 0, 0, 0], ends=[direction_offset + 1, -1, -1])) if direction_offset == 1: if sequence_lens is not None: seq_lens_for_reverse = sequence_lens else: input_shape = pred_mh.net.Shape(input_blob, name + '/input_shape') batch_size = pred_mh.net.Slice(input_shape, name + '/batch_size_slice', starts=[1], ends=[2]) seq_len = pred_mh.net.Slice(input_shape, name + '/seq_len_slice', starts=[0], ends=[1]) dummy_sequence_lens = pred_mh.net.Tile([seq_len, batch_size], name + '/dummy_sequence_lens', axis=0) pred_mh.net.Reshape( dummy_sequence_lens, [dummy_sequence_lens, cls.dummy_name()], shape=[-1]) seq_lens_for_reverse = pred_mh.net.Cast(dummy_sequence_lens, name + '/seq_lens_for_reverse', to=core.DataType.INT32) reform(Bi, Br, W_, R_, name, hidden_size, init_net) if direction_offset == 1: input = pred_mh.net.ReversePackedSegs( [input_blob, seq_lens_for_reverse], name + "/input-reversed") else: input = input_blob outputs = keep_outputs( list( make_cell( pred_mh, input, sequence_lens, initial_states_sliced, input_size, hidden_size, name, drop_states=False, forward_only=True, ))) if direction_offset == 1: outputs[0] = pred_mh.net.ReversePackedSegs( [outputs[0], seq_lens_for_reverse], name + "/output-reversed") return outputs @classmethod def _create_rnn_variant(cls, init_model, pred_model, n, opset_version): assert init_model is not None, "cannot convert RNNs without access to the full model" assert pred_model is not None, "cannot convert RNNs without access to the full model" attrs = dict(n.attrs) # make a copy, which is safe to mutate hidden_size = attrs.pop('hidden_size') direction = force_unicode(attrs.pop('direction', 'forward')) if n.op_type == 'RNN': activation = force_unicode( attrs.pop('activations', ('tanh', ))[0].lower()) elif n.op_type == 'GRU': linear_before_reset = attrs.pop('linear_before_reset', 0) assert not attrs, "unsupported RNN attributes: " + str(attrs.keys()) assert direction in ['forward', 'bidirectional' ], "unsupported backwards RNN/GRU/LSTM" if n.op_type in ['RNN', 'GRU']: input_blob, W, R, B, sequence_lens, initial_h = n.inputs elif n.op_type == 'LSTM': input_blob, W, R, B, sequence_lens, initial_h, initial_c = n.inputs if sequence_lens == "": sequence_lens = None for x in itertools.chain(init_model.graph.input, init_model.graph.value_info, pred_model.graph.input, pred_model.graph.value_info): if x.name == W: input_size = x.type.tensor_type.shape.dim[2].dim_value break else: raise RuntimeError( "best-effort shape inference for RNN/GRU/LSTM failed") pred_mh = ModelHelper() init_net = core.Net("init-net") init_net.Reshape(W, [W, cls.dummy_name()], shape=[1, -1, 0]) init_net.Squeeze(W, W, dims=[0]) init_net.Reshape(R, [R, cls.dummy_name()], shape=[1, -1, 0]) init_net.Squeeze(R, R, dims=[0]) init_net.Reshape(B, [B, cls.dummy_name()], shape=[1, -1]) init_net.Squeeze(B, B, dims=[0]) if n.op_type == 'RNN': def reform(*args): pass def make_cell(*args, **kwargs): return rnn_cell.BasicRNN(*args, activation=activation, **kwargs) def make_rnn(direction_offset): return cls._make_rnn_direction( input_blob, B, W, R, [(initial_h, '/initial_h')], sequence_lens, pred_mh, init_net, input_size, hidden_size, 1, direction_offset, "/i2h_b", "/gates_t_b", "/i2h_w", "/gates_t_w", reform, make_cell, lambda x: x) elif n.op_type == 'GRU': def reform(Bi, Br, W_, R_, name, hidden_size, init_net): # caffe2 has a different order from onnx. We need to rearrange # z r h -> r z h reforms = ((W_, 'i2h_w', True, [(0, -1)]), (R_, 'gate_t_w', False, [(0, -1)]), (Bi, 'i2h_b', True, []), (Br, 'gate_t_b', False, [])) cls._rnn_reform_weights(reforms, name, hidden_size, init_net, ['update', 'reset', 'output'], [1, 0, 2]) def make_cell(*args, **kwargs): return gru_cell.GRU(*args, linear_before_reset=linear_before_reset, **kwargs) def make_rnn(direction_offset): return cls._make_rnn_direction( input_blob, B, W, R, [(initial_h, '/initial_h')], sequence_lens, pred_mh, init_net, input_size, hidden_size, 3, direction_offset, "_bias_i2h", "_bias_gates", "/i2h_w_pre", "/gates_t_w_pre", reform, make_cell, lambda x: x) elif n.op_type == 'LSTM': def reform(Bi, Br, W_, R_, name, hidden_size, init_net): # caffe2 has a different order from onnx. We need to rearrange # i o f c -> i f o c reforms = ((W_, 'i2h_w', True, [(0, -1)]), (R_, 'gates_t_w', True, [(0, -1)]), (Bi, 'i2h_b', True, []), (Br, 'gates_t_b', True, [])) cls._rnn_reform_weights(reforms, name, hidden_size, init_net, ['input', 'output', 'forget', 'cell'], [0, 2, 1, 3]) def make_cell(*args, **kwargs): return rnn_cell.LSTM(*args, **kwargs) def make_rnn(direction_offset): return cls._make_rnn_direction( input_blob, B, W, R, [(initial_h, '/initial_h'), (initial_c, '/initial_c')], sequence_lens, pred_mh, init_net, input_size, hidden_size, 4, direction_offset, "/i2h_b", "/gates_t_b", "/i2h_w", "/gates_t_w", reform, make_cell, lambda x: [x[0], x[1], x[3]]) if direction == 'forward': outputs = make_rnn(0) # in the forward case, storage is shared between the # last outputs. We need to decouple them so that the # VariableLengthSequencePadding only mutates # n.outputs[0] for i in range(1, len(outputs)): pred_mh.net.Copy(outputs[i], n.outputs[i]) if sequence_lens is not None: pred_mh.net.VariableLengthSequencePadding( [outputs[0], sequence_lens], [outputs[0]]) pred_mh.net.ExpandDims([outputs[0]], [n.outputs[0]], dims=[1]) elif direction == 'bidirectional': outputs_f = make_rnn(0) outputs_b = make_rnn(1) concatted_output, _ = pred_mh.net.Concat( [outputs_f[0], outputs_b[0]], [cls.dummy_name(), cls.dummy_name()], axis=2) if sequence_lens is not None: pred_mh.net.VariableLengthSequencePadding( [concatted_output, sequence_lens], [concatted_output]) reshaped_output, _ = pred_mh.net.Reshape( concatted_output, [cls.dummy_name(), cls.dummy_name()], shape=[0, 0, -1, 2]) pred_mh.net.Transpose(reshaped_output, n.outputs[0], axes=[0, 2, 1, 3]) for i in range(1, len(n.outputs)): pred_mh.net.Concat( [outputs_f[i], outputs_b[i]], [n.outputs[i], cls.dummy_name()], axis=0) # We want to decide whether to put all of our weight-reshaping # operators in the init net or the predict net. We can put # them in the init net iff the inputs to those operators are # already available, either as graph initializers, or as the # output of other operators in the init net. The latter case # occurs, for example, when exporting from pytorch to onnx. # In most production use, we expect has_initializers to be # true. initializers = {i.name for i in init_model.graph.initializer} outputs = { output for node in init_model.graph.node for output in node.output } has_initializers = all(x in initializers or x in outputs for x in (W, R, B)) pred_ops = [] init_ops = [] (init_ops if has_initializers else pred_ops).extend(init_net.Proto().op) pred_ops.extend(pred_mh.Proto().op) return Caffe2Ops(pred_ops, init_ops, list(pred_mh.Proto().external_input)) @classmethod def _create_control_op(cls, init_model, pred_model, n, opset_version): control_inputs = [] if '__control_inputs' in n.attrs: control_inputs.extend(n.attrs['__control_inputs']) node = cls._common_onnx_node_to_caffe2_op(init_model, pred_model, n, opset_version) node.control_input.extend(control_inputs) return Caffe2Ops([node], [], []) @classmethod def _remove_ssa(cls, net, remap_dict): for op in net.op: for i, name in enumerate(op.output): if name in remap_dict: op.output[i] = remap_dict[name] for i, out in enumerate(net.external_output): if out in remap_dict: net.external_output[i] = remap_dict[out] @classmethod def _create_if(cls, init_model, pred_model, n, opset_version): ops = cls._create_control_op(init_model, pred_model, n, opset_version) assert ops[0][0].type == 'If' if_op = ops[0][0] then_net = else_net = None control_inputs = [] for arg in if_op.arg: if arg.name == 'then_net': then_net = arg.n if arg.name == 'else_net': else_net = arg.n if arg.name == '__control_inputs': control_inputs = arg.strings assert then_net and else_net then_net_outs = then_net.external_output else_net_outs = else_net.external_output op_outputs = if_op.output assert len(then_net_outs) == len(else_net_outs) assert len(else_net_outs) == len(op_outputs) for arg in if_op.arg: if arg.name == 'then_net': arg.n.external_input.extend(control_inputs) if arg.name == 'else_net': arg.n.external_input.extend(control_inputs) return ops @classmethod def _create_loop(cls, init_model, pred_model, n, opset_version): ops = cls._create_control_op(init_model, pred_model, n, opset_version) assert ops[0][0].type == 'ONNXWhile' while_op = ops[0][0] while_op.arg.extend( [caffe2.python.utils.MakeArgument('has_trip_count', True)]) while_op.arg.extend( [caffe2.python.utils.MakeArgument('has_cond', True)]) while_op.arg.extend( [caffe2.python.utils.MakeArgument('disable_scopes', True)]) control_inputs = [] for arg in while_op.arg: if arg.name == '__control_inputs': control_inputs = arg.strings num_loop_carried_deps = 0 for arg in while_op.arg: if arg.name == 'body': num_loop_carried_deps = len(arg.n.external_input) - 2 arg.n.external_input.extend(control_inputs) while_op.arg.extend([ caffe2.python.utils.MakeArgument('num_loop_carried_deps', num_loop_carried_deps) ]) return ops @classmethod def _substitute_raw_value(cls, tp, raw_values_dict): if tp.HasField('raw_data') and tp.raw_data == bytes(b'__EXTERNAL'): if tp.name not in raw_values_dict: raise RuntimeError( 'TensorProto for value {} referenced raw data but it was not found!' .format(tp.name)) else: tp.raw_data = raw_values_dict[tp.name] @classmethod def _visit_and_substitute_raw_values(cls, nodes, raw_values_dict): for node in nodes: for attr in node.attribute: if attr.HasField('t'): cls._substitute_raw_value(attr.t, raw_values_dict) for t in attr.tensors: cls._substitute_raw_value(t, raw_values_dict) if attr.HasField('g'): cls._visit_and_substitute_raw_values( attr.g.node, raw_values_dict) for g in attr.graphs: cls._visit_and_substitute_raw_values( g.node, raw_values_dict) @classmethod def _external_value_resolution_pass(cls, model, raw_values_dict): for init in model.graph.initializer: cls._substitute_raw_value(init, raw_values_dict) cls._visit_and_substitute_raw_values(model.graph.node, raw_values_dict) @classmethod def _direct_initialize_parameters(cls, initializer, ws, device_option): for tp in initializer: ws.FeedBlob(tp.name, onnx.numpy_helper.to_array(tp), device_option) @classmethod def _direct_initialize_inputs(cls, inputs, initialized, ws, device_option): for value_info in inputs: if value_info.name in initialized: continue shape = list(d.dim_value for d in value_info.type.tensor_type.shape.dim) ws.FeedBlob( value_info.name, np.ones(shape, dtype=onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[ value_info.type.tensor_type.elem_type]), device_option) @staticmethod def optimize_onnx(input, init=False, predict=False): passes = [ 'fuse_consecutive_transposes', 'eliminate_nop_transpose', 'fuse_transpose_into_gemm', 'lift_lexical_references' ] if init: passes.append('split_init') if predict: passes.append('split_predict') out = onnx.optimizer.optimize(input, passes) return out @classmethod def prepare_zip_archive(cls, file, device='CPU', **kwargs): with zipfile.ZipFile(file, mode='r') as z: with z.open('__MODEL_PROTO', 'r') as f: model = onnx.load(f) blob_names = set(z.namelist()) - set('__MODEL_PROTO') # TODO: make this more efficient raw_values_dict = {} for name in blob_names: with z.open(name, 'r') as blob_file: raw_values_dict[name] = blob_file.read() return cls.prepare(model, device, raw_values_dict=raw_values_dict, **kwargs) @classmethod 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 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 @classmethod # TODO: This method needs a refactor for clarity def _onnx_node_to_caffe2_op(cls, init_model, pred_model, node_def, opset_version): cbackend = C.Caffe2Backend(cls._dummy_name) if cbackend.support_onnx_import(node_def.op_type): # extract value infos from pred model (value infos of # node's inputs that are in init model should be all # available in pred model) value_infos = [] for name in node_def.input: if pred_model is not None: for vi in itertools.chain(pred_model.graph.input, pred_model.graph.output, pred_model.graph.value_info): if vi.name == name: value_infos.append(vi.SerializeToString()) op_strs = cbackend.convert_node(node_def.SerializeToString(), value_infos, opset_version) init_ops = [] for s in op_strs[0]: op = caffe2_pb2.OperatorDef() op.ParseFromString(s) init_ops.append(op) ops = [] for s in op_strs[1]: op = caffe2_pb2.OperatorDef() op.ParseFromString(s) ops.append(op) return Caffe2Ops(ops, init_ops, []) if node_def.op_type in cls._special_operators: translator = getattr(cls, cls._special_operators[node_def.op_type]) else: translator = cls._common_onnx_node_to_caffe2_op ops = translator(init_model, pred_model, OnnxNode(node_def), opset_version) if isinstance(ops, Caffe2Ops): return ops if not isinstance(ops, container_abcs.Iterable): ops = [ops] return Caffe2Ops(ops, [], []) _broadcast_operators = { 'Add', 'Sub', } @classmethod def _common_onnx_node_to_caffe2_op(cls, init_model, pred_model, onnx_node, opset_version): """ This translator performs the basic translation of ONNX nodes into Caffe2 operators. Besides doing a straightforward marshalling from one format to another, it also does these extra things: - Renames operators based on '_renamed_operators' - Renames attributes based on '_global_renamed_attrs' and '_per_op_renamed_attrs' If you're writing a custom translator, consider calling this first, and then fixing things up further. """ c2_op = caffe2_pb2.OperatorDef() c2_op.input.extend(onnx_node.inputs) c2_op.output.extend(onnx_node.outputs) c2_op.name = onnx_node.name onnx_op_type = onnx_node.op_type broken_version = cls._broken_operators.get(onnx_op_type, float('Inf')) if broken_version <= opset_version: raise ValueError( "Don't know how to translate op {} in ONNX operator set v{} (I only support prior to v{})" .format(onnx_op_type, opset_version, broken_version)) c2_op.type = cls._renamed_operators.get(onnx_op_type, onnx_op_type) if not core.IsOperator(c2_op.type): raise ValueError( "Don't know how to translate op {}".format(onnx_op_type)) def kmap(k): if (onnx_op_type in cls._per_op_renamed_attrs and k in cls._per_op_renamed_attrs[onnx_op_type]): return cls._per_op_renamed_attrs[onnx_op_type][k] if k in cls._global_renamed_attrs: return cls._global_renamed_attrs[k] return k c2_op.arg.extend(onnx_node.attrs.caffe2(kmap=kmap)) if opset_version < 7: # onnx opset 7 and newest caffe2 have adopted full onnx broadcast semantics # so we don't need this hack anymore if c2_op.type in cls._broadcast_operators: already_broadcast = False for arg in c2_op.arg: if arg.name == 'broadcast': already_broadcast = True if not already_broadcast: c2_op.arg.extend( [caffe2.python.utils.MakeArgument('broadcast', 1)]) return c2_op @staticmethod def _all_names_in_graph(graph): if graph is None: return set() names = set() names.update(value_info.name for value_info in graph.input) names.update(value_info.name for value_info in graph.output) for node in graph.node: names.update(node.input) names.update(node.output) return names @classmethod def _graph_to_net(cls, onnx_graph, opset_version): net = caffe2_pb2.NetDef() for node in onnx_graph.node: try: c2ops = cls._onnx_node_to_caffe2_op(None, None, node, opset_version) except Exception as e: print('ONNX FATAL:', e) continue net.op.extend(c2ops.init_ops) net.op.extend(c2ops.ops) net.external_input.extend(c2ops.interface_blobs) net.external_output.extend(value_info.name for value_info in onnx_graph.output) net.external_input.extend(value_info.name for value_info in onnx_graph.input) return net @classmethod def _onnx_model_to_caffe2_net(cls, onnx_model, device, opset_version, include_initializers): device_option = get_device_option(Device(device)) # init_model = cls.optimize_onnx(onnx_model, init=True) # pred_model = cls.optimize_onnx(onnx_model, predict=True) init_model = onnx_model pred_model = onnx_model init_net = caffe2_pb2.NetDef() pred_net = caffe2_pb2.NetDef() init_net.name = onnx_model.graph.name + '_init' pred_net.name = onnx_model.graph.name + '_predict' if include_initializers: init_net.op.extend( cls._create_tensor_filling_op(tp) for tp in onnx_model.graph.initializer) cls._dummy_name.reset( cls._all_names_in_graph(init_model.graph) | cls._all_names_in_graph(pred_model.graph)) success = True for net, model in ((init_net, init_model), (pred_net, pred_model)): net.device_option.CopyFrom(device_option) for node in model.graph.node: try: c2ops = cls._onnx_node_to_caffe2_op(init_model, pred_model, node, opset_version) except Exception as e: success = False print('ONNX FATAL:', e) continue init_net.op.extend(c2ops.init_ops) net.op.extend(c2ops.ops) net.external_input.extend(c2ops.interface_blobs) net.external_output.extend(value_info.name for value_info in model.graph.output) net.external_input.extend(value_info.name for value_info in model.graph.input) if not success: raise RuntimeError('ONNX conversion failed') return init_net, pred_net # wrapper for backwards compatability @classmethod def onnx_graph_to_caffe2_net(cls, model, device="CPU", opset_version=_known_opset_version): return cls._onnx_model_to_caffe2_net(model, device=device, opset_version=opset_version, include_initializers=True) @classmethod def supports_device(cls, device_str): device = Device(device_str) if device.type == DeviceType.CPU: return True elif core.IsGPUDeviceType(device.type): return workspace.has_gpu_support return False @classmethod def is_compatible(cls, model, device='CPU', **kwargs): if hasattr(super(Caffe2Backend, cls), 'is_compatible') \ and callable(super(Caffe2Backend, cls).is_compatible): if not super(Caffe2Backend, cls).is_compatible( model, device, **kwargs): return False # TODO: should have an unspported list of operators, be optimistic for now return True prepare = Caffe2Backend.prepare prepare_zip_archive = Caffe2Backend.prepare_zip_archive run_node = Caffe2Backend.run_node run_model = Caffe2Backend.run_model supports_device = Caffe2Backend.supports_device # noqa is_compatible = Caffe2Backend.is_compatible