## @package model_helper # Module caffe2.python.model_helper from caffe2.python import core, scope, workspace from caffe2.python.helpers.db_input import db_input from caffe2.python.modeling import parameter_info from caffe2.python.modeling.parameter_sharing import ( parameter_sharing_context, ) from caffe2.python.optimizer_context import ( OptimizerContext, DEFAULT_OPTIM, ) from caffe2.python.regularizer_context import RegularizerContext from future.utils import viewitems, viewkeys from itertools import chain import logging # _known_working_ops are operators that do not need special care. _known_working_ops = [ "Accuracy", "Adam", "Add", "Adagrad", "SparseAdagrad", "Adadelta", "SparseAdadelta", "AveragedLoss", "Cast", "Checkpoint", "ConstantFill", "Copy", "CopyGPUToCPU", "CopyCPUToGPU", "DequeueBlobs", "EnsureCPUOutput", "ExpandDims", "Flatten", "FlattenToVec", "LabelCrossEntropy", "LearningRate", "MakeTwoClass", "MatMul", "NCCLAllreduce", "NHWC2NCHW", "PackSegments", "Print", "PRelu", "ReduceFrontSum", "Scale", "ScatterWeightedSum", "Sigmoid", "SortedSegmentSum", "Snapshot", # Note: snapshot is deprecated, use Checkpoint "Softmax", "SoftmaxWithLoss", "SquaredL2Distance", "Squeeze", "StopGradient", "Summarize", "Tanh", "Transpose", "UnpackSegments", "WeightedSum", "YellowFin" ] class ModelHelper(object): """A helper model so we can manange models more easily. It contains net def and parameter storages. You can add an Operator yourself, e.g. model = model_helper.ModelHelper(name="train_net") # init your weight and bias as w and b w = model.param_init_net.XavierFill(...) b = model.param_init_net.ConstantFill(...) fc1 = model.FC([input, w, b], output, **kwargs) or you can use helper functions in brew module without manually defining parameter initializations and operators. model = model_helper.ModelHelper(name="train_net") fc1 = brew.fc(model, input, output, dim_in, dim_out, **kwargs) """ def __init__(self, name=None, init_params=True, allow_not_known_ops=True, skip_sparse_optim=False, param_model=None, arg_scope=None): self.name = name or "model" self.net = core.Net(self.name) if param_model is not None: self.param_init_net = param_model.param_init_net self.param_to_grad = param_model.param_to_grad self.params = param_model.params self._parameters_info = param_model._parameters_info self._computed_params = param_model._computed_params else: self.param_init_net = core.Net(self.name + '_init') self.param_to_grad = {} self.params = [] self._parameters_info = {} self._computed_params = [] self._param_info_deprecated = [] self._devices = [] self.gradient_ops_added = False self.init_params = init_params self.allow_not_known_ops = allow_not_known_ops self.skip_sparse_optim = skip_sparse_optim self.weights = [] self.biases = [] self._arg_scope = { 'order': "NCHW", 'use_cudnn': True, 'cudnn_exhaustive_search': False, } if arg_scope is not None: # Please notice value as None is not acceptable. We are not checking it # here because we already have check in MakeArgument. self._arg_scope.update(arg_scope) @property def arg_scope(self): return self._arg_scope def get_name(self): return self.name def _infer_param_shape(self, param): for op in self.param_init_net.Proto().op: if str(param) in op.output: for arg in op.arg: if arg.name == "shape": return list(arg.ints) return None def _update_param_info_deprecated(self): assert len(self._param_info_deprecated) <= len(self.params) for param in self.params[len(self._param_info_deprecated):]: if not isinstance(param, core.BlobReference): raise ValueError( "Param %s must be a BlobReference!" % str(param)) self._param_info_deprecated.append(parameter_info.ParameterInfo( param_id=len(self._param_info_deprecated), param=param, shape=self._infer_param_shape(param))) for info in self._param_info_deprecated: info.grad = self.param_to_grad.get(info.name) def _normalize_tags(self, tags): tags = tags or [] return set(tags) if isinstance(tags, list) else set([tags]) def create_param(self, param_name, shape, initializer, tags=None): """ Creates parameter with a given name and initializer. If param_name is instance of BlobRefernce - then this blob will be used to store parameter (no any logic will affect it's location). If param_name is instance of a string type, then the final blob will be created in the CurrentNameScope with the respect of all parameter sharing logic, i.e. 'resolved_name_scope/param_name'. Parameter sharing logic is going to override CurrentNameScope according to the rules that are specified through ParameterSharing contexts, all ParameterSharing contexts are applied recursively until there are no extra overrides present, where on each step the best match will be applied first. The following examples should clarify the way ParameterSharing logic works: As an example if this function is called with parameter 'w': a. Call from some scope 'global_scope' with no Parameter sharing: 'global_scope/w' b. Call from scope 'scope_b', with override {'scope_b': 'scope_a'}: 'scope_a/w' c. Call from scope 'scope_a', with override {'scope_a': ''}: 'scope_a/w' d. Call from scope 'scope_b/shared', with overrides {'scope_b/shared': 'scope_b', 'scope_b': 'scope_a'}: 'scope_a/w' d. Call from scope 'scope_b/unshared', with overrides {'scope_b/shared': 'scope_b', 'scope_b': 'scope_a'}: 'scope_a/unshared/w' """ # ParameterSharing works only for case when param_name is instance of # a string type. If param_name is a BlobReference - no attempt for # ParameterSharing will be applied. if isinstance(param_name, core.BlobReference): param_name = str(param_name) elif isinstance(param_name, str): # Parameter name will be equal to current Namescope that got # resolved with the respect of parameter sharing of the scopes. param_name = parameter_sharing_context.get_parameter_name( param_name) else: raise TypeError("Unsupported type for param_name") if param_name in self._parameters_info: assert self._parameters_info[param_name].shape == shape return self._parameters_info[param_name].blob param_info = initializer.create_param( param_name=core.BlobReference(param_name), init_net=self.param_init_net, shape=shape, ) optim_context = OptimizerContext.current() for tag in self._normalize_tags(tags): if optim_context.has_optimizer(tag): # param_info will check optimizer has not been set param_info.optimizer = optim_context.get_optimizer(tag) if not param_info.optimizer and optim_context.has_optimizer(DEFAULT_OPTIM): param_info.optimizer = optim_context.get_optimizer(DEFAULT_OPTIM) reg_context = RegularizerContext.current() param_info.regularizer = reg_context self._parameters_info[param_name] = param_info # Add param to legacy structs as well, so all other functions for # parameters are still working. self.AddParameter(param_info.blob, tags) return param_info.blob def get_param_info(self, param): assert isinstance(param, core.BlobReference), \ "Param {} is not a BlobReference".format(param) return self._parameters_info.get(param, None) # This method is deprecated, use create_param method which # also does parameter initialization when needed def add_param_DEPRECATED(self, param, key=None, shape=None, length=None): logging.warning("add_param method is DEPRECATED") self._update_param_info_deprecated() self.AddParameter(param) if key is not None and self.net.input_record() is not None: idx = self.net.input_record().field_blobs().index(key) key = self.net.input_record().field_names()[idx] shape = shape if shape is not None else self._infer_param_shape(param) if not isinstance(param, core.BlobReference): raise ValueError("Param %s must be a BlobReference!" % str(param)) self._param_info_deprecated.append(parameter_info.ParameterInfo( param_id=len(self._param_info_deprecated), param=param, shape=shape, key=key, length=length, )) return self._param_info_deprecated[-1] def AddParameter(self, param, tags=None): assert isinstance(param, core.BlobReference) tags = self._normalize_tags(tags) if parameter_info.ParameterTags.COMPUTED_PARAM in tags: self._computed_params.append(param) else: self.params.append(param) if parameter_info.ParameterTags.WEIGHT in tags: self.weights.append(param) if parameter_info.ParameterTags.BIAS in tags: self.biases.append(param) @staticmethod def _NormalizeNamescope(namescope): if namescope is None: return scope.CurrentNameScope() elif namescope == '' or namescope.endswith(scope._NAMESCOPE_SEPARATOR): return namescope else: return namescope + scope._NAMESCOPE_SEPARATOR def GetParams(self, namescope=None, top_scope=False): ''' Returns the params in current namescope ''' namescope = ModelHelper._NormalizeNamescope(namescope) if namescope == '': return self.params[:] else: return [p for p in self.params if p.GetNameScope().startswith(namescope)] def Proto(self): return self.net.Proto() def InitProto(self): return self.param_init_net.Proto() def RunAllOnGPU(self, *args, **kwargs): self.param_init_net.RunAllOnGPU(*args, **kwargs) self.net.RunAllOnGPU(*args, **kwargs) def CreateDB(self, blob_out, db, db_type, **kwargs): dbreader = self.param_init_net.CreateDB( [], blob_out, db=db, db_type=db_type, **kwargs) return dbreader def AddGradientOperators(self, *args, **kwargs): if self.gradient_ops_added: raise RuntimeError("You cannot run AddGradientOperators twice.") self.Validate() self.gradient_ops_added = True self.grad_map = self.net.AddGradientOperators(*args, **kwargs) self.param_to_grad = self.get_param_to_grad(self.params) # Populate ParameterInfo for all parameters if missing # and add gradient blob information. So optimizers can use it for param, grad in self.param_to_grad.items(): param_info = self.get_param_info(param) if param_info: param_info.grad = grad else: self._parameters_info[param] = parameter_info.ParameterInfo( param_id=None, param=param, grad=grad, ) return self.grad_map def get_param_to_grad(self, params): ''' Given a list of parameters returns a dict from a parameter to a corresponding gradient ''' param_to_grad = {} if not self.gradient_ops_added: raise RuntimeError("You need to run AddGradientOperators first.") # We need to use empty namescope when creating the gradients # to prevent duplicating the namescope prefix for gradient blobs. for p in params: if str(p) in self.grad_map: param_to_grad[p] = self.grad_map[str(p)] return param_to_grad def GetOptimizationParamInfo(self, params=None): ''' Returns a map for param => grad. If params is not specified, all parameters will be considered. ''' if not self.gradient_ops_added: raise RuntimeError("Need to call AddGradientOperators first") param_to_grad = self.param_to_grad if params: param_to_grad = self.get_param_to_grad(params) return [ self.get_param_info(param) for param, grad in viewitems(param_to_grad) if ( not self.skip_sparse_optim or not isinstance(grad, core.GradientSlice) ) ] def _Validate(self): ''' Check for duplicate params ''' params_list = [str(p) for p in self.params] params_set = set(params_list) dupes = [] if len(params_set) != len(params_list): params_list = sorted(params_list) for j, p in enumerate(params_list): if j > 0 and params_list[j - 1] == p: if p not in dupes: dupes.append(p) return dupes def Validate(self): dupes = self._Validate() assert dupes == [], "Duplicate params: {}".format(dupes) def GetComputedParams(self, namescope=None): ''' Returns the computed params in current namescope. 'Computed params' are such parameters that are not optimized via gradient descent but are directly computed from data, such as the running mean and variance of Spatial Batch Normalization. ''' namescope = ModelHelper._NormalizeNamescope(namescope) if namescope == '': return self._computed_params[:] else: return [p for p in self._computed_params if p.GetNameScope().startswith(namescope)] def GetAllParams(self, namescope=None): return self.GetParams(namescope) + self.GetComputedParams(namescope) def TensorProtosDBInput( self, unused_blob_in, blob_out, batch_size, db, db_type, **kwargs ): """TensorProtosDBInput.""" assert len(unused_blob_in) == 0, \ """You cannot pass reader to model_helper.TensorProtosDBInput. Use model.net.TensorProtosDBInput instead to create the op.""" return db_input( self, blob_out, batch_size, db, db_type, **kwargs) def GetDevices(self): assert len(self._devices) > 0, \ "Use data_parallel_model to run model on multiple GPUs." return self._devices def __getattr__(self, op_type): """Catch-all for all other operators, mostly those without params.""" if op_type.startswith('__'): raise AttributeError(op_type) if not core.IsOperator(op_type): raise AttributeError( 'Method ' + op_type + ' is not a registered operator.' + ' Did you mean: [' + ','.join(workspace.C.nearby_opnames(op_type)) + ']' ) if op_type not in _known_working_ops: if not self.allow_not_known_ops: raise AttributeError( "Operator {} is not known to be safe".format(op_type)) logging.warning("You are creating an op that the ModelHelper " "does not recognize: {}.".format(op_type)) return self.net.__getattr__(op_type) def __dir__(self): return sorted(set(chain( dir(type(self)), viewkeys(self.__dict__), _known_working_ops ))) def GetCompleteNet(self): r""" Return param_init_net + net Net. Returns: 'core.Net' containing param_init_net and net """ new_net = self.param_init_net.Clone( self.name + "_complete_net", keep_schema=True) # add init net info to debug info for op in new_net.Proto().op: op.debug_info = op.debug_info + "/param_init_net" new_net.AppendNet(self.net) # keep the execution optimization if self.net.Proto().HasField("type"): new_net.Proto().type = self.net.Proto().type return new_net def ConstructInitTrainNetfromNet(self, net): r""" construct init net and train net from complete_net Inputs: net: 'core.Net' containing param_init_net and train net """ param_op_mask = [] train_op_mask = [] for idx, op in enumerate(net.Proto().op): if op.debug_info.endswith("/param_init_net"): param_op_mask.append(idx) else: train_op_mask.append(idx) self.param_init_net = net.Clone( net.Name() + "/generated_param_init_net", keep_schema=True, op_id_mask=param_op_mask, update_external_list=True, ) self.net = net.Clone( net.Name() + "/generated_net", keep_schema=True, op_id_mask=train_op_mask, update_external_list=True, ) def ExtractPredictorNet( net_proto, input_blobs, output_blobs, device=None, renames=None, disabled_inputs=None, ): ''' Takes a model net for training and returns a net which can be used for prediction. For example, all gradient operators and input operators are removed. @param net_proto protobuf of the net you want to process (net.Proto()) @param input_blobs list/set of blob names that are the inputs of predictor @param output_blobs list/set of blob names that are outputs of predictor @param device optional device option that is assigned @param renames dictionary of blob name to a new name (optional) @param disabled_inputs optional set of blobs that are 'switched off'. This will cause branches with those blobs as inputs to be removed ''' predict_net = core.Net(net_proto.name + "_predict") predict_proto = predict_net.Proto() orig_external_inputs = set(net_proto.external_input) orig_external_outputs = set(net_proto.external_output) input_blobs = {str(b) for b in input_blobs} known_blobs = set(orig_external_inputs).union(input_blobs) output_blobs = {str(b) for b in output_blobs} external_inputs = set(input_blobs) external_outputs = set(output_blobs) if renames is None: renames = {} if disabled_inputs is not None: known_blobs = known_blobs - set(disabled_inputs) ops = list(net_proto.op) # Find the range of ops that we should include try: first_op_with_input = min( [ j for j in range(len(ops)) if input_blobs.intersection(ops[j].input) and ops[j].type != 'StopGradient' ] ) except ValueError: raise Exception("No ops with input={}".format(input_blobs)) try: last_op_with_output = max( [ j for j in range(len(ops)) if output_blobs.intersection(ops[j].output) ] ) except ValueError: raise Exception("No ops with output={}".format(output_blobs)) def validate_op(op): # Check that the op does not have is_test = 0 set. This is a common # pitfall with SpatialBN op, at lest. for arg in op.arg: if arg.name == "is_test" and arg.i == 0: raise Exception( "An operator had is_test=0, did you try to extract a " + "predictor from a train model (instead of test model)?" + " Op was: {}".format(str(op)) ) def rename_list(proto_list): # proto lists don't support assignments new_list = proto_list[:] for j, b in enumerate(new_list): if b in renames: new_list[j] = renames[b] del proto_list[:] proto_list.extend(new_list) # Iterate through the ops and only include those whose inputs # we can satisfy. for op in ops[first_op_with_input:(last_op_with_output + 1)]: if known_blobs.issuperset(op.input): # Special handling for recurrent nets # TODO: when standard argument type for "nets" is introduced, # this can be more general if op.type == 'RecurrentNetwork': for arg in op.arg: if arg.name == 'backward_step_net': arg.ClearField(str('n')) elif arg.name == 'step_net': for step_op in arg.n.op: rename_list(step_op.input) rename_list(step_op.output) if device is not None: step_op.device_option.device_type = device.device_type step_op.device_option.device_id = device.device_id rename_list(arg.n.external_input) rename_list(arg.n.external_output) # Add additional external inputs external_inputs.update( set(arg.n.external_input).intersection( orig_external_inputs ) ) if device is not None: op.device_option.device_type = device.device_type op.device_option.device_id = device.device_id validate_op(op) predict_proto.op.extend([op]) known_blobs.update(op.output) external_inputs.update( set(op.input).intersection(orig_external_inputs) ) external_outputs.update( set(op.output).intersection(orig_external_outputs) ) else: logging.debug( "Op {} had unknown inputs: {}".format( op.type, set(op.input).difference(known_blobs) ) ) # Predictor net's external inputs and outputs include only those # that are part of this net. predict_proto.external_input.extend(external_inputs) predict_proto.external_output.extend(external_outputs) rename_list(predict_proto.external_input) rename_list(predict_proto.external_output) renamed_input_blobs = [] for b in input_blobs: if b in renames: renamed_input_blobs.append(renames[b]) else: renamed_input_blobs.append(b) for op in predict_proto.op: rename_list(op.input) rename_list(op.output) return predict_net, list( set(predict_proto.external_input) - set(renamed_input_blobs) )