def compatible_shapes(tf_shape, inf_shape): def compare_elem(dt, ds): if dt is None or dt < 0: return True elif dt == ds: return True else: return False if tf_shape is None or any_variadic(inf_shape): return True else: return all(compare_elem(dt, ds) for dt, ds in zip(tf_shape, inf_shape))
def type_inference(self): x_type = self.x.dtype perm = self.perm.val x_shape = np.array(self.x.shape) if len(perm) != self.x.rank: msg = "perm should have the same length as rank(x): {} != {}" raise ValueError(msg.format(len(perm), self.x.rank)) if self.x.rank == 0: return self.x.sym_type # scalar cannot be transposed if any_variadic(self.x.shape): ret_shape = get_new_variadic_symbol() else: ret_shape = x_shape[perm] return types.tensor(x_type, tuple(ret_shape))
def compatible_shapes(tf_shape, inf_shape): def compare_elem(dt, ds): if dt is None or dt < 0: return True elif dt == ds: return True elif is_symbolic(ds): if is_symbolic(dt) and dt != ds: logging.warning("Symbolic dim {} and {}".format(ds, dt) +\ " assumed to be equal") return True else: return False if tf_shape is None or any_variadic(inf_shape): return True else: return all(compare_elem(dt, ds) for dt, ds in zip(tf_shape, inf_shape))
def _remove_symbolic_reshape_block(block): num_changes = 0 for op in list(block.operations): for b in op.blocks: num_changes += _remove_symbolic_reshape_block(b) if op.op_type != "reshape": continue if op.shape.val is not None: # shape does not contain symbol. continue if op.shape.sym_val is None: # shape is runtime determined. continue if len(op.shape.child_ops) > 1: continue # Use output shape as `shape` shape = op.outputs[0].shape if any_variadic(shape): msg = ("Cannot reshape to variadic from a compile time " + "shape argument. Variadic shape can only be achieved " + "via runtime shape argument. op: {}") raise ValueError(msg.format(op)) num_symbols = num_symbolic(shape) if num_symbols > 1: continue # Convert the one symbol to -1 integer_shape = [-1 if is_symbolic(i) else i for i in shape] shape_const = mb.const( val=integer_shape, name=op.shape.name + "x", before_op=op, ) reshaped = mb.reshape(x=op.x, shape=shape_const, name=op.name, before_op=op) op.enclosing_block.replace_uses_of_var_after_op(anchor_op=op, old_var=op.outputs[0], new_var=reshaped) # Remove all the ops at once block.remove_ops([op, op.shape.op]) num_changes += 1 return num_changes
def load(prog, **kwargs): if "main" not in prog.functions: msg = "main function not found in program {}" raise ValueError(msg.format(prog)) if len(prog.functions) != 1: msg = ("Program must have exactly one `main` function to " "convert to NN. Program: {}") raise ValueError(msg.format(prog)) nn_backend_passes(prog) input_types = prog.main_input_types output_types = prog.main_output_types v1_inputs = [] symbolic_inputs = {} for name, var in prog.functions["main"].inputs.items(): if types.is_tensor(var.sym_type): sym_shape = var.sym_type.get_shape() if any_variadic(sym_shape): raise NotImplementedError("Variadic rank is not supported") if any_symbolic(sym_shape): user_specified = False for input_type in input_types: if name == input_type.name: sym_shape = input_type.shape.default user_specified = True break # Use dummy static shape, and will set it later. shape = [1 if is_symbolic(d) else d for d in sym_shape] if not user_specified: symbolic_inputs[name] = sym_shape else: shape = sym_shape v1_inputs.append((name, Array(*shape))) elif types.is_scalar(var.sym_type): v1_inputs.append((name, Array(1))) else: raise NotImplementedError() v1_outputs = [] for var in prog.functions["main"].outputs: if types.is_tensor(var.sym_type) or types.is_primitive(var.sym_type): # Disregard the output types v1_outputs.append((var.name, None)) else: raise NotImplementedError() # create neural network builder builder = neural_network.NeuralNetworkBuilder( v1_inputs, v1_outputs, disable_rank5_shape_mapping=True, use_float_arraytype=True, ) # const in V2 are added lazily to V1 by each op whenever needed. # `const_context` stores the const names we've added so far and avoid # adding a const more than once. # const_context: list[set of str] (const name for v1 & v2 # (the same)). Note that in NN in outer layer is visible from the inner # layer, so the const_context is simply a stack of set. const_context = [] # Iterate through ops and add to builder convert_ops( const_context, builder, prog.functions["main"].operations, prog.functions["main"].outputs, ) proto = builder.spec # image input has_image_input = any([isinstance(s, ImageType) for s in input_types]) if has_image_input: proto = _convert_to_image_input(proto, input_types, skip_model_load=kwargs.get( "skip_model_load", False)) # image output if output_types is not None: assert len(output_types) == len(prog.functions["main"].outputs), \ "number of mil program outputs do not match the number of outputs provided by the user" for i, output_proto_desc in enumerate(proto.description.output): output_var = prog.functions["main"].outputs[i] if isinstance(output_types[i], ImageType): if not types.is_tensor(var.sym_type): raise ValueError( "Image output, '{}', is a scalar, but it should be a tensor of rank 4" .format(var.name)) shape = var.sym_type.get_shape() if any_variadic(shape): raise ValueError( "Variable rank model outputs, that are ImageTypes, are not supported" ) if any([is_symbolic(d) for d in shape]): raise NotImplementedError( "Image output '{}' has symbolic dimensions in its shape" .format(var.name)) _validate_image_input_output_shapes( output_types[i].color_layout, shape, var.name, is_input=False) clr_space = _get_colorspace_enum(output_types[i].color_layout) output_proto_desc.type.imageType.colorSpace = clr_space output_proto_desc.type.imageType.width = shape[-1] output_proto_desc.type.imageType.height = shape[-2] # classifier flag classifier_config = kwargs.get("classifier_config", None) if classifier_config is not None: # verify that classifier_config.predicted_probabilities_output if its exists. # And if its empty/None, fill it with the last non const op's output # this is done in "_get_probability_var_for_classifier()" probability_var = _get_probability_var_for_classifier( prog, classifier_config) if classifier_config.predicted_probabilities_output != probability_var.name: classifier_config.predicted_probabilities_output = probability_var.name # add classifier related fields to the proto spec proto = _convert_to_classifier(proto, classifier_config, skip_model_load=kwargs.get( "skip_model_load", False)) _set_user_inputs(proto, input_types) _set_symbolic_inputs(proto, symbolic_inputs) _set_optional_inputs(proto, input_types) return proto
def load(prog, weights_dir, resume_on_errors=False, **kwargs): if "main" not in prog.functions: raise ValueError("main function not found in program") mil_passes.mil_backend_passes(prog) # if user has specified "ClassifierConfig", then add the "classify" op to the prog classifier_config = kwargs.get("classifier_config", None) predicted_feature_name = None predicted_probabilities_name = None if classifier_config is not None: predicted_feature_name, predicted_probabilities_name = _add_classify_op( prog, classifier_config) input_types = prog.main_input_types weight_path = os.path.join(weights_dir, _WEIGHTS_FILE_NAME) blob_writer = BlobWriter(weight_path) function_protos = {} for func_name, func in prog.functions.items(): function_protos[func_name] = convert_function(func, prog.parameters, blob_writer) proto = pm.Program( version=1, functions=function_protos, ) input_features = [] output_features = [] symbolic_inputs = [] image_input_names = { } # these are the model inputs marked as image by the user input_shape_map = {} for input_type in input_types: if isinstance(input_type, ImageType): image_input_names[input_type.name] = input_type # error checking for input(s) marked as images if input_type.name not in list( prog.functions["main"].inputs.keys()): msg = "Provided image input '{}' is not one of the inputs of the MIL program" raise ValueError(msg.format(input_type.name)) input_shape_map[input_type.name] = input_type for name, var in prog.functions["main"].inputs.items(): input_feature_type = ft.FeatureType() # error checking for input(s) marked as images # an image input must be of type tensor in program proto # (since an image type does not exist in MIL program) if name in image_input_names and \ not types.is_tensor(var.sym_type): raise ValueError( "For the image input, '{}', its type in the MIL program must be tensor. " "Instead it is {}.".format(name, var.sym_type.__type_info__())) if types.is_tensor(var.sym_type): shape = var.sym_type.get_shape() if any_variadic(shape): raise ValueError( "Variable rank model inputs are not supported!") if any_symbolic(shape): symbolic_inputs.append(name) # We extract the default input shape given by user first if name in input_shape_map: shape = input_shape_map[name].shape.default else: logging.warning( "Input shape not fully specified by enumerated shapes or range dim! 1 will be used for dimension not specified instead." ) # If no input shape is provided (ex. auto conversion of -1 in Tensorflow) shape = [1 if is_symbolic(d) else d for d in shape] if name not in image_input_names: # make a feature type of Type "multiArrayType" array_type = ft.ArrayFeatureType( shape=shape, dataType=cast_to_framework_io_dtype(var, False)) input_feature_type.multiArrayType.CopyFrom(array_type) else: if len(shape) < 3: raise ValueError( "Image input, '{}', must have rank at least 3. Instead it has rank {}" .format(name, len(shape))) # make a feature type of Type "imageType" input_type = image_input_names[name] if not input_type.channel_first: raise ValueError( "Image input, '{}', must be in the channel_first format" .format(name)) if input_type.color_layout == "G": clr_space = ft.ImageFeatureType.ColorSpace.GRAYSCALE elif input_type.color_layout == "BGR": clr_space = ft.ImageFeatureType.ColorSpace.BGR else: clr_space = ft.ImageFeatureType.ColorSpace.RGB image_type = ft.ImageFeatureType(width=shape[-1], height=shape[-2], colorSpace=clr_space) input_feature_type.imageType.CopyFrom(image_type) input_features.append( ml.FeatureDescription(name=name, type=input_feature_type)) elif types.is_scalar(var.sym_type): array_type = ft.ArrayFeatureType( shape=[1], dataType=cast_to_framework_io_dtype(var, False)) input_feature_type.multiArrayType.CopyFrom(array_type) input_features.append( ml.FeatureDescription(name=var.name, type=input_feature_type)) else: raise NotImplementedError() for var in prog.functions["main"].outputs: output_feature_type = ft.FeatureType() if types.is_tensor(var.sym_type) or types.is_primitive(var.sym_type): dataType = None if classifier_config is None or var.name != predicted_feature_name: # Not a classifier output, make sure model output type matches with ML Program type. dataType = cast_to_framework_io_dtype(var, True) else: # Classifier outputs are set up separately, so default to fp32 for now. dataType = ft.ArrayFeatureType.ArrayDataType.FLOAT32 array_type = ft.ArrayFeatureType(shape=None, dataType=dataType) output_feature_type.multiArrayType.CopyFrom(array_type) output_features.append( ml.FeatureDescription(name=var.name, type=output_feature_type)) elif (types.is_dict(var.sym_type)): output_feature_type.dictionaryType.MergeFromString(b"") keytype, valtype = var.sym_type.T if types.is_str(keytype): output_feature_type.dictionaryType.stringKeyType.MergeFromString( b"") elif (keytype == types_int64): output_feature_type.dictionaryType.int64KeyType.MergeFromString( b"") else: raise ValueError("Dictionary key type not supported.") output_features.append( ml.FeatureDescription(name=var.name, type=output_feature_type)) else: raise NotImplementedError() # Model description desc = ml.ModelDescription(input=input_features, output=output_features) if classifier_config is not None: desc.predictedFeatureName = predicted_feature_name desc.predictedProbabilitiesName = predicted_probabilities_name # Manually edit output type of predictedFeatureName. # It doesn't use MLMultiArray and really uses a "primitive" type. for output in desc.output: if output.name == predicted_feature_name: if type(classifier_config.class_labels[0]) == int: output.type.int64Type.MergeFromString(b"") else: output.type.stringType.MergeFromString(b"") break # Create ML Model model = ml.Model(description=desc, specificationVersion=_SPECIFICATION_VERSION_IOS_15) model.mlProgram.CopyFrom(proto) # Set symbolic shapes for input_name in symbolic_inputs: input_type = input_shape_map.get(input_name, None) if isinstance(input_type, ImageType): if isinstance(input_type.shape, EnumeratedShapes): enumerated_shapes = [] for s in input_type.shape.shapes: enumerated_shapes.append( NeuralNetworkImageSize(height=s.shape[-2], width=s.shape[-1])) add_enumerated_image_sizes(model, input_name, sizes=enumerated_shapes) else: img_range = NeuralNetworkImageSizeRange() H = input_type.shape.shape[-2] W = input_type.shape.shape[-1] if isinstance(H, RangeDim): img_range.add_height_range((H.lower_bound, H.upper_bound)) elif is_symbolic(H): img_range.add_height_range((1, -1)) else: img_range.add_height_range((H, H)) if isinstance(W, RangeDim): img_range.add_width_range((W.lower_bound, W.upper_bound)) elif is_symbolic(W): img_range.add_width_range((1, -1)) else: img_range.add_width_range((W, W)) update_image_size_range(model, input_name, img_range) elif isinstance(input_type, TensorType): if isinstance(input_type.shape, EnumeratedShapes): add_multiarray_ndshape_enumeration( model, input_name, [tuple(s.shape) for s in input_type.shape.shapes]) else: lb = [] ub = [] for s in input_type.shape.shape: if isinstance(s, RangeDim): lb.append(s.lower_bound) ub.append(s.upper_bound) elif is_symbolic(s): lb.append(1) ub.append(-1) else: lb.append(s) ub.append(s) set_multiarray_ndshape_range(model, input_name, lower_bounds=lb, upper_bounds=ub) elif input_type is None: sym_type = prog.functions["main"].inputs[input_name].sym_type lb = [] ub = [] for s in sym_type.get_shape(): if is_symbolic(s): lb.append(1) ub.append(-1) else: lb.append(s) ub.append(s) set_multiarray_ndshape_range(model, input_name, lower_bounds=lb, upper_bounds=ub) # Set optional inputs _set_optional_inputs(model, input_types) return model
def load(prog, **kwargs): if "main" not in prog.functions: msg = "main function not found in program {}" raise ValueError(msg.format(prog)) if len(prog.functions) != 1: msg = ("Program must have exactly one `main` function to " "convert to NN. Program: {}") raise ValueError(msg.format(prog)) nn_backend_passes(prog) input_types = prog.main_input_types v1_inputs = [] symbolic_inputs = {} for name, var in prog.functions["main"].inputs.items(): if types.is_tensor(var.sym_type): sym_shape = var.sym_type.get_shape() if any_variadic(sym_shape): # TODO: rdar://59559656 raise NotImplementedError("Variadic rank is not supported") if any_symbolic(sym_shape): user_specified = False for input_type in input_types: if name == input_type.name: sym_shape = input_type.shape.default user_specified = True break # Use dummy static shape, and will set it later. shape = [1 if is_symbolic(d) else d for d in sym_shape] if not user_specified: symbolic_inputs[name] = sym_shape else: shape = sym_shape v1_inputs.append((name, datatypes.Array(*shape))) elif types.is_scalar(var.sym_type): v1_inputs.append((name, datatypes.Array(1))) else: raise NotImplementedError() v1_outputs = [] for var in prog.functions["main"].outputs: if types.is_tensor(var.sym_type) or types.is_primitive(var.sym_type): # Disregard the output types v1_outputs.append((var.name, None)) else: raise NotImplementedError() # create neural network builder builder = neural_network.NeuralNetworkBuilder( v1_inputs, v1_outputs, disable_rank5_shape_mapping=True, use_float_arraytype=True, ) # const in V2 are added lazily to V1 by each op whenever needed. # `const_context` stores the const names we've added so far and avoid # adding a const more than once. # const_context: list[set of str] (const name for v1 & v2 # (the same)). Note that in NN in outer layer is visible from the inner # layer, so the const_context is simply a stack of set. const_context = [] # Iterate through ops and add to builder convert_ops( const_context, builder, prog.functions["main"].operations, prog.functions["main"].outputs, ) # Replace model outputs's name with v1_outputs output_names = [x[0] for x in v1_outputs] for i, spec_layer in enumerate(builder.nn_spec.layers): for j, name in enumerate(spec_layer.output): for output_name in output_names: if output_name.split(":")[0] == name: spec_layer.output[j] = output_name proto = builder.spec # image input has_image_input = any([isinstance(s, ImageType) for s in input_types]) if has_image_input: proto = _convert_to_image_input(proto, input_types) # classifier flag classifier_config = kwargs.get("classifier_config", None) if classifier_config is not None: proto = _convert_to_classifier(proto, classifier_config) _set_user_inputs(proto, input_types) _set_symbolic_inputs(proto, symbolic_inputs) return proto