import re import numbers from collections import namedtuple import custom_layers from .shapes import * LAYER_DESCRIPTORS = { # Caffe Types 'AbsVal': shape_identity, 'Accuracy': shape_scalar, 'ArgMax': shape_not_implemented, 'BatchNorm': shape_identity, 'BNLL': shape_not_implemented, 'Concat': shape_concat, 'ContrastiveLoss': shape_scalar, 'Convolution': shape_convolution, 'Deconvolution': shape_deconvolution, 'Data': shape_data, 'Dropout': shape_identity, 'DummyData': shape_data, 'Crop': shape_crop, 'EuclideanLoss': shape_scalar, 'Eltwise': shape_identity, 'Exp': shape_identity, 'Flatten': shape_not_implemented, 'HDF5Data': shape_data, 'HDF5Output': shape_identity, 'HingeLoss': shape_scalar, 'Im2col': shape_not_implemented, 'ImageData': shape_data, 'InfogainLoss': shape_scalar, 'InnerProduct': shape_inner_product, 'Input': shape_data, 'LRN': shape_identity, 'MemoryData': shape_mem_data, 'MultinomialLogisticLoss': shape_scalar, 'MVN': shape_not_implemented, 'Pooling': shape_pool, 'Power': shape_power, 'ReLU': shape_identity, 'PReLU': shape_identity, 'Scale': shape_identity, 'Sigmoid': shape_identity, 'SigmoidCrossEntropyLoss': shape_scalar, 'Silence': shape_not_implemented, 'Softmax': shape_identity, 'SoftmaxWithLoss': shape_scalar, 'Split': shape_not_implemented, 'Slice': shape_not_implemented, 'TanH': shape_identity, 'WindowData': shape_not_implemented, 'Threshold': shape_identity, } # layer types in 'V1LayerParameter' # (v1layertype name, enum value, mapped to layer type) v1_layertypes = [ ('ABSVAL', 35), ('ACCURACY', 1), ('ARGMAX', 30), ('BNLL', 2), ('CONCAT', 3), ('CONVOLUTION', 4), ('DATA', 5), ('DECONVOLUTION', 39), ('DROPOUT', 6), ('ELTWISE', 25), ('EXP', 38), ('FLATTEN', 8), ('IM2COL', 11), ('INNERPRODUCT', 14), ('LRN', 15), ('MEMORYDATA', 29), ('MULTINOMIALLOGISTICLOSS', 16), ('MVN', 34), ('POOLING', 17), ('POWER', 26), ('RELU', 18), ('SIGMOID', 19), ('SIGMOIDCROSSENTROPYLOSS', 27), ('SILENCE', 36), ('SOFTMAX', 20), ('SPLIT', 22), ('SLICE', 33), ('TANH', 23), ('WINDOWDATA', 24), ('THRESHOLD', 31), ] LAYER_TYPES = LAYER_DESCRIPTORS.keys() LayerType = type('LayerType', (), {t: t for t in LAYER_TYPES}) #map the layer name in V1 to standard name V1_LAYER_MAP = {'_not_init_': True} def get_v1_layer_map(): global V1_LAYER_MAP if '_not_init_' not in V1_LAYER_MAP: return V1_LAYER_MAP else: del V1_LAYER_MAP['_not_init_'] name2layer = {} for n in LAYER_TYPES: name2layer[n.upper()] = n for l in v1_layertypes: n, v = l if n in name2layer and v not in V1_LAYER_MAP: V1_LAYER_MAP[v] = name2layer[n] else: raise KaffeError('not found v1 layer type %s' % n) return V1_LAYER_MAP class NodeKind(LayerType): @staticmethod def map_raw_kind(kind): if custom_layers.has_layer(kind): return kind if kind in LAYER_TYPES: return kind v1_layers = get_v1_layer_map() if kind in v1_layers: return v1_layers[kind] else: return None @staticmethod def compute_output_shape(node): if custom_layers.has_layer(node.kind): return custom_layers.compute_output_shape(node.kind, node) try: val = LAYER_DESCRIPTORS[node.kind](node) return val except NotImplementedError: raise KaffeError( 'Output shape computation not implemented for type: %s' % node.kind) class NodeDispatchError(KaffeError): pass class NodeDispatch(object): @staticmethod def get_handler_name(node_kind): if len(node_kind) <= 6: # A catch-all for things like ReLU and tanh return node_kind.lower() # Convert from CamelCase to under_scored name = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', node_kind) return re.sub('([a-z0-9])([A-Z])', r'\1_\2', name).lower() def get_handler(self, node_kind, prefix): if custom_layers.has_layer(node_kind): return getattr(self, 'map_custom') name = self.get_handler_name(node_kind) name = '_'.join((prefix, name)) try: return getattr(self, name) except AttributeError: raise NodeDispatchError( 'No handler found for node kind: %s (expected: %s)' % (node_kind, name)) class LayerAdapter(object): def __init__(self, layer, kind): self.layer = layer self.kind = kind @property def parameters(self): name = NodeDispatch.get_handler_name(self.kind) if self.kind.lower() == "normalize": name = "norm" elif self.kind.lower() == "deconvolution": name = "convolution" name = '_'.join((name, 'param')) try: return getattr(self.layer, name) except AttributeError: print(dir(self.layer)) raise NodeDispatchError( 'Caffe parameters not found attr[%s] for layer kind[%s]' % (name, self.kind)) @staticmethod def get_kernel_value(scalar, repeated, idx, default=None): if scalar: return scalar if repeated: if isinstance(repeated, numbers.Number): return repeated if len(repeated) == 1: # Same value applies to all spatial dimensions return int(repeated[0]) assert idx < len(repeated) # Extract the value for the given spatial dimension return repeated[idx] if default is None: raise ValueError('Unable to determine kernel parameter!') return default @property def kernel_parameters(self): assert self.kind in (NodeKind.Convolution, NodeKind.Pooling,\ NodeKind.Deconvolution) params = self.parameters k_h = self.get_kernel_value(params.kernel_h, params.kernel_size, 0) k_w = self.get_kernel_value(params.kernel_w, params.kernel_size, 1) s_h = self.get_kernel_value( params.stride_h, params.stride, 0, default=1) s_w = self.get_kernel_value( params.stride_w, params.stride, 1, default=1) p_h = self.get_kernel_value(params.pad_h, params.pad, 0, default=0) p_w = self.get_kernel_value(params.pad_w, params.pad, 1, default=0) dila_h = dila_w = 1 if self.kind in (NodeKind.Convolution, NodeKind.Deconvolution): dila_len = len(params.dilation) if dila_len == 2: dila_h = params.dilation[0] dila_w = params.dilation[1] elif dila_len == 1: dila_h = dila_w = params.dilation[0] else: assert dila_len == 0, "invalid length[%s] of dilation in convolution" % ( dila_len) return KernelParameters(k_h, k_w, s_h, s_w, p_h, p_w, dila_h, dila_w) KernelParameters = namedtuple( 'KernelParameters', [ 'kernel_h', 'kernel_w', 'stride_h', 'stride_w', 'pad_h', 'pad_w', 'dila_h', 'dila_w' ], )