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How to use set_attr method in Testify

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caffe_export_utils.py

Source:caffe_export_utils.py

...48        op_type = self.op.type49        if op_type in CaffeOpExporter.onnx_to_caffe:50            op_type = CaffeOpExporter.onnx_to_caffe[op_type]51        return op_type52    def set_attr(self):53        pass54    def parse(self) -> ppl_caffe_pb2.LayerParameter:55        self.set_attr()56        self.layer.bottom[:] = [var.name for var in self.op.inputs if not var.is_parameter]57        self.layer.top[:] = [var.name for var in self.op.outputs]58        for var in self.op.parameters:59            blob = ppl_caffe_pb2.BlobProto()60            value = var.value61            value = convert_any_to_numpy(value)62            if var.meta is not None:63                shape = var.meta.shape64                dtype = DataType.to_numpy(var.meta.dtype)65            else:66                shape, dtype = value.shape, value.dtype67            blob.shape.dim.extend(shape)68            blob.data.extend(value.astype(dtype).flat)69            self.layer.blobs.extend([blob])70        return self.layer71@register_class72class Conv(CaffeOpExporter):73    def set_attr(self):74        kernel_h, kernel_w = refine_value(self.op.attributes.get('kernel_shape'))75        stride_h, stride_w = refine_value(self.op.attributes.get('strides', [1, 1]))76        dilations_h, dilations_w = refine_value(self.op.attributes.get('dilations', [1, 1]))77        pads = refine_value(self.op.attributes.get('pads', [0, 0]))78        if len(pads) == 2:79            pad_h, pad_w = pads80        elif len(pads) == 4:81            begin_pad = pads[:2]82            end_pad = pads[2:]83            if begin_pad == end_pad:84                pad_h, pad_w = begin_pad85            else:86                logger.error('Caffe only support begin_pad == end_pad in layer')87        else:88            logger.error(f'Unsupported pads attributes with the length of {len(pads)} in Caffe')89        self.layer.convolution_param.num_output = self.op.parameters[0].value.shape[0]90        self.layer.convolution_param.group = refine_value(self.op.attributes.get('group', 1))91        self.layer.convolution_param.kernel_h = kernel_h92        self.layer.convolution_param.kernel_w = kernel_w93        self.layer.convolution_param.pad_h = pad_h94        self.layer.convolution_param.pad_w = pad_w95        self.layer.convolution_param.stride_h = stride_h96        self.layer.convolution_param.stride_w = stride_w97        self.layer.convolution_param.hole_h = dilations_h98        self.layer.convolution_param.hole_w = dilations_w99        if len(self.op.parameters) == 2:100            self.layer.convolution_param.bias_term = True101        else:102            self.layer.convolution_param.bias_term = False103@register_class104class BatchNormalization(CaffeOpExporter):105    def set_attr(self):106        self.layer.bn_param.moving_average = bool(refine_value(self.op.attributes.get('training_mode', 0)))107        self.layer.bn_param.var_eps = refine_value(self.op.attributes.get('epsilon', 1e-05))108        if self.layer.bn_param.moving_average:109            self.layer.bn_param.decay = 1 - refine_value(self.op.attributes.get('momentum', 0.9))110    def parse(self):111        super(BatchNormalization, self).parse()112        channel = len(self.layer.blobs[3].data)113        for i in range(4):114            self.layer.blobs[i].shape.ClearField('dim')115            self.layer.blobs[i].shape.dim.extend([1, channel, 1, 1])116        return self.layer117@register_class118class Relu(CaffeOpExporter):119    pass120@register_class121class PRelu(CaffeOpExporter):122    pass123@register_class124class LeakyRelu(CaffeOpExporter):125    def set_attr(self):126        self.layer.relu_param.negative_slope = refine_value(self.op.attributes.get('alpha', 0.01))127class _Pooling(CaffeOpExporter):128    def set_attr(self):129        kernel_h, kernel_w = refine_value(self.op.attributes.get('kernel_shape'))130        stride_h, stride_w = refine_value(self.op.attributes.get('strides', [1, 1]))131        ceil_mode = refine_value(self.op.attributes.get('ceil_mode', 0))132        pads = refine_value(self.op.attributes.get('pads', [0, 0]))133        if len(pads) == 2:134            pad_h, pad_w = pads135        elif len(pads) == 4:136            begin_pad = pads[:2]137            end_pad = pads[2:]138            if begin_pad == end_pad:139                pad_h, pad_w = begin_pad140            else:141                logger.error('Caffe only support begin_pad == end_pad in layer')142        else:143            logger.error(f'Unsupported pads attributes with the length of {len(pads)} in Caffe')144        self.layer.pooling_param.kernel_h = kernel_h145        self.layer.pooling_param.kernel_w = kernel_w146        self.layer.pooling_param.pad_h = pad_h147        self.layer.pooling_param.pad_w = pad_w148        self.layer.pooling_param.stride_h = stride_h149        self.layer.pooling_param.stride_w = stride_w150        if ceil_mode == 0:151            # ceil_mode is True by CaffeOpExporter in caffe152            self.layer.pooling_param.ceil_mode = False153@register_class154class GlobalAveragePool(_Pooling):155    def set_attr(self):156        self.layer.pooling_param.global_pooling = True157        self.layer.pooling_param.pool = ppl_caffe_pb2.PoolingParameter.AVE158@register_class159class AveragePool(_Pooling):160    def set_attr(self):161        super(AveragePool, self).set_attr()162        self.layer.pooling_param.global_pooling = False163        self.layer.pooling_param.pool = ppl_caffe_pb2.PoolingParameter.AVE164@register_class165class GlobalMaxPool(_Pooling):166    def set_attr(self):167        self.layer.pooling_param.global_pooling = True168        self.layer.pooling_param.pool = ppl_caffe_pb2.PoolingParameter.MAX169@register_class170class MaxPool(_Pooling):171    def set_attr(self):172        super(MaxPool, self).set_attr()173        self.layer.pooling_param.global_pooling = False174        self.layer.pooling_param.pool = ppl_caffe_pb2.PoolingParameter.MAX175@register_class176class Concat(CaffeOpExporter):177    def set_attr(self):178        self.layer.concat_param.axis = refine_value(self.op.attributes['axis'])179@register_class180class Softmax(CaffeOpExporter):181    def set_attr(self):182        axis = refine_value(self.op.attributes.get('axis', -1))183        if not (axis == -1 or axis == len(self.op.inputs[0].meta.shape) - 1):184            logger.warning(f'Converting to caffe Softmax, the axis={axis}, which is not the last axis. '185                           'This may result to incorrect caffe model')186        self.layer.softmax_param.axis = axis187@register_class188class Transpose(CaffeOpExporter):189    def set_attr(self):190        perm = refine_value(self.op.attributes['perm'])191        self.layer.transpose_param.dim.extend(perm)192@register_class193class ReduceL2(CaffeOpExporter):194    def set_attr(self):195        self.layer.reducel2_param.axes = refine_value(self.op.attributes.get('axes'))196        self.layer.reducel2_param.keepdims = refine_value(self.op.attributes.get('keepdims', 1))197@register_class198class ReduceMean(CaffeOpExporter):199    def set_attr(self):200        axis = None201        if 'axis' in self.op.attributes:202            axis = self.op.attributes.get('axis')203        elif 'axes' in self.op.attributes:204            axis = self.op.attributes.get('axes')205        if isinstance(axis, list):206            assert len(axis) == 1, (207                'You are trying to dump a RuduceMean op to caffe, '208                f'however caffe support 1 axis only, your mean opeartion has {len(axis)} working axis')209            axis = axis[0]210        self.layer.reduce_param.axis = axis211@register_class212class Div(CaffeOpExporter):213    pass214@register_class215class Mul(CaffeOpExporter):216    # TODO: Can optimize some case to Scale + Reshape217    #  (lhs_shape[1] == rhs_shape[1] and all(i == 1 for i in rhs_shape[2:]))218    def set_attr(self):219        self.layer.eltwise_param.operation = ppl_caffe_pb2.EltwiseParameter.PROD220    def parse(self):221        self.set_attr()222        self.layer.bottom[:] = [var.name for var in self.op.inputs if not var.is_parameter]223        self.layer.top[:] = [var.name for var in self.op.outputs]224        for var in self.op.parameters:225            value = convert_any_to_numpy(var.value)226            if value.size != 1:227                raise AttributeError(f'Now don\'t support Mul op with initializer in shape {value.shape} convert to caffe')228            # Mul olny has two inputs, thus in this loop means the bottom has only one item229            self.layer.eltwise_param.coeff.append(value.item())230        return self.layer231@register_class232class Add(CaffeOpExporter):233    def set_attr(self):234        self.layer.eltwise_param.operation = ppl_caffe_pb2.EltwiseParameter.SUM235        # ONNX op only support no coeff add now236        # https://github.com/onnx/onnx/blob/master/docs/Operators.md#add237        self.layer.eltwise_param.coeff[:] = [1.0] * len(self.op.inputs)238    def parse(self):239        parameter_layers, extend_bottom = [], []240        for i, var in enumerate(self.op.parameters):241            param_layer = ppl_caffe_pb2.LayerParameter(type='Parameter', name=self.op.name + '_param_' + str(i))242            param_layer.top[:] = [var.name]243            extend_bottom.append(var.name)244            blob = ppl_caffe_pb2.BlobProto()245            value = convert_any_to_numpy(var.value)246            if var.meta is not None:247                shape = var.meta.shape248                dtype = DataType.to_numpy(var.meta.dtype)249            else:250                shape, dtype = value.shape, value.dtype251            blob.shape.dim.extend(shape)252            blob.data.extend(value.astype(dtype).flat)253            param_layer.blobs.extend([blob])254            shape_param = param_layer.parameter_param255            if len(shape) == 3:256                shape_param.batch, shape_param.m, shape_param.n = shape257            elif len(shape) == 4:258                shape_param.batch, shape_param.channel, shape_param.height, shape_param.width = shape259            else:260                raise AttributeError(f'Cannot convert {self.op.name} to Eltwise op.')261            parameter_layers.append(param_layer)262        super(Add, self).parse()263        if len(extend_bottom) != 0:264            self.layer.bottom.extend(extend_bottom)265            self.layer.eltwise_param.coeff.extend([1.0] * len(extend_bottom))266        return [*parameter_layers, self.layer]267@register_class268class Max(CaffeOpExporter):269    def set_attr(self):270        self.layer.eltwise_param.operation = ppl_caffe_pb2.EltwiseParameter.MAX271@register_class272class Sub(CaffeOpExporter):273    def set_attr(self):274        self.layer.eltwise_param.operation = ppl_caffe_pb2.EltwiseParameter.SUM275        # ONNX op only support no coeff sub now276        # https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sub277        self.layer.eltwise_param.coeff[:] = [1.0, -1.0]278@register_class279class Reshape(CaffeOpExporter):280    def set_attr(self):281        if len(self.op.inputs) == 0:282            raise AttributeError(f'{self.op.name} has no inputs. Cannot convert to caffe op. '283                        'Please optimize the onnx model.')284        shape = convert_any_to_numpy(self.op.parameters[0].value)285        self.layer.reshape_param.shape.dim.extend(shape)286    def parse(self):287        self.set_attr()288        self.layer.bottom[:] = [var.name for var in self.op.inputs if not var.is_parameter]289        self.layer.top[:] = [var.name for var in self.op.outputs]290        return self.layer291@register_class292class Clip(CaffeOpExporter):293    def parse(self):294        self.layer.bottom[:] = [var.name for var in self.op.inputs if not var.is_parameter]295        self.layer.top[:] = [var.name for var in self.op.outputs]296        min_val = refine_value(self.op.attributes.get('min'))297        max_val = refine_value(self.op.attributes.get('max'))298        if len(self.op.parameters) == 2:299            min_val = convert_any_to_numpy(self.op.parameters[0].value).item()300            max_val = convert_any_to_numpy(self.op.parameters[1].value).item()301        if min_val == 0.0 and max_val == 6.0:302            self.layer.type = 'ReLU6'303        else:304            self.layer.clip_param.min = min_val305            self.layer.clip_param.max = max_val306        return self.layer307@register_class308class Gemm(CaffeOpExporter):309    def parse(self):310        super(Gemm, self).parse()311        # Whether need to add transpose layer312        transpose_layer = None313        if refine_value(self.op.attributes.get('transA', 0)) != 0:314            A = self.op.inputs[0]315            shape = A.meta.shape316            if len(shape) == 2:317                transpose_layer = ppl_caffe_pb2.LayerParameter(type='Transpose', name=self.op.name + '_transposed')318                transpose_layer.bottom[:] = [A.name]319                transpose_layer.top[:] = [A.name + '_trans']320                transpose_layer.transpose_param.dim[:] = [1, 0]321                # Modify InnerProduct input322                self.layer.bottom[:] = [A.name + '_trans']323            else:324                raise ValueError('Cannot support transposed gemm with non-2D input.')325        if self.op.attributes.get('transB', 0) == 0:326            B = convert_any_to_numpy(self.op.parameters[0].value)327            BT_value = np.transpose(B, [1, 0])328            self.layer.blobs[0].shape.dim[:] = BT_value.shape329            self.layer.blobs[0].data[:] = BT_value.astype('float32').flat330        self.layer.inner_product_param.num_output = self.layer.blobs[0].shape.dim[0]331        self.layer.inner_product_param.bias_term = True if len(self.op.parameters) == 2 else False332        if transpose_layer is None:333            return self.layer334        else:335            return [transpose_layer, self.layer]336@register_class337class Pad(CaffeOpExporter):338    def set_attr(self):339        mode = refine_value(self.op.attributes.get('mode', 'constant'))340        if mode != 'reflect':341            raise TypeError(f'Unsupport pad mode {mode} in caffe op')342        pads = convert_any_to_numpy(self.op.inputs[1].value) if len(self.op.inputs) > 1 else refine_value(self.op.attributes['pads'])343        if len(pads) == 2:344            pads = [pads[0], 0, pads[1], 0]345        phs, pws, phe, pwe = pads346        assert phs == phe and pws == pwe347        self.layer.pad_param.pad_h = phs348        self.layer.pad_param.pad_w = pws349# TODO: InterP and other cases350@register_class351class Resize(CaffeOpExporter):352    def __init__(self, op):353        self.mode = refine_value(op.attributes.get('mode'))354        self.scales = convert_any_to_numpy(op.inputs[2].value) if len(op.inputs) > 2 else []355        self.sizes = convert_any_to_numpy(op.inputs[-1].value) if len(op.inputs) == 4 else []356        super().__init__(op)357    def set_type(self):358        if self.mode == 'nearest' and len(self.sizes) == 0 and len(self.scales) > 0:359            return 'NNUpsample'360        elif self.mode == 'linear' and len(self.scales) == 0 and len(self.sizes) > 0:361            return 'Interp'362        else:363            raise TypeError(f'Cannot convert {self.op.name} to caffe op')364    def set_attr(self):365        if self.op_type == 'NNUpsample':366            assert len(self.scales) == 4367            valid_flag = (self.scales[0] == 1.0) and (self.scales[1] == 1.0) and (self.scales[2] == self.scales[3])368            if not valid_flag:369                raise AttributeError(f'Cannot convert {self.op.name} to NNUpsample due to different scales')370            self.layer.nn_upsample_param.resize = int(self.scales[2])371        if self.op_type == 'Interp':372            self.layer.interp_param.height = int(self.sizes[-2])373            self.layer.interp_param.width = int(self.sizes[-1])374            trans_mode = refine_value(self.op.attributes.get('coordinate_transformation_mode', 'half_pixel'))375            if trans_mode == 'align_corners':376                self.layer.interp_param.align_corners = True377            elif trans_mode == 'half_pixel':378                self.layer.interp_param.align_corners = False379            else:380                raise AttributeError(f'Cannot convert {self.op.name} in {trans_mode} mode to Interp.')381    def parse(self):382        self.set_attr()383        self.layer.bottom[:] = [var.name for var in self.op.inputs if not var.is_parameter]384        self.layer.top[:] = [var.name for var in self.op.outputs]385        return self.layer386@register_class387class ConvTranspose(Conv):388    pass389@register_class390class Sigmoid(CaffeOpExporter):391    pass392@register_class393class Slice(CaffeOpExporter):394    def parse(self):395        # assert (len(self.op.inputs) == 1)396        input_shape = self.op.inputs[0].meta.shape397        starts, ends = convert_any_to_numpy(self.op.parameters[0].value), convert_any_to_numpy(self.op.parameters[1].value)398        axes = convert_any_to_numpy(self.op.parameters[2].value) if len(self.op.parameters) >= 3 else [i for i in range(len(input_shape))]399        if len(self.op.parameters) >= 4 and any(convert_any_to_numpy(self.op.parameters[3].value) != 1):400            raise AttributeError('Slice op with steps cannot dump to caffe model')401        layers = []402        self.layer = ppl_caffe_pb2.LayerParameter(type=self.op_type, name=self.op.name)403        for i, (start_point, end_point, axis) in enumerate(list(zip(starts, ends, axes))):404            current_layer = ppl_caffe_pb2.LayerParameter(type=self.op_type, name=self.op.name + '_' + str(i))405            current_layer.slice_param.axis = axis406            current_layer.bottom[:] = [var.name for var in self.op.inputs if not var.is_parameter]407            current_layer.top[:] = [var.name for var in self.op.outputs]408            slice_points = [start_point, end_point]409            if start_point == 0:410                slice_points.remove(start_point)411            else:412                current_layer.top.insert(0, self.op.outputs[0].name + '_front')413            if end_point == -1 or end_point == input_shape[axis]:414                slice_points.remove(end_point)415            else:416                current_layer.top.append(self.op.outputs[-1].name + '_behind')417            current_layer.slice_param.slice_point.extend(slice_points)418            layers.append(current_layer)419        return layers420@register_class421class Tanh(CaffeOpExporter):422    pass423@register_class424class Pow(CaffeOpExporter):425    def set_attr(self):426        self.layer.power_param.power = refine_value(self.op.attributes.get('power', 1))427        self.layer.power_param.scale = refine_value(self.op.attributes.get('scale', 1))428        self.layer.power_param.shift = refine_value(self.op.attributes.get('shift', 0))429@register_class430class Scale(CaffeOpExporter):431    def set_attr(self):432        self.layer.scale_param.axis = refine_value(self.op.attributes.get('axis', 1))433        self.layer.scale_param.num_axes = refine_value(self.op.attributes.get('num_axes', 1))434        self.layer.scale_param.bias_term = refine_value(self.op.attributes.get('bias_term', False))435@register_class436class ChannelShuffle(CaffeOpExporter):437    def set_attr(self):438        self.layer.channel_shuffle_param.group = refine_value(self.op.attributes.get('group', 1))439@register_class440class InstanceNormalization(CaffeOpExporter):441    def set_attr(self):442        self.layer.instance_norm_param.num_features = refine_value(self.op.attributes.get('num_features'))443        self.layer.instance_norm_param.eps = refine_value(self.op.attributes.get('eps', 1e-5))444        self.layer.instance_norm_param.affine = refine_value(self.op.attributes.get('affine', False))445@register_class446class Parameter(CaffeOpExporter):447    def set_attr(self):448        self.layer.parameter_param.m = refine_value(self.op.attributes.get('m', -1))449        self.layer.parameter_param.n = refine_value(self.op.attributes.get('n', -1))450        self.layer.parameter_param.batch = refine_value(self.op.attributes.get('batch', 1))451        self.layer.parameter_param.channel = refine_value(self.op.attributes.get('channel', -1))452        self.layer.parameter_param.height = refine_value(self.op.attributes.get('height', -1))453        self.layer.parameter_param.width = refine_value(self.op.attributes.get('width', -1))454@register_class455class Interp(CaffeOpExporter):456    def set_attr(self):457        if refine_value(self.op.attributes.get('shrink_factor')) != 1:458            self.layer.interp_param.shrink_factor = refine_value(self.op.attributes.get('shrink_factor'))459        if refine_value(self.op.attributes.get('zoom_factor')) != 1:460            self.layer.interp_param.zoom_factor = refine_value(self.op.attributes.get('zoom_factor'))461        if refine_value(self.op.attributes.get('width')) and refine_value(self.op.attributes.get('height')):462            self.layer.interp_param.height = refine_value(self.op.attributes.get('height'))463            self.layer.interp_param.width = refine_value(self.op.attributes.get('width'))464        self.layer.interp_param.pad_beg = refine_value(self.op.attributes.get('pad_beg'))465        self.layer.interp_param.pad_end = refine_value(self.op.attributes.get('pad_end'))466        self.layer.interp_param.align_corners = refine_value(self.op.attributes.get('align_corners'))467@register_class468class Tile(CaffeOpExporter):469    def set_attr(self):470        self.layer.tile_param.axis = refine_value(self.op.attributes.get('axis'))471        self.layer.tile_param.tiles = refine_value(self.op.attributes.get('tiles'))472@register_class473class Flatten(CaffeOpExporter):474    def set_attr(self):475        self.layer.flatten_param.axis = refine_value(self.op.attributes.get('axis', 1))476        self.layer.flatten_param.end_axis = refine_value(self.op.attributes.get('end_axis', -1))477@register_class478class SpaceToDepth(CaffeOpExporter):479    def set_attr(self):480        self.layer.subpixel_down_param.downsample = refine_value(self.op.attributes.get('blocksize', 1))481@register_class482class DepthToSpace(CaffeOpExporter):483    def set_attr(self):484        self.layer.subpixel_up_param.upsample = refine_value(self.op.attributes.get('blocksize', 1))485@register_class486class CaffeArgMax(CaffeOpExporter):487    def set_attr(self):488        self.layer.argmax_param.out_max_val = refine_value(self.op.attributes.get('out_max_val'))489        self.layer.argmax_param.top_k = refine_value(self.op.attributes.get('top_k'))490        self.layer.argmax_param.axis = refine_value(self.op.attributes.get('axis'))491@register_class492class HardSwish(CaffeOpExporter):493    pass494@register_class495class HardSigmoid(CaffeOpExporter):...

ops.py

Source:ops.py

...4from op import OpsParam, OpsRegister5from op_io import *6############################# IO define ##############################7# graph may has mult-inputs, so graph will have multi-input8OpsRegister.Register("Input").set_attr(input_shape=list(),9                                       max_len = int(),10                                       max_batch = int(),11                                       alias="NULL",12                                       data_type="NULL",13                                       layout="NCHW")14# graph out , only hold place for edge15OpsRegister.Register("Output").set_attr()16OpsRegister.Register("Split").set_attr(split_num=int())17############################# Basic Op define ##############################18# two input19OpsRegister.Register("Dot").set_attr(axes=list())20# one or two input21# enum type {22#    Add,23#    Subtract,24#    Multiply,25#    Avg,26#    Max27#   }28#  note : coeff only used by caffe for "Add"29OpsRegister.Register("Eltwise").set_attr(type="Add",30                                         coeff=list())31# list input32OpsRegister.Register("Concat").set_attr(axis=int())33# one input34OpsRegister.Register("Exp").set_attr(base=float(),35                                     scale=float(),36                                     shift=float())37# one input38# y = log(shift + scale * x)39OpsRegister.Register("Log").set_attr(base=float(),40                                     scale=float(),41                                     shift=float())42# one input43# y =  (shift + scale * x) ^ power44OpsRegister.Register("Power").set_attr(shift=float(),45                                       scale=float(),46                                       power=float())47# one input48OpsRegister.Register("Softmax").set_attr(axis=int())49# applies an activation parameter function to an output50# enum type:51#     enum type {52#       TanH,53#       Sigmoid,54#       }55OpsRegister.Register("Activation").set_attr(type="",56                                            clip_relu_num=float())57# Leaky version of a Rectified Linear Unit ( alpha != 0 ).58#   f(x) = alpha * x     : x < 059#   f(x) =       x     : x >= 060# Standard ReLU ( alpha = 0 )61#   f(x) = 0 * x     : x < 062#   f(x) =     x     : x >= 063#   note:  alpha is fixed value64OpsRegister.Register("ReLU").set_attr(alpha=float())65# Parametric Rectified Linear Unit66#   f(x) = alpha * x   : x < 067#   f(x) = x       : x >= 068#   note: alpha is learned array with the same shape as x.69#   ref: Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:70#         <<Surpassing Human-Level Performance on ImageNet Classification>>, 2015.71OpsRegister.Register("PReLU").set_attr(channel_shared=bool())72# Exponential Linear Unit.73#   f(x) =  alpha * (exp(x) - 1.0)  : x < 074#   f(x) = x            : x >= 075OpsRegister.Register("ELU").set_attr(alpha=int())76# dense op parameter77OpsRegister.Register("Dense").set_attr(out_dim=int(),78                                       axis=int(),79                                       bias_term=bool())80# dropout parameter81OpsRegister.Register("Dropout").set_attr(ratio=float())82OpsRegister.Register("Flatten").set_attr(start_axis=int(),83                                         end_axis=int())84# caffe unique layer85OpsRegister.Register("Reshape").set_attr(dims=list(),86                                         axis=int(),87                                         num_axes=int(),88                                         layout='')89# Permutes the dimensions of the input according to a given pattern(list type)90OpsRegister.Register("Permute").set_attr(dims=list())91# Cropping op for cropping data of (1/2/3D) by using axis info92# cropping is the same as tf cropping parameter, which saved as tuple or int.93OpsRegister.Register("Crop").set_attr(cropping=list(),94                                          axis=int())95# slices an input layer to multiple output layers along a given dimension with given slice indices96OpsRegister.Register("Slice").set_attr(axis=int(),97                                       slice_point=list(),98                                       slice_dim=int(),99                                       num=int(),100                                       sections=list())101############################# Normalization Op define ##############################102# Batch normalization op103# explanation:104# Normalize the activations of the previous layer at each batch,105# i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.106OpsRegister.Register("BatchNorm").set_attr(momentum=float(),107                                           epsilon=float())108# caffe need may use scale layer after batchnorm layer which tf/mxnet/keras needn't109OpsRegister.Register("Scale").set_attr(axis=int(),110                                       num_axes=int(),111                                       bias_term=bool())112# Local Response Normalization op same as caffe,113# which performs a kind of "lateral inhibition" by normalizing over local input regions114# enum NormRegion {115# ACROSS_CHANNELS116# WITHIN_CHANNEL117# }118OpsRegister.Register("LRN").set_attr(local_size=int(),119                                     alpha=float(),120                                     beta=float(),121                                     norm_region="ACROSS_CHANNELS",122                                     k=float())123# Mean-Variance Normalization124OpsRegister.Register("MVN").set_attr(normalize_variance=bool(),125                                     across_channels=bool(),126                                     epsilon=float())127############################# Pooling (1D/2D/3D) Op define ##############################128# enum type:129#      enum method {130#           MAX,    // [default]131#     AVG,132#           AVGEXC, average_exclude_padding_value133#     STOCHASTIC,134#      }135OpsRegister.Register("Pooling").set_attr(pool_size=list(),136                                         strides=list(),137                                         padding=list(),138                                         method="MAX",139                                         global_pooling=bool(),140                                         cmp_out_shape_floor_as_conv=False)141# Spatial Pyramid Pooling142# enum type:143#      enum method {144#           MAX,    // [default]145#     AVG,146#     STOCHASTIC,147#      }148OpsRegister.Register("SPP").set_attr(pyramid_height=int(),149                                     method="MAX",)150############################# Convolution (1D/2D/3D) Op define ##############################151# convolution parameter152OpsRegister.Register("Convolution").set_attr(filter_num=int(),153                                             kernel_size=list(),154                                             strides=list(),155                                             padding=list(),156                                             dilation_rate=list(),157                                             group=int(),158                                             axis=int(),159                                             bias_term=bool())160# Depthwise separable convolution, commonly called "separable convolution" in tf161OpsRegister.Register("DeSepConvolution").set_attr(filter_num=int(),162                                                  kernel_size=list(),163                                                  strides=list(),164                                                  padding=list(),165                                                  dilation_rate=list(),166                                                  group=int(),167                                                  axis=int(),168                                                  depth_multiplier=int())169# also called transposed convolution170OpsRegister.Register("Deconvolution").set_attr(filter_num=int(),171                                               kernel_size=list(),172                                               strides=list(),173                                               padding=list(),174                                               dilation_rate=list(),175                                               group=int(),176                                               axis=int(),177                                               bias_term=bool())178# DeformableConvolution179OpsRegister.Register("DeformConvolution").set_attr(filter_num=int(),180                                                   kernel_size=list(),181                                                   strides=list(),182                                                   padding=list(),183                                                   dilation_rate=list(),184                                                   group=int(),185                                                   axis=int(),186                                                   bias_term=bool())187############################# Rnn Op define ##############################188# Standard  RNN (LSTM/GRU)189# enum rnn type:190#      enum type {191#        TANH,    // base192#      SIGMOID, // base193#        RELU,    // base194#        LSTM,195#      GRU,196#    }197OpsRegister.Register("RNN").set_attr(hidden_size=int(),198                                     input_size=int(),199                                     bias_term=bool(),200                                     dropout=float(),201                                     type="GRU")202############################# embedding Op define ##############################203# embedding layer, input_dim in tf or caffe means the voc num and output_dim means the emb size204OpsRegister.Register("Embedding").set_attr(input_dim=int(),205                                           output_dim=int(),206                                           bias_term=bool())207############################# Accuracy Op define ##############################208# NULL209########### Object track and detection (for adu(caffe layer type)) Op define #############210# RPNProposalSSD for SSD and RPN211OpsRegister.Register("RPNProposalSSD").set_attr(**RPNProposalSSD_param())212OpsRegister.Register("RCNNDetOutputWithAttr").set_attr(**detection_output_ssd_param())213OpsRegister.Register("DFMBPSROIAlign").set_attr(**dfmb_psroi_pooling_param())214OpsRegister.Register("RCNNProposal").set_attr(**RPNProposalSSD_param())215OpsRegister.Register("ProposalImgScaleToCamCoords").set_attr(**proposal_img_scale_to_cam_coords_param())216########### VIS Op define #############217OpsRegister.Register("Axpy").set_attr()218OpsRegister.Register("PriorBox").set_attr(min_size=list(),219                                          max_size=list(),220                                          aspect_ratio=list(),221                                          fixed_size=list(),222                                          fixed_ratio=list(),223                                          density=list(),224                                          is_flip=bool(),225                                          is_clip=bool(),226                                          variance=list(),227                                          img_h=int(),228                                          img_w=int(),229                                          step_h=float(),230                                          step_w=float(),231                                          offset=float(),232                                          order=list())233# enum code_type {234#  CORNER,235#  CENTER_SIZE,236#  CORNER_SIZE,237# }238OpsRegister.Register("DetectionOutput").set_attr(share_location=bool(),239                                                 variance_encode_in_target=bool(),240                                                 class_num=int(),241                                                 background_id=int(),242                                                 keep_top_k=int(),243                                                 code_type="CORNER",244                                                 conf_thresh=float(),245                                                 nms_top_k=int(),246                                                 nms_thresh=float(),247                                                 nms_eta=float())248########### ADU Op define #############249OpsRegister.Register("Argmax").set_attr(out_max_val=bool(),250                                        top_k=int(),251                                        axis=int(),252                                        axis_term=bool())253########### OCR Op define #############254OpsRegister.Register("Im2Sequence").set_attr(paddings=list(),255                                             strides=list(),256                                             window_size=list(),257                                             dilations=list())258OpsRegister.Register("Cast").set_attr(in_type=int(),259                                      out_type=int())260OpsRegister.Register("Gru").set_attr(is_reverse=bool(),261                                     gate_activation="sigmoid",262                                     activation="relu",263                                     gru_formula="")264OpsRegister.Register("CtcAlign").set_attr(merge_repeated=bool(),265                                          blank=int())266########### RNN Op define #############267OpsRegister.Register("Embedding").set_attr(word_num=int(),268                                           emb_dim=int(),269                       padding_idx=int())270OpsRegister.Register("SequencePool").set_attr(pooltype="LAST")271OpsRegister.Register("SequenceConv").set_attr(filter_num=int(),272                                              kernel_size=list(),273                                              padding_trainable=bool(),274                                              context_stride=int(),275                                              context_start=int(),276                                              context_length=int())277OpsRegister.Register("CrfDecoding").set_attr()278OpsRegister.Register("LSTM").set_attr(candidate_activation="tanh",279                                      cell_activation="tanh",280                                      gate_activation="sigmoid",281                                      is_reverse=bool(),282                                      use_peepholes=bool(),283                                      num_direction=int(),284                                      dropout_param=float(),285                                      num_layers=int(),286                                      input_activation="null")287OpsRegister.Register("LSTMP").set_attr(outDim=int(),288                                       skipNum=int(),289                                       reActType='tanh',290                                       cellDim=int())291OpsRegister.Register("MatMul").set_attr(transpose_x=bool(),292                                        transpose_y=bool(),293                                        coeff=float())294OpsRegister.Register("LayerNorm").set_attr(is_across_spatial=bool(),295                                           is_shared_channel=bool(),296                                           begin_norm_axis=int(),297                                           eps=float())298OpsRegister.Register("Resize").set_attr(method="BILINEAR_ALIGN",299                                        height_scale=float(),300                                        width_scale=float(),301                                        out_width=int(),302                                        out_height=int())303OpsRegister.Register("Normalize").set_attr(begin_norm_axis=int(),304                                           is_across_spatial=bool(),305                                           is_shared_channel=bool(),306                                           eps=float(),307                                           p=int())308OpsRegister.Register("Pad").set_attr(pad_c=list(),309                                     pad_h=list(),310                                     pad_w=list())311OpsRegister.Register("ShuffleChannel").set_attr(group=int())312OpsRegister.Register("RoisAnchorFeature").set_attr(min_anchor_size=float(),313                                                   num_anchor_scales=int(),314                                                   anchor_scale_pow_base=float(),315                                                   anchor_wph_ratios=list(),316                                                   num_top_iou_anchor=int(),317                                                   min_num_top_iou_anchor=int(),318                                                   iou_thr=float(),319                                                   ft_ratio_h=bool(),320                                                   ft_ratio_w=bool(),321                                                   ft_log_ratio_h=bool(),322                                                   ft_log_ratio_w=bool(),323                                                   bbox_size_add_one=bool())324OpsRegister.Register("Interp").set_attr(height=int(),325                                        width=int(),326                                        zoom_factor=int(),327                                        shrink_factor=int(),328                                        pad_beg=int(),329                                        pad_end=int())330##################################### reverse_sequence op define ############################    #########331####### it is named BatchReverseSequenceLayer in lego332#333OpsRegister.Register("ReverseSequence").set_attr()  ##no prams , no weights.334##################################### reverse op define #####################################335####### it is named BatchReverseInputLayer in lego336OpsRegister.Register("Reverse").set_attr()   ## no prams, no weights.337##################################### embedding_lg op define ################################    #####338####### it is named BatchEmbeddingLayer in lego339OpsRegister.Register("EmbeddingLg").set_attr() ## ???? is it same to Embedding?340##################################### grnn(single-layer, single-direction GRU) op define ####    #################################341####### it is named BatchGrnnLayer in lego342OpsRegister.Register("GRNN").set_attr() ## ???? is it same to RNN?343##################################### match_matrix op define ################################    #####344####### it is named BatchMatchMatrixTensorLayer in lego345OpsRegister.Register("MatchMatrix").set_attr(dim_in = int(),346                                             dim_t = int(),347                                             linear_term = bool(),348                                             bias_term = bool(),349                                             diag_init = int(),350                                             diag_init_dim_num = int(),351                                             init_low = int(),352                                             init_up = int())353##################################### var_size_conv op define ###############################    ######354####### it is named BatchVarSizeConvLayer in lego355OpsRegister.Register("VarSizeConv").set_attr()  ## it is same to convolution????356##################################### topk_pooling op define ################################    #####357###### it is named BatchTopKPoolingLayer in lego358OpsRegister.Register("TopKPooling").set_attr(top_k = int(),359                                             feat_map_num = int())360##################################### topk_avg_pooling op define ############################    #########361###### it is named BatchTopKAvgPoolingByRowLayer in lego362OpsRegister.Register("TopKAvgPooling").set_attr(top_ks = list(),363                                                feat_map_num = int(),364                                                is_pooling_by_row = bool())365##################################### extract_last op define ################################    #####366###### it is named BatchExtractLastLayer in lego,367OpsRegister.Register("SequencePool").set_attr(pooltype = str())  #no paras, no weights.368#####################################Unpadding_padding op define ############################    #########369###### it is named UnpaddingPaddingLayer in lego,370OpsRegister.Register("ConvUnpaddingPadding").set_attr()  #no paras, no weights.371# Fast-RCNN372OpsRegister.Register("AffineChannel").set_attr()  #no paras, no weights.373OpsRegister.Register("AnchorGenerator").set_attr(anchor_sizes=list(),374                                                 aspect_ratios=list(),375                                                 variances=list(),376                                                 stride=list(),377                                                 offset=float())378OpsRegister.Register("GenerateProposals").set_attr(pre_nms_top_n=int(),379                                                 post_nms_top_n=int(),380                                                 nms_thresh=float(),381                                                 min_size=float(),382                                                 eta=float())383OpsRegister.Register("RoiAlign").set_attr(spatial_scale=float(),384                                          pooled_height=int(),385                                          pooled_width=int(),386                                          sampling_ratio=int())387OpsRegister.Register("RoiPool").set_attr(spatial_scale=float(),388                                          pooled_height=int(),389                                          pooled_width=int())390##################################### pytorch edsr model PixelShuffle op define ################################391# PixelShuffle in_shape = [n, r * r * c, h, w] scale_factor = r ==> out_shape = [n, c, r * h, r * w]392OpsRegister.Register("PixelShuffle").set_attr(scale_factor=int())393OpsRegister.Register("Coord2Patch").set_attr(img_h=int(),394                                             output_h=int(),395                                             output_w=int())396OpsRegister.Register("DataNorm").set_attr(epsilon=float())397OpsRegister.Register("Pad2D").set_attr(mode="constant",398                                       value=float(),399                                       pad_h=list(),400                                       pad_w=list())401OpsRegister.Register("SequencePoolConcat").set_attr(pooltype=str(),402                                                    slot_num=int(),403                                                    axis=int())404OpsRegister.Register("SRoiAlign").set_attr(pooled_h=int(),405                                            pooled_w=int(),406                                            spatial_scale=float())407OpsRegister.Register("SProposal").set_attr(feat_stride=int(),408                                            basesize=int(),409                                            boxminsize=int(),410                                            pre_nms_topn=int(),411                                            post_nms_topn=int(),412                                            nms_thresh=float(),413                                            scale=list(),...

vmmanagertest.py

Source:vmmanagertest.py

...5class VMManagerTest(testbase.ClustoTestBase):6    def data(self):7        vmm = VMManager('vmm')8        s1 = BasicServer('s1')9        s1.set_attr('system', subkey='memory', value=1000)10        s1.set_attr('system', subkey='disk', value=5000)11        s1.set_attr('system', subkey='cpucount', value=2)12        13        s2 = BasicServer('s2')14        s2.set_attr('system', subkey='memory', value=16000)15        s2.set_attr('system', subkey='disk', value=2500)16        s2.set_attr('system', subkey='cpucount', value=2)17        18        vmm.insert(s1)19        vmm.insert(s2)20        21    def testVMManagerAllocate(self):22        s1 = clusto.get_by_name('s1')23        s2 = clusto.get_by_name('s2')24        25        vs1 = BasicVirtualServer('vs1')26        vs1.set_attr('system', subkey='memory', value=1000)27        vs1.set_attr('system', subkey='disk', value=50)28        vs1.set_attr('system', subkey='cpucount', value=1)29        vs2 = BasicVirtualServer('vs2')30        vs2.set_attr('system', subkey='memory', value=8000)31        vs2.set_attr('system', subkey='disk', value=1000)32        vs2.set_attr('system', subkey='cpucount', value=1)33        vs3 = BasicVirtualServer('vs3')34        vs3.set_attr('system', subkey='memory', value=800)35        vs3.set_attr('system', subkey='disk', value=100)36        vs3.set_attr('system', subkey='cpucount', value=3)37        vmm = clusto.get_by_name('vmm')38        vmm.allocate(vs1)39        self.assertEqual(len(vmm.resources(vs1)), 1)40        self.assert_(vmm.resources(vs1)[0].value in [s1, s2])41        vmm.allocate(vs2)42        self.assertEqual([r.value for r in vmm.resources(vs2)], [s2])43        self.assertRaises(ResourceException, vmm.allocate, vs3)44    def testVMDestroy(self):45        vmm = clusto.get_by_name('vmm')46        vs1 = BasicVirtualServer('vs1')47        vs1.set_attr('system', subkey='memory', value=1000)48        vs1.set_attr('system', subkey='disk', value=50)49        vs1.set_attr('system', subkey='cpucount', value=2)50        vs2 = BasicVirtualServer('vs2')51        vs2.set_attr('system', subkey='memory', value=5000)52        vs2.set_attr('system', subkey='disk', value=50)53        vs2.set_attr('system', subkey='cpucount', value=2)54        vs3 = BasicVirtualServer('vs3')55        vs3.set_attr('system', subkey='memory', value=1000)56        vs3.set_attr('system', subkey='disk', value=50)57        vs3.set_attr('system', subkey='cpucount', value=1)58        s1 = clusto.get_by_name('s1')59        s2 = clusto.get_by_name('s2')60        61        vmm.allocate(vs1)62        vmm.allocate(vs2)63        self.assertRaises(ResourceException, vmm.allocate, vs3)64        vmm.deallocate(vs2)65        vmm.allocate(vs3)66        self.assertEqual([r.value for r in vmm.resources(vs3)],67                         [clusto.get_by_name('s2')])68                          69    def testVMAllocateToSpecificHost(self):70        vs1 = BasicVirtualServer('vs1')71        vs1.set_attr('system', subkey='memory', value=1000)72        vs1.set_attr('system', subkey='disk', value=50)73        vs1.set_attr('system', subkey='cpucount', value=2)74        vs2 = BasicVirtualServer('vs2')75        vs2.set_attr('system', subkey='memory', value=5000)76        vs2.set_attr('system', subkey='disk', value=50)77        vs2.set_attr('system', subkey='cpucount', value=2)78        vs3 = BasicVirtualServer('vs3')79        vs3.set_attr('system', subkey='memory', value=1000)80        vs3.set_attr('system', subkey='disk', value=50)81        vs3.set_attr('system', subkey='cpucount', value=1)82        s1 = clusto.get_by_name('s1')83        s2 = clusto.get_by_name('s2')84        s3 = BasicServer('s3')85        86        vmm = clusto.get_by_name('vmm')87        vmm.allocate(vs1, s1)88        self.assertRaises(ResourceException, vmm.allocate, vs2, s3)89        self.assertRaises(ResourceException, vmm.allocate, vs1, s1)90        self.assertRaises(ResourceException, vmm.allocate, vs1, s2)91        self.assertEqual([r.value for r in vmm.resources(vs1)],92                         [clusto.get_by_name('s1')])93        self.assertRaises(ResourceException, vmm.allocate, vs2, s1)94        self.assertEqual([r.value for r in vmm.resources(vs2)],95                         [])96        vmm.allocate(vs2, s1, force=True)97        self.assertEqual([r.value for r in vmm.resources(vs2)],98                         [clusto.get_by_name('s1')])99    def testAddingAndRemovingHosts(self):100        s1 = clusto.get_by_name('s1')101        s2 = clusto.get_by_name('s2')102        s3 = BasicServer('s3')103        s3.set_attr('system', subkey='memory', value=16000)104        s3.set_attr('system', subkey='disk', value=2500)105        s3.set_attr('system', subkey='cpucount', value=2)106        107        vmm = clusto.get_by_name('vmm')108        vs1 = BasicVirtualServer('vs1')109        vs1.set_attr('system', subkey='memory', value=1000)110        vs1.set_attr('system', subkey='disk', value=50)111        vs1.set_attr('system', subkey='cpucount', value=2)112        self.assertRaises(ResourceException, vmm.allocate, vs1, s3)113        vmm.allocate(vs1, s1)114        self.assertRaises(ResourceException, vmm.remove, s1)115        vmm.deallocate(vs1)116        vmm.remove(s1)117        vmm.insert(s3)118        vmm.allocate(vs1, s3)119    def testReservingResource(self):120        s1 = clusto.get_by_name('s1')121        s2 = clusto.get_by_name('s2')122        vmm = clusto.get_by_name('vmm')123        vs1 = BasicVirtualServer('vs1')124        vs1.set_attr('system', subkey='memory', value=1000)125        vs1.set_attr('system', subkey='disk', value=50)126        vs1.set_attr('system', subkey='cpucount', value=2)127        vmm.allocate(vmm, s1)128        self.assertRaises(ResourceException, vmm.allocate, vs1, s1)129        130class EC2VMManagerTest(testbase.ClustoTestBase):131    def data(self):132        vmm = clusto.drivers.EC2VMManager('ec2man')...

ops_fluid.py

Source:ops_fluid.py

2# Copyright (c) 2017, Cuichaowen. All rights reserved.3# -*- coding: utf-8 -*-4from op import OpsParam, OpsRegister5from op_io import *6OpsRegister.Register("elementwise_mul").set_attr()7OpsRegister.Register("depthwise_conv2d").set_attr()8OpsRegister.Register("transpose").set_attr()9OpsRegister.Register("reshape").set_attr()10OpsRegister.Register("concat").set_attr()11OpsRegister.Register("box_coder").set_attr()12OpsRegister.Register("im2sequence").set_attr()13OpsRegister.Register("sum").set_attr()14OpsRegister.Register("top_k").set_attr()15OpsRegister.Register("ctc_align").set_attr()16OpsRegister.Register("cast").set_attr()17OpsRegister.Register("elementwise_add_fulid").set_attr()18OpsRegister.Register("lookup_table").set_attr()19OpsRegister.Register("lstm").set_attr()20OpsRegister.Register("sequence_pool").set_attr()21OpsRegister.Register("tanh").set_attr()22OpsRegister.Register("sequence_conv").set_attr()23OpsRegister.Register("stanh").set_attr()24OpsRegister.Register("matmul").set_attr()25OpsRegister.Register("layer_norm").set_attr()26OpsRegister.Register("dropout").set_attr()27OpsRegister.Register("scale").set_attr()28OpsRegister.Register("norm").set_attr()29OpsRegister.Register("lod_reset").set_attr()30OpsRegister.Register("fill_constant").set_attr()31OpsRegister.Register("lod_rank_table").set_attr()32OpsRegister.Register("max_sequence_len").set_attr()33OpsRegister.Register("less_than").set_attr()34OpsRegister.Register("lod_tensor_to_array").set_attr()35OpsRegister.Register("write_to_array").set_attr()36OpsRegister.Register("reorder_lod_tensor_by_rank").set_attr()37OpsRegister.Register("while").set_attr()38OpsRegister.Register("array_to_lod_tensor").set_attr()39OpsRegister.Register("assign").set_attr()40OpsRegister.Register("assign_value").set_attr()41OpsRegister.Register("shape").set_attr()42OpsRegister.Register("fake_quantize_abs_max").set_attr()43OpsRegister.Register("fake_dequantize_max_abs").set_attr()44OpsRegister.Register("fake_quantize_range_abs_max").set_attr()45OpsRegister.Register("fake_dequantize_range_max_abs").set_attr()46OpsRegister.Register("increment").set_attr()47OpsRegister.Register("fusion_dropout_add_ln_quant").set_attr()48OpsRegister.Register("dequantize_max_abs_rowwise").set_attr()49OpsRegister.Register("quantize_abs_max_rowwise").set_attr()50OpsRegister.Register("fusion_add_relu_dropout_quant").set_attr()51OpsRegister.Register("fill_constant_batch_size_like").set_attr()52OpsRegister.Register("beam_search_decode").set_attr()53OpsRegister.Register('reduce').set_attr(54    reduce_type=str(),55    keep_dim=bool(),56    reduce_dim=list(),57    reduce_all=bool(),58    coeff=float(),59)60OpsRegister.Register('arg_max').set_attr(61    out_max_val=bool(),62    top_k=int(),63    axis=int(),64)65OpsRegister.Register('sequence_expand').set_attr(66    ref_level=int(),67)68OpsRegister.Register('eltwise').set_attr(69    type=str(),70    coeff=float(),71)72OpsRegister.Register('cast').set_attr(73    int_type=int(),74    out_type=int(),75)76OpsRegister.Register('yolo_box').set_attr(77    anchors=list(),78    class_num=int(),79    conf_thresh=float(),80    downsample_ratio=int(),81)82OpsRegister.Register('slice').set_attr(83    slice_dim=int(),84    slice_point=list(),85    axis=int(),86)87OpsRegister.Register('box_coder').set_attr(88    axis=int(),89    box_normalized=bool(),90    variance=list(),91)92OpsRegister.Register('GroupNormal').set_attr(93    has_scale=bool(),94    has_bias=bool(),95    eps=float(),96    group=int(),97)98OpsRegister.Register('slice_v2').set_attr(99    starts=list(),100    ends=list(),101    axes=list(),102)103OpsRegister.Register('arithmetic').set_attr(104    op_type=int(),105)106OpsRegister.Register('aligned_mat_mul').set_attr(107    is_transpose_X=bool(),108    is_transpose_Y=bool(),109    scale=float(),110)111OpsRegister.Register('attention_padding_mask').set_attr(112    mask=float(),113    pad_id=int(),114)115OpsRegister.Register('topk_avg_pooling').set_attr(116    top_ks=list(),117    feat_map_num=int(),118    is_pooling_by_row=bool(),119)120OpsRegister.Register('Dense').set_attr(121    axis=int(),122    out_dim=int(),123    bias_term=bool(),124)125OpsRegister.Register('MatchMatrix').set_attr(126    dim_in=int(),127    dim_t=int(),128    linear_term=bool(),129    bias_term=bool(),130    is_l_same=bool(),...

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