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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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