import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.regularizer import L2Decay from ppdet.core.workspace import register import math __all__ = ['MobileNetV3'] @register class MobileNetV3(): def __init__(self, scale=1.0, model_name='small', with_extra_blocks=False, conv_decay=0.0, bn_decay=0.0, extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]]): self.scale = scale self.model_name = model_name self.with_extra_blocks = with_extra_blocks self.extra_block_filters = extra_block_filters self.conv_decay = conv_decay self.bn_decay = bn_decay self.inplanes = 16 self.end_points = [] self.block_stride = 1 if model_name == "large": self.cfg = [ # kernel_size, expand, channel, se_block, act_mode, stride [3, 16, 16, False, 'relu', 1], [3, 64, 24, False, 'relu', 2], [3, 72, 24, False, 'relu', 1], [5, 72, 40, True, 'relu', 2], [5, 120, 40, True, 'relu', 1], [5, 120, 40, True, 'relu', 1], [3, 240, 80, False, 'hard_swish', 2], [3, 200, 80, False, 'hard_swish', 1], [3, 184, 80, False, 'hard_swish', 1], [3, 184, 80, False, 'hard_swish', 1], [3, 480, 112, True, 'hard_swish', 1], [3, 672, 112, True, 'hard_swish', 1], [5, 672, 160, True, 'hard_swish', 2], [5, 960, 160, True, 'hard_swish', 1], [5, 960, 160, True, 'hard_swish', 1], ] elif model_name == "small": self.cfg = [ # kernel_size, expand, channel, se_block, act_mode, stride [3, 16, 16, True, 'relu', 2], [3, 72, 24, False, 'relu', 2], [3, 88, 24, False, 'relu', 1], [5, 96, 40, True, 'hard_swish', 2], [5, 240, 40, True, 'hard_swish', 1], [5, 240, 40, True, 'hard_swish', 1], [5, 120, 48, True, 'hard_swish', 1], [5, 144, 48, True, 'hard_swish', 1], [5, 288, 96, True, 'hard_swish', 2], [5, 576, 96, True, 'hard_swish', 1], [5, 576, 96, True, 'hard_swish', 1], ] else: raise NotImplementedError def _conv_bn_layer(self, input, filter_size, num_filters, stride, padding, num_groups=1, if_act=True, act=None, name=None, use_cudnn=True): conv_param_attr = ParamAttr( name=name + '_weights', regularizer=L2Decay(self.conv_decay)) conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=padding, groups=num_groups, act=None, use_cudnn=use_cudnn, param_attr=conv_param_attr, bias_attr=False) bn_name = name + '_bn' bn_param_attr = ParamAttr( name=bn_name + "_scale", regularizer=L2Decay(self.bn_decay)) bn_bias_attr = ParamAttr( name=bn_name + "_offset", regularizer=L2Decay(self.bn_decay)) bn = fluid.layers.batch_norm( input=conv, param_attr=bn_param_attr, bias_attr=bn_bias_attr, moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance') if if_act: if act == 'relu': bn = fluid.layers.relu(bn) elif act == 'hard_swish': bn = self._hard_swish(bn) elif act == 'relu6': bn = fluid.layers.relu6(bn) return bn def _hard_swish(self, x): return x * fluid.layers.relu6(x + 3) / 6. def _se_block(self, input, num_out_filter, ratio=4, name=None): num_mid_filter = int(num_out_filter // ratio) pool = fluid.layers.pool2d( input=input, pool_type='avg', global_pooling=True, use_cudnn=False) conv1 = fluid.layers.conv2d( input=pool, filter_size=1, num_filters=num_mid_filter, act='relu', param_attr=ParamAttr(name=name + '_1_weights'), bias_attr=ParamAttr(name=name + '_1_offset')) conv2 = fluid.layers.conv2d( input=conv1, filter_size=1, num_filters=num_out_filter, act='hard_sigmoid', param_attr=ParamAttr(name=name + '_2_weights'), bias_attr=ParamAttr(name=name + '_2_offset')) scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0) return scale def _residual_unit(self, input, num_in_filter, num_mid_filter, num_out_filter, stride, filter_size, act=None, use_se=False, name=None): input_data = input conv0 = self._conv_bn_layer( input=input, filter_size=1, num_filters=num_mid_filter, stride=1, padding=0, if_act=True, act=act, name=name + '_expand') if self.block_stride == 16 and stride == 2: self.end_points.append(conv0) conv1 = self._conv_bn_layer( input=conv0, filter_size=filter_size, num_filters=num_mid_filter, stride=stride, padding=int((filter_size - 1) // 2), if_act=True, act=act, num_groups=num_mid_filter, use_cudnn=False, name=name + '_depthwise') if use_se: conv1 = self._se_block( input=conv1, num_out_filter=num_mid_filter, name=name + '_se') conv2 = self._conv_bn_layer( input=conv1, filter_size=1, num_filters=num_out_filter, stride=1, padding=0, if_act=False, name=name + '_linear') if num_in_filter != num_out_filter or stride != 1: return conv2 else: return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None) def _extra_block_dw(self, input, num_filters1, num_filters2, stride, name=None): pointwise_conv = self._conv_bn_layer( input=input, filter_size=1, num_filters=int(num_filters1), stride=1, padding="SAME", act='relu6', name=name + "_extra1") depthwise_conv = self._conv_bn_layer( input=pointwise_conv, filter_size=3, num_filters=int(num_filters2), stride=stride, padding="SAME", num_groups=int(num_filters1), act='relu6', use_cudnn=False, name=name + "_extra2_dw") normal_conv = self._conv_bn_layer( input=depthwise_conv, filter_size=1, num_filters=int(num_filters2), stride=1, padding="SAME", act='relu6', name=name + "_extra2_sep") return normal_conv def __call__(self, input): scale = self.scale inplanes = self.inplanes cfg = self.cfg blocks = [] #conv1 conv = self._conv_bn_layer( input, filter_size=3, num_filters=inplanes if scale <= 1.0 else int(inplanes * scale), stride=2, padding=1, num_groups=1, if_act=True, act='hard_swish', name='conv1') i = 0 for layer_cfg in cfg: self.block_stride *= layer_cfg[5] conv = self._residual_unit( input=conv, num_in_filter=inplanes, num_mid_filter=int(scale * layer_cfg[1]), num_out_filter=int(scale * layer_cfg[2]), act=layer_cfg[4], stride=layer_cfg[5], filter_size=layer_cfg[0], use_se=layer_cfg[3], name='conv' + str(i + 2)) inplanes = int(scale * layer_cfg[2]) i += 1 if not self.with_extra_blocks: return conv # extra block conv_extra = self._conv_bn_layer( conv, filter_size=1, num_filters=int(scale * cfg[-1][1]), stride=1, padding="SAME", num_groups=1, if_act=True, act='hard_swish', name='conv' + str(i + 2)) self.end_points.append(conv_extra) i += 1 for block_filter in self.extra_block_filters: conv_extra = self._extra_block_dw(conv_extra, block_filter[0], block_filter[1], 2, 'conv' + str(i + 2)) self.end_points.append(conv_extra) i += 1 return self.end_points