import paddle.fluid as fluid from paddle.fluid.initializer import MSRA from paddle.fluid.param_attr import ParamAttr import math __all__ = [ 'MobileNetV3', 'MobileNetV3_small_x0_25', 'MobileNetV3_small_x0_5', 'MobileNetV3_small_x0_75', 'MobileNetV3_small_x1_0', 'MobileNetV3_small_x1_25', 'MobileNetV3_large_x0_25', 'MobileNetV3_large_x0_5', 'MobileNetV3_large_x0_75', 'MobileNetV3_large_x1_0', 'MobileNetV3_large_x1_25', 'MobileNetV3_large_x2_0' ] class MobileNetV3(): def __init__(self, scale=1.0, model_name='small'): self.scale = scale self.inplanes = 16 if model_name == "large": self.cfg = [ # k, exp, c, se, nl, s, [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], ] self.cls_ch_squeeze = 960 self.cls_ch_expand = 1280 elif model_name == "small": self.cfg = [ # k, exp, c, se, nl, s, [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], ] self.cls_ch_squeeze = 576 self.cls_ch_expand = 1280 else: raise NotImplementedError def net(self, input, class_dim=1000): scale = self.scale inplanes = self.inplanes cfg = self.cfg cls_ch_squeeze = self.cls_ch_squeeze cls_ch_expand = self.cls_ch_expand #conv1 conv = self.conv_bn_layer( input, filter_size=3, #num_filters=int(scale*inplanes), 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') print(conv.shape) i = 0 for layer_cfg in cfg: 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 conv = self.conv_bn_layer( input=conv, filter_size=1, num_filters=int(scale * cls_ch_squeeze), stride=1, padding=0, num_groups=1, if_act=True, act='hard_swish', name='conv_last') conv = fluid.layers.pool2d( input=conv, pool_type='avg', global_pooling=True, use_cudnn=False) conv = fluid.layers.conv2d( input=conv, num_filters=cls_ch_expand, filter_size=1, stride=1, padding=0, act=None, param_attr=ParamAttr(name='last_1x1_conv_weights'), bias_attr=False) #conv = fluid.layers.hard_swish(conv) conv = self.hard_swish(conv) out = fluid.layers.fc(input=conv, size=class_dim, act='softmax', param_attr=ParamAttr(name='fc_weights'), bias_attr=ParamAttr(name='fc_offset')) return out 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 = 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=ParamAttr(name=name + '_weights'), bias_attr=False) bn_name = name + '_bn' bn = fluid.layers.batch_norm( input=conv, param_attr=ParamAttr( name=bn_name + "_scale", regularizer=fluid.regularizer.L2DecayRegularizer( regularization_coeff=0.0)), bias_attr=ParamAttr( name=bn_name + "_offset", regularizer=fluid.regularizer.L2DecayRegularizer( regularization_coeff=0.0)), 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 = fluid.layers.hard_swish(bn) bn = self.hard_swish(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') 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: with fluid.name_scope('se_block_skip'): 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 MobileNetV3_small_x0_25(): model = MobileNetV3(model_name='small', scale=0.25) return model def MobileNetV3_small_x0_5(): model = MobileNetV3(model_name='small', scale=0.5) return model def MobileNetV3_small_x0_75(): model = MobileNetV3(model_name='small', scale=0.75) return model def MobileNetV3_small_x1_0(): model = MobileNetV3(model_name='small', scale=1.0) return model def MobileNetV3_small_x1_25(): model = MobileNetV3(model_name='small', scale=1.25) return model def MobileNetV3_large_x0_25(): model = MobileNetV3(model_name='large', scale=0.25) return model def MobileNetV3_large_x0_5(): model = MobileNetV3(model_name='large', scale=0.5) return model def MobileNetV3_large_x0_75(): model = MobileNetV3(model_name='large', scale=0.75) return model def MobileNetV3_large_x1_0(): model = MobileNetV3(model_name='large', scale=1.0) return model def MobileNetV3_large_x1_25(): model = MobileNetV3(model_name='large', scale=1.25) return model def MobileNetV3_large_x2_0(): model = MobileNetV3(model_name='large', scale=2.0) return model