# paddleslim.models package

`paddleslim.models.``image_classification`(model, image_shape, class_num, use_gpu=False)

## Submodules

## paddleslim.models.classification_models module

class `paddleslim.models.classification_models.``MobileNet`
  `conv_bn_layer`(input, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act='relu', use_cudnn=True, name=None)
  `depthwise_separable`(input, num_filters1, num_filters2, num_groups, stride, scale, name=None)
  `net`(input, class_dim=1000, scale=1.0)

`paddleslim.models.classification_models.``ResNet34`(prefix_name='')

`paddleslim.models.classification_models.``ResNet50`(prefix_name='')

class `paddleslim.models.classification_models.``MobileNetV2`(scale=1.0, change_depth=False)
  `conv_bn_layer`(input, filter_size, num_filters, stride, padding, channels=None, num_groups=1, if_act=True, name=None, use_cudnn=True)
  `inverted_residual_unit`(input, num_in_filter, num_filters, ifshortcut, stride, filter_size, padding, expansion_factor, name=None)
  `invresi_blocks`(input, in_c, t, c, n, s, name=None)
  `net`(input, class_dim=1000)
  `shortcut`(input, data_residual)

## paddleslim.models.mobilenet module

class `paddleslim.models.mobilenet.``MobileNet`
  `conv_bn_layer`(input, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act='relu', use_cudnn=True, name=None)
  `depthwise_separable`(input, num_filters1, num_filters2, num_groups, stride, scale, name=None)
  `net`(input, class_dim=1000, scale=1.0)

## paddleslim.models.mobilenet_v2 module

class `paddleslim.models.mobilenet_v2.``MobileNetV2`(scale=1.0, change_depth=False)
  `conv_bn_layer`(input, filter_size, num_filters, stride, padding, channels=None, num_groups=1, if_act=True, name=None, use_cudnn=True)
  `inverted_residual_unit`(input, num_in_filter, num_filters, ifshortcut, stride, filter_size, padding, expansion_factor, name=None)
  `invresi_blocks`(input, in_c, t, c, n, s, name=None)
  `net`(input, class_dim=1000)
  `shortcut`(input, data_residual)

`paddleslim.models.mobilenet_v2.``MobileNetV2_x1_0`()

`paddleslim.models.mobilenet_v2.``MobileNetV2_x1_5`()

`paddleslim.models.mobilenet_v2.``MobileNetV2_x2_0`()

`paddleslim.models.mobilenet_v2.``MobileNetV2_scale`()

## paddleslim.models.resnet module

class `paddleslim.models.resnet.``ResNet`(layers=50, prefix_name='')
  `basic_block`(input, num_filters, stride, is_first, name)
  `bottleneck_block`(input, num_filters, stride, name)
  `conv_bn_layer`(input, num_filters, filter_size, stride=1, groups=1, act=None, name=None)
  `net`(input, class_dim=1000, conv1_name='conv1', fc_name=None)
  `shortcut`(input, ch_out, stride, is_first, name)

`paddleslim.models.resnet.``ResNet34`(prefix_name='')

`paddleslim.models.resnet.``ResNet50`(prefix_name='')

`paddleslim.models.resnet.``ResNet101`()

`paddleslim.models.resnet.``ResNet152`()

## paddleslim.models.util module

`paddleslim.models.util.``image_classification`(model, image_shape, class_num, use_gpu=False)