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- import paddle
- from paddle import ParamAttr
- import paddle.nn as nn
- import paddle.nn.functional as F
- from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
- from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
- from paddleseg.cvlibs import manager
- import ppmatting
- class ConvBlock(nn.Layer):
- def __init__(self, input_channels, output_channels, groups, name=None):
- super(ConvBlock, self).__init__()
- self.groups = groups
- self._conv_1 = Conv2D(
- in_channels=input_channels,
- out_channels=output_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- weight_attr=ParamAttr(name=name + "1_weights"),
- bias_attr=False)
- if groups == 2 or groups == 3 or groups == 4:
- self._conv_2 = Conv2D(
- in_channels=output_channels,
- out_channels=output_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- weight_attr=ParamAttr(name=name + "2_weights"),
- bias_attr=False)
- if groups == 3 or groups == 4:
- self._conv_3 = Conv2D(
- in_channels=output_channels,
- out_channels=output_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- weight_attr=ParamAttr(name=name + "3_weights"),
- bias_attr=False)
- if groups == 4:
- self._conv_4 = Conv2D(
- in_channels=output_channels,
- out_channels=output_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- weight_attr=ParamAttr(name=name + "4_weights"),
- bias_attr=False)
- self._pool = MaxPool2D(
- kernel_size=2, stride=2, padding=0, return_mask=True)
- def forward(self, inputs):
- x = self._conv_1(inputs)
- x = F.relu(x)
- if self.groups == 2 or self.groups == 3 or self.groups == 4:
- x = self._conv_2(x)
- x = F.relu(x)
- if self.groups == 3 or self.groups == 4:
- x = self._conv_3(x)
- x = F.relu(x)
- if self.groups == 4:
- x = self._conv_4(x)
- x = F.relu(x)
- skip = x
- x, max_indices = self._pool(x)
- return x, max_indices, skip
- class VGGNet(nn.Layer):
- def __init__(self, input_channels=3, layers=11, pretrained=None):
- super(VGGNet, self).__init__()
- self.pretrained = pretrained
- self.layers = layers
- self.vgg_configure = {
- 11: [1, 1, 2, 2, 2],
- 13: [2, 2, 2, 2, 2],
- 16: [2, 2, 3, 3, 3],
- 19: [2, 2, 4, 4, 4]
- }
- assert self.layers in self.vgg_configure.keys(), \
- "supported layers are {} but input layer is {}".format(
- self.vgg_configure.keys(), layers)
- self.groups = self.vgg_configure[self.layers]
-
- self._conv_block_1 = ConvBlock(
- input_channels, 64, self.groups[0], name="conv1_")
- self._conv_block_2 = ConvBlock(64, 128, self.groups[1], name="conv2_")
- self._conv_block_3 = ConvBlock(128, 256, self.groups[2], name="conv3_")
- self._conv_block_4 = ConvBlock(256, 512, self.groups[3], name="conv4_")
- self._conv_block_5 = ConvBlock(512, 512, self.groups[4], name="conv5_")
-
- self._conv_6 = Conv2D(
- 512, 512, kernel_size=3, padding=1, bias_attr=False)
- self.init_weight()
- def forward(self, inputs):
- fea_list = []
- ids_list = []
- x, ids, skip = self._conv_block_1(inputs)
- fea_list.append(skip)
- ids_list.append(ids)
- x, ids, skip = self._conv_block_2(x)
- fea_list.append(skip)
- ids_list.append(ids)
- x, ids, skip = self._conv_block_3(x)
- fea_list.append(skip)
- ids_list.append(ids)
- x, ids, skip = self._conv_block_4(x)
- fea_list.append(skip)
- ids_list.append(ids)
- x, ids, skip = self._conv_block_5(x)
- fea_list.append(skip)
- ids_list.append(ids)
- x = F.relu(self._conv_6(x))
- fea_list.append(x)
- return fea_list
- def init_weight(self):
- if self.pretrained is not None:
- ppmatting.utils.load_pretrained_model(self, self.pretrained)
- @manager.BACKBONES.add_component
- def VGG11(**args):
- model = VGGNet(layers=11, **args)
- return model
- @manager.BACKBONES.add_component
- def VGG13(**args):
- model = VGGNet(layers=13, **args)
- return model
- @manager.BACKBONES.add_component
- def VGG16(**args):
- model = VGGNet(layers=16, **args)
- return model
- @manager.BACKBONES.add_component
- def VGG19(**args):
- model = VGGNet(layers=19, **args)
- return model
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