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add swnet by Jeralyn123
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| Original file line number | Diff line number | Diff line change |
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| """Efficient Neural Network""" | ||
| import torch | ||
| import torch.nn as nn | ||
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| __all__ = ['ENet', 'get_enet', 'get_enet_citys'] | ||
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| class ENet(nn.Module): | ||
| """Efficient Neural Network""" | ||
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| def __init__(self, nclass, backbone='', aux=False, jpu=False, pretrained_base=None, **kwargs): | ||
| super(ENet, self).__init__() | ||
| self.initial = InitialBlock(13, **kwargs) | ||
| #block 1: | ||
| self.bottleneck1_0 = Bottleneck(16, 16, 64, downsampling=True, **kwargs) | ||
| self.bottleneck1_1 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_2 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_3 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_4 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_5 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_6 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_7 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_8 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_9 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck1_10 = Bottleneck(64, 16, 64, **kwargs) | ||
| #blcok 2: | ||
| self.bottleneck2_0 = Bottleneck(64, 32, 128, downsampling=True, **kwargs) | ||
| self.bottleneck2_1 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck2_2 = Bottleneck(128, 32, 128, dilation=2, **kwargs) | ||
| self.bottleneck2_3 = Bottleneck(128, 32, 128, asymmetric=True, **kwargs) | ||
| self.bottleneck2_4 = Bottleneck(128, 32, 128, dilation=4, **kwargs) | ||
| self.bottleneck2_5 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck2_6 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck2_7 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck2_8 = Bottleneck(128, 32, 128, dilation=8, **kwargs) | ||
| self.bottleneck2_9 = Bottleneck(128, 32, 128, asymmetric=True, **kwargs) | ||
| self.bottleneck2_10 = Bottleneck(128, 32, 128, dilation=16, **kwargs) | ||
| #block 3: | ||
| self.bottleneck3_0 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck3_1 = Bottleneck(128, 32, 128, dilation=2, **kwargs) | ||
| self.bottleneck3_2 = Bottleneck(128, 32, 128, asymmetric=True, **kwargs) | ||
| self.bottleneck3_3 = Bottleneck(128, 32, 128, dilation=4, **kwargs) | ||
| self.bottleneck3_4 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck3_5 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck3_6 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck3_7 = Bottleneck(128, 32, 128, **kwargs) | ||
| self.bottleneck3_8 = Bottleneck(128, 32, 128, dilation=8, **kwargs) | ||
| self.bottleneck3_9 = Bottleneck(128, 32, 128, asymmetric=True, **kwargs) | ||
| self.bottleneck3_10 = Bottleneck(128, 32, 128, dilation=16, **kwargs) | ||
| #block 4: | ||
| self.bottleneck4_0 = UpsamplingBottleneck(128, 16, 64, **kwargs) | ||
| self.bottleneck4_1 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_2 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_3 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_4 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_5 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_6 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_7 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_8 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_9 = Bottleneck(64, 16, 64, **kwargs) | ||
| self.bottleneck4_10 = Bottleneck(64, 16, 64, **kwargs) | ||
| #block 5: | ||
| self.bottleneck5_0 = UpsamplingBottleneck(64, 4, 16, **kwargs) | ||
| self.bottleneck5_1 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_2 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_3 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_4 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_5 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_6 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_7 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_8 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_9 = Bottleneck(16, 4, 16, **kwargs) | ||
| self.bottleneck5_10 = Bottleneck(16, 4, 16, **kwargs) | ||
| #block 6: | ||
| self.fullconv = nn.ConvTranspose2d(16, nclass, 2, 2, bias=False) | ||
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| self.__setattr__('exclusive', ['bottleneck1_0', 'bottleneck1_1', 'bottleneck1_2', 'bottleneck1_3', | ||
| 'bottleneck1_4', 'bottleneck1_5', 'bottleneck1_6', 'bottleneck1_7', | ||
| 'bottleneck1_8', 'bottleneck1_9', 'bottleneck1_10','bottleneck2_0', | ||
| 'bottleneck2_1', 'bottleneck2_2', 'bottleneck2_3', 'bottleneck2_4', | ||
| 'bottleneck2_5', 'bottleneck2_6', 'bottleneck2_7', 'bottleneck2_8', | ||
| 'bottleneck2_9', 'bottleneck2_10','bottleneck3_0', 'bottleneck3_1', | ||
| 'bottleneck3_2', 'bottleneck3_3', 'bottleneck3_4', 'bottleneck3_5', | ||
| 'bottleneck3_6', 'bottleneck3_7', 'bottleneck3_8', 'bottleneck3_9', | ||
| 'bottleneck3_10','bottleneck4_0', 'bottleneck4_1', 'bottleneck4_2', | ||
| 'bottleneck4_3', 'bottleneck4_4', 'bottleneck4_5', 'bottleneck4_6', | ||
| 'bottleneck4_7', 'bottleneck4_8', 'bottleneck4_9', 'bottleneck4_10', | ||
| 'bottleneck5_0', 'bottleneck5_1', 'bottleneck5_2', 'bottleneck5_3', | ||
| 'bottleneck5_4', 'bottleneck5_5', 'bottleneck5_6', 'bottleneck5_7', | ||
| 'bottleneck5_8', 'bottleneck5_9', 'bottleneck5_10','fullconv']) | ||
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| def forward(self, x): | ||
| # init | ||
| x = self.initial(x) | ||
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| # stage 1 | ||
| x, max_indices1 = self.bottleneck1_0(x) | ||
| x = self.bottleneck1_1(x) | ||
| x = self.bottleneck1_2(x) | ||
| x = self.bottleneck1_3(x) | ||
| x = self.bottleneck1_4(x) | ||
| x = self.bottleneck1_5(x) | ||
| x = self.bottleneck1_6(x) | ||
| x = self.bottleneck1_7(x) | ||
| x = self.bottleneck1_8(x) | ||
| x = self.bottleneck1_9(x) | ||
| x = self.bottleneck1_10(x) | ||
| # stage 2 | ||
| x, max_indices2 = self.bottleneck2_0(x) | ||
| x = self.bottleneck2_1(x) | ||
| x = self.bottleneck2_2(x) | ||
| x = self.bottleneck2_3(x) | ||
| x = self.bottleneck2_4(x) | ||
| x = self.bottleneck2_5(x) | ||
| x = self.bottleneck2_6(x) | ||
| x = self.bottleneck2_7(x) | ||
| x = self.bottleneck2_8(x) | ||
| x = self.bottleneck2_9(x) | ||
| x = self.bottleneck2_10(x) | ||
| # stage 3 | ||
| x = self.bottleneck3_0(x) | ||
| x = self.bottleneck3_1(x) | ||
| x = self.bottleneck3_2(x) | ||
| x = self.bottleneck3_3(x) | ||
| x = self.bottleneck3_4(x) | ||
| x = self.bottleneck3_5(x) | ||
| x = self.bottleneck3_6(x) | ||
| x = self.bottleneck3_7(x) | ||
| x = self.bottleneck3_8(x) | ||
| x = self.bottleneck3_9(x) | ||
| x = self.bottleneck3_10(x) | ||
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| # stage 4 | ||
| x = self.bottleneck4_0(x, max_indices2) | ||
| x = self.bottleneck4_1(x) | ||
| x = self.bottleneck4_2(x) | ||
| x = self.bottleneck4_3(x) | ||
| x = self.bottleneck4_4(x) | ||
| x = self.bottleneck4_5(x) | ||
| x = self.bottleneck4_6(x) | ||
| x = self.bottleneck4_7(x) | ||
| x = self.bottleneck4_8(x) | ||
| x = self.bottleneck4_9(x) | ||
| x = self.bottleneck4_10(x) | ||
| # stage 5 | ||
| x = self.bottleneck5_0(x, max_indices1) | ||
| x = self.bottleneck5_1(x) | ||
| x = self.bottleneck5_2(x) | ||
| x = self.bottleneck5_3(x) | ||
| x = self.bottleneck5_4(x) | ||
| x = self.bottleneck5_5(x) | ||
| x = self.bottleneck5_6(x) | ||
| x = self.bottleneck5_7(x) | ||
| x = self.bottleneck5_8(x) | ||
| x = self.bottleneck5_9(x) | ||
| x = self.bottleneck5_10(x) | ||
| # out | ||
| x = self.fullconv(x) | ||
| return tuple([x]) | ||
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| class InitialBlock(nn.Module): | ||
| """ENet initial block""" | ||
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| def __init__(self, out_channels, norm_layer=nn.BatchNorm2d, **kwargs): | ||
| super(InitialBlock, self).__init__() | ||
| self.conv = nn.Conv2d(3, out_channels, 3, 2, 1, bias=False) | ||
| self.maxpool = nn.MaxPool2d(2, 2) | ||
| self.bn = norm_layer(out_channels + 3) | ||
| self.act = nn.RReLU() | ||
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| def forward(self, x): | ||
| x_conv = self.conv(x) | ||
| x_pool = self.maxpool(x) | ||
| x = torch.cat([x_conv, x_pool], dim=1) | ||
| x = self.bn(x) | ||
| x = self.act(x) | ||
| return x | ||
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| class Bottleneck(nn.Module): | ||
| """Bottlenecks include regular, asymmetric, downsampling, dilated""" | ||
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| def __init__(self, in_channels, inter_channels, out_channels, dilation=1, asymmetric=False, | ||
| downsampling=False, norm_layer=nn.BatchNorm2d, **kwargs): | ||
| super(Bottleneck, self).__init__() | ||
| self.downsamping = downsampling | ||
| if downsampling: | ||
| self.maxpool = nn.MaxPool2d(2, 2, return_indices=True) | ||
| self.conv_down = nn.Sequential( | ||
| nn.Conv2d(in_channels, out_channels, 1, bias=False), | ||
| norm_layer(out_channels) | ||
| ) | ||
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| self.conv1 = nn.Sequential( | ||
| nn.Conv2d(in_channels, inter_channels, 1, bias=False), | ||
| norm_layer(inter_channels), | ||
| nn.RReLU() | ||
| ) | ||
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| if downsampling: | ||
| self.conv2 = nn.Sequential( | ||
| nn.Conv2d(inter_channels, inter_channels, 2, stride=2, bias=False), | ||
| norm_layer(inter_channels), | ||
| nn.RReLU() | ||
| ) | ||
| else: | ||
| if asymmetric: | ||
| self.conv2 = nn.Sequential( | ||
| nn.Conv2d(inter_channels, inter_channels, (5, 1), padding=(2, 0), bias=False), | ||
| nn.Conv2d(inter_channels, inter_channels, (1, 5), padding=(0, 2), bias=False), | ||
| norm_layer(inter_channels), | ||
| nn.RReLU() | ||
| ) | ||
| else: | ||
| self.conv2 = nn.Sequential( | ||
| nn.Conv2d(inter_channels, inter_channels, 3, dilation=dilation, padding=dilation, bias=False), | ||
| norm_layer(inter_channels), | ||
| nn.RReLU() | ||
| ) | ||
| self.conv3 = nn.Sequential( | ||
| nn.Conv2d(inter_channels, out_channels, 1, bias=False), | ||
| norm_layer(out_channels), | ||
| nn.Dropout2d(0.1) | ||
| ) | ||
| self.act = nn.RReLU() | ||
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| def forward(self, x): | ||
| identity = x | ||
| if self.downsamping: | ||
| identity, max_indices = self.maxpool(identity) | ||
| identity = self.conv_down(identity) | ||
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| out = self.conv1(x) | ||
| out = self.conv2(out) | ||
| out = self.conv3(out) | ||
| out = self.act(out + identity) | ||
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| if self.downsamping: | ||
| return out, max_indices | ||
| else: | ||
| return out | ||
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| class UpsamplingBottleneck(nn.Module): | ||
| """upsampling Block""" | ||
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| def __init__(self, in_channels, inter_channels, out_channels, norm_layer=nn.BatchNorm2d, **kwargs): | ||
| super(UpsamplingBottleneck, self).__init__() | ||
| self.conv = nn.Sequential( | ||
| nn.Conv2d(in_channels, out_channels, 1, bias=False), | ||
| norm_layer(out_channels) | ||
| ) | ||
| self.upsampling = nn.MaxUnpool2d(2) | ||
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| self.block = nn.Sequential( | ||
| nn.Conv2d(in_channels, inter_channels, 1, bias=False), | ||
| norm_layer(inter_channels), | ||
| nn.RReLU(), | ||
| nn.ConvTranspose2d(inter_channels, inter_channels, 2, 2, bias=False), | ||
| norm_layer(inter_channels), | ||
| nn.RReLU(), | ||
| nn.Conv2d(inter_channels, out_channels, 1, bias=False), | ||
| norm_layer(out_channels), | ||
| nn.Dropout2d(0.1) | ||
| ) | ||
| self.act = nn.RReLU() | ||
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| def forward(self, x, max_indices): | ||
| out_up = self.conv(x) | ||
| out_up = self.upsampling(out_up, max_indices) | ||
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| out_ext = self.block(x) | ||
| out = self.act(out_up + out_ext) | ||
| return out | ||
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| def get_enet(dataset='citys', backbone='', pretrained=False, root='~/.torch/models', pretrained_base=True, **kwargs): | ||
| acronyms = { | ||
| 'pascal_voc': 'pascal_voc', | ||
| 'pascal_aug': 'pascal_aug', | ||
| 'ade20k': 'ade', | ||
| 'coco': 'coco', | ||
| 'citys': 'citys', | ||
| } | ||
| from core.data.dataloader import datasets | ||
| model = ENet(datasets[dataset].NUM_CLASS, backbone=backbone, pretrained_base=pretrained_base, **kwargs) | ||
| if pretrained: | ||
| from .model_store import get_model_file | ||
| device = torch.device(kwargs['local_rank']) | ||
| model.load_state_dict(torch.load(get_model_file('enet_%s' % (acronyms[dataset]), root=root), | ||
| map_location=device)) | ||
| return model | ||
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| def get_enet_citys(**kwargs): | ||
| return get_enet('citys', '', **kwargs) | ||
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| if __name__ == '__main__': | ||
| img = torch.randn(1, 3, 512, 512) | ||
| model = get_enet_citys() | ||
| output = model(img) |
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