185 lines
6.6 KiB
Python
185 lines
6.6 KiB
Python
"""Pyramid Scene Parsing Network"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from core.models.segbase import SegBaseModel
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from core.models.fcn import _FCNHead
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__all__ = ['PSPNet', 'get_psp', 'get_psp_resnet50_voc', 'get_psp_resnet50_ade', 'get_psp_resnet101_voc',
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'get_psp_resnet101_ade', 'get_psp_resnet101_citys', 'get_psp_resnet101_coco']
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class PSPNet(SegBaseModel):
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r"""Pyramid Scene Parsing Network
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Parameters
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----------
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nclass : int
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Number of categories for the training dataset.
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backbone : string
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Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
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'resnet101' or 'resnet152').
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norm_layer : object
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Normalization layer used in backbone network (default: :class:`nn.BatchNorm`;
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for Synchronized Cross-GPU BachNormalization).
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aux : bool
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Auxiliary loss.
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Reference:
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Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia.
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"Pyramid scene parsing network." *CVPR*, 2017
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"""
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def __init__(self, nclass, backbone='resnet50', aux=False, pretrained_base=True, **kwargs):
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super(PSPNet, self).__init__(nclass, aux, backbone, pretrained_base=pretrained_base, **kwargs)
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self.head = _PSPHead(nclass, **kwargs)
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if self.aux:
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self.auxlayer = _FCNHead(1024, nclass, **kwargs)
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self.__setattr__('exclusive', ['head', 'auxlayer'] if aux else ['head'])
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def forward(self, x):
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size = x.size()[2:]
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_, _, c3, c4 = self.base_forward(x)
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outputs = []
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x = self.head(c4)
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x = F.interpolate(x, size, mode='bilinear', align_corners=True)
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outputs.append(x)
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if self.aux:
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auxout = self.auxlayer(c3)
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auxout = F.interpolate(auxout, size, mode='bilinear', align_corners=True)
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outputs.append(auxout)
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#return tuple(outputs)
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return outputs[0]
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def _PSP1x1Conv(in_channels, out_channels, norm_layer, norm_kwargs):
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return nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 1, bias=False),
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norm_layer(out_channels, **({} if norm_kwargs is None else norm_kwargs)),
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nn.ReLU(True)
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)
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class _PyramidPooling(nn.Module):
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def __init__(self, in_channels, **kwargs):
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super(_PyramidPooling, self).__init__()
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out_channels = int(in_channels / 4)
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self.avgpool1 = nn.AdaptiveAvgPool2d(1)
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self.avgpool2 = nn.AdaptiveAvgPool2d(2)
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self.avgpool3 = nn.AdaptiveAvgPool2d(3)
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self.avgpool4 = nn.AdaptiveAvgPool2d(6)
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self.conv1 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
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self.conv2 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
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self.conv3 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
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self.conv4 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
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def forward(self, x):
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size = x.size()[2:]
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feat1 = F.interpolate(self.conv1(self.avgpool1(x)), size, mode='bilinear', align_corners=True)
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feat2 = F.interpolate(self.conv2(self.avgpool2(x)), size, mode='bilinear', align_corners=True)
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feat3 = F.interpolate(self.conv3(self.avgpool3(x)), size, mode='bilinear', align_corners=True)
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feat4 = F.interpolate(self.conv4(self.avgpool4(x)), size, mode='bilinear', align_corners=True)
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return torch.cat([x, feat1, feat2, feat3, feat4], dim=1)
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class _PSPHead(nn.Module):
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def __init__(self, nclass, norm_layer=nn.BatchNorm2d, norm_kwargs=None, **kwargs):
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super(_PSPHead, self).__init__()
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self.psp = _PyramidPooling(2048, norm_layer=norm_layer, norm_kwargs=norm_kwargs)
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self.block = nn.Sequential(
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nn.Conv2d(4096, 512, 3, padding=1, bias=False),
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norm_layer(512, **({} if norm_kwargs is None else norm_kwargs)),
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nn.ReLU(True),
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nn.Dropout(0.1),
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nn.Conv2d(512, nclass, 1)
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)
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def forward(self, x):
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x = self.psp(x)
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return self.block(x)
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def get_psp(dataset='pascal_voc', backbone='resnet50', pretrained=False, root='~/.torch/models',
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pretrained_base=True, **kwargs):
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r"""Pyramid Scene Parsing Network
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Parameters
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----------
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dataset : str, default pascal_voc
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The dataset that model pretrained on. (pascal_voc, ade20k)
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pretrained : bool or str
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Boolean value controls whether to load the default pretrained weights for model.
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String value represents the hashtag for a certain version of pretrained weights.
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root : str, default '~/.torch/models'
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Location for keeping the model parameters.
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pretrained_base : bool or str, default True
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This will load pretrained backbone network, that was trained on ImageNet.
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Examples
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--------
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>>> model = get_psp(dataset='pascal_voc', backbone='resnet50', pretrained=False)
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>>> print(model)
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"""
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acronyms = {
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'pascal_voc': 'pascal_voc',
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'pascal_aug': 'pascal_aug',
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'ade20k': 'ade',
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'coco': 'coco',
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'citys': 'citys',
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}
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from ..data.dataloader import datasets
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model = PSPNet(datasets[dataset].NUM_CLASS, backbone=backbone, pretrained_base=pretrained_base, **kwargs)
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if pretrained:
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from .model_store import get_model_file
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device = torch.device(kwargs['local_rank'])
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model.load_state_dict(torch.load(get_model_file('psp_%s_%s' % (backbone, acronyms[dataset]), root=root),
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map_location=device))
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return model
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def get_psp_resnet50_voc(**kwargs):
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return get_psp('pascal_voc', 'resnet50', **kwargs)
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def get_psp_resnet50_ade(**kwargs):
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return get_psp('ade20k', 'resnet50', **kwargs)
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def get_psp_resnet101_voc(**kwargs):
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return get_psp('pascal_voc', 'resnet101', **kwargs)
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def get_psp_resnet101_ade(**kwargs):
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return get_psp('ade20k', 'resnet101', **kwargs)
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def get_psp_resnet101_citys(**kwargs):
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return get_psp('citys', 'resnet101', **kwargs)
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def get_psp_resnet101_coco(**kwargs):
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return get_psp('coco', 'resnet101', **kwargs)
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if __name__ == '__main__':
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# model = get_psp_resnet50_voc()
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# img = torch.randn(4, 3, 480, 480)
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# output = model(img)
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input = torch.rand(2, 3, 512, 512)
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model = PSPNet(4, pretrained_base=False)
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# target = torch.zeros(4, 512, 512).cuda()
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# model.eval()
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# print(model)
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loss = model(input)
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print(loss, loss.shape)
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# from torchsummary import summary
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#
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# summary(model, (3, 224, 224)) # 打印表格，按顺序输出每层的输出形状和参数
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import torch
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from thop import profile
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from torchsummary import summary
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flop, params = profile(model, input_size=(1, 3, 512, 512))
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print('flops:{:.3f}G\nparams:{:.3f}M'.format(flop / 1e9, params / 1e6)) |