"""Point-wise Spatial Attention Network""" import torch import torch.nn as nn import torch.nn.functional as F from core.nn import CollectAttention, DistributeAttention from core.models.segbase import SegBaseModel from core.models.fcn import _FCNHead #运行失败，name '_C' is not defined。也是跟psa_block模块的实现有关：用到了自定义的torch.autograd.Function（里面用到了cpp文件，找不到文件出错） __all__ = ['PSANet', 'get_psanet', 'get_psanet_resnet50_voc', 'get_psanet_resnet101_voc', 'get_psanet_resnet152_voc', 'get_psanet_resnet50_citys', 'get_psanet_resnet101_citys', 'get_psanet_resnet152_citys'] class PSANet(SegBaseModel): r"""PSANet Parameters ---------- nclass : int Number of categories for the training dataset. backbone : string Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50', 'resnet101' or 'resnet152'). norm_layer : object Normalization layer used in backbone network (default: :class:`nn.BatchNorm`; for Synchronized Cross-GPU BachNormalization). aux : bool Auxiliary loss. Reference: Hengshuang Zhao, et al. "PSANet: Point-wise Spatial Attention Network for Scene Parsing." ECCV-2018. """ def __init__(self, nclass, backbone='resnet', aux=False, pretrained_base=False, **kwargs): super(PSANet, self).__init__(nclass, aux, backbone, pretrained_base, **kwargs) self.head = _PSAHead(nclass, **kwargs) if aux: self.auxlayer = _FCNHead(1024, nclass, **kwargs) self.__setattr__('exclusive', ['head', 'auxlayer'] if aux else ['head']) def forward(self, x): size = x.size()[2:] _, _, c3, c4 = self.base_forward(x) outputs = list() x = self.head(c4) x = F.interpolate(x, size, mode='bilinear', align_corners=True) outputs.append(x) if self.aux: auxout = self.auxlayer(c3) auxout = F.interpolate(auxout, size, mode='bilinear', align_corners=True) outputs.append(auxout) return tuple(outputs) class _PSAHead(nn.Module): def __init__(self, nclass, norm_layer=nn.BatchNorm2d, **kwargs): super(_PSAHead, self).__init__() self.collect = _CollectModule(2048, 512, 60, 60, norm_layer, **kwargs) self.distribute = _DistributeModule(2048, 512, 60, 60, norm_layer, **kwargs) self.conv_post = nn.Sequential( nn.Conv2d(1024, 2048, 1, bias=False), norm_layer(2048), nn.ReLU(True)) self.project = nn.Sequential( nn.Conv2d(4096, 512, 3, padding=1, bias=False), norm_layer(512), nn.ReLU(True), nn.Conv2d(512, nclass, 1) ) def forward(self, x): global_feature_collect = self.collect(x) global_feature_distribute = self.distribute(x) global_feature = torch.cat([global_feature_collect, global_feature_distribute], dim=1) out = self.conv_post(global_feature) out = F.interpolate(out, scale_factor=2, mode='bilinear', align_corners=True) out = torch.cat([x, out], dim=1) out = self.project(out) return out class _CollectModule(nn.Module): def __init__(self, in_channels, reduced_channels, feat_w, feat_h, norm_layer, **kwargs): super(_CollectModule, self).__init__() self.conv_reduce = nn.Sequential( nn.Conv2d(in_channels, reduced_channels, 1, bias=False), norm_layer(reduced_channels), nn.ReLU(True)) self.conv_adaption = nn.Sequential( nn.Conv2d(reduced_channels, reduced_channels, 1, bias=False), norm_layer(reduced_channels), nn.ReLU(True), nn.Conv2d(reduced_channels, (feat_w - 1) * (feat_h), 1, bias=False)) self.collect_attention = CollectAttention() self.reduced_channels = reduced_channels self.feat_w = feat_w self.feat_h = feat_h def forward(self, x): x = self.conv_reduce(x) # shrink x_shrink = F.interpolate(x, scale_factor=1 / 2, mode='bilinear', align_corners=True) x_adaption = self.conv_adaption(x_shrink) ca = self.collect_attention(x_adaption) global_feature_collect_list = list() for i in range(x_shrink.shape[0]): x_shrink_i = x_shrink[i].view(self.reduced_channels, -1) ca_i = ca[i].view(ca.shape[1], -1) global_feature_collect_list.append( torch.mm(x_shrink_i, ca_i).view(1, self.reduced_channels, self.feat_h // 2, self.feat_w // 2)) global_feature_collect = torch.cat(global_feature_collect_list) return global_feature_collect class _DistributeModule(nn.Module): def __init__(self, in_channels, reduced_channels, feat_w, feat_h, norm_layer, **kwargs): super(_DistributeModule, self).__init__() self.conv_reduce = nn.Sequential( nn.Conv2d(in_channels, reduced_channels, 1, bias=False), norm_layer(reduced_channels), nn.ReLU(True)) self.conv_adaption = nn.Sequential( nn.Conv2d(reduced_channels, reduced_channels, 1, bias=False), norm_layer(reduced_channels), nn.ReLU(True), nn.Conv2d(reduced_channels, (feat_w - 1) * (feat_h), 1, bias=False)) self.distribute_attention = DistributeAttention() self.reduced_channels = reduced_channels self.feat_w = feat_w self.feat_h = feat_h def forward(self, x): x = self.conv_reduce(x) x_shrink = F.interpolate(x, scale_factor=1 / 2, mode='bilinear', align_corners=True) x_adaption = self.conv_adaption(x_shrink) da = self.distribute_attention(x_adaption) global_feature_distribute_list = list() for i in range(x_shrink.shape[0]): x_shrink_i = x_shrink[i].view(self.reduced_channels, -1) da_i = da[i].view(da.shape[1], -1) global_feature_distribute_list.append( torch.mm(x_shrink_i, da_i).view(1, self.reduced_channels, self.feat_h // 2, self.feat_w // 2)) global_feature_distribute = torch.cat(global_feature_distribute_list) return global_feature_distribute def get_psanet(dataset='pascal_voc', backbone='resnet50', pretrained=False, root='~/.torch/models', pretrained_base=False, **kwargs): acronyms = { 'pascal_voc': 'pascal_voc', 'pascal_aug': 'pascal_aug', 'ade20k': 'ade', 'coco': 'coco', 'citys': 'citys', } # from ..data.dataloader import datasets model = PSANet(4, 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('deeplabv3_%s_%s' % (backbone, acronyms[dataset]), root=root), # map_location=device)) return model def get_psanet_resnet50_voc(**kwargs): return get_psanet('pascal_voc', 'resnet50', **kwargs) def get_psanet_resnet101_voc(**kwargs): return get_psanet('pascal_voc', 'resnet101', **kwargs) def get_psanet_resnet152_voc(**kwargs): return get_psanet('pascal_voc', 'resnet152', **kwargs) def get_psanet_resnet50_citys(**kwargs): return get_psanet('citys', 'resnet50', **kwargs) def get_psanet_resnet101_citys(**kwargs): return get_psanet('citys', 'resnet101', **kwargs) def get_psanet_resnet152_citys(**kwargs): return get_psanet('citys', 'resnet152', **kwargs) if __name__ == '__main__': model = get_psanet_resnet50_voc() img = torch.randn(1, 3, 480, 480) output = model(img)