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import torch
import torch.nn as nn
import torch.nn.functional as F
from . import backbone_picie as backbone
class PanopticFPN(nn.Module):
def __init__(self, args):
super(PanopticFPN, self).__init__()
self.backbone = backbone.__dict__[args.arch](pretrained=args.pretrain)
if args.arch == 'vit_small':
self.decoder = FPNDecoderViT(args)
else:
self.decoder = FPNDecoder(args)
def forward(self, x, encoder_features=False, decoder_features=False):
feats = self.backbone(x)
dec_outs = self.decoder(feats)
if encoder_features:
return feats['res5'], dec_outs
else:
return dec_outs
class FPNDecoder(nn.Module):
def __init__(self, args):
super(FPNDecoder, self).__init__()
if args.arch == 'resnet18':
mfactor = 1
out_dim = 128
else:
mfactor = 4
out_dim = 256
self.layer4 = nn.Conv2d(512 * mfactor // 8, out_dim, kernel_size=1, stride=1, padding=0)
self.layer3 = nn.Conv2d(512 * mfactor // 4, out_dim, kernel_size=1, stride=1, padding=0)
self.layer2 = nn.Conv2d(512 * mfactor // 2, out_dim, kernel_size=1, stride=1, padding=0)
self.layer1 = nn.Conv2d(512 * mfactor, out_dim, kernel_size=1, stride=1, padding=0)
def forward(self, x):
o1 = self.layer1(x['res5'])
o2 = self.upsample_add(o1, self.layer2(x['res4']))
o3 = self.upsample_add(o2, self.layer3(x['res3']))
o4 = self.upsample_add(o3, self.layer4(x['res2']))
return o4
def upsample_add(self, x, y):
_, _, H, W = y.size()
return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=False) + y
class FPNDecoderViT(nn.Module):
def __init__(self, args):
super(FPNDecoderViT, self).__init__()
if args.arch == 'resnet18' or args.arch == 'vit_small':
mfactor = 1
out_dim = 128
else:
mfactor = 4
out_dim = 256
self.upsample_rate = 4
self.layer4 = nn.Conv2d(384, out_dim, kernel_size=1, stride=1, padding=0)
self.layer3 = nn.Conv2d(384, out_dim, kernel_size=1, stride=1, padding=0)
self.layer2 = nn.Conv2d(384, out_dim, kernel_size=1, stride=1, padding=0)
self.layer1 = nn.Conv2d(384, out_dim, kernel_size=1, stride=1, padding=0)
def forward(self, x):
o1 = self.layer1(x[3])
o1 = F.interpolate(o1, scale_factor=4, mode='bilinear', align_corners=False)
o2 = self.upsample_add(o1, self.layer2(x[2]))
o3 = self.upsample_add(o2, self.layer3(x[1]))
o4 = self.upsample_add(o3, self.layer4(x[0]))
return o4
def upsample_add(self, x, y):
return F.interpolate(y, scale_factor=self.upsample_rate, mode='bilinear', align_corners=False) + x
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