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import torch
import torch.nn as nn
from torchvision.models import resnet
import torch.utils.model_zoo as model_zoo
from IndicPhotoOCR.detection.textbpn.cfglib.config import config as cfg
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
class ResNet(nn.Module):
def __init__(self, name="resnet50", pretrain=True):
super().__init__()
if name == "resnet50":
base_net = resnet.resnet50(pretrained=False)
elif name == "resnet101":
base_net = resnet.resnet101(pretrained=False)
elif name == "resnet18":
base_net = resnet.resnet18(pretrained=False)
elif name == "resnet34":
base_net = resnet.resnet34(pretrained=False)
else:
print(" base model is not support !")
if pretrain:
print("load the {} weight from ./cache".format(name))
base_net.load_state_dict(model_zoo.load_url(model_urls[name], model_dir="./cache",
map_location=torch.device(cfg.device)), strict=False)
# print(base_net)
self.stage1 = nn.Sequential(
base_net.conv1,
base_net.bn1,
base_net.relu,
base_net.maxpool
)
self.stage2 = base_net.layer1
self.stage3 = base_net.layer2
self.stage4 = base_net.layer3
self.stage5 = base_net.layer4
self.up2 = nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1)
def forward(self, x):
C1 = self.stage1(x)
C2 = self.stage2(C1)
C3 = self.stage3(C2)
C4 = self.stage4(C3)
C5 = self.stage5(C4)
if cfg.scale == 2 or cfg.scale == 1:
# up2 --> 1/2
C1 = self.up2(C1)
return C1, C2, C3, C4, C5
if __name__ == '__main__':
import torch
input = torch.randn((4, 3, 512, 512))
net = ResNet()
C1, C2, C3, C4, C5 = net(input)
print(C1.size())
print(C2.size())
print(C3.size())
print(C4.size())
print(C5.size())
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