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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import numpy as np
import time
# net_stride output_size
# 128 2x2
# 64 4x4
# 32 8x8
# pip regression, resnet18, for GSSL
class Pip_resnet18(nn.Module):
def __init__(self, resnet, num_nb, num_lms=68, input_size=256, net_stride=32):
super(Pip_resnet18, self).__init__()
self.num_nb = num_nb
self.num_lms = num_lms
self.input_size = input_size
self.net_stride = net_stride
self.conv1 = resnet.conv1
self.bn1 = resnet.bn1
self.maxpool = resnet.maxpool
self.sigmoid = nn.Sigmoid()
self.layer1 = resnet.layer1
self.layer2 = resnet.layer2
self.layer3 = resnet.layer3
self.layer4 = resnet.layer4
self.my_maxpool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
self.cls_layer = nn.Conv2d(512, num_lms, kernel_size=1, stride=1, padding=0)
self.x_layer = nn.Conv2d(512, num_lms, kernel_size=1, stride=1, padding=0)
self.y_layer = nn.Conv2d(512, num_lms, kernel_size=1, stride=1, padding=0)
self.nb_x_layer = nn.Conv2d(512, num_nb*num_lms, kernel_size=1, stride=1, padding=0)
self.nb_y_layer = nn.Conv2d(512, num_nb*num_lms, kernel_size=1, stride=1, padding=0)
# init
nn.init.normal_(self.cls_layer.weight, std=0.001)
if self.cls_layer.bias is not None:
nn.init.constant_(self.cls_layer.bias, 0)
nn.init.normal_(self.x_layer.weight, std=0.001)
if self.x_layer.bias is not None:
nn.init.constant_(self.x_layer.bias, 0)
nn.init.normal_(self.y_layer.weight, std=0.001)
if self.y_layer.bias is not None:
nn.init.constant_(self.y_layer.bias, 0)
nn.init.normal_(self.nb_x_layer.weight, std=0.001)
if self.nb_x_layer.bias is not None:
nn.init.constant_(self.nb_x_layer.bias, 0)
nn.init.normal_(self.nb_y_layer.weight, std=0.001)
if self.nb_y_layer.bias is not None:
nn.init.constant_(self.nb_y_layer.bias, 0)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
cls1 = self.cls_layer(x)
offset_x = self.x_layer(x)
offset_y = self.y_layer(x)
nb_x = self.nb_x_layer(x)
nb_y = self.nb_y_layer(x)
x = self.my_maxpool(x)
cls2 = self.cls_layer(x)
x = self.my_maxpool(x)
cls3 = self.cls_layer(x)
return cls1, cls2, cls3, offset_x, offset_y, nb_x, nb_y
if __name__ == '__main__':
pass
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