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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 | |