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| import torchvision | |
| import torch.nn as nn | |
| import pretrainedmodels | |
| import torch.nn.functional as F | |
| from collections import OrderedDict | |
| from constant import SCALE_FACTOR | |
| import math | |
| class Resnet(nn.Module): | |
| def __init__(self, variant): | |
| super(Resnet, self).__init__() | |
| assert variant in ['resnet50', 'resnet101', 'resnet152'] | |
| # load retrain model | |
| model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet') | |
| self.features = nn.Sequential(OrderedDict([ | |
| ('conv1', model.conv1), | |
| ('bn1', model.bn1), | |
| ('relu', model.relu), | |
| ('maxpool', model.maxpool), | |
| ('layer1', model.layer1), | |
| ('layer2', model.layer2), | |
| ('layer3', model.layer3), | |
| ('layer4', model.layer4) | |
| ])) | |
| num_ftrs = model.last_linear.in_features | |
| self.classifier = nn.Sequential( | |
| nn.Linear(num_ftrs, 14), | |
| nn.Sigmoid() | |
| ) | |
| # load other info | |
| # load other info | |
| self.mean = model.mean | |
| self.std = model.std | |
| self.input_size = model.input_size[1] # assume every input is a square image | |
| self.input_range = model.input_range | |
| self.input_space = model.input_space | |
| self.resize_size = int(math.floor(self.input_size / SCALE_FACTOR)) | |
| def forward(self, x): | |
| x = self.features(x) # 1x2048x7x7 | |
| s = x.size()[3] # 7 if input image is 224x224, 16 if input image is 512x512 | |
| x = F.avg_pool2d(x, kernel_size=s, stride=1) # 1x2048x1x1 | |
| x = x.view(x.size(0), -1) # 1x2048 | |
| x = self.classifier(x) # 1x1000 | |
| return x | |
| def extract(self, x): | |
| return self.features(x) | |
| def build(variant): | |
| net = Resnet(variant).cuda() | |
| return net | |
| architect='resnet' |