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| import torchvision | |
| import torch.nn as nn | |
| import pretrainedmodels | |
| import torch.nn.functional as F | |
| import torch | |
| from constant import SCALE_FACTOR | |
| import math | |
| import pdb | |
| class DenseNet(nn.Module): | |
| def __init__(self, variant): | |
| super(DenseNet, self).__init__() | |
| assert variant in ['densenet121', 'densenet161', 'densenet201'] | |
| # load retrain model | |
| model = pretrainedmodels.__dict__[variant](num_classes=1000, pretrained='imagenet') | |
| self.features = model.features | |
| num_ftrs = model.last_linear.in_features | |
| self.classifier = nn.Sequential( | |
| nn.Linear(num_ftrs, 14), | |
| nn.Sigmoid() | |
| ) | |
| # TODO: BCELoss with logit for numeric stable | |
| # self.classifier = nn.Linear(num_ftrs, 14) | |
| # 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, **kwargs): | |
| x = self.features(x) # 1x1024x7x7 | |
| s = x.size()[3] # 7 if input image is 224x224, 16 if input image is 512x512 | |
| x = F.relu(x, inplace=True) # 1x1024x7x7 | |
| pooling = kwargs['pooling'] | |
| if pooling == 'MAX': | |
| x = F.max_pool2d(x, kernel_size=s, stride=1) | |
| x = x.view(x.size(0), -1) # 1x1024 | |
| elif pooling == 'AVG': | |
| x = F.avg_pool2d(x, kernel_size=s, stride=1) # 1x1024x1x1 | |
| x = x.view(x.size(0), -1) # 1x1024 | |
| elif pooling == 'LSE': | |
| r = kwargs.lse_r | |
| x_max = F.max_pool2d(x, kernel_size=s, stride=1) | |
| p = ((1/r) * torch.log((1 / (s*s)) * torch.exp(r*(x - x_max)).sum(3).sum(2))) | |
| x_max = x_max.view(x.size(0), -1) | |
| x = x_max + p | |
| else: | |
| raise ValueError('Invalid pooling') | |
| x = self.classifier(x) # 1x1000 | |
| return x | |
| def extract(self, x): | |
| return self.features(x) | |
| # def count_params(self): | |
| # return sum(p.numel() for p in self.parameters() if p.requires_grad) | |
| def build(variant): | |
| net = DenseNet(variant).cuda() | |
| return net | |
| architect='densenet' | |