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'''
# author: Zhiyuan Yan
# email: [email protected]
# date: 2023-0706
# description: Class for the ResnetDetector
Functions in the Class are summarized as:
1. __init__: Initialization
2. build_backbone: Backbone-building
3. build_loss: Loss-function-building
4. features: Feature-extraction
5. classifier: Classification
6. get_losses: Loss-computation
7. get_train_metrics: Training-metrics-computation
8. get_test_metrics: Testing-metrics-computation
9. forward: Forward-propagation
Reference:
@inproceedings{wang2020cnn,
title={CNN-generated images are surprisingly easy to spot... for now},
author={Wang, Sheng-Yu and Wang, Oliver and Zhang, Richard and Owens, Andrew and Efros, Alexei A},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={8695--8704},
year={2020}
}
Notes:
We chose to use ResNet-34 as the backbone instead of ResNet-50 because the number of parameters in ResNet-34 is relatively similar to that of Xception. This similarity allows us to make a more meaningful and fair comparison between different architectures.
'''
import os
import datetime
import logging
import numpy as np
from sklearn import metrics
from typing import Union
from collections import defaultdict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn import DataParallel
from torch.utils.tensorboard import SummaryWriter
from metrics.base_metrics_class import calculate_metrics_for_train
from .base_detector import AbstractDetector
from detectors import DETECTOR
from networks import BACKBONE
from loss import LOSSFUNC
logger = logging.getLogger(__name__)
@DETECTOR.register_module(module_name='resnet34')
class ResnetDetector(AbstractDetector):
def __init__(self, config):
super().__init__()
self.config = config
self.backbone = self.build_backbone(config)
self.loss_func = self.build_loss(config)
def build_backbone(self, config):
# prepare the backbone
backbone_class = BACKBONE[config['backbone_name']]
model_config = config['backbone_config']
backbone = backbone_class(model_config)
#FIXME: current load pretrained weights only from the backbone, not here
# # if donot load the pretrained weights, fail to get good results
# state_dict = torch.load(config['pretrained'])
# state_dict = {'resnet.'+k:v for k, v in state_dict.items() if 'fc' not in k}
# backbone.load_state_dict(state_dict, False)
# logger.info('Load pretrained model successfully!')
return backbone
def build_loss(self, config):
# prepare the loss function
loss_class = LOSSFUNC[config['loss_func']]
loss_func = loss_class()
return loss_func
def features(self, data_dict: dict) -> torch.tensor:
return self.backbone.features(data_dict['image'])
def classifier(self, features: torch.tensor) -> torch.tensor:
return self.backbone.classifier(features)
def get_losses(self, data_dict: dict, pred_dict: dict) -> dict:
label = data_dict['label']
pred = pred_dict['cls']
loss = self.loss_func(pred, label)
loss_dict = {'overall': loss}
return loss_dict
def get_train_metrics(self, data_dict: dict, pred_dict: dict) -> dict:
label = data_dict['label']
pred = pred_dict['cls']
# compute metrics for batch data
auc, eer, acc, ap = calculate_metrics_for_train(label.detach(), pred.detach())
metric_batch_dict = {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap}
return metric_batch_dict
def forward(self, data_dict: dict, inference=False) -> dict:
# get the features by backbone
features = self.features(data_dict)
# get the prediction by classifier
pred = self.classifier(features)
# get the probability of the pred
prob = torch.softmax(pred, dim=1)[:, 1]
# build the prediction dict for each output
pred_dict = {'cls': pred, 'prob': prob, 'feat': features}
return pred_dict
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