├── LICENSE.txt
├── README.md
├── data
├── __init__.py
└── datasets.py
├── dataset
├── test
│ └── download_testset.sh
├── train
│ └── download_trainset.sh
└── val
│ └── download_valset.sh
├── demo.py
├── demo_dir.py
├── earlystop.py
├── eval.py
├── eval_config.py
├── examples
├── fake.png
├── real.png
└── realfakedir
│ ├── 0_real
│ └── real.png
│ └── 1_fake
│ └── fake.png
├── networks
├── __init__.py
├── base_model.py
├── lpf.py
├── resnet.py
├── resnet_lpf.py
└── trainer.py
├── options
├── __init__.py
├── base_options.py
├── test_options.py
└── train_options.py
├── requirements.txt
├── train.py
├── util.py
├── validate.py
└── weights
└── download_weights.sh
/LICENSE.txt:
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/README.md:
--------------------------------------------------------------------------------
1 | ## Detecting CNN-Generated Images [[Project Page]](https://peterwang512.github.io/CNNDetection/)
2 |
3 | **CNN-generated images are surprisingly easy to spot...for now**
4 | [Sheng-Yu Wang](https://peterwang512.github.io/), [Oliver Wang](http://www.oliverwang.info/), [Richard Zhang](https://richzhang.github.io/), [Andrew Owens](http://andrewowens.com/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/).
5 |
In [CVPR](https://arxiv.org/abs/1912.11035), 2020.
6 |
7 |
8 |
9 | This repository contains models, evaluation code, and training code on datasets from our paper. **If you would like to run our pretrained model on your image/dataset see [(2) Quick start](https://github.com/PeterWang512/CNNDetection#2-quick-start).**
10 |
11 | **Jun 20th 2020 Update** Training code and dataset released; test results on uncropped images added (recommended for best performance).
12 |
13 | **Oct 26th 2020 Update** Some reported the download link for training data does not work. If this happens, please try the updated alternative links: [1](https://drive.google.com/drive/u/2/folders/14E_R19lqIE9JgotGz09fLPQ4NVqlYbVc) and [2](https://cmu.app.box.com/folder/124997172518?s=4syr4womrggfin0tsfhxohaec5dh6n48)
14 |
15 | **Oct 18th 2021 Update** Our method gets 92% AUC on the recently released StyleGAN3 model! For more details, please visit this [link](https://github.com/NVlabs/stylegan3-detector).
16 |
17 | **Jul 24th, 2024 Update** Unfortunately, the previous Google Drive link for the dataset is no longer available. Please use this temporary download [link](https://drive.google.com/drive/folders/1RwCSaraEUctIwFgoQXWMKFvW07gM80_3?usp=drive_link). I am planning to host the dataset on Huggingface within a week.
18 |
19 | **Jul 26th, 2024 Update** The link has been fixed! Please follow the README to download the dataset. You will need to install 7z to prepare the dataset. For linux, run `sudo apt-get install p7zip-full` to install.
20 |
21 | ## (1) Setup
22 |
23 | ### Install packages
24 | - Install PyTorch ([pytorch.org](http://pytorch.org))
25 | - `pip install -r requirements.txt`
26 |
27 | ### Download model weights
28 | - Run `bash weights/download_weights.sh`
29 |
30 |
31 | ## (2) Quick start
32 |
33 | ### Run on a single image
34 |
35 | This command runs the model on a single image, and outputs the uncalibrated prediction.
36 |
37 | ```
38 | # Model weights need to be downloaded.
39 | python demo.py -f examples/real.png -m weights/blur_jpg_prob0.5.pth
40 | python demo.py -f examples/fake.png -m weights/blur_jpg_prob0.5.pth
41 | ```
42 |
43 | ### Run on a dataset
44 |
45 | This command computes AP and accuracy on a dataset. See the [provided directory](examples/realfakedir) for an example. Put your real/fake images into the appropriate subfolders to test.
46 |
47 | ```
48 | python demo_dir.py -d examples/realfakedir -m weights/blur_jpg_prob0.5.pth
49 | ```
50 |
51 | ## (3) Dataset
52 |
53 | ### Testset
54 | The testset evaluated in the paper can be downloaded [here](https://drive.google.com/file/d/1z_fD3UKgWQyOTZIBbYSaQ-hz4AzUrLC1/view?usp=sharing).
55 |
56 | The zip file contains images from 13 CNN-based synthesis algorithms, including the 12 testsets from the paper and images downloaded from whichfaceisreal.com. Images from each algorithm are stored in a separate folder. In each category, real images are in the `0_real` folder, and synthetic images are in the `1_fake` folder.
57 |
58 | Note: ProGAN, StyleGAN, StyleGAN2, CycleGAN testset contains multiple classes, which are stored in separate subdirectories.
59 |
60 | ### Training set
61 | The training set used in the paper can be downloaded [here](https://drive.google.com/file/d/1iVNBV0glknyTYGA9bCxT_d0CVTOgGcKh/view?usp=sharing) (Try alternative links [1](https://drive.google.com/drive/u/2/folders/14E_R19lqIE9JgotGz09fLPQ4NVqlYbVc),[2](https://cmu.app.box.com/folder/124997172518?s=4syr4womrggfin0tsfhxohaec5dh6n48) if the previous link does not work). All images are from LSUN or generated by ProGAN, and they are separated in 20 object categories. Similarly, in each category, real images are in the `0_real` folder, and synthetic images are in the `1_fake` folder.
62 |
63 | ### Validation set
64 | The validation set consists of held-out ProGAN real and fake images, and can be downloaded [here](https://drive.google.com/file/d/1FU7xF8Wl_F8b0tgL0529qg2nZ_RpdVNL/view?usp=sharing). The directory structure is identical to that of the training set.
65 |
66 | ### Download the dataset
67 | Before downloading, install 7z if needed.
68 | ```
69 | # Download script for linux
70 | sudo apt-get install p7zip-full
71 | ```
72 |
73 | A script for downloading the dataset is as follows:
74 | ```
75 | # Download the testset
76 | cd dataset/test
77 | bash download_testset.sh
78 | cd ../..
79 |
80 | # Download the training set
81 | cd dataset/train
82 | bash download_trainset.sh
83 | cd ../..
84 |
85 | # Download the validation set
86 | cd dataset/val
87 | bash download_valset.sh
88 | cd ../..
89 | ```
90 |
91 | **If the script doesn't work, an alternative will be to download the zip files manually from the above google drive links. One can place the testset, training, and validation set zip files in `dataset/test`, `dataset/train`, and `dataset/val` folders, respectively, and then unzip the zip files to set everything up.**
92 |
93 | ## (4) Train your models
94 | We provide two example scripts to train our `Blur+JPEG(0.5)` and `Blur+JPEG(0.1)` models. We use `checkpoints/[model_name]/model_epoch_best.pth` as our final model.
95 | ```
96 | # Train Blur+JPEG(0.5)
97 | python train.py --name blur_jpg_prob0.5 --blur_prob 0.5 --blur_sig 0.0,3.0 --jpg_prob 0.5 --jpg_method cv2,pil --jpg_qual 30,100 --dataroot ./dataset/ --classes airplane,bird,bicycle,boat,bottle,bus,car,cat,cow,chair,diningtable,dog,person,pottedplant,motorbike,tvmonitor,train,sheep,sofa,horse
98 |
99 | # Train Blur+JPEG(0.1)
100 | python train.py --name blur_jpg_prob0.1 --blur_prob 0.1 --blur_sig 0.0,3.0 --jpg_prob 0.1 --jpg_method cv2,pil --jpg_qual 30,100 --dataroot ./dataset/ --classes airplane,bird,bicycle,boat,bottle,bus,car,cat,cow,chair,diningtable,dog,person,pottedplant,motorbike,tvmonitor,train,sheep,sofa,horse
101 | ```
102 |
103 | ## (5) Evaluation
104 |
105 | After the testset and the model weights are downloaded, one can evaluate the models by running:
106 |
107 | ```
108 | # Run evaluation script. Model weights need to be downloaded. See eval_config.py for flags
109 | python eval.py
110 | ```
111 |
112 | Besides print-outs, the results will also be stored in a csv file in the `results` folder. Configurations such as the path of the dataset, model weight are in `eval_config.py`, and one can modify the evaluation by changing the configurations.
113 |
114 |
115 | **6/13/2020 Update** Additionally, we tested on uncropped images, and observed better performances on most categories. To evaluate without center-cropping:
116 | ```
117 | # Run evaluation script without cropping. Model weights need to be downloaded.
118 | python eval.py --no_crop --batch_size 1
119 | ```
120 |
121 | The following are the models' performances on the released set, with cropping to 224x224 (as in the paper), and without cropping.
122 |
123 | [Blur+JPEG(0.5)]
124 |
125 | |Testset |Acc (224)| AP (224) |Acc (No crop)| AP (No crop)|
126 | |:--------:|:------:|:----:|:------:|:----:|
127 | |ProGAN | 100.0% |100.0%| 100.0% |100.0%|
128 | |StyleGAN | 73.4% |98.5% | 77.5% |99.3% |
129 | |BigGAN | 59.0% |88.2% | 59.5% |90.4% |
130 | |CycleGAN | 80.8% |96.8% | 84.6% |97.9% |
131 | |StarGAN | 81.0% |95.4% | 84.7% |97.5% |
132 | |GauGAN | 79.3% |98.1% | 82.9% |98.8% |
133 | |CRN | 87.6% |98.9% | 97.8% |100.0% |
134 | |IMLE | 94.1% |99.5% | 98.8% |100.0% |
135 | |SITD | 78.3% |92.7% | 93.9% |99.6% |
136 | |SAN | 50.0% |63.9% | 50.0% |62.8% |
137 | |Deepfake | 51.1% |66.3% | 50.4% |63.1% |
138 | |StyleGAN2 | 68.4% |98.0% | 72.4% |99.1% |
139 | |Whichfaceisreal| 63.9% |88.8% | 75.2% |100.0% |
140 |
141 |
142 | [Blur+JPEG(0.1)]
143 |
144 | |Testset |Acc (224)| AP (224) |Acc (No crop)| AP (No crop)|
145 | |:--------:|:------:|:----:|:------:|:----:|
146 | |ProGAN |100.0% |100.0%| 100.0% |100.0%|
147 | |StyleGAN |87.1% |99.6%| 90.2% |99.8% |
148 | |BigGAN |70.2% |84.5%| 71.2% |86.0% |
149 | |CycleGAN |85.2% |93.5%| 87.6% |94.9% |
150 | |StarGAN |91.7% |98.2%| 94.6% |99.0% |
151 | |GauGAN |78.9% |89.5%| 81.4% |90.8% |
152 | |CRN |86.3% |98.2%| 86.3% |99.8% |
153 | |IMLE |86.2% |98.4%| 86.3% |99.8% |
154 | |SITD |90.3% |97.2%| 98.1% |99.8% |
155 | |SAN |50.5% |70.5%| 50.0% |68.6% |
156 | |Deepfake |53.5% |89.0%| 50.7% |84.5% |
157 | |StyleGAN2 |84.4% |99.1%| 86.9% |99.5% |
158 | |Whichfaceisreal|83.6% |93.2%| 91.6% |99.8%|
159 |
160 | ## (A) Acknowledgments
161 |
162 | This repository borrows partially from the [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix), and the PyTorch [torchvision models](https://github.com/pytorch/vision/tree/master/torchvision/models) repositories.
163 |
164 | ## (B) Citation, Contact
165 |
166 | If you find this useful for your research, please consider citing this [bibtex](https://peterwang512.github.io/CNNDetection/bibtex.txt). Please contact Sheng-Yu Wang \ with any comments or feedback.
167 |
168 |
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/data/__init__.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import numpy as np
3 | from torch.utils.data.sampler import WeightedRandomSampler
4 |
5 | from .datasets import dataset_folder
6 |
7 |
8 | def get_dataset(opt):
9 | dset_lst = []
10 | for cls in opt.classes:
11 | root = opt.dataroot + '/' + cls
12 | dset = dataset_folder(opt, root)
13 | dset_lst.append(dset)
14 | return torch.utils.data.ConcatDataset(dset_lst)
15 |
16 |
17 | def get_bal_sampler(dataset):
18 | targets = []
19 | for d in dataset.datasets:
20 | targets.extend(d.targets)
21 |
22 | ratio = np.bincount(targets)
23 | w = 1. / torch.tensor(ratio, dtype=torch.float)
24 | sample_weights = w[targets]
25 | sampler = WeightedRandomSampler(weights=sample_weights,
26 | num_samples=len(sample_weights))
27 | return sampler
28 |
29 |
30 | def create_dataloader(opt):
31 | shuffle = not opt.serial_batches if (opt.isTrain and not opt.class_bal) else False
32 | dataset = get_dataset(opt)
33 | sampler = get_bal_sampler(dataset) if opt.class_bal else None
34 |
35 | data_loader = torch.utils.data.DataLoader(dataset,
36 | batch_size=opt.batch_size,
37 | shuffle=shuffle,
38 | sampler=sampler,
39 | num_workers=int(opt.num_threads))
40 | return data_loader
41 |
--------------------------------------------------------------------------------
/data/datasets.py:
--------------------------------------------------------------------------------
1 | import cv2
2 | import numpy as np
3 | import torchvision.datasets as datasets
4 | import torchvision.transforms as transforms
5 | import torchvision.transforms.functional as TF
6 | from random import random, choice
7 | from io import BytesIO
8 | from PIL import Image
9 | from PIL import ImageFile
10 | from scipy.ndimage.filters import gaussian_filter
11 |
12 |
13 | ImageFile.LOAD_TRUNCATED_IMAGES = True
14 |
15 | def dataset_folder(opt, root):
16 | if opt.mode == 'binary':
17 | return binary_dataset(opt, root)
18 | if opt.mode == 'filename':
19 | return FileNameDataset(opt, root)
20 | raise ValueError('opt.mode needs to be binary or filename.')
21 |
22 |
23 | def binary_dataset(opt, root):
24 | if opt.isTrain:
25 | crop_func = transforms.RandomCrop(opt.cropSize)
26 | elif opt.no_crop:
27 | crop_func = transforms.Lambda(lambda img: img)
28 | else:
29 | crop_func = transforms.CenterCrop(opt.cropSize)
30 |
31 | if opt.isTrain and not opt.no_flip:
32 | flip_func = transforms.RandomHorizontalFlip()
33 | else:
34 | flip_func = transforms.Lambda(lambda img: img)
35 | if not opt.isTrain and opt.no_resize:
36 | rz_func = transforms.Lambda(lambda img: img)
37 | else:
38 | rz_func = transforms.Lambda(lambda img: custom_resize(img, opt))
39 |
40 | dset = datasets.ImageFolder(
41 | root,
42 | transforms.Compose([
43 | rz_func,
44 | transforms.Lambda(lambda img: data_augment(img, opt)),
45 | crop_func,
46 | flip_func,
47 | transforms.ToTensor(),
48 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
49 | ]))
50 | return dset
51 |
52 |
53 | class FileNameDataset(datasets.ImageFolder):
54 | def name(self):
55 | return 'FileNameDataset'
56 |
57 | def __init__(self, opt, root):
58 | self.opt = opt
59 | super().__init__(root)
60 |
61 | def __getitem__(self, index):
62 | # Loading sample
63 | path, target = self.samples[index]
64 | return path
65 |
66 |
67 | def data_augment(img, opt):
68 | img = np.array(img)
69 |
70 | if random() < opt.blur_prob:
71 | sig = sample_continuous(opt.blur_sig)
72 | gaussian_blur(img, sig)
73 |
74 | if random() < opt.jpg_prob:
75 | method = sample_discrete(opt.jpg_method)
76 | qual = sample_discrete(opt.jpg_qual)
77 | img = jpeg_from_key(img, qual, method)
78 |
79 | return Image.fromarray(img)
80 |
81 |
82 | def sample_continuous(s):
83 | if len(s) == 1:
84 | return s[0]
85 | if len(s) == 2:
86 | rg = s[1] - s[0]
87 | return random() * rg + s[0]
88 | raise ValueError("Length of iterable s should be 1 or 2.")
89 |
90 |
91 | def sample_discrete(s):
92 | if len(s) == 1:
93 | return s[0]
94 | return choice(s)
95 |
96 |
97 | def gaussian_blur(img, sigma):
98 | gaussian_filter(img[:,:,0], output=img[:,:,0], sigma=sigma)
99 | gaussian_filter(img[:,:,1], output=img[:,:,1], sigma=sigma)
100 | gaussian_filter(img[:,:,2], output=img[:,:,2], sigma=sigma)
101 |
102 |
103 | def cv2_jpg(img, compress_val):
104 | img_cv2 = img[:,:,::-1]
105 | encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), compress_val]
106 | result, encimg = cv2.imencode('.jpg', img_cv2, encode_param)
107 | decimg = cv2.imdecode(encimg, 1)
108 | return decimg[:,:,::-1]
109 |
110 |
111 | def pil_jpg(img, compress_val):
112 | out = BytesIO()
113 | img = Image.fromarray(img)
114 | img.save(out, format='jpeg', quality=compress_val)
115 | img = Image.open(out)
116 | # load from memory before ByteIO closes
117 | img = np.array(img)
118 | out.close()
119 | return img
120 |
121 |
122 | jpeg_dict = {'cv2': cv2_jpg, 'pil': pil_jpg}
123 | def jpeg_from_key(img, compress_val, key):
124 | method = jpeg_dict[key]
125 | return method(img, compress_val)
126 |
127 |
128 | rz_dict = {'bilinear': Image.BILINEAR,
129 | 'bicubic': Image.BICUBIC,
130 | 'lanczos': Image.LANCZOS,
131 | 'nearest': Image.NEAREST}
132 | def custom_resize(img, opt):
133 | interp = sample_discrete(opt.rz_interp)
134 | return TF.resize(img, opt.loadSize, interpolation=rz_dict[interp])
135 |
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/dataset/test/download_testset.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/CNN_synth_testset.zip
3 |
4 | unzip CNN_synth_testset.zip
5 | rm CNN_synth_testset.zip
6 |
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/dataset/train/download_trainset.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.001 &
3 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.002 &
4 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.003 &
5 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.004 &
6 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.005 &
7 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.006 &
8 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_train.7z.007 &
9 | wait $(jobs -p)
10 |
11 | 7z x progan_train.7z.001
12 | rm progan_train.7z.*
13 | unzip progan_train.zip
14 | rm progan_train.zip
15 |
--------------------------------------------------------------------------------
/dataset/val/download_valset.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | wget https://huggingface.co/datasets/sywang/CNNDetection/resolve/main/progan_val.zip
3 |
4 | unzip progan_val.zip
5 | rm progan_val.zip
6 |
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/demo.py:
--------------------------------------------------------------------------------
1 | import os
2 | import sys
3 | import torch
4 | import torch.nn
5 | import argparse
6 | import numpy as np
7 | import torchvision.transforms as transforms
8 | import torchvision.datasets as datasets
9 | from PIL import Image
10 | from networks.resnet import resnet50
11 |
12 | parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
13 | parser.add_argument('-f','--file', default='examples_realfakedir')
14 | parser.add_argument('-m','--model_path', type=str, default='weights/blur_jpg_prob0.5.pth')
15 | parser.add_argument('-c','--crop', type=int, default=None, help='by default, do not crop. specify crop size')
16 | parser.add_argument('--use_cpu', action='store_true', help='uses gpu by default, turn on to use cpu')
17 |
18 | opt = parser.parse_args()
19 |
20 | model = resnet50(num_classes=1)
21 | state_dict = torch.load(opt.model_path, map_location='cpu')
22 | model.load_state_dict(state_dict['model'])
23 | if(not opt.use_cpu):
24 | model.cuda()
25 | model.eval()
26 |
27 | # Transform
28 | trans_init = []
29 | if(opt.crop is not None):
30 | trans_init = [transforms.CenterCrop(opt.crop),]
31 | print('Cropping to [%i]'%opt.crop)
32 | else:
33 | print('Not cropping')
34 | trans = transforms.Compose(trans_init + [
35 | transforms.ToTensor(),
36 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
37 | ])
38 |
39 | img = trans(Image.open(opt.file).convert('RGB'))
40 |
41 | with torch.no_grad():
42 | in_tens = img.unsqueeze(0)
43 | if(not opt.use_cpu):
44 | in_tens = in_tens.cuda()
45 | prob = model(in_tens).sigmoid().item()
46 |
47 | print('probability of being synthetic: {:.2f}%'.format(prob * 100))
48 |
--------------------------------------------------------------------------------
/demo_dir.py:
--------------------------------------------------------------------------------
1 |
2 | import argparse
3 | import os
4 | import csv
5 | import torch
6 | import torchvision.datasets as datasets
7 | import torchvision.transforms as transforms
8 | import torch.utils.data
9 | import numpy as np
10 | from sklearn.metrics import average_precision_score, precision_recall_curve, accuracy_score
11 |
12 | from networks.resnet import resnet50
13 |
14 | from tqdm import tqdm
15 |
16 | parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
17 | parser.add_argument('-d','--dir', nargs='+', type=str, default='examples/realfakedir')
18 | parser.add_argument('-m','--model_path', type=str, default='weights/blur_jpg_prob0.5.pth')
19 | parser.add_argument('-b','--batch_size', type=int, default=32)
20 | parser.add_argument('-j','--workers', type=int, default=4, help='number of workers')
21 | parser.add_argument('-c','--crop', type=int, default=None, help='by default, do not crop. specify crop size')
22 | parser.add_argument('--use_cpu', action='store_true', help='uses gpu by default, turn on to use cpu')
23 | parser.add_argument('--size_only', action='store_true', help='only look at sizes of images in dataset')
24 |
25 | opt = parser.parse_args()
26 |
27 | # Load model
28 | if(not opt.size_only):
29 | model = resnet50(num_classes=1)
30 | if(opt.model_path is not None):
31 | state_dict = torch.load(opt.model_path, map_location='cpu')
32 | model.load_state_dict(state_dict['model'])
33 | model.eval()
34 | if(not opt.use_cpu):
35 | model.cuda()
36 |
37 | # Transform
38 | trans_init = []
39 | if(opt.crop is not None):
40 | trans_init = [transforms.CenterCrop(opt.crop),]
41 | print('Cropping to [%i]'%opt.crop)
42 | else:
43 | print('Not cropping')
44 | trans = transforms.Compose(trans_init + [
45 | transforms.ToTensor(),
46 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
47 | ])
48 |
49 | # Dataset loader
50 | if(type(opt.dir)==str):
51 | opt.dir = [opt.dir,]
52 |
53 | print('Loading [%i] datasets'%len(opt.dir))
54 | data_loaders = []
55 | for dir in opt.dir:
56 | dataset = datasets.ImageFolder(dir, transform=trans)
57 | data_loaders+=[torch.utils.data.DataLoader(dataset,
58 | batch_size=opt.batch_size,
59 | shuffle=False,
60 | num_workers=opt.workers),]
61 |
62 | y_true, y_pred = [], []
63 | Hs, Ws = [], []
64 | with torch.no_grad():
65 | for data_loader in data_loaders:
66 | for data, label in tqdm(data_loader):
67 | # for data, label in data_loader:
68 | Hs.append(data.shape[2])
69 | Ws.append(data.shape[3])
70 |
71 | y_true.extend(label.flatten().tolist())
72 | if(not opt.size_only):
73 | if(not opt.use_cpu):
74 | data = data.cuda()
75 | y_pred.extend(model(data).sigmoid().flatten().tolist())
76 |
77 | Hs, Ws = np.array(Hs), np.array(Ws)
78 | y_true, y_pred = np.array(y_true), np.array(y_pred)
79 |
80 | print('Average sizes: [{:2.2f}+/-{:2.2f}] x [{:2.2f}+/-{:2.2f}] = [{:2.2f}+/-{:2.2f} Mpix]'.format(np.mean(Hs), np.std(Hs), np.mean(Ws), np.std(Ws), np.mean(Hs*Ws)/1e6, np.std(Hs*Ws)/1e6))
81 | print('Num reals: {}, Num fakes: {}'.format(np.sum(1-y_true), np.sum(y_true)))
82 |
83 | if(not opt.size_only):
84 | r_acc = accuracy_score(y_true[y_true==0], y_pred[y_true==0] > 0.5)
85 | f_acc = accuracy_score(y_true[y_true==1], y_pred[y_true==1] > 0.5)
86 | acc = accuracy_score(y_true, y_pred > 0.5)
87 | ap = average_precision_score(y_true, y_pred)
88 |
89 | print('AP: {:2.2f}, Acc: {:2.2f}, Acc (real): {:2.2f}, Acc (fake): {:2.2f}'.format(ap*100., acc*100., r_acc*100., f_acc*100.))
90 |
91 |
92 |
--------------------------------------------------------------------------------
/earlystop.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import torch
3 |
4 |
5 | class EarlyStopping:
6 | """Early stops the training if validation loss doesn't improve after a given patience."""
7 | def __init__(self, patience=1, verbose=False, delta=0):
8 | """
9 | Args:
10 | patience (int): How long to wait after last time validation loss improved.
11 | Default: 7
12 | verbose (bool): If True, prints a message for each validation loss improvement.
13 | Default: False
14 | delta (float): Minimum change in the monitored quantity to qualify as an improvement.
15 | Default: 0
16 | """
17 | self.patience = patience
18 | self.verbose = verbose
19 | self.counter = 0
20 | self.best_score = None
21 | self.early_stop = False
22 | self.score_max = -np.Inf
23 | self.delta = delta
24 |
25 | def __call__(self, score, model):
26 | if self.best_score is None:
27 | self.best_score = score
28 | self.save_checkpoint(score, model)
29 | elif score < self.best_score - self.delta:
30 | self.counter += 1
31 | print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
32 | if self.counter >= self.patience:
33 | self.early_stop = True
34 | else:
35 | self.best_score = score
36 | self.save_checkpoint(score, model)
37 | self.counter = 0
38 |
39 | def save_checkpoint(self, score, model):
40 | '''Saves model when validation loss decrease.'''
41 | if self.verbose:
42 | print(f'Validation accuracy increased ({self.score_max:.6f} --> {score:.6f}). Saving model ...')
43 | model.save_networks('best')
44 | self.score_max = score
45 |
--------------------------------------------------------------------------------
/eval.py:
--------------------------------------------------------------------------------
1 | import os
2 | import csv
3 | import torch
4 |
5 | from validate import validate
6 | from networks.resnet import resnet50
7 | from options.test_options import TestOptions
8 | from eval_config import *
9 |
10 |
11 | # Running tests
12 | opt = TestOptions().parse(print_options=False)
13 | model_name = os.path.basename(model_path).replace('.pth', '')
14 | rows = [["{} model testing on...".format(model_name)],
15 | ['testset', 'accuracy', 'avg precision']]
16 |
17 | print("{} model testing on...".format(model_name))
18 | for v_id, val in enumerate(vals):
19 | opt.dataroot = '{}/{}'.format(dataroot, val)
20 | opt.classes = os.listdir(opt.dataroot) if multiclass[v_id] else ['']
21 | opt.no_resize = True # testing without resizing by default
22 |
23 | model = resnet50(num_classes=1)
24 | state_dict = torch.load(model_path, map_location='cpu')
25 | model.load_state_dict(state_dict['model'])
26 | model.cuda()
27 | model.eval()
28 |
29 | acc, ap, _, _, _, _ = validate(model, opt)
30 | rows.append([val, acc, ap])
31 | print("({}) acc: {}; ap: {}".format(val, acc, ap))
32 |
33 | csv_name = results_dir + '/{}.csv'.format(model_name)
34 | with open(csv_name, 'w') as f:
35 | csv_writer = csv.writer(f, delimiter=',')
36 | csv_writer.writerows(rows)
37 |
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/eval_config.py:
--------------------------------------------------------------------------------
1 | from util import mkdir
2 |
3 |
4 | # directory to store the results
5 | results_dir = './results/'
6 | mkdir(results_dir)
7 |
8 | # root to the testsets
9 | dataroot = './dataset/test/'
10 |
11 | # list of synthesis algorithms
12 | vals = ['progan', 'stylegan', 'biggan', 'cyclegan', 'stargan', 'gaugan',
13 | 'crn', 'imle', 'seeingdark', 'san', 'deepfake', 'stylegan2', 'whichfaceisreal']
14 |
15 | # indicates if corresponding testset has multiple classes
16 | multiclass = [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0]
17 |
18 | # model
19 | model_path = 'weights/blur_jpg_prob0.5.pth'
20 |
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/examples/fake.png:
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https://raw.githubusercontent.com/PeterWang512/CNNDetection/ea0b5622365e3a9cd31d1b54b6b5971131a839ab/examples/fake.png
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/examples/real.png:
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https://raw.githubusercontent.com/PeterWang512/CNNDetection/ea0b5622365e3a9cd31d1b54b6b5971131a839ab/examples/real.png
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/examples/realfakedir/0_real/real.png:
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https://raw.githubusercontent.com/PeterWang512/CNNDetection/ea0b5622365e3a9cd31d1b54b6b5971131a839ab/examples/realfakedir/0_real/real.png
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/examples/realfakedir/1_fake/fake.png:
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https://raw.githubusercontent.com/PeterWang512/CNNDetection/ea0b5622365e3a9cd31d1b54b6b5971131a839ab/examples/realfakedir/1_fake/fake.png
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/networks/__init__.py:
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https://raw.githubusercontent.com/PeterWang512/CNNDetection/ea0b5622365e3a9cd31d1b54b6b5971131a839ab/networks/__init__.py
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/networks/base_model.py:
--------------------------------------------------------------------------------
1 | # from pix2pix
2 | import os
3 | import torch
4 | import torch.nn as nn
5 | from torch.nn import init
6 | from torch.optim import lr_scheduler
7 |
8 |
9 | class BaseModel(nn.Module):
10 | def __init__(self, opt):
11 | super(BaseModel, self).__init__()
12 | self.opt = opt
13 | self.total_steps = 0
14 | self.isTrain = opt.isTrain
15 | self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
16 | self.device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')
17 |
18 | def save_networks(self, epoch):
19 | save_filename = 'model_epoch_%s.pth' % epoch
20 | save_path = os.path.join(self.save_dir, save_filename)
21 |
22 | # serialize model and optimizer to dict
23 | state_dict = {
24 | 'model': self.model.state_dict(),
25 | 'optimizer' : self.optimizer.state_dict(),
26 | 'total_steps' : self.total_steps,
27 | }
28 |
29 | torch.save(state_dict, save_path)
30 |
31 | # load models from the disk
32 | def load_networks(self, epoch):
33 | load_filename = 'model_epoch_%s.pth' % epoch
34 | load_path = os.path.join(self.save_dir, load_filename)
35 |
36 | print('loading the model from %s' % load_path)
37 | # if you are using PyTorch newer than 0.4 (e.g., built from
38 | # GitHub source), you can remove str() on self.device
39 | state_dict = torch.load(load_path, map_location=self.device)
40 | if hasattr(state_dict, '_metadata'):
41 | del state_dict._metadata
42 |
43 | self.model.load_state_dict(state_dict['model'])
44 | self.total_steps = state_dict['total_steps']
45 |
46 | if self.isTrain and not self.opt.new_optim:
47 | self.optimizer.load_state_dict(state_dict['optimizer'])
48 | ### move optimizer state to GPU
49 | for state in self.optimizer.state.values():
50 | for k, v in state.items():
51 | if torch.is_tensor(v):
52 | state[k] = v.to(self.device)
53 |
54 | for g in self.optimizer.param_groups:
55 | g['lr'] = self.opt.lr
56 |
57 | def eval(self):
58 | self.model.eval()
59 |
60 | def test(self):
61 | with torch.no_grad():
62 | self.forward()
63 |
64 |
65 | def init_weights(net, init_type='normal', gain=0.02):
66 | def init_func(m):
67 | classname = m.__class__.__name__
68 | if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
69 | if init_type == 'normal':
70 | init.normal_(m.weight.data, 0.0, gain)
71 | elif init_type == 'xavier':
72 | init.xavier_normal_(m.weight.data, gain=gain)
73 | elif init_type == 'kaiming':
74 | init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
75 | elif init_type == 'orthogonal':
76 | init.orthogonal_(m.weight.data, gain=gain)
77 | else:
78 | raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
79 | if hasattr(m, 'bias') and m.bias is not None:
80 | init.constant_(m.bias.data, 0.0)
81 | elif classname.find('BatchNorm2d') != -1:
82 | init.normal_(m.weight.data, 1.0, gain)
83 | init.constant_(m.bias.data, 0.0)
84 |
85 | print('initialize network with %s' % init_type)
86 | net.apply(init_func)
87 |
--------------------------------------------------------------------------------
/networks/lpf.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) 2019, Adobe Inc. All rights reserved.
2 | #
3 | # This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike
4 | # 4.0 International Public License. To view a copy of this license, visit
5 | # https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.
6 |
7 | import torch
8 | import torch.nn.parallel
9 | import numpy as np
10 | import torch.nn as nn
11 | import torch.nn.functional as F
12 | from IPython import embed
13 |
14 | class Downsample(nn.Module):
15 | def __init__(self, pad_type='reflect', filt_size=3, stride=2, channels=None, pad_off=0):
16 | super(Downsample, self).__init__()
17 | self.filt_size = filt_size
18 | self.pad_off = pad_off
19 | self.pad_sizes = [int(1.*(filt_size-1)/2), int(np.ceil(1.*(filt_size-1)/2)), int(1.*(filt_size-1)/2), int(np.ceil(1.*(filt_size-1)/2))]
20 | self.pad_sizes = [pad_size+pad_off for pad_size in self.pad_sizes]
21 | self.stride = stride
22 | self.off = int((self.stride-1)/2.)
23 | self.channels = channels
24 |
25 | # print('Filter size [%i]'%filt_size)
26 | if(self.filt_size==1):
27 | a = np.array([1.,])
28 | elif(self.filt_size==2):
29 | a = np.array([1., 1.])
30 | elif(self.filt_size==3):
31 | a = np.array([1., 2., 1.])
32 | elif(self.filt_size==4):
33 | a = np.array([1., 3., 3., 1.])
34 | elif(self.filt_size==5):
35 | a = np.array([1., 4., 6., 4., 1.])
36 | elif(self.filt_size==6):
37 | a = np.array([1., 5., 10., 10., 5., 1.])
38 | elif(self.filt_size==7):
39 | a = np.array([1., 6., 15., 20., 15., 6., 1.])
40 |
41 | filt = torch.Tensor(a[:,None]*a[None,:])
42 | filt = filt/torch.sum(filt)
43 | self.register_buffer('filt', filt[None,None,:,:].repeat((self.channels,1,1,1)))
44 |
45 | self.pad = get_pad_layer(pad_type)(self.pad_sizes)
46 |
47 | def forward(self, inp):
48 | if(self.filt_size==1):
49 | if(self.pad_off==0):
50 | return inp[:,:,::self.stride,::self.stride]
51 | else:
52 | return self.pad(inp)[:,:,::self.stride,::self.stride]
53 | else:
54 | return F.conv2d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
55 |
56 | def get_pad_layer(pad_type):
57 | if(pad_type in ['refl','reflect']):
58 | PadLayer = nn.ReflectionPad2d
59 | elif(pad_type in ['repl','replicate']):
60 | PadLayer = nn.ReplicationPad2d
61 | elif(pad_type=='zero'):
62 | PadLayer = nn.ZeroPad2d
63 | else:
64 | print('Pad type [%s] not recognized'%pad_type)
65 | return PadLayer
66 |
67 |
68 | class Downsample1D(nn.Module):
69 | def __init__(self, pad_type='reflect', filt_size=3, stride=2, channels=None, pad_off=0):
70 | super(Downsample1D, self).__init__()
71 | self.filt_size = filt_size
72 | self.pad_off = pad_off
73 | self.pad_sizes = [int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2))]
74 | self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]
75 | self.stride = stride
76 | self.off = int((self.stride - 1) / 2.)
77 | self.channels = channels
78 |
79 | # print('Filter size [%i]' % filt_size)
80 | if(self.filt_size == 1):
81 | a = np.array([1., ])
82 | elif(self.filt_size == 2):
83 | a = np.array([1., 1.])
84 | elif(self.filt_size == 3):
85 | a = np.array([1., 2., 1.])
86 | elif(self.filt_size == 4):
87 | a = np.array([1., 3., 3., 1.])
88 | elif(self.filt_size == 5):
89 | a = np.array([1., 4., 6., 4., 1.])
90 | elif(self.filt_size == 6):
91 | a = np.array([1., 5., 10., 10., 5., 1.])
92 | elif(self.filt_size == 7):
93 | a = np.array([1., 6., 15., 20., 15., 6., 1.])
94 |
95 | filt = torch.Tensor(a)
96 | filt = filt / torch.sum(filt)
97 | self.register_buffer('filt', filt[None, None, :].repeat((self.channels, 1, 1)))
98 |
99 | self.pad = get_pad_layer_1d(pad_type)(self.pad_sizes)
100 |
101 | def forward(self, inp):
102 | if(self.filt_size == 1):
103 | if(self.pad_off == 0):
104 | return inp[:, :, ::self.stride]
105 | else:
106 | return self.pad(inp)[:, :, ::self.stride]
107 | else:
108 | return F.conv1d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
109 |
110 |
111 | def get_pad_layer_1d(pad_type):
112 | if(pad_type in ['refl', 'reflect']):
113 | PadLayer = nn.ReflectionPad1d
114 | elif(pad_type in ['repl', 'replicate']):
115 | PadLayer = nn.ReplicationPad1d
116 | elif(pad_type == 'zero'):
117 | PadLayer = nn.ZeroPad1d
118 | else:
119 | print('Pad type [%s] not recognized' % pad_type)
120 | return PadLayer
121 |
--------------------------------------------------------------------------------
/networks/resnet.py:
--------------------------------------------------------------------------------
1 | import torch.nn as nn
2 | import torch.utils.model_zoo as model_zoo
3 |
4 |
5 | __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
6 | 'resnet152']
7 |
8 |
9 | model_urls = {
10 | 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
11 | 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
12 | 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
13 | 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
14 | 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
15 | }
16 |
17 |
18 | def conv3x3(in_planes, out_planes, stride=1):
19 | """3x3 convolution with padding"""
20 | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
21 | padding=1, bias=False)
22 |
23 |
24 | def conv1x1(in_planes, out_planes, stride=1):
25 | """1x1 convolution"""
26 | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
27 |
28 |
29 | class BasicBlock(nn.Module):
30 | expansion = 1
31 |
32 | def __init__(self, inplanes, planes, stride=1, downsample=None):
33 | super(BasicBlock, self).__init__()
34 | self.conv1 = conv3x3(inplanes, planes, stride)
35 | self.bn1 = nn.BatchNorm2d(planes)
36 | self.relu = nn.ReLU(inplace=True)
37 | self.conv2 = conv3x3(planes, planes)
38 | self.bn2 = nn.BatchNorm2d(planes)
39 | self.downsample = downsample
40 | self.stride = stride
41 |
42 | def forward(self, x):
43 | identity = x
44 |
45 | out = self.conv1(x)
46 | out = self.bn1(out)
47 | out = self.relu(out)
48 |
49 | out = self.conv2(out)
50 | out = self.bn2(out)
51 |
52 | if self.downsample is not None:
53 | identity = self.downsample(x)
54 |
55 | out += identity
56 | out = self.relu(out)
57 |
58 | return out
59 |
60 |
61 | class Bottleneck(nn.Module):
62 | expansion = 4
63 |
64 | def __init__(self, inplanes, planes, stride=1, downsample=None):
65 | super(Bottleneck, self).__init__()
66 | self.conv1 = conv1x1(inplanes, planes)
67 | self.bn1 = nn.BatchNorm2d(planes)
68 | self.conv2 = conv3x3(planes, planes, stride)
69 | self.bn2 = nn.BatchNorm2d(planes)
70 | self.conv3 = conv1x1(planes, planes * self.expansion)
71 | self.bn3 = nn.BatchNorm2d(planes * self.expansion)
72 | self.relu = nn.ReLU(inplace=True)
73 | self.downsample = downsample
74 | self.stride = stride
75 |
76 | def forward(self, x):
77 | identity = x
78 |
79 | out = self.conv1(x)
80 | out = self.bn1(out)
81 | out = self.relu(out)
82 |
83 | out = self.conv2(out)
84 | out = self.bn2(out)
85 | out = self.relu(out)
86 |
87 | out = self.conv3(out)
88 | out = self.bn3(out)
89 |
90 | if self.downsample is not None:
91 | identity = self.downsample(x)
92 |
93 | out += identity
94 | out = self.relu(out)
95 |
96 | return out
97 |
98 |
99 | class ResNet(nn.Module):
100 |
101 | def __init__(self, block, layers, num_classes=1000, zero_init_residual=False):
102 | super(ResNet, self).__init__()
103 | self.inplanes = 64
104 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
105 | bias=False)
106 | self.bn1 = nn.BatchNorm2d(64)
107 | self.relu = nn.ReLU(inplace=True)
108 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
109 | self.layer1 = self._make_layer(block, 64, layers[0])
110 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
111 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
112 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
113 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
114 | self.fc = nn.Linear(512 * block.expansion, num_classes)
115 |
116 | for m in self.modules():
117 | if isinstance(m, nn.Conv2d):
118 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
119 | elif isinstance(m, nn.BatchNorm2d):
120 | nn.init.constant_(m.weight, 1)
121 | nn.init.constant_(m.bias, 0)
122 |
123 | # Zero-initialize the last BN in each residual branch,
124 | # so that the residual branch starts with zeros, and each residual block behaves like an identity.
125 | # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
126 | if zero_init_residual:
127 | for m in self.modules():
128 | if isinstance(m, Bottleneck):
129 | nn.init.constant_(m.bn3.weight, 0)
130 | elif isinstance(m, BasicBlock):
131 | nn.init.constant_(m.bn2.weight, 0)
132 |
133 | def _make_layer(self, block, planes, blocks, stride=1):
134 | downsample = None
135 | if stride != 1 or self.inplanes != planes * block.expansion:
136 | downsample = nn.Sequential(
137 | conv1x1(self.inplanes, planes * block.expansion, stride),
138 | nn.BatchNorm2d(planes * block.expansion),
139 | )
140 |
141 | layers = []
142 | layers.append(block(self.inplanes, planes, stride, downsample))
143 | self.inplanes = planes * block.expansion
144 | for _ in range(1, blocks):
145 | layers.append(block(self.inplanes, planes))
146 |
147 | return nn.Sequential(*layers)
148 |
149 | def forward(self, x):
150 | x = self.conv1(x)
151 | x = self.bn1(x)
152 | x = self.relu(x)
153 | x = self.maxpool(x)
154 |
155 | x = self.layer1(x)
156 | x = self.layer2(x)
157 | x = self.layer3(x)
158 | x = self.layer4(x)
159 |
160 | x = self.avgpool(x)
161 | x = x.view(x.size(0), -1)
162 | x = self.fc(x)
163 |
164 | return x
165 |
166 |
167 | def resnet18(pretrained=False, **kwargs):
168 | """Constructs a ResNet-18 model.
169 | Args:
170 | pretrained (bool): If True, returns a model pre-trained on ImageNet
171 | """
172 | model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
173 | if pretrained:
174 | model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
175 | return model
176 |
177 |
178 | def resnet34(pretrained=False, **kwargs):
179 | """Constructs a ResNet-34 model.
180 | Args:
181 | pretrained (bool): If True, returns a model pre-trained on ImageNet
182 | """
183 | model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
184 | if pretrained:
185 | model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
186 | return model
187 |
188 |
189 | def resnet50(pretrained=False, **kwargs):
190 | """Constructs a ResNet-50 model.
191 | Args:
192 | pretrained (bool): If True, returns a model pre-trained on ImageNet
193 | """
194 | model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
195 | if pretrained:
196 | model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
197 | return model
198 |
199 |
200 | def resnet101(pretrained=False, **kwargs):
201 | """Constructs a ResNet-101 model.
202 | Args:
203 | pretrained (bool): If True, returns a model pre-trained on ImageNet
204 | """
205 | model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
206 | if pretrained:
207 | model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
208 | return model
209 |
210 |
211 | def resnet152(pretrained=False, **kwargs):
212 | """Constructs a ResNet-152 model.
213 | Args:
214 | pretrained (bool): If True, returns a model pre-trained on ImageNet
215 | """
216 | model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
217 | if pretrained:
218 | model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
219 | return model
220 |
--------------------------------------------------------------------------------
/networks/resnet_lpf.py:
--------------------------------------------------------------------------------
1 | # This code is built from the PyTorch examples repository: https://github.com/pytorch/vision/tree/master/torchvision/models.
2 | # Copyright (c) 2017 Torch Contributors.
3 | # The Pytorch examples are available under the BSD 3-Clause License.
4 | #
5 | # ==========================================================================================
6 | #
7 | # Adobe’s modifications are Copyright 2019 Adobe. All rights reserved.
8 | # Adobe’s modifications are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike
9 | # 4.0 International Public License (CC-NC-SA-4.0). To view a copy of the license, visit
10 | # https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.
11 | #
12 | # ==========================================================================================
13 | #
14 | # BSD-3 License
15 | #
16 | # Redistribution and use in source and binary forms, with or without
17 | # modification, are permitted provided that the following conditions are met:
18 | #
19 | # * Redistributions of source code must retain the above copyright notice, this
20 | # list of conditions and the following disclaimer.
21 | #
22 | # * Redistributions in binary form must reproduce the above copyright notice,
23 | # this list of conditions and the following disclaimer in the documentation
24 | # and/or other materials provided with the distribution.
25 | #
26 | # * Neither the name of the copyright holder nor the names of its
27 | # contributors may be used to endorse or promote products derived from
28 | # this software without specific prior written permission.
29 | #
30 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
31 | # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
32 | # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
33 | # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
34 | # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
35 | # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
36 | # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
37 | # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
38 | # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
39 |
40 | import torch.nn as nn
41 | import torch.utils.model_zoo as model_zoo
42 | from .lpf import *
43 |
44 | __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
45 | 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d']
46 |
47 |
48 | # model_urls = {
49 | # 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
50 | # 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
51 | # 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
52 | # 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
53 | # 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
54 | # }
55 |
56 |
57 | def conv3x3(in_planes, out_planes, stride=1, groups=1):
58 | """3x3 convolution with padding"""
59 | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
60 | padding=1, groups=groups, bias=False)
61 |
62 | def conv1x1(in_planes, out_planes, stride=1):
63 | """1x1 convolution"""
64 | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
65 |
66 | class BasicBlock(nn.Module):
67 | expansion = 1
68 |
69 | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, norm_layer=None, filter_size=1):
70 | super(BasicBlock, self).__init__()
71 | if norm_layer is None:
72 | norm_layer = nn.BatchNorm2d
73 | if groups != 1:
74 | raise ValueError('BasicBlock only supports groups=1')
75 | # Both self.conv1 and self.downsample layers downsample the input when stride != 1
76 | self.conv1 = conv3x3(inplanes, planes)
77 | self.bn1 = norm_layer(planes)
78 | self.relu = nn.ReLU(inplace=True)
79 | if(stride==1):
80 | self.conv2 = conv3x3(planes,planes)
81 | else:
82 | self.conv2 = nn.Sequential(Downsample(filt_size=filter_size, stride=stride, channels=planes),
83 | conv3x3(planes, planes),)
84 | self.bn2 = norm_layer(planes)
85 | self.downsample = downsample
86 | self.stride = stride
87 |
88 | def forward(self, x):
89 | identity = x
90 |
91 | out = self.conv1(x)
92 | out = self.bn1(out)
93 | out = self.relu(out)
94 |
95 | out = self.conv2(out)
96 | out = self.bn2(out)
97 |
98 | if self.downsample is not None:
99 | identity = self.downsample(x)
100 |
101 | out += identity
102 | out = self.relu(out)
103 |
104 | return out
105 |
106 |
107 | class Bottleneck(nn.Module):
108 | expansion = 4
109 |
110 | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, norm_layer=None, filter_size=1):
111 | super(Bottleneck, self).__init__()
112 | if norm_layer is None:
113 | norm_layer = nn.BatchNorm2d
114 | # Both self.conv2 and self.downsample layers downsample the input when stride != 1
115 | self.conv1 = conv1x1(inplanes, planes)
116 | self.bn1 = norm_layer(planes)
117 | self.conv2 = conv3x3(planes, planes, groups) # stride moved
118 | self.bn2 = norm_layer(planes)
119 | if(stride==1):
120 | self.conv3 = conv1x1(planes, planes * self.expansion)
121 | else:
122 | self.conv3 = nn.Sequential(Downsample(filt_size=filter_size, stride=stride, channels=planes),
123 | conv1x1(planes, planes * self.expansion))
124 | self.bn3 = norm_layer(planes * self.expansion)
125 | self.relu = nn.ReLU(inplace=True)
126 | self.downsample = downsample
127 | self.stride = stride
128 |
129 | def forward(self, x):
130 | identity = x
131 |
132 | out = self.conv1(x)
133 | out = self.bn1(out)
134 | out = self.relu(out)
135 |
136 | out = self.conv2(out)
137 | out = self.bn2(out)
138 | out = self.relu(out)
139 |
140 | out = self.conv3(out)
141 | out = self.bn3(out)
142 |
143 | if self.downsample is not None:
144 | identity = self.downsample(x)
145 |
146 | out += identity
147 | out = self.relu(out)
148 |
149 | return out
150 |
151 |
152 | class ResNet(nn.Module):
153 |
154 | def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
155 | groups=1, width_per_group=64, norm_layer=None, filter_size=1, pool_only=True):
156 | super(ResNet, self).__init__()
157 | if norm_layer is None:
158 | norm_layer = nn.BatchNorm2d
159 | planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
160 | self.inplanes = planes[0]
161 |
162 | if(pool_only):
163 | self.conv1 = nn.Conv2d(3, planes[0], kernel_size=7, stride=2, padding=3, bias=False)
164 | else:
165 | self.conv1 = nn.Conv2d(3, planes[0], kernel_size=7, stride=1, padding=3, bias=False)
166 | self.bn1 = norm_layer(planes[0])
167 | self.relu = nn.ReLU(inplace=True)
168 |
169 | if(pool_only):
170 | self.maxpool = nn.Sequential(*[nn.MaxPool2d(kernel_size=2, stride=1),
171 | Downsample(filt_size=filter_size, stride=2, channels=planes[0])])
172 | else:
173 | self.maxpool = nn.Sequential(*[Downsample(filt_size=filter_size, stride=2, channels=planes[0]),
174 | nn.MaxPool2d(kernel_size=2, stride=1),
175 | Downsample(filt_size=filter_size, stride=2, channels=planes[0])])
176 |
177 | self.layer1 = self._make_layer(block, planes[0], layers[0], groups=groups, norm_layer=norm_layer)
178 | self.layer2 = self._make_layer(block, planes[1], layers[1], stride=2, groups=groups, norm_layer=norm_layer, filter_size=filter_size)
179 | self.layer3 = self._make_layer(block, planes[2], layers[2], stride=2, groups=groups, norm_layer=norm_layer, filter_size=filter_size)
180 | self.layer4 = self._make_layer(block, planes[3], layers[3], stride=2, groups=groups, norm_layer=norm_layer, filter_size=filter_size)
181 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
182 | self.fc = nn.Linear(planes[3] * block.expansion, num_classes)
183 |
184 | for m in self.modules():
185 | if isinstance(m, nn.Conv2d):
186 | if(m.in_channels!=m.out_channels or m.out_channels!=m.groups or m.bias is not None):
187 | # don't want to reinitialize downsample layers, code assuming normal conv layers will not have these characteristics
188 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
189 | else:
190 | print('Not initializing')
191 | elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
192 | nn.init.constant_(m.weight, 1)
193 | nn.init.constant_(m.bias, 0)
194 |
195 | # Zero-initialize the last BN in each residual branch,
196 | # so that the residual branch starts with zeros, and each residual block behaves like an identity.
197 | # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
198 | if zero_init_residual:
199 | for m in self.modules():
200 | if isinstance(m, Bottleneck):
201 | nn.init.constant_(m.bn3.weight, 0)
202 | elif isinstance(m, BasicBlock):
203 | nn.init.constant_(m.bn2.weight, 0)
204 |
205 | def _make_layer(self, block, planes, blocks, stride=1, groups=1, norm_layer=None, filter_size=1):
206 | if norm_layer is None:
207 | norm_layer = nn.BatchNorm2d
208 | downsample = None
209 | if stride != 1 or self.inplanes != planes * block.expansion:
210 | # downsample = nn.Sequential(
211 | # conv1x1(self.inplanes, planes * block.expansion, stride, filter_size=filter_size),
212 | # norm_layer(planes * block.expansion),
213 | # )
214 |
215 | downsample = [Downsample(filt_size=filter_size, stride=stride, channels=self.inplanes),] if(stride !=1) else []
216 | downsample += [conv1x1(self.inplanes, planes * block.expansion, 1),
217 | norm_layer(planes * block.expansion)]
218 | # print(downsample)
219 | downsample = nn.Sequential(*downsample)
220 |
221 | layers = []
222 | layers.append(block(self.inplanes, planes, stride, downsample, groups, norm_layer, filter_size=filter_size))
223 | self.inplanes = planes * block.expansion
224 | for _ in range(1, blocks):
225 | layers.append(block(self.inplanes, planes, groups=groups, norm_layer=norm_layer, filter_size=filter_size))
226 |
227 | return nn.Sequential(*layers)
228 |
229 | def forward(self, x):
230 | x = self.conv1(x)
231 | x = self.bn1(x)
232 | x = self.relu(x)
233 | x = self.maxpool(x)
234 |
235 | x = self.layer1(x)
236 | x = self.layer2(x)
237 | x = self.layer3(x)
238 | x = self.layer4(x)
239 |
240 | x = self.avgpool(x)
241 | x = x.view(x.size(0), -1)
242 | x = self.fc(x)
243 |
244 | return x
245 |
246 |
247 | def resnet18(pretrained=False, filter_size=1, pool_only=True, **kwargs):
248 | """Constructs a ResNet-18 model.
249 | Args:
250 | pretrained (bool): If True, returns a model pre-trained on ImageNet
251 | """
252 | model = ResNet(BasicBlock, [2, 2, 2, 2], filter_size=filter_size, pool_only=pool_only, **kwargs)
253 | if pretrained:
254 | model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
255 | return model
256 |
257 |
258 | def resnet34(pretrained=False, filter_size=1, pool_only=True, **kwargs):
259 | """Constructs a ResNet-34 model.
260 | Args:
261 | pretrained (bool): If True, returns a model pre-trained on ImageNet
262 | """
263 | model = ResNet(BasicBlock, [3, 4, 6, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
264 | if pretrained:
265 | model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
266 | return model
267 |
268 |
269 | def resnet50(pretrained=False, filter_size=1, pool_only=True, **kwargs):
270 | """Constructs a ResNet-50 model.
271 | Args:
272 | pretrained (bool): If True, returns a model pre-trained on ImageNet
273 | """
274 | model = ResNet(Bottleneck, [3, 4, 6, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
275 | if pretrained:
276 | model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
277 | return model
278 |
279 |
280 | def resnet101(pretrained=False, filter_size=1, pool_only=True, **kwargs):
281 | """Constructs a ResNet-101 model.
282 | Args:
283 | pretrained (bool): If True, returns a model pre-trained on ImageNet
284 | """
285 | model = ResNet(Bottleneck, [3, 4, 23, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
286 | if pretrained:
287 | model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
288 | return model
289 |
290 |
291 | def resnet152(pretrained=False, filter_size=1, pool_only=True, **kwargs):
292 | """Constructs a ResNet-152 model.
293 | Args:
294 | pretrained (bool): If True, returns a model pre-trained on ImageNet
295 | """
296 | model = ResNet(Bottleneck, [3, 8, 36, 3], filter_size=filter_size, pool_only=pool_only, **kwargs)
297 | if pretrained:
298 | model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
299 | return model
300 |
301 |
302 | def resnext50_32x4d(pretrained=False, filter_size=1, pool_only=True, **kwargs):
303 | model = ResNet(Bottleneck, [3, 4, 6, 3], groups=4, width_per_group=32, filter_size=filter_size, pool_only=pool_only, **kwargs)
304 | # if pretrained:
305 | # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
306 | return model
307 |
308 |
309 | def resnext101_32x8d(pretrained=False, filter_size=1, pool_only=True, **kwargs):
310 | model = ResNet(Bottleneck, [3, 4, 23, 3], groups=8, width_per_group=32, filter_size=filter_size, pool_only=pool_only, **kwargs)
311 | # if pretrained:
312 | # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
313 | return model
314 |
--------------------------------------------------------------------------------
/networks/trainer.py:
--------------------------------------------------------------------------------
1 | import functools
2 | import torch
3 | import torch.nn as nn
4 | from networks.resnet import resnet50
5 | from networks.base_model import BaseModel, init_weights
6 |
7 |
8 | class Trainer(BaseModel):
9 | def name(self):
10 | return 'Trainer'
11 |
12 | def __init__(self, opt):
13 | super(Trainer, self).__init__(opt)
14 |
15 | if self.isTrain and not opt.continue_train:
16 | self.model = resnet50(pretrained=True)
17 | self.model.fc = nn.Linear(2048, 1)
18 | torch.nn.init.normal_(self.model.fc.weight.data, 0.0, opt.init_gain)
19 |
20 | if not self.isTrain or opt.continue_train:
21 | self.model = resnet50(num_classes=1)
22 |
23 | if self.isTrain:
24 | self.loss_fn = nn.BCEWithLogitsLoss()
25 | # initialize optimizers
26 | if opt.optim == 'adam':
27 | self.optimizer = torch.optim.Adam(self.model.parameters(),
28 | lr=opt.lr, betas=(opt.beta1, 0.999))
29 | elif opt.optim == 'sgd':
30 | self.optimizer = torch.optim.SGD(self.model.parameters(),
31 | lr=opt.lr, momentum=0.0, weight_decay=0)
32 | else:
33 | raise ValueError("optim should be [adam, sgd]")
34 |
35 | if not self.isTrain or opt.continue_train:
36 | self.load_networks(opt.epoch)
37 | self.model.to(opt.gpu_ids[0])
38 |
39 |
40 | def adjust_learning_rate(self, min_lr=1e-6):
41 | for param_group in self.optimizer.param_groups:
42 | param_group['lr'] /= 10.
43 | if param_group['lr'] < min_lr:
44 | return False
45 | return True
46 |
47 | def set_input(self, input):
48 | self.input = input[0].to(self.device)
49 | self.label = input[1].to(self.device).float()
50 |
51 |
52 | def forward(self):
53 | self.output = self.model(self.input)
54 |
55 | def get_loss(self):
56 | return self.loss_fn(self.output.squeeze(1), self.label)
57 |
58 | def optimize_parameters(self):
59 | self.forward()
60 | self.loss = self.loss_fn(self.output.squeeze(1), self.label)
61 | self.optimizer.zero_grad()
62 | self.loss.backward()
63 | self.optimizer.step()
64 |
65 |
--------------------------------------------------------------------------------
/options/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/PeterWang512/CNNDetection/ea0b5622365e3a9cd31d1b54b6b5971131a839ab/options/__init__.py
--------------------------------------------------------------------------------
/options/base_options.py:
--------------------------------------------------------------------------------
1 | import argparse
2 | import os
3 | import util
4 | import torch
5 | #import models
6 | #import data
7 |
8 |
9 | class BaseOptions():
10 | def __init__(self):
11 | self.initialized = False
12 |
13 | def initialize(self, parser):
14 | parser.add_argument('--mode', default='binary')
15 | parser.add_argument('--arch', type=str, default='res50', help='architecture for binary classification')
16 |
17 | # data augmentation
18 | parser.add_argument('--rz_interp', default='bilinear')
19 | parser.add_argument('--blur_prob', type=float, default=0)
20 | parser.add_argument('--blur_sig', default='0.5')
21 | parser.add_argument('--jpg_prob', type=float, default=0)
22 | parser.add_argument('--jpg_method', default='cv2')
23 | parser.add_argument('--jpg_qual', default='75')
24 |
25 | parser.add_argument('--dataroot', default='./dataset/', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
26 | parser.add_argument('--classes', default='', help='image classes to train on')
27 | parser.add_argument('--class_bal', action='store_true')
28 | parser.add_argument('--batch_size', type=int, default=64, help='input batch size')
29 | parser.add_argument('--loadSize', type=int, default=256, help='scale images to this size')
30 | parser.add_argument('--cropSize', type=int, default=224, help='then crop to this size')
31 | parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
32 | parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
33 | parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
34 | parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
35 | parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
36 | parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
37 | parser.add_argument('--resize_or_crop', type=str, default='scale_and_crop', help='scaling and cropping of images at load time [resize_and_crop|crop|scale_width|scale_width_and_crop|none]')
38 | parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
39 | parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal|xavier|kaiming|orthogonal]')
40 | parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
41 | parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{loadSize}')
42 | self.initialized = True
43 | return parser
44 |
45 | def gather_options(self):
46 | # initialize parser with basic options
47 | if not self.initialized:
48 | parser = argparse.ArgumentParser(
49 | formatter_class=argparse.ArgumentDefaultsHelpFormatter)
50 | parser = self.initialize(parser)
51 |
52 | # get the basic options
53 | opt, _ = parser.parse_known_args()
54 | self.parser = parser
55 |
56 | return parser.parse_args()
57 |
58 | def print_options(self, opt):
59 | message = ''
60 | message += '----------------- Options ---------------\n'
61 | for k, v in sorted(vars(opt).items()):
62 | comment = ''
63 | default = self.parser.get_default(k)
64 | if v != default:
65 | comment = '\t[default: %s]' % str(default)
66 | message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
67 | message += '----------------- End -------------------'
68 | print(message)
69 |
70 | # save to the disk
71 | expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
72 | util.mkdirs(expr_dir)
73 | file_name = os.path.join(expr_dir, 'opt.txt')
74 | with open(file_name, 'wt') as opt_file:
75 | opt_file.write(message)
76 | opt_file.write('\n')
77 |
78 | def parse(self, print_options=True):
79 |
80 | opt = self.gather_options()
81 | opt.isTrain = self.isTrain # train or test
82 |
83 | # process opt.suffix
84 | if opt.suffix:
85 | suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
86 | opt.name = opt.name + suffix
87 |
88 | if print_options:
89 | self.print_options(opt)
90 |
91 | # set gpu ids
92 | str_ids = opt.gpu_ids.split(',')
93 | opt.gpu_ids = []
94 | for str_id in str_ids:
95 | id = int(str_id)
96 | if id >= 0:
97 | opt.gpu_ids.append(id)
98 | if len(opt.gpu_ids) > 0:
99 | torch.cuda.set_device(opt.gpu_ids[0])
100 |
101 | # additional
102 | opt.classes = opt.classes.split(',')
103 | opt.rz_interp = opt.rz_interp.split(',')
104 | opt.blur_sig = [float(s) for s in opt.blur_sig.split(',')]
105 | opt.jpg_method = opt.jpg_method.split(',')
106 | opt.jpg_qual = [int(s) for s in opt.jpg_qual.split(',')]
107 | if len(opt.jpg_qual) == 2:
108 | opt.jpg_qual = list(range(opt.jpg_qual[0], opt.jpg_qual[1] + 1))
109 | elif len(opt.jpg_qual) > 2:
110 | raise ValueError("Shouldn't have more than 2 values for --jpg_qual.")
111 |
112 | self.opt = opt
113 | return self.opt
114 |
--------------------------------------------------------------------------------
/options/test_options.py:
--------------------------------------------------------------------------------
1 | from .base_options import BaseOptions
2 |
3 |
4 | class TestOptions(BaseOptions):
5 | def initialize(self, parser):
6 | parser = BaseOptions.initialize(self, parser)
7 | parser.add_argument('--model_path')
8 | parser.add_argument('--no_resize', action='store_true')
9 | parser.add_argument('--no_crop', action='store_true')
10 | parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
11 |
12 | self.isTrain = False
13 | return parser
14 |
--------------------------------------------------------------------------------
/options/train_options.py:
--------------------------------------------------------------------------------
1 | from .base_options import BaseOptions
2 |
3 |
4 | class TrainOptions(BaseOptions):
5 | def initialize(self, parser):
6 | parser = BaseOptions.initialize(self, parser)
7 | parser.add_argument('--earlystop_epoch', type=int, default=5)
8 | parser.add_argument('--data_aug', action='store_true', help='if specified, perform additional data augmentation (photometric, blurring, jpegging)')
9 | parser.add_argument('--optim', type=str, default='adam', help='optim to use [sgd, adam]')
10 | parser.add_argument('--new_optim', action='store_true', help='new optimizer instead of loading the optim state')
11 | parser.add_argument('--loss_freq', type=int, default=400, help='frequency of showing loss on tensorboard')
12 | parser.add_argument('--save_latest_freq', type=int, default=2000, help='frequency of saving the latest results')
13 | parser.add_argument('--save_epoch_freq', type=int, default=20, help='frequency of saving checkpoints at the end of epochs')
14 | parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
15 | parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...')
16 | parser.add_argument('--last_epoch', type=int, default=-1, help='starting epoch count for scheduler intialization')
17 | parser.add_argument('--train_split', type=str, default='train', help='train, val, test, etc')
18 | parser.add_argument('--val_split', type=str, default='val', help='train, val, test, etc')
19 | parser.add_argument('--niter', type=int, default=10000, help='# of iter at starting learning rate')
20 | parser.add_argument('--beta1', type=float, default=0.9, help='momentum term of adam')
21 | parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam')
22 |
23 | self.isTrain = True
24 | return parser
25 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | scipy
2 | scikit-learn
3 | numpy
4 | opencv_python
5 | Pillow
6 | torch>=1.2.0
7 | torchvision
8 |
--------------------------------------------------------------------------------
/train.py:
--------------------------------------------------------------------------------
1 | import os
2 | import sys
3 | import time
4 | import torch
5 | import torch.nn
6 | import argparse
7 | from PIL import Image
8 | from tensorboardX import SummaryWriter
9 |
10 | from validate import validate
11 | from data import create_dataloader
12 | from earlystop import EarlyStopping
13 | from networks.trainer import Trainer
14 | from options.train_options import TrainOptions
15 |
16 |
17 | """Currently assumes jpg_prob, blur_prob 0 or 1"""
18 | def get_val_opt():
19 | val_opt = TrainOptions().parse(print_options=False)
20 | val_opt.dataroot = '{}/{}/'.format(val_opt.dataroot, val_opt.val_split)
21 | val_opt.isTrain = False
22 | val_opt.no_resize = False
23 | val_opt.no_crop = False
24 | val_opt.serial_batches = True
25 | val_opt.jpg_method = ['pil']
26 | if len(val_opt.blur_sig) == 2:
27 | b_sig = val_opt.blur_sig
28 | val_opt.blur_sig = [(b_sig[0] + b_sig[1]) / 2]
29 | if len(val_opt.jpg_qual) != 1:
30 | j_qual = val_opt.jpg_qual
31 | val_opt.jpg_qual = [int((j_qual[0] + j_qual[-1]) / 2)]
32 |
33 | return val_opt
34 |
35 |
36 | if __name__ == '__main__':
37 | opt = TrainOptions().parse()
38 | opt.dataroot = '{}/{}/'.format(opt.dataroot, opt.train_split)
39 | val_opt = get_val_opt()
40 |
41 | data_loader = create_dataloader(opt)
42 | dataset_size = len(data_loader)
43 | print('#training images = %d' % dataset_size)
44 |
45 | train_writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, "train"))
46 | val_writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, "val"))
47 |
48 | model = Trainer(opt)
49 | early_stopping = EarlyStopping(patience=opt.earlystop_epoch, delta=-0.001, verbose=True)
50 | for epoch in range(opt.niter):
51 | epoch_start_time = time.time()
52 | iter_data_time = time.time()
53 | epoch_iter = 0
54 |
55 | for i, data in enumerate(data_loader):
56 | model.total_steps += 1
57 | epoch_iter += opt.batch_size
58 |
59 | model.set_input(data)
60 | model.optimize_parameters()
61 |
62 | if model.total_steps % opt.loss_freq == 0:
63 | print("Train loss: {} at step: {}".format(model.loss, model.total_steps))
64 | train_writer.add_scalar('loss', model.loss, model.total_steps)
65 |
66 | if model.total_steps % opt.save_latest_freq == 0:
67 | print('saving the latest model %s (epoch %d, model.total_steps %d)' %
68 | (opt.name, epoch, model.total_steps))
69 | model.save_networks('latest')
70 |
71 | # print("Iter time: %d sec" % (time.time()-iter_data_time))
72 | # iter_data_time = time.time()
73 |
74 | if epoch % opt.save_epoch_freq == 0:
75 | print('saving the model at the end of epoch %d, iters %d' %
76 | (epoch, model.total_steps))
77 | model.save_networks('latest')
78 | model.save_networks(epoch)
79 |
80 | # Validation
81 | model.eval()
82 | acc, ap = validate(model.model, val_opt)[:2]
83 | val_writer.add_scalar('accuracy', acc, model.total_steps)
84 | val_writer.add_scalar('ap', ap, model.total_steps)
85 | print("(Val @ epoch {}) acc: {}; ap: {}".format(epoch, acc, ap))
86 |
87 | early_stopping(acc, model)
88 | if early_stopping.early_stop:
89 | cont_train = model.adjust_learning_rate()
90 | if cont_train:
91 | print("Learning rate dropped by 10, continue training...")
92 | early_stopping = EarlyStopping(patience=opt.earlystop_epoch, delta=-0.002, verbose=True)
93 | else:
94 | print("Early stopping.")
95 | break
96 | model.train()
97 |
98 |
--------------------------------------------------------------------------------
/util.py:
--------------------------------------------------------------------------------
1 | import os
2 | import torch
3 |
4 |
5 | def mkdirs(paths):
6 | if isinstance(paths, list) and not isinstance(paths, str):
7 | for path in paths:
8 | mkdir(path)
9 | else:
10 | mkdir(paths)
11 |
12 |
13 | def mkdir(path):
14 | if not os.path.exists(path):
15 | os.makedirs(path)
16 |
17 |
18 | def unnormalize(tens, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
19 | # assume tensor of shape NxCxHxW
20 | return tens * torch.Tensor(std)[None, :, None, None] + torch.Tensor(
21 | mean)[None, :, None, None]
22 |
--------------------------------------------------------------------------------
/validate.py:
--------------------------------------------------------------------------------
1 | import torch
2 | import numpy as np
3 | from networks.resnet import resnet50
4 | from sklearn.metrics import average_precision_score, precision_recall_curve, accuracy_score
5 | from options.test_options import TestOptions
6 | from data import create_dataloader
7 |
8 |
9 | def validate(model, opt):
10 | data_loader = create_dataloader(opt)
11 |
12 | with torch.no_grad():
13 | y_true, y_pred = [], []
14 | for img, label in data_loader:
15 | in_tens = img.cuda()
16 | y_pred.extend(model(in_tens).sigmoid().flatten().tolist())
17 | y_true.extend(label.flatten().tolist())
18 |
19 | y_true, y_pred = np.array(y_true), np.array(y_pred)
20 | r_acc = accuracy_score(y_true[y_true==0], y_pred[y_true==0] > 0.5)
21 | f_acc = accuracy_score(y_true[y_true==1], y_pred[y_true==1] > 0.5)
22 | acc = accuracy_score(y_true, y_pred > 0.5)
23 | ap = average_precision_score(y_true, y_pred)
24 | return acc, ap, r_acc, f_acc, y_true, y_pred
25 |
26 |
27 | if __name__ == '__main__':
28 | opt = TestOptions().parse(print_options=False)
29 |
30 | model = resnet50(num_classes=1)
31 | state_dict = torch.load(opt.model_path, map_location='cpu')
32 | model.load_state_dict(state_dict['model'])
33 | model.cuda()
34 | model.eval()
35 |
36 | acc, avg_precision, r_acc, f_acc, y_true, y_pred = validate(model, opt)
37 |
38 | print("accuracy:", acc)
39 | print("average precision:", avg_precision)
40 |
41 | print("accuracy of real images:", r_acc)
42 | print("accuracy of fake images:", f_acc)
43 |
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/weights/download_weights.sh:
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1 | wget https://www.dropbox.com/s/2g2jagq2jn1fd0i/blur_jpg_prob0.5.pth?dl=0 -O ./weights/blur_jpg_prob0.5.pth
2 | wget https://www.dropbox.com/s/h7tkpcgiwuftb6g/blur_jpg_prob0.1.pth?dl=0 -O ./weights/blur_jpg_prob0.1.pth
3 |
4 |
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