update

.gitattributes

@@ -13,6 +13,7 @@
 *.msgpack filter=lfs diff=lfs merge=lfs -text
 *.npy filter=lfs diff=lfs merge=lfs -text
 *.npz filter=lfs diff=lfs merge=lfs -text
+*.so filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,9 @@
+# Introduction
+We opened the inference API of our text image forgery detetion model, we are very glad you could try it. Unfortunately, we cannot open the model code for business reasons.
+
+# Notes
+In order to ensure that the model can predict correctly, the forged image you submit has not been reprocessed, such as resizing, cropping, network transmission, etc.
+
 ---
 title: Text Image Forgery Detection
 emoji: 🐢
@@ -9,9 +15,4 @@ app_file: app.py
 pinned: false
 license: mit
 ---
-# Introduction
-We opened the inference API of our text image forgery detetion model, we are very glad you could try it. Unfortunately, we cannot open the model code for business reasons.
-
-# Notes
-In order to ensure that the model can predict correctly, the forged image you submit has not been reprocessed, such as resizing, cropping, network transmission, etc.
 

app.py

@@ -1,51 +1,90 @@
 import gradio as gr
-import json
-import requests
 import os
+import sys
+import numpy as np
+import numpy as np
+import torch.backends.cudnn as cudnn
+import torch.utils.data
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from mmcv.utils import Config
+sys.path.append('.')
+from image_forgery_detection import build_detector
+from image_forgery_detection import Compose
 
 
-def test():
-    os.system('ping -c 5 www.baidu.com ')
-    os.system('ping -c 5 aip.baidubce.com')
-    os.system('ping -c 5 39.156.66.110')
-    os.system('ping -c 5 www.google.com ')
-    os.system('ping -c 5 81.70.158.155 ')
-
-    print('testing backend interface...')
-    url = 'http://81.70.158.155:5005/predict'
-    with open('./2680.jpg', 'rb') as f:
-        try:
-            r = requests.post(url, files = {'image' : f}, timeout=10)
-            ret = json.loads(r.text)
-        except Exception as e:
-            print('backend test failed: ', str(e))
-            return
-
-    # image level cls score
-    pred_cls_score = ret['cls_score']
-    print('--------', pred_cls_score)
-    print('backend test success')
-
-def predict(img):
-    url = 'http://81.70.158.155:5005/predict'
-    with open(img, 'rb') as f:
-        r = requests.post(url, files = {'image' : f})
-        ret = json.loads(r.text)
-
-    # image level cls score
-    pred_cls_score = ret['cls_score']
-    pred_seg = json.loads(ret['result'])
-    pred_seg = np.array(pred_seg, dtype=np.uint8)
-    
-    return pred_seg
-
-
-test()
-iface = gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload image to detect", type="filepath"),
-    outputs='image',
-    title="Forged? Or Not?",
-)
-iface.launch()
+transform_pil = transforms.Compose([
+    transforms.ToPILImage(),
+])
+
+
+def predict(f_path):
+    print(f_path)
+
+    results = dict(img_info=dict(filename=f_path, ann=dict(seg_map='None')))
+    results['seg_fields'] = []
+    results['img_prefix'] = None
+    results['seg_prefix'] = None
+
+    inputs = pipelines(results)
+
+    img = inputs['img'].data
+    img_meta = inputs['img_metas'].data
+    if 'dct_vol' in inputs:
+        dct_vol = inputs['dct_vol'].data
+        qtables = inputs['qtables'].data
+
+    with torch.no_grad():
+        img = img.unsqueeze(dim=0)
+        if 'dct_vol' in inputs:
+            dct_vol = dct_vol.unsqueeze(dim=0)
+            qtables = qtables.unsqueeze(dim=0)
+            cls_pred, seg_pred = model(img, dct_vol, qtables, [img_meta, ], return_loss=False, rescale=True)
+        else:
+            cls_pred, seg_pred = model(img, [img_meta, ], return_loss=False, rescale=True)
+        cls_pred = cls_pred[0]
+        seg = seg_pred[0, 0]
+    seg = np.array(transform_pil(torch.from_numpy(seg)))
+    thresh_int = 255 * thresh 
+    seg[seg>=thresh_int] = 255
+    seg[seg<thresh_int] = 0
+
+    return '{:.3f}'.format(cls_pred), seg
+
+
+if __name__ == '__main__':
+    model_path = './models/latest.pth'
+    cfg = Config.fromfile('./models/config.py')
+
+    global model
+    global pipelines
+    global thresh
+
+    thresh = 0.5
+    if hasattr(cfg.model.base_model, 'backbone'):
+        cfg.model.base_model.backbone.pretrained = None
+    else:
+        cfg.model.base_model.pretrained = None
+    model = build_detector(cfg.model)
+    if os.path.exists(model_path):
+        checkpoint = torch.load(model_path, map_location='cpu')['state_dict']
+        model.load_state_dict(checkpoint, strict=True)
+        print("load %s finish" % (os.path.basename(model_path)))
+    else:
+        print("%s not exist" % model_path)
+        exit(1)
+    model.eval()
+
+    pipelines = Compose(cfg.data.val[0].pipeline)
+
+    iface = gr.Interface(
+        predict,
+        inputs=gr.inputs.Image(label="Upload image to detect", type="filepath"),
+        # outputs=['text', 'image'],
+        outputs=[gr.outputs.Textbox(type="text", label="image forgery score"),
+                gr.outputs.Image(type="numpy", label="predict mask")],
+        title="Forged? Or Not?",
+    )
+    # iface.launch(server_name='0.0.0.0', share=True)
+    iface.launch()
 

image_forgery_detection/__init__.py

@@ -0,0 +1,2 @@
+from .models import *
+from .datasets import *

image_forgery_detection/datasets/__init__.py

@@ -0,0 +1,2 @@
+from .builder import PIPELINES
+from .pipelines import Compose

image_forgery_detection/datasets/builder.cpython-38-x86_64-linux-gnu.so

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image_forgery_detection/datasets/dct_pipelines.cpython-38-x86_64-linux-gnu.so

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image_forgery_detection/models/__init__.py

@@ -0,0 +1,21 @@
+from .builder import build_detector, build_loss, build_base_model, build_backbone
+from .detector import Detector
+from .losses import BinaryDiceLoss, CrossEntropyLoss, FocalLoss
+from .pan import PAN
+from .deeplabv3 import DeepLabV3, DeepLabV3Plus
+from .fpn import FPN
+from .hooks import CosineAnnealingWarmRestartsLrUpdaterHook
+from .pvtv2 import PVTv2B2, PVTv2B0
+from .transforensics import TransForensics, TransForensics_v2, TransForensics_v3, TransForensics_v4
+from .transforensics_detector import TransForensicsDetector
+from .psccnet import PSCCNet
+from .psccnet_detector import PSCCDetector
+from .network_CAT import CAT_Net, CAT_Net_ORI
+
+
+__all__ = ['build_detector', 'build_loss', 'build_base_model', 'FocalLoss',
+           'Detector', 'BinaryDiceLoss', 'CrossEntropyLoss', 'PAN', 'DeepLabV3',
+           'DeepLabV3Plus', 'FPN', 'CosineAnnealingWarmRestartsLrUpdaterHook',
+           'PVTv2B2', 'PVTv2B0', 
+           'build_backbone', 
+           'TransForensics', 'TransForensicsDetector', 'TransForensics_v2', 'TransForensics_v3']

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image_forgery_detection/models/deeplabv3.cpython-38-x86_64-linux-gnu.so

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models/config.py

@@ -0,0 +1,145 @@
+work_dir = 'records/guoshoucai_auto_gen_ps_with_tianchi_psccnet_baseline_dct_balance_scale_0_05_1_0_15_epochs_cls_weight_1_5_more_negs_seed_4567'
+dataset_type = 'MaskSegDatasetv2'
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+input_size = (512, 512)
+train_pre_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations', binary=True, train=True, img_label_binary=True)
+]
+train_post_pipeline = [
+    dict(type='SimpleResize', size=(512, 512)),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='Normalizev2',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg', 'img_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='SimpleResize', size=(512, 512)),
+    dict(
+        type='Normalizev2',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=4,
+    train=dict(
+        type='MaskSegDatasetv2',
+        data_root='/mnt/disk1/data/image_forgery/text_forgery',
+        ann_path='guoshoucai_auto_gen_ps_with_tianchi_1.txt',
+        pipeline=[[{
+            'type': 'LoadImageFromFile'
+        }, {
+            'type': 'LoadAnnotations',
+            'binary': True,
+            'train': True,
+            'img_label_binary': True
+        }],
+                  [{
+                      'type': 'SimpleResize',
+                      'size': (512, 512)
+                  }, {
+                      'type': 'RandomFlip',
+                      'prob': 0.5
+                  }, {
+                      'type': 'Normalizev2',
+                      'mean': [0.485, 0.456, 0.406],
+                      'std': [0.229, 0.224, 0.225]
+                  }, {
+                      'type': 'DefaultFormatBundle'
+                  }, {
+                      'type': 'Collect',
+                      'keys': ['img', 'gt_semantic_seg', 'img_label']
+                  }]]),
+    val=[
+        dict(
+            type='MaskSegDatasetv2',
+            data_root=
+            '/mnt/disk1/data/image_forgery/text_forgery/guoshoucai_auto_gen/test_forged_with_ps',
+            ann_path='test_1.txt',
+            test_mode=True,
+            pipeline=[
+                dict(type='LoadImageFromFile'),
+                dict(type='SimpleResize', size=(512, 512)),
+                dict(
+                    type='Normalizev2',
+                    mean=[0.485, 0.456, 0.406],
+                    std=[0.229, 0.224, 0.225]),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img'])
+            ],
+            dataset_name='guoshoucai_text',
+            gt_seg_map_loader_cfg=dict(binary=True, img_label_binary=True)),
+        dict(
+            type='MaskSegDatasetv2',
+            data_root=
+            '/mnt/disk1/data/image_forgery/text_forgery/tianchi_text_forgory',
+            ann_path='val.txt',
+            test_mode=True,
+            pipeline=[
+                dict(type='LoadImageFromFile'),
+                dict(type='SimpleResize', size=(512, 512)),
+                dict(
+                    type='Normalizev2',
+                    mean=[0.485, 0.456, 0.406],
+                    std=[0.229, 0.224, 0.225]),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img'])
+            ],
+            dataset_name='tianchi',
+            gt_seg_map_loader_cfg=dict(binary=True, img_label_binary=True))
+    ])
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='PSCCDetector',
+    base_model=dict(
+        type='PSCCNet',
+        crop_size=(512, 512),
+        pretrained=
+        '/home/yangwu/.cache/torch/checkpoints/hrnet_w18_small_v2.pth'),
+    train_cfg=dict(
+        seg_loss=dict(type='BCELoss', reduction='none'),
+        seg_loss_weights=(1.0, 1.0),
+        mask_loss_weights=(1.0, 1.0, 1.0, 1.0),
+        cls_loss=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            class_weight=(1.0, 1.0)),
+        p_balance_scale=0.05,
+        n_balance_scale=1.0),
+    test_cfg=dict())
+optimizer = dict(type='Adam', lr=0.0001, weight_decay=1e-05)
+optimizer_config = dict()
+lr_config = dict(policy='CosineAnnealing', min_lr=1e-07, by_epoch=False)
+runner = dict(type='IterBasedRunner', max_iters=121960)
+checkpoint_config = dict(by_epoch=False, interval=4065, max_keep_ckpts=1)
+evaluation = dict(
+    interval=4065,
+    metric='mFscore',
+    pre_eval=True,
+    mean=False,
+    thresh=0.5,
+    img_thresh=0.5)
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook')
+    ])
+ext_test_dataset = ['CASIA1']
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+cudnn_benchmark = True
+find_unused_parameters = False
+auto_resume = False
+gpu_ids = range(0, 4)

models/latest.pth

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requirements.txt

@@ -1 +1,24 @@
 requests
+torch
+torchvision
+pytorch-lightning
+lightning-bolts
+tqdm
+wandb
+segmentation_models_pytorch
+albumentations==0.4.3
+pandas
+loguru
+matplotlib
+tensorboard
+mmcv
+docutils
+PyYAML
+prettytable
+pytesseract
+sklearn
+einops
+pyclipper
+shapely
+lmdb
+jpegio