Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+TruFor_train_test/dataset/data/bcm_COCO_train_list.txt filter=lfs diff=lfs merge=lfs -text
+TruFor_train_test/dataset/data/bcmc_COCO_train_list.txt filter=lfs diff=lfs merge=lfs -text
+TruFor_train_test/dataset/data/cm_COCO_train_list.txt filter=lfs diff=lfs merge=lfs -text
+TruFor_train_test/dataset/data/sp_COCO_train_list.txt filter=lfs diff=lfs merge=lfs -text
+TruFor_train_test/pretrained_models/noiseprint++/noiseprint++.th filter=lfs diff=lfs merge=lfs -text

TruFor_train_test/LICENSE.txt

@@ -0,0 +1,31 @@
+THIS DOCUMENT CONSTITUTES A LICENCE TO USE THE SOFTWARE ON THE TERMS AND CONDITIONS APPEARING BELOW.
+
+
+Preamble
+
+This License applies to the software with which this license is distributed.
+The software is intellectual property of Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA')
+and is placed under the protection of copyright laws, including Italian legislation and international treaties.
+BY USING THE PROGRAM, YOU INDICATE YOUR ACCEPTANCE OF THIS LICENSE TO DO SO.
+
+
+Terms and Conditions
+
+Reproduction, modification, and usage of the software covered by this license is allowed free of charge provided that:
+(i) this software should be used, reproduced and modified only for informational and nonprofit purposes; any unauthorized use of this software for industrial or profit-oriented activities is expressly prohibited; and
+(ii) any reproduction or modification retains all original notices including proprietary or copyright notices; and
+(iii) reference to the original authors is given whenever results, which arise from the use of this software or any modification of it, are made public.
+No other use of the materials and of any information incorporated thereto is hereby authorized.
+In addition, be informed that some names are protected by trademarks which are the property of GRIP-UNINA, its researchers and/or other third parties whether a specific mention in that respect is made or not.
+
+
+Disclaimers
+
+This software is provided 'as-is', without any express or implied warranty.
+In no event will the authors be held liable for any damages arising from the use of this software.
+
+
+Transmission of user information
+
+Any and all information or request for information you may direct to GRIP
+through e-mail as may be linked to website http://www.grip.unina.it/ 

TruFor_train_test/LICENSE_CMX.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Huayao Liu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

TruFor_train_test/README.md

@@ -0,0 +1,129 @@
+# Setup
+
+To set up a **conda environment**, run:
+```
+conda env create -f trufor_conda.yaml
+```
+
+# Train
+
+## Pretrained weights
+
+The pretrained Noiseprint++ and SegFormer-B2 weights are already included in the github in the `pretrained_models` folder.
+
+To download the final TruFor trained weights (not required for training) see instructions in the inference section.
+
+## Training datasets
+
+Before running the training, update the datasets paths in `project_config.py`.
+
+To download the datasets used in the paper:
+- tampCOCO and compRAISE: https://github.com/mjkwon2021/CAT-Net
+- FantasticReality: there is a link by CAT-Net authors here https://github.com/mjkwon2021/CAT-Net/issues/51
+- CASIA 2.0 revised: https://github.com/namtpham/casia2groundtruth
+- IMD: https://staff.utia.cas.cz/novozada/db/IMD2020.zip
+
+To add your own dataset:
+- create a dataloader in the `dataset` folder (you may use the existing ones as a reference)
+- add it in the `data_core.py` file (both in section `mode == "train"` and `mode == "valid"`)
+- to use the dataset add it to the list in the `DATASET.TRAIN` and/or `DATASET.VALID` options in the config file
+
+## Flags and outputs
+
+Flags:
+- `-g` or `--gpu`: default is gpu '0'. Put '-1' if you want to use cpu. You can run on multiple gpus on the same device (e.g. `-g 0 1`).
+- `-exp` or `--experiment`: name of the experiment. It must have the same name as the config file (without the extension).
+
+Any other config option (to change the values without editing the .yaml file) has to be put in the end of the command in the form `NAME.OF.PARAMETER value_of_parameter`, using the parameter names included in the config file. 
+For example, to perform an extra validation step before the training starts, you can add at the end of the command
+`VALID.FIRST_VALID True`. 
+To change the batch size, change it in the `TRAIN.BATCH_SIZE_PER_GPU` setting.
+
+
+## Training using provided configs (to replicate the paper's results)
+### Phase 1: training the *Noiseprint++ extractor* (optional)
+This step is optional, as you can use our Noiseprint++ weights.
+Code for the training of Noiseprint++ is not yet available.
+
+### Phase 2: training the *localization network*
+
+```
+python train.py -exp trufor_ph2
+```
+
+### Phase 3: training the *detection network* and the *confidence estimator*
+
+First of all, make sure that `TRAIN.PRETRAINING` in `lib/config/trufor_ph3.yaml` contains the path to the weights of phase 2. Then run:
+
+```
+python train.py -exp trufor_ph3
+```
+
+You can also specify it directly in the command, without editing the yaml:
+
+```
+python train.py -exp trufor_ph3 TRAIN.PRETRAINING "weights/trufor_ph2/best.pth.tar"
+```
+
+
+## Custom training
+
+If you want to create your own training, duplicate `trufor_ph2.yaml` and `trufor_ph3.yaml` in the `lib/config` folder, rename and edit them according to your needs.
+Then, follow the same training instructions as above, using the name of your config files in `-exp`.
+
+**Remember to update the `TRAIN.PRETRAINING` value either in the yaml of ph3 (or in the command itself) with the path to the ph2 weights.**
+
+
+# Inference
+
+## Flags and outputs
+
+Flags:
+- `-g` or `--gpu`: default is gpu '0'. Put '-1' if you want to use cpu.
+- `-in` or `--input`:  default is "images/". It can be a single file, a directory, or a glob statement
+- `-out` or `--output`: output folder
+- `-exp` or `--experiment`: name of the experiment. It must have the same name as the config file (without the extension).
+- `--save_np`: if you want to save the Noiseprint++ aswell
+
+Any other config option (to change the values without editing the .yaml file) has to be put in the end of the command in the form `NAME.OF.PARAMETER value_of_parameter`, using the parameter names included in the config file. 
+For example, `TEST.MODEL_FILE "pretrained_models/trufor.pth.tar"`
+
+
+The output is a .npz containing the following files:
+- **'map'**: anomaly localization map
+- **'conf'**: confidence map
+- **'score'**: score in the range [0,1]
+- **'np++'**: Noiseprint++ (if flag `--save_np` is specified)
+- **'imgsize'**: size of the image
+
+## Inference using our provided weights (no training required)
+
+Download the [weights](https://www.grip.unina.it/download/prog/TruFor/TruFor_weights.zip) and unzip them in the "pretrained_models" folder (or wherever you prefer). 
+MD5 is 7bee48f3476c75616c3c5721ab256ff8.
+
+Then run:
+```
+python test.py -in path/to/image_or_folder -out path/to/output_folder -exp trufor_ph3 TEST.MODEL_FILE "pretrained_models/trufor.pth.tar" 
+```
+
+## Inference using your trained weights
+The `TEST.MODEL_FILE` option is not required as it uses the name specified in `-exp`.
+```
+python test.py -in path/to/image_or_folder -out path/to/output_folder -exp name_of_your_yaml_ph2
+```
+
+# Metrics
+
+In the file `metrics.py` you can find the functions we used to compute the metrics. <br/>
+Localization metrics have to be computed only on fake images, and the ground truth **has to be 0 for pristine pixels and 1 for forged pixels**. <br/>
+When computing F1 score, we take the maximum between the F1 using the localization map and the F1 using the inverse of the localization map.
+We do not consider pixels close to the borders of the forged area in the ground truth, since in most cases they are not accurate. 
+
+
+# Visualization
+
+To visualize the output for an image, run the following:
+```
+python visualize.py --image image_path --output output_path [--mask mask_path]
+```
+Providing the mask is optional.

TruFor_train_test/dataset/AbstractDataset.py

@@ -0,0 +1,146 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+from abc import ABC, abstractmethod
+from PIL import Image
+import numpy as np
+import torch
+import random
+import cv2
+
+
+class AbstractDataset(ABC):
+
+    def __init__(self, crop_size, grid_crop: bool, max_dim=None, aug=None):
+        """
+        :param crop_size: (H, W) or None. H and W must be the multiple of 8 if grid_crop==True.
+        :param grid_crop: T: crop within 8x8 grid. F: crop anywhere.
+        :param max_dim: if image is bigger than this size, it is cropped
+        :param aug: augmentation
+        """
+        self._crop_size = crop_size
+        self._max_dim   = max_dim
+        self._grid_crop = grid_crop
+
+        if grid_crop and crop_size is not None:
+            assert crop_size[0] % 8 == 0 and crop_size[1] % 8 == 0
+
+        self.img_list = None
+        self.aug = aug
+        #if self.aug is not None:
+        #    print('Augmentation:', self.aug)
+
+
+    def _create_tensor(self, mask=None, rgb_path=None):
+        ignore_index = -1
+
+        try:
+            img_RGB = np.array(Image.open(rgb_path).convert("RGB"))
+        except:
+            raise ValueError(f'error path: {rgb_path}')
+
+        h, w = img_RGB.shape[0], img_RGB.shape[1]
+        
+        if mask is None:
+            mask = np.zeros((h, w))
+        elif mask.shape[0]!=h or mask.shape[1]!=w:
+            # a small number of images have a mask that mismatches the size of the image
+            print(f'MASK MISMATCH: {rgb_path} \n h:{h}, w:{w}, mask: {mask.shape}', flush=True)
+            try:
+                mask = np.ascontiguousarray(np.rot90(mask))
+                assert mask.shape[0]==h and mask.shape[1]==w
+            except:
+                mask = cv2.resize(np.uint8(mask), (h, w), interpolation=cv2.INTER_NEAREST)>0
+
+        # augmentation
+        if self.aug is not None:
+            mask = np.uint8(mask)
+            dat = self.aug(image=img_RGB, mask=mask)
+            assert dat['image'].dtype==img_RGB.dtype
+            assert dat['mask'].dtype==mask.dtype
+            img_RGB = dat['image']
+            mask = dat['mask']>0
+            h, w = img_RGB.shape[0], img_RGB.shape[1]
+            del dat
+
+        # cropping
+        if self._crop_size is None and self._grid_crop:
+            crop_size = (-(-h//8) * 8, -(-w//8) * 8)  # smallest 8x8 grid crop that contains image
+        elif self._crop_size is None and not self._grid_crop:
+            crop_size = None  # use entire image! no crop, no pad
+        else:
+            crop_size = self._crop_size
+
+        if crop_size is not None:
+            # Pad if crop_size is larger than image size
+            if h < crop_size[0] or w < crop_size[1]:
+                
+                # pad RGB
+                if img_RGB is not None:
+                    temp = np.full((max(h, crop_size[0]), max(w, crop_size[1]), 3), 127.5)
+                    temp[:img_RGB.shape[0], :img_RGB.shape[1], :] = img_RGB
+                    img_RGB = temp
+
+                # pad mask
+                temp = np.full((max(h, crop_size[0]), max(w, crop_size[1])), ignore_index)  # pad with ignore_index(-1)
+                try:
+                    temp[:mask.shape[0], :mask.shape[1]] = mask
+                    mask = temp
+                except:
+                    raise ValueError(f'{rgb_path}\nh:{h}, w:{w}, temp:{temp.shape}, mask: {mask.shape}')
+
+            # Determine where to crop
+            if self._grid_crop:
+                s_r = (random.randint(0, max(h - crop_size[0], 0)) // 8) * 8
+                s_c = (random.randint(0, max(w - crop_size[1], 0)) // 8) * 8
+            else:
+                s_r = random.randint(0, max(h - crop_size[0], 0))
+                s_c = random.randint(0, max(w - crop_size[1], 0))
+
+            # crop
+            mask    = mask[s_r:s_r+crop_size[0], s_c:s_c+crop_size[1]]
+            img_RGB = img_RGB[s_r:s_r+crop_size[0], s_c:s_c+crop_size[1], :]
+                
+        # cropping big images
+        if self._max_dim is not None:
+            max_dim = self._max_dim
+            # Determine where to crop
+            s_r = (max((h - max_dim)//2, 0) // 8) * 8
+            s_c = (max((w - max_dim)//2, 0) // 8) * 8
+
+            # crop
+            mask    = mask[s_r:s_r+max_dim, s_c:s_c+max_dim]
+            img_RGB = img_RGB[s_r:s_r+max_dim, s_c:s_c+max_dim, :]
+        
+        t_mask = torch.tensor(mask, dtype=torch.long)
+        t_RGB  = torch.tensor(img_RGB.transpose(2,0,1), dtype=torch.float)/256.0
+        return t_RGB, t_mask
+    
+
+    @abstractmethod
+    def get_img(self, index):
+        pass
+
+    def get_img_name(self, index):
+        item = self.img_list[index]
+        if isinstance(item, list):
+            return item[0]
+        else:
+            return item
+
+    def __len__(self):
+        return len(self.img_list)
+

TruFor_train_test/dataset/data/CASIA_v2_auth_valid_list.txt

@@ -0,0 +1,100 @@
+Au/Au_pla_30132.jpg,None
+Au/Au_ani_00037.jpg,None
+Au/Au_pla_30252.jpg,None
+Au/Au_art_30491.jpg,None
+Au/Au_art_20018.jpg,None
+Au/Au_pla_00064.jpg,None
+Au/Au_arc_30598.jpg,None
+Au/Au_sec_30302.jpg,None
+Au/Au_cha_30332.jpg,None
+Au/Au_ani_30387.jpg,None
+Au/Au_nat_30453.jpg,None
+Au/Au_pla_30062.jpg,None
+Au/Au_cha_30232.jpg,None
+Au/Au_ani_30312.jpg,None
+Au/Au_cha_30185.jpg,None
+Au/Au_ani_30472.jpg,None
+Au/Au_pla_30443.jpg,None
+Au/Au_nat_20042.jpg,None
+Au/Au_pla_30190.jpg,None
+Au/Au_pla_20049.jpg,None
+Au/Au_nat_30282.jpg,None
+Au/Au_pla_20060.jpg,None
+Au/Au_ani_30466.jpg,None
+Au/Au_pla_30038.jpg,None
+Au/Au_ind_20044.jpg,None
+Au/Au_ani_20067.jpg,None
+Au/Au_ani_30068.jpg,None
+Au/Au_sec_30418.jpg,None
+Au/Au_ani_10150.jpg,None
+Au/Au_arc_30177.jpg,None
+Au/Au_nat_10107.jpg,None
+Au/Au_pla_30313.jpg,None
+Au/Au_pla_30245.jpg,None
+Au/Au_cha_30479.jpg,None
+Au/Au_sec_30547.jpg,None
+Au/Au_art_30257.jpg,None
+Au/Au_arc_20001.jpg,None
+Au/Au_arc_30467.jpg,None
+Au/Au_pla_30693.jpg,None
+Au/Au_ani_30483.jpg,None
+Au/Au_sec_30187.jpg,None
+Au/Au_cha_30666.jpg,None
+Au/Au_cha_00084.jpg,None
+Au/Au_nat_30085.jpg,None
+Au/Au_art_20035.jpg,None
+Au/Au_nat_30722.jpg,None
+Au/Au_arc_30627.jpg,None
+Au/Au_sec_30642.jpg,None
+Au/Au_ani_30051.jpg,None
+Au/Au_cha_30001.jpg,None
+Au/Au_nat_30032.jpg,None
+Au/Au_ani_10193.jpg,None
+Au/Au_sec_30292.jpg,None
+Au/Au_arc_30057.jpg,None
+Au/Au_ani_20030.jpg,None
+Au/Au_ind_30158.jpg,None
+Au/Au_sec_30633.jpg,None
+Au/Au_cha_10142.jpg,None
+Au/Au_nat_00013.jpg,None
+Au/Au_cha_30604.jpg,None
+Au/Au_art_30073.jpg,None
+Au/Au_cha_30638.jpg,None
+Au/Au_pla_30153.jpg,None
+Au/Au_sec_00045.jpg,None
+Au/Au_pla_00002.jpg,None
+Au/Au_cha_30168.jpg,None
+Au/Au_pla_30278.jpg,None
+Au/Au_nat_10128.jpg,None
+Au/Au_cha_20010.jpg,None
+Au/Au_art_30191.jpg,None
+Au/Au_ind_00046.jpg,None
+Au/Au_sec_30536.jpg,None
+Au/Au_pla_20094.jpg,None
+Au/Au_pla_30318.jpg,None
+Au/Au_ani_30457.jpg,None
+Au/Au_pla_30187.jpg,None
+Au/Au_ani_20044.jpg,None
+Au/Au_pla_30436.jpg,None
+Au/Au_arc_30200.jpg,None
+Au/Au_nat_30680.jpg,None
+Au/Au_pla_30115.jpg,None
+Au/Au_pla_30388.jpg,None
+Au/Au_nat_30507.jpg,None
+Au/Au_pla_30183.jpg,None
+Au/Au_sec_30398.jpg,None
+Au/Au_ani_30766.jpg,None
+Au/Au_cha_20044.jpg,None
+Au/Au_sec_30002.jpg,None
+Au/Au_ani_30157.jpg,None
+Au/Au_ind_20042.jpg,None
+Au/Au_arc_30698.jpg,None
+Au/Au_pla_30269.jpg,None
+Au/Au_arc_30308.jpg,None
+Au/Au_sec_30091.jpg,None
+Au/Au_cha_30419.jpg,None
+Au/Au_nat_30352.jpg,None
+Au/Au_ani_10160.jpg,None
+Au/Au_pla_30346.jpg,None
+Au/Au_arc_30502.jpg,None
+Au/Au_pla_30094.jpg,None

TruFor_train_test/dataset/data/CASIA_v2_valid_list.txt

@@ -0,0 +1,100 @@
+Tp/Tp_D_NRN_M_B_nat00041_arc00065_11444.jpg,groundtruth/Tp_D_NRN_M_B_nat00041_arc00065_11444_gt.png
+Tp/Tp_S_NNN_S_N_nat00058_nat00058_00665.tif,groundtruth/Tp_S_NNN_S_N_nat00058_nat00058_00665_gt.png
+Tp/Tp_S_CRN_S_N_pla00092_pla00092_10607.jpg,groundtruth/Tp_S_CRN_S_N_pla00092_pla00092_10607_gt.png
+Tp/Tp_S_NND_S_N_ind00040_ind00040_10881.jpg,groundtruth/Tp_S_NND_S_N_ind00040_ind00040_10881_gt.png
+Tp/Tp_D_NRN_S_N_ind00071_cha00063_00472.tif,groundtruth/Tp_D_NRN_S_N_ind00071_cha00063_00472_gt.png
+Tp/Tp_S_NNN_S_N_sec20003_sec20003_02453.tif,groundtruth/Tp_S_NNN_S_N_sec20003_sec20003_02453_gt.png
+Tp/Tp_S_CNN_M_N_art00064_art00064_10260.tif,groundtruth/Tp_S_CNN_M_N_art00064_art00064_10260_gt.png
+Tp/Tp_S_NRN_S_B_ani10002_ani10002_20005.jpg,groundtruth/Tp_S_NRN_S_B_ani10002_ani10002_20005_gt.png
+Tp/Tp_S_NNN_S_N_ani00085_ani00085_00205.tif,groundtruth/Tp_S_NNN_S_N_ani00085_ani00085_00205_gt.png
+Tp/Tp_S_NND_S_N_sec20028_sec20028_01618.tif,groundtruth/Tp_S_NND_S_N_sec20028_sec20028_01618_gt.png
+Tp/Tp_S_CNN_M_N_txt00011_txt00011_10845.jpg,groundtruth/Tp_S_CNN_M_N_txt00011_txt00011_10845_gt.png
+Tp/Tp_S_CNN_M_N_pla10114_pla10114_10430.tif,groundtruth/Tp_S_CNN_M_N_pla10114_pla10114_10430_gt.png
+Tp/Tp_S_NNN_S_N_nat00085_nat00085_00680.tif,groundtruth/Tp_S_NNN_S_N_nat00085_nat00085_00680_gt.png
+Tp/Tp_D_NRN_M_N_nat10110_cha00086_11370.jpg,groundtruth/Tp_D_NRN_M_N_nat10110_cha00086_11370_gt.png
+Tp/Tp_S_NNN_S_N_ind00040_ind00040_10880.jpg,groundtruth/Tp_S_NNN_S_N_ind00040_ind00040_10880_gt.png
+Tp/Tp_S_NNN_S_N_ani00012_ani00012_00836.tif,groundtruth/Tp_S_NNN_S_N_ani00012_ani00012_00836_gt.png
+Tp/Tp_D_NND_M_N_arc00058_arc00086_00278.tif,groundtruth/Tp_D_NND_M_N_arc00058_arc00086_00278_gt.png
+Tp/Tp_S_NNN_S_B_arc00002_arc00002_00222.tif,groundtruth/Tp_S_NNN_S_B_arc00002_arc00002_00222_gt.png
+Tp/Tp_D_NRN_S_N_ani10215_ani00081_12382.jpg,groundtruth/Tp_D_NRN_S_N_ani10215_ani00081_12382_gt.png
+Tp/Tp_S_NRN_S_B_art00072_art00072_01015.tif,groundtruth/Tp_S_NRN_S_B_art00072_art00072_01015_gt.png
+Tp/Tp_S_NNN_S_N_nat20040_nat20040_01544.tif,groundtruth/Tp_S_NNN_S_N_nat20040_nat20040_01544_gt.png
+Tp/Tp_S_NRN_S_N_ind00012_ind00012_01310.tif,groundtruth/Tp_S_NRN_S_N_ind00012_ind00012_01310_gt.png
+Tp/Tp_S_NNN_S_B_cha20037_cha20037_02430.tif,groundtruth/Tp_S_NNN_S_B_cha20037_cha20037_02430_gt.png
+Tp/Tp_D_CRN_S_N_txt00077_txt00082_11295.jpg,groundtruth/Tp_D_CRN_S_N_txt00077_txt00082_11295_gt.png
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TruFor_train_test/dataset/data/sp_COCO_train_list.txt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:148cd2354e927e2f2b37fc7827238d8da724d8480c90555fdab0982cb5b5ca43
+size 22102484

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TruFor_train_test/dataset/data_core.py

@@ -0,0 +1,137 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+from torch.utils.data import Dataset
+import random
+
+from dataset.dataset_FantasticReality import FantasticReality
+from dataset.dataset_IMD2020 import IMD2020
+from dataset.dataset_CASIA import CASIA
+from dataset.dataset_TampCOCO import tampCOCO
+from dataset.dataset_CompRAISE import compRAISE
+
+
+class myDataset(Dataset):
+    def __init__(self, config, crop_size, grid_crop, mode="train", max_dim=None, aug=None):
+        self.dataset_list = []
+        training_set = config.DATASET.TRAIN
+        valid_set    = config.DATASET.VALID
+        
+        if mode == "train":
+            if 'FR' in training_set:
+                self.dataset_list.append(FantasticReality(crop_size, grid_crop, "dataset/data/FR_train_list.txt", aug=aug))
+                self.dataset_list.append(FantasticReality(crop_size, grid_crop, "dataset/data/FR_auth_train_list.txt", is_auth_list=True, aug=aug))
+                
+            if 'IMD' in training_set:
+                self.dataset_list.append(IMD2020(crop_size, grid_crop, "dataset/data/IMD_train_list.txt", aug=aug))
+                
+            if 'CA' in training_set:
+                self.dataset_list.append(CASIA(crop_size, grid_crop, "dataset/data/CASIA_v2_train_list.txt", aug=aug))
+                self.dataset_list.append(CASIA(crop_size, grid_crop, "dataset/data/CASIA_v2_auth_train_list.txt", aug=aug))
+
+            if 'COCO' in training_set:
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/cm_COCO_train_list.txt",   aug=aug))
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/sp_COCO_train_list.txt",   aug=aug))
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/bcm_COCO_train_list.txt",  aug=aug))
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/bcmc_COCO_train_list.txt", aug=aug))
+            
+            if 'RAISE' in training_set:
+                self.dataset_list.append(compRAISE(crop_size, grid_crop, "dataset/data/compRAISE_train.txt", aug=aug))
+
+
+        elif mode == "valid":
+            if 'FR' in valid_set:
+                self.dataset_list.append(FantasticReality(crop_size, grid_crop, "dataset/data/FR_valid_list.txt", max_dim=max_dim, aug=aug))
+                self.dataset_list.append(FantasticReality(crop_size, grid_crop, "dataset/data/FR_auth_valid_list.txt", is_auth_list=True, max_dim=max_dim, aug=aug))
+                
+            if 'IMD' in valid_set:
+                self.dataset_list.append(IMD2020(crop_size, grid_crop, "dataset/data/IMD_valid_list.txt", max_dim=max_dim, aug=aug))
+            
+            if 'CA' in valid_set:
+                self.dataset_list.append(CASIA(crop_size, grid_crop, "dataset/data/CASIA_v2_valid_list.txt", max_dim=max_dim, aug=aug))
+                self.dataset_list.append(CASIA(crop_size, grid_crop, "dataset/data/CASIA_v2_auth_valid_list.txt", max_dim=max_dim, aug=aug))
+            
+            if 'COCO' in valid_set:
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/cm_COCO_valid_list.txt",   max_dim=max_dim, aug=aug))
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/sp_COCO_valid_list.txt",   max_dim=max_dim, aug=aug))
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/bcm_COCO_valid_list.txt",  max_dim=max_dim, aug=aug))
+                self.dataset_list.append(tampCOCO(crop_size, grid_crop, "dataset/data/bcmc_COCO_valid_list.txt", max_dim=max_dim, aug=aug))
+            
+            if 'RAISE' in valid_set:
+                self.dataset_list.append(compRAISE(crop_size, grid_crop, "dataset/data/compRAISE_valid.txt", max_dim=max_dim, aug=aug))
+
+        else:
+            raise KeyError("Invalid mode: " + mode)
+
+        self.crop_size = crop_size
+        self.grid_crop = grid_crop
+        self.mode = mode
+        lengths = [len(ds) for ds in self.dataset_list]
+        self.smallest = min(lengths)
+        if config.TRAIN.NUM_SAMPLES > 0 and config.TRAIN.NUM_SAMPLES < self.smallest:
+            self.smallest = config.TRAIN.NUM_SAMPLES
+
+
+    def shuffle(self):
+        for dataset in self.dataset_list:
+            random.shuffle(dataset.img_list)
+
+
+    def get_filename(self, index):
+        it = 0
+        while True:
+            if index >= len(self.dataset_list[it]):
+                index -= len(self.dataset_list[it])
+                it += 1
+                continue
+            return self.dataset_list[it].get_img_name(index)
+
+
+    def __len__(self):
+        if self.mode == 'train':
+            # class-balanced sampling
+            return self.smallest * len(self.dataset_list)
+        else:
+            return sum([len(lst) for lst in self.dataset_list])
+
+
+    def __getitem__(self, index):
+        if self.mode == 'train':
+            # class-balanced sampling
+            if index < self.smallest * len(self.dataset_list):
+                return self.dataset_list[index//self.smallest].get_img(index % self.smallest)
+            else:
+                raise ValueError("Something wrong.")
+        else:
+            it = 0
+            while True:
+                if index >= len(self.dataset_list[it]):
+                    index -= len(self.dataset_list[it])
+                    it += 1
+                    continue
+                return self.dataset_list[it].get_img(index)
+
+
+    def get_info(self):
+        s = ''
+        for ds in self.dataset_list:
+            s += f'{ds.__class__.__name__}: \t{len(ds)} images \n'
+        s += f'Smallest: {self.smallest}'
+        return s
+
+
+
+

TruFor_train_test/dataset/dataset_CASIA.py

@@ -0,0 +1,52 @@
+"""
+Created by Myung-Joon Kwon
+mjkwon2021@gmail.com
+July 14, 2020
+
+modified by Fabrizio Guillaro
+fabrizio.guillaro@unina.it
+September 2022
+"""
+
+from project_config import project_root, dataset_paths
+from dataset.AbstractDataset import AbstractDataset
+
+import os
+import numpy as np
+from PIL import Image
+
+
+class CASIA(AbstractDataset):
+    """
+    directory structure:
+    CASIA2.0_revised
+    ├── Au
+    ├── Tp
+    └── groundtruth
+    """
+
+    def __init__(self, crop_size, grid_crop, img_list: str, max_dim=None, aug=None):
+        super().__init__(crop_size, grid_crop, max_dim, aug=aug)
+        self._root_path    = dataset_paths['CA']
+        
+        with open(project_root / img_list, "r") as f:
+            self.img_list = [t.strip().split(',') for t in f.readlines()]
+        
+        
+
+    def get_img(self, index):
+        assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+        
+        root = self._root_path
+
+        mask_path = os.path.join(root, self.img_list[index][1])
+        rgb_path  = os.path.join(root, self.img_list[index][0])
+            
+        if self.img_list[index][1] == 'None':
+            mask = None
+        else:
+            mask = np.array(Image.open(mask_path).convert("L"))
+            mask[mask > 0] = 1
+
+        assert os.path.isfile(rgb_path)
+        return self._create_tensor(mask=mask, rgb_path=rgb_path)

TruFor_train_test/dataset/dataset_CompRAISE.py

@@ -0,0 +1,36 @@
+"""
+Created by Myung-Joon Kwon
+mjkwon2021@gmail.com
+27 Jan 2021
+
+modified by Fabrizio Guillaro
+fabrizio.guillaro@unina.it
+September 2022
+"""
+
+from project_config import project_root, dataset_paths
+from dataset.AbstractDataset import AbstractDataset
+import os
+
+class compRAISE(AbstractDataset):
+    """
+    directory structure
+    compRAISE
+    ├── r000da54ft_Q67.jpg
+    ├── r000da54ft_Q67_aligned_Q87.jpg
+    └── r000da54ft_Q67_resize_1.15_Q90.jpg ...
+    """
+    def __init__(self, crop_size, grid_crop, img_list: str, max_dim=None, aug=None):
+        super().__init__(crop_size, grid_crop, max_dim, aug=aug)
+        self._root_path = dataset_paths['compRAISE']
+        with open(project_root / img_list, "r") as f:
+            lines = f.readlines()            
+        self.img_list = [t.strip() for t in lines]
+        
+
+    def get_img(self, index):
+        assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+        rgb_path = os.path.join(self._root_path, self.img_list[index])
+        assert os.path.isfile(rgb_path)
+        return self._create_tensor(mask=None, rgb_path=rgb_path)
+    

TruFor_train_test/dataset/dataset_FantasticReality.py

@@ -0,0 +1,54 @@
+"""
+Created by Myung-Joon Kwon
+mjkwon2021@gmail.com
+July 7, 2020
+
+modified by Fabrizio Guillaro
+fabrizio.guillaro@unina.it
+September 2022
+"""
+
+from project_config import project_root, dataset_paths
+from dataset.AbstractDataset import AbstractDataset
+
+import os
+import numpy as np
+
+
+class FantasticReality(AbstractDataset):
+    """
+    directory structure:
+    FantasticReality
+    ├── ColorFakeImages
+    ├── ColorRealImages
+    └── SegmentationFake
+    """
+
+    def __init__(self, crop_size, grid_crop, img_list: str=None, is_auth_list: bool=False, max_dim=None, aug=None):
+        super().__init__(crop_size, grid_crop, max_dim, aug=aug)
+        self._root_path    = dataset_paths['FR']
+        with open(project_root / img_list, "r") as f:
+            self.img_list = [t.strip() for t in f.readlines()]
+        self._is_auth_list = is_auth_list
+        
+
+    def get_img(self, index):
+        
+        root = self._root_path
+
+        if not self._is_auth_list:
+            # tampered image
+            assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+            rgb_path = os.path.join(root, 'ColorFakeImages', self.img_list[index])
+            mask_path = os.path.join(root, "SegmentationFake", self.img_list[index].replace('.jpg', '.npz'))
+            matrix = np.load(mask_path)
+            mask = matrix['arr_0'].squeeze()
+            mask[mask > 0] = 1
+            assert os.path.isfile(rgb_path)
+            return self._create_tensor(mask=mask, rgb_path=rgb_path)
+        else:
+            # authentic image
+            assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+            rgb_path = os.path.join(root, 'ColorRealImages', self.img_list[index])
+            assert os.path.isfile(rgb_path)
+            return self._create_tensor(mask=None, rgb_path=rgb_path)

TruFor_train_test/dataset/dataset_IMD2020.py

@@ -0,0 +1,49 @@
+"""
+Created by Myung-Joon Kwon
+mjkwon2021@gmail.com
+July 12, 2020
+
+modified by Fabrizio Guillaro
+fabrizio.guillaro@unina.it
+September 2022
+"""
+
+from project_config import project_root, dataset_paths
+from dataset.AbstractDataset import AbstractDataset
+
+import os
+import numpy as np
+from PIL import Image
+
+
+class IMD2020(AbstractDataset):
+    """
+    directory structure
+    IMD2020_wild
+    ├── 1a07yi
+    ├── 1a16mu
+    └── 1a1ogs ...
+    """
+    def __init__(self, crop_size, grid_crop, img_list: str, max_dim=None, aug=None):
+        super().__init__(crop_size, grid_crop, max_dim, aug=aug)
+        self._root_path = dataset_paths['IMD']
+        with open(project_root / img_list, "r") as f:
+            self.img_list = [t.strip().split(',') for t in f.readlines()]
+        
+
+    def get_img(self, index):
+        assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+        root = self._root_path
+            
+        rgb_path = os.path.join(root, self.img_list[index][0])
+            
+        if self.img_list[index][1] == 'None':
+            mask = None
+        else:
+            mask_path = os.path.join(root, self.img_list[index][1])
+            mask = np.array(Image.open(mask_path).convert("L"))
+            mask[mask > 0] = 1
+        assert os.path.isfile(rgb_path)
+        return self._create_tensor(mask=mask, rgb_path=rgb_path)
+    
+    #Note: removed z14/00030_fake.jpg (shape mismatch)

TruFor_train_test/dataset/dataset_TampCOCO.py

@@ -0,0 +1,42 @@
+"""
+Created by Myung-Joon Kwon
+mjkwon2021@gmail.com
+27 Jan 2021
+
+modified by Fabrizio Guillaro
+fabrizio.guillaro@unina.it
+September 2022
+"""
+from project_config import project_root, dataset_paths
+from dataset.AbstractDataset import AbstractDataset
+
+import os
+import numpy as np
+from PIL import Image
+
+
+class tampCOCO(AbstractDataset):
+    """
+    directory structure
+    tampCOCO
+    ├── cm_images
+    ├── cm_masks
+    └── sp_images ...
+    """
+    def __init__(self, crop_size, grid_crop, img_list: str, max_dim=None, aug=None):
+        super().__init__(crop_size, grid_crop, max_dim, aug=aug)
+        self._root_path = dataset_paths['tampCOCO']
+        with open(project_root / img_list, "r") as f:
+            lines = f.readlines()
+        self.img_list = [t.strip().split(',') for t in lines if os.path.getsize(os.path.join(self._root_path, t.strip().split(',')[0]))]
+
+
+
+    def get_img(self, index):
+        assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+        rgb_path  = os.path.join(self._root_path, self.img_list[index][0])
+        mask_path = os.path.join(self._root_path, self.img_list[index][1])
+        mask = np.array(Image.open(mask_path).convert('L'))
+        mask[mask > 1] = 1
+        assert os.path.isfile(rgb_path)
+        return self._create_tensor(mask=mask, rgb_path=rgb_path)

TruFor_train_test/dataset/dataset_test.py

@@ -0,0 +1,46 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+from torch.utils.data import Dataset
+import random
+import numpy as np
+import torch
+from PIL import Image
+
+
+class TestDataset(Dataset):
+    def __init__(self, list_img=None):
+        self.img_list = list_img
+
+    def shuffle(self):
+        random.shuffle(self.img_list)
+
+    def __len__(self):
+        return len(self.img_list)
+
+    def __getitem__(self, index):
+        assert self.img_list
+        assert 0 <= index < len(self.img_list), f"Index {index} is not available!"
+        rgb_path = self.img_list[index]
+        img_RGB = np.array(Image.open(rgb_path).convert("RGB"))
+        return torch.tensor(img_RGB.transpose(2, 0, 1), dtype=torch.float) / 256.0, rgb_path
+
+    def get_filename(self, index):
+        item = self.img_list[index]
+        if isinstance(item, list):
+            return item[0]
+        else:
+            return item

TruFor_train_test/lib/config/__init__.py

@@ -0,0 +1,2 @@
+from .default import _C as config
+from .default import update_config

TruFor_train_test/lib/config/aug_res_comp.yaml

@@ -0,0 +1,15 @@
+__version__: 1.2.1
+transform:
+  __class_fullname__: Compose
+  transforms:
+  - __class_fullname__: RandomScale
+    p: 0.5
+    interpolation: 1
+    scale_limit:
+    - -0.5
+    - 0.5
+
+  - __class_fullname__: JpegCompression
+    p: 0.5
+    quality_lower: 30
+    quality_upper: 100

TruFor_train_test/lib/config/default.py

@@ -0,0 +1,123 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+import os
+from yacs.config import CfgNode as CN
+
+
+_C = CN()
+
+_C.OUTPUT_DIR = 'weights'
+_C.LOG_DIR = 'log'
+_C.GPUS = (0,)
+_C.WORKERS = 4
+
+# Cudnn parameters
+_C.CUDNN = CN()
+_C.CUDNN.BENCHMARK = True
+_C.CUDNN.DETERMINISTIC = False
+_C.CUDNN.ENABLED = True
+
+# Model parameters
+_C.MODEL = CN()
+_C.MODEL.NAME = 'detconfcmx'
+_C.MODEL.PRETRAINED = 'pretrained_models/segformers/mit_b2.pth'
+_C.MODEL.MODS = ('RGB','NP++')
+_C.MODEL.EXTRA = CN(new_allowed=True)
+_C.MODEL.EXTRA.BACKBONE = 'mit_b2'
+_C.MODEL.EXTRA.DETECTION = None
+_C.MODEL.EXTRA.MODULES = ['NP++','backbone','loc_head','conf_head','det_head']  # modules
+# ['NP++',     -> Noiseprint++ extraction module
+#  'backbone', -> encoder backbone
+#  'loc_head', -> localization head
+#  'conf_head',-> confidence head
+#  'det_head'] -> detection head
+_C.MODEL.EXTRA.FIX_MODULES = ['NP++']  # freezed modules
+
+_C.LOSS = CN()
+_C.LOSS.USE_OHEM = False
+_C.LOSS.LOSSES = [['LOC', 1.0, 'cross_entropy']] # tuples (Loss, weight, criterion)
+    # 'LOC' -> Localization Loss
+    # 'CONF'-> Confidence Loss
+    # 'DET' -> Detection Loss
+# es:
+    # -['LOC', 1.0, 'cross_entropy']
+    # -['CONF', 1.0, 'mse']
+    # -['DET',0.5,'cross_entropy']
+_C.LOSS.SMOOTH = 0
+
+# Dataset parameters
+_C.DATASET = CN()
+_C.DATASET.ROOT = ''
+_C.DATASET.TRAIN = []
+_C.DATASET.VALID = []
+_C.DATASET.NUM_CLASSES = 2
+_C.DATASET.CLASS_WEIGHTS = [0.5, 2.5]
+
+# Training parameters
+_C.TRAIN = CN()
+
+_C.TRAIN.IMAGE_SIZE = [512, 512]  # width * height
+
+_C.TRAIN.LR = 0.01
+_C.TRAIN.OPTIMIZER = 'sgd'
+_C.TRAIN.MOMENTUM = 0.9
+_C.TRAIN.WD = 0.0001
+_C.TRAIN.NESTEROV = False
+_C.TRAIN.IGNORE_LABEL = -1
+
+_C.TRAIN.BEGIN_EPOCH = 0
+_C.TRAIN.END_EPOCH = 100  # also used to compute LR adjustment!
+_C.TRAIN.STOP_EPOCH = -1  # to stop before end_epoch
+_C.TRAIN.EXTRA_EPOCH = 0
+
+_C.TRAIN.RESUME = True
+_C.TRAIN.PRETRAINING = ''  # to start from a fully pretrained network
+_C.TRAIN.AUG = None
+_C.TRAIN.BATCH_SIZE_PER_GPU = 18
+_C.TRAIN.SHUFFLE = True
+_C.TRAIN.NUM_SAMPLES = 0  # number of images for each dataset (upper limit is the size of the smaller dataset)
+
+# Validation parameters
+_C.VALID = CN()
+_C.VALID.IMAGE_SIZE = None  # width * height
+_C.VALID.AUG = None
+_C.VALID.FIRST_VALID = True # To run a validation before training
+_C.VALID.MAX_SIZE = None
+_C.VALID.BEST_KEY = 'avg_mIoU'
+
+# Testing parameters
+_C.TEST = CN()
+_C.TEST.MODEL_FILE = ''
+
+
+
+def update_config(cfg, args):
+    cfg.defrost()
+
+    cfg.merge_from_file(f'/home/daniil.orel/detect_fake_images/TruFor_train_test/lib/config/trufor_ph3.yaml')
+    if cfg.TEST.MODEL_FILE == '':
+        cfg.merge_from_list(['TEST.MODEL_FILE', f'weights/trufor_ph3/best.pth.tar'])
+    try:
+        cfg.merge_from_list(['GPUS', tuple(args.gpu)])
+    except:
+        pass
+
+    # if args.opts:
+    #     cfg.merge_from_list(args.opts)
+
+    cfg.freeze()
+

TruFor_train_test/lib/config/trufor_ph2.yaml

@@ -0,0 +1,48 @@
+CUDNN:
+  BENCHMARK: false
+  DETERMINISTIC: false
+  ENABLED: false
+WORKERS: 16
+
+DATASET:
+  TRAIN: [IMD, FR, CA, COCO, RAISE]
+  VALID: [IMD, FR, CA, COCO, RAISE]
+  NUM_CLASSES: 2
+  CLASS_WEIGHTS: [0.5, 2.5]
+MODEL:
+  NAME: detconfcmx
+  PRETRAINED: 'pretrained_models/segformers/mit_b2.pth'
+  MODS: ('RGB','NP++')
+  EXTRA:
+      BACKBONE: mit_b2
+      DECODER: MLPDecoder
+      DECODER_EMBED_DIM: 512
+      PREPRC: 'imagenet'
+      BN_EPS: 0.001
+      BN_MOMENTUM: 0.1
+      NP_WEIGHTS: 'pretrained_models/noiseprint++/noiseprint++.th'
+      MODULES: ['NP++','backbone','loc_head']
+      FIX_MODULES: ['NP++']
+LOSS:
+  LOSSES:
+    - [ 'LOC', 1.0, 'dice_entropy' ]
+  SMOOTH: 0
+TRAIN:
+  PRETRAINING:
+  IMAGE_SIZE: [512,512]
+  BATCH_SIZE_PER_GPU: 18
+  SHUFFLE: true
+  BEGIN_EPOCH: 0
+  END_EPOCH: 100
+  OPTIMIZER: sgd
+  LR: 0.005
+  WD: 0.0005
+  MOMENTUM: 0.9
+  NESTEROV: false
+  IGNORE_LABEL: -1
+  AUG: 'lib/config/aug_res_comp.yaml'
+VALID:
+  FIRST_VALID: false
+  MAX_SIZE: 2048
+  BEST_KEY: 'avg_p-F1_smooth'
+  AUG: 'lib/config/aug_res_comp.yaml'

TruFor_train_test/lib/config/trufor_ph3.yaml

@@ -0,0 +1,50 @@
+CUDNN:
+  BENCHMARK: false
+  DETERMINISTIC: false
+  ENABLED: false
+WORKERS: 16
+
+DATASET:
+  TRAIN: [IMD, FR, CA, COCO, RAISE]
+  VALID: [IMD, FR, CA, COCO, RAISE]
+  NUM_CLASSES: 2
+  CLASS_WEIGHTS: [0.5, 2.5]
+MODEL:
+  NAME: detconfcmx
+  PRETRAINED:
+  MODS: ('RGB','NP++')
+  EXTRA:
+      BACKBONE: mit_b2
+      DECODER: MLPDecoder
+      DECODER_EMBED_DIM: 512
+      PREPRC: 'imagenet'
+      BN_EPS: 0.001
+      BN_MOMENTUM: 0.1
+      NP_WEIGHTS:
+      MODULES: ['NP++','backbone','loc_head','conf_head','det_head']
+      FIX_MODULES: ['NP++','backbone','loc_head']
+      DETECTION: 'confpool'
+LOSS:
+  LOSSES:
+    - ['CONF', 1.0, 'mse']
+    - ['DET',0.5,'cross_entropy']
+  SMOOTH: 0
+TRAIN:
+  PRETRAINING: 'weights/trufor_ph2/best.pth.tar'
+  IMAGE_SIZE: [512,512]
+  BATCH_SIZE_PER_GPU: 18
+  SHUFFLE: true
+  BEGIN_EPOCH: 0
+  END_EPOCH: 100
+  OPTIMIZER: sgd
+  LR: 0.005
+  WD: 0.0005
+  MOMENTUM: 0.9
+  NESTEROV: false
+  IGNORE_LABEL: -1
+  AUG: 'lib/config/aug_res_comp.yaml'
+VALID:
+  FIRST_VALID: true
+  MAX_SIZE: 2048
+  BEST_KEY: 'avg_det_bacc'
+  AUG: 'lib/config/aug_res_comp.yaml'

TruFor_train_test/lib/core/criterion.py

@@ -0,0 +1,207 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+
+class CrossEntropy(nn.Module):
+    def __init__(self, ignore_label=-1, weight=None):
+        super(CrossEntropy, self).__init__()
+        self.ignore_label = ignore_label
+        self.criterion = nn.CrossEntropyLoss(weight=weight, 
+                                             ignore_index=ignore_label)
+
+    def forward(self, score, target):        
+        ph, pw = score.size(2), score.size(3)
+        h, w = target.size(1), target.size(2)
+        if ph != h or pw != w:
+            score = F.upsample(
+                    input=score, size=(h, w), mode='bilinear')
+
+        loss = self.criterion(score, target)
+        return loss
+
+    
+    
+class DiceLoss(nn.Module):
+    def __init__(self, ignore_label=-1, smooth=1, exponent=2): #because padding adds -1 to the targets
+        super(DiceLoss, self).__init__()  
+        self.ignore_index = ignore_label
+        self.smooth = smooth
+        self.exponent = exponent
+        
+    def dice_loss(self, pred, target, valid_mask):
+        assert pred.shape[0] == target.shape[0]
+        total_loss = 0
+        num_classes = pred.shape[1]
+        for i in range(num_classes):
+            if i != self.ignore_index:
+                dice_loss = self.binary_dice_loss(
+                    pred[:, i],
+                    target[..., i],
+                    valid_mask=valid_mask,)
+                total_loss += dice_loss
+        return total_loss / num_classes
+
+    def binary_dice_loss(self, pred, target, valid_mask):
+        assert pred.shape[0] == target.shape[0]
+        pred = pred.reshape(pred.shape[0], -1)
+        target = target.reshape(target.shape[0], -1)
+        valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
+
+        num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + self.smooth
+        den = torch.sum(pred.pow(self.exponent)*valid_mask + target.pow(self.exponent)*valid_mask, dim=1) + max(self.smooth, 1e-5)
+        
+        dice = num / den
+        dice = torch.mean(dice)
+        return 1 - dice
+        
+    def forward(self, score, target):
+        ph, pw = score.size(2), score.size(3)
+        h, w = target.size(1), target.size(2)
+        if ph != h or pw != w:
+            score = F.upsample(
+                    input=score, size=(h, w), mode='bilinear')
+        
+        score = F.softmax(score,dim=1)
+        num_classes = score.shape[1]
+        
+        one_hot_target = F.one_hot(
+            torch.clamp(target.long(), 0, num_classes - 1),
+            num_classes=num_classes)
+        valid_mask = (target != self.ignore_index).long()
+        
+        loss = self.dice_loss(score, one_hot_target, valid_mask)
+        return loss
+    
+    
+
+class BinaryDiceLoss(nn.Module):
+    def __init__(self, smooth=1, exponent=2, ignore_label=-1): #because padding adds -1 to the targets
+        super(BinaryDiceLoss, self).__init__()  
+        self.ignore_index = ignore_label
+        self.smooth = smooth
+        self.exponent = exponent
+
+    def binary_dice_loss(self, pred, target, valid_mask):
+        assert pred.shape[0] == target.shape[0]
+        pred = pred.reshape(pred.shape[0], -1)
+        target = target.reshape(target.shape[0], -1)
+        valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
+
+        num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + self.smooth
+        den = torch.sum(pred.pow(self.exponent)*valid_mask + target.pow(self.exponent)*valid_mask, dim=1) + max(self.smooth, 1e-5)
+        
+        dice = num / den
+        dice = torch.mean(dice)
+        return 1 - dice
+        
+    def forward(self, score, target):
+        ph, pw = score.size(2), score.size(3)
+        h, w = target.size(1), target.size(2)
+        if ph != h or pw != w:
+            score = F.upsample(
+                    input=score, size=(h, w), mode='bilinear')
+        
+        score = F.softmax(score,dim=1)
+        num_classes = score.shape[1]
+        
+        one_hot_target = F.one_hot(
+            torch.clamp(target.long(), 0, num_classes - 1),
+            num_classes=num_classes)
+        valid_mask = (target != self.ignore_index).long()
+        
+        loss = self.binary_dice_loss(
+                    score[:, 1],
+                    one_hot_target[..., 1],
+                    valid_mask)
+        return loss
+    
+
+class DiceEntropyLoss(nn.Module):
+    def __init__(self, smooth=1, exponent=2, ignore_label=-1, weight=None): #because padding adds -1 to the targets
+        super(DiceEntropyLoss, self).__init__()  
+        self.ignore_label = ignore_label
+        self.smooth = smooth
+        self.exponent = exponent
+        self.cross_entropy = nn.CrossEntropyLoss(weight=weight, 
+                                             ignore_index=ignore_label)
+    
+    def binary_dice_loss(self, pred, target, valid_mask):
+        assert pred.shape[0] == target.shape[0]
+        pred = pred.reshape(pred.shape[0], -1)
+        target = target.reshape(target.shape[0], -1)
+        valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
+
+        num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + self.smooth
+        den = torch.sum(pred.pow(self.exponent)*valid_mask + target.pow(self.exponent)*valid_mask, dim=1) + max(self.smooth, 1e-5)
+        
+        dice = num / den
+        dice = torch.mean(dice)
+        return 1 - dice
+        
+    def forward(self, score, target):
+        ph, pw = score.size(2), score.size(3)
+        h, w = target.size(1), target.size(2)
+        if ph != h or pw != w:
+            score = F.upsample(
+                    input=score, size=(h, w), mode='bilinear')
+        
+        CE_loss   = self.cross_entropy(score, target)
+        
+        
+        score = F.softmax(score,dim=1)
+        num_classes = score.shape[1]
+        
+        one_hot_target = F.one_hot(
+            torch.clamp(target.long(), 0, num_classes - 1),
+            num_classes=num_classes)
+        valid_mask = (target != self.ignore_label).long()
+        
+        dice_loss = self.binary_dice_loss(
+                    score[:, 1],
+                    one_hot_target[..., 1],
+                    valid_mask)
+
+        return 0.3*CE_loss + 0.7*dice_loss
+
+
+
+    
+class FocalLoss(nn.Module):
+
+    def __init__(self, alpha=0.25, gamma=2., ignore_label=-1):  #alpha 0.25, gamma=2.
+        super(FocalLoss, self).__init__()
+        self.alpha=alpha
+        self.gamma= gamma
+        self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label, reduction="none")
+       
+    def forward(self, score, target):  
+        ph, pw = score.size(2), score.size(3)
+        h, w = target.size(1), target.size(2)
+        if ph != h or pw != w:
+            score = F.upsample(
+                    input=score, size=(h, w), mode='bilinear')
+            
+        ce_loss = self.criterion(score, target)
+        pt = torch.exp(-ce_loss)
+        f_loss = self.alpha * (1-pt)**self.gamma * ce_loss
+        return f_loss.mean()
+        
+        

TruFor_train_test/lib/core/criterion_conf.py

@@ -0,0 +1,70 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+
+class MSE(nn.Module):
+    def __init__(self, ignore_label=-1, criterion='mse'):
+        super(MSE, self).__init__()
+        self.ignore_label = ignore_label
+        if criterion=='mse':
+            self.criterion = nn.MSELoss()
+        else:
+            assert False
+    
+    def calcolaGTs(self, gt, erodeKernSize=15, dilateKernSize=11):
+        from torch.nn.functional import max_pool2d
+        gt1 = 1 - max_pool2d(1-gt[:,None,:,:], erodeKernSize, stride=1, padding=(erodeKernSize-1)//2)[:,0]
+        gt0 = 1 - max_pool2d(gt[:,None,:,:], dilateKernSize, stride=1, padding=(dilateKernSize-1)//2)[:,0]
+        return gt0, gt1
+
+
+    def forward(self, pred, target, conf):
+        # conf: confidence prediction (1 channel)
+        # pred: 2 channels cmx prediction
+        ch, cw = conf.size(2), conf.size(3)
+        ph, pw = pred.size(2), pred.size(3)
+        h, w = target.size(1), target.size(2)
+        
+        if ph != h or pw != w:
+            pred = F.upsample(input=pred, size=(h, w), mode='bilinear')
+        if ch != h or cw != w:
+            conf = F.upsample(input=conf, size=(h, w), mode='bilinear')
+        
+        conf = torch.sigmoid(conf)
+        pred = F.softmax(pred, dim=1)
+        
+        target0, target1 = self.calcolaGTs((target==1).float())
+        conf = conf.squeeze(dim=1)
+        tcp  = pred[:,1]*target1 + pred[:,0]*target0
+        
+        assert conf.shape == tcp.shape
+        
+        valid = torch.logical_and(target!=self.ignore_label, torch.logical_or(target1>0, target0>0))
+                
+        conf = conf[valid]
+        tcp  = tcp[valid]
+        loss = self.criterion(conf, tcp)
+        return loss
+    
+
+
+
+

TruFor_train_test/lib/core/criterion_det.py

@@ -0,0 +1,31 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+
+class CrossEntropy(nn.Module):
+    def __init__(self):
+        super(CrossEntropy, self).__init__()
+
+    def forward(self, score, target):
+        target_det = (torch.count_nonzero(target * (target >= 0), (-1, -2)) > 3).float().clamp(0, 1)
+        weights_det = target_det * 0.5 / 0.7 + (1 - target_det) * 0.5 / 0.3
+        loss_det = F.binary_cross_entropy_with_logits(score[:, 0], target_det, reduction='mean', weight=weights_det)
+        return loss_det

TruFor_train_test/lib/core/function.py

@@ -0,0 +1,273 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2022
+@author: fabrizio.guillaro
+"""
+
+import logging
+import os
+import time
+
+import numpy as np
+from tqdm import tqdm
+
+import torch
+from torch.nn import functional as F
+
+from lib.utils import AverageMeter
+from lib.utils import get_confusion_matrix, get_confusion_matrix_1ch
+from lib.utils import adjust_learning_rate as default_adjust_learning_rate
+
+def train(epoch, num_epoch, epoch_iters, base_lr, num_iters,
+          trainloader, optimizer, model, writer_dict,
+          adjust_learning_rate=default_adjust_learning_rate):
+    
+    # Training
+    model.train()
+    batch_time = AverageMeter()
+    avg_loss = AverageMeter()
+    tic = time.time()
+    cur_iters = epoch*epoch_iters
+    writer = writer_dict['writer']
+    global_steps = writer_dict['train_global_steps']
+    
+    for i_iter, (rgbs, labels) in enumerate(tqdm(trainloader)):
+        rgbs = rgbs.cuda()
+        labels = labels.long().cuda()
+
+        losses, *_ = model(labels=labels, rgbs=rgbs)
+        loss = losses.mean()
+
+        model.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # measure elapsed time
+        batch_time.update(time.time() - tic)
+        tic = time.time()
+
+        # update average loss
+        avg_loss.update(loss.item())
+
+        lr = adjust_learning_rate(optimizer, base_lr, num_iters, i_iter+cur_iters)
+      
+    print_loss = avg_loss.average()
+    msg = 'Epoch: [{}/{}], Time: {:.2f}, ' \
+          'lr: {:.6f}, Loss: {:.6f}' .format(
+              epoch, num_epoch, batch_time.average(), lr, print_loss)
+    logging.info(msg)
+
+    writer.add_scalar('train_loss', print_loss, global_steps)
+    writer.add_scalar('learning_rate', lr, global_steps)
+    global_steps += 1
+    writer_dict['train_global_steps'] = global_steps
+
+
+
+def validate(config, testloader, model, writer_dict, valid_set="valid"):
+
+    model.eval()
+    avg_loss = AverageMeter()
+    confusion_matrix      = np.zeros((config.DATASET.NUM_CLASSES, config.DATASET.NUM_CLASSES))
+    confusion_matrix_CONF = np.zeros((config.DATASET.NUM_CLASSES, config.DATASET.NUM_CLASSES))
+    
+
+    # PRED metrics
+    avg_mse      = AverageMeter()
+
+    avg_mIoU     = AverageMeter()
+    avg_p_mIoU   = AverageMeter()
+    avg_mIoU_s   = AverageMeter() # smoothed
+    avg_p_mIoU_s = AverageMeter() # smoothed
+    avg_IoU_1_s  = AverageMeter() # smoothed
+
+    avg_p_F1     = AverageMeter()
+    avg_p_F1_s   = AverageMeter()
+
+    # CONF metrics
+    c_avg_mse    = AverageMeter()
+    c_avg_mIoU   = AverageMeter()
+    c_avg_mIoU_s = AverageMeter() # smoothed
+
+    # DET metrics
+    avg_det_tpr = AverageMeter()
+    avg_det_tnr = AverageMeter()
+
+    
+    with torch.no_grad():
+        for it, (rgb, label) in enumerate(tqdm(testloader)):
+            size = label.size()
+
+            rgb = rgb.cuda()
+            label = label.long().cuda()
+
+            losses, pred, conf, det = model(labels=label, rgbs=rgb)
+            
+            if pred is not None:
+                pred = F.upsample(input=pred, size=(size[-2], size[-1]), mode='bilinear')
+                pred_prob = F.softmax(pred, dim=1)
+                
+            if conf is not None:
+                conf = F.upsample(input=conf, size=(size[-2], size[-1]), mode='bilinear')
+                tcp  = pred_prob[:,1]*(label==1) + pred_prob[:,0]*(label==0)
+            
+            loss = losses.mean()
+            avg_loss.update(loss.item())
+
+            smooth = 1.
+
+            # PRED METRICS
+            current_confusion_matrix = get_confusion_matrix(
+                label,
+                pred,
+                size,
+                config.DATASET.NUM_CLASSES,
+                config.TRAIN.IGNORE_LABEL)
+
+            val_mse = torch.mean((pred_prob[:,1]-label)**2).item()
+            avg_mse.update(val_mse)
+            confusion_matrix += current_confusion_matrix
+
+            TN = current_confusion_matrix[0, 0]
+            FN = current_confusion_matrix[1, 0]
+            FP = current_confusion_matrix[0, 1]
+            TP = current_confusion_matrix[1, 1]
+            pos = current_confusion_matrix.sum(1)    # ground truth label count
+            res = current_confusion_matrix.sum(0)    # prediction count
+            tp  = np.diag(current_confusion_matrix)  # Intersection part
+            
+            # mIoU
+            IoU_array = (tp / np.maximum(1.0, pos + res - tp))  # Union part
+            mean_IoU = IoU_array.mean()                         # mean of the classes IoU            
+            avg_mIoU.update(mean_IoU)
+            
+            # mIoU_s
+            IoU_array_smooth = ((tp + smooth)/ (pos + res - tp + smooth))  # Union part
+            mean_IoU_smooth = IoU_array_smooth.mean()                      # mean of the classes IoU      
+            avg_mIoU_s.update(mean_IoU_smooth)
+            avg_IoU_1_s.update(IoU_array_smooth[1])                        # IoU of class 1
+            
+            # p_mIoU
+            p_mIoU = 0.5 * (FN / np.maximum(1.0, FN + TP + TN)) + 0.5 * (FP / np.maximum(1.0, FP + TP + TN))
+            avg_p_mIoU.update(np.maximum(mean_IoU, p_mIoU))
+            
+            # p_mIoU_smooth
+            p_mIoU_smooth = 0.5 * ((FN + smooth)/ (FN + TP + TN + smooth)) + 0.5 * ((FP + smooth)/ (FP + TP + TN + smooth))
+            avg_p_mIoU_s.update(np.maximum(mean_IoU_smooth, p_mIoU_smooth))
+
+            # p_F1
+            F1   = 2 * TP / np.maximum(2 * TP + FN + FP, 1.0)
+            p_F1 = 2 * FN / np.maximum(2 * TP + FN + TN, 1.0)
+            avg_p_F1.update(np.maximum(F1, p_F1))
+
+            # p_F1_smooth
+            F1_s   = (2 * TP + smooth) / (2 * TP + FN + FP + smooth)
+            p_F1_s = (2 * FN + smooth) / (2 * TP + FN + TN + smooth)
+            avg_p_F1_s.update(np.maximum(F1_s, p_F1_s))
+
+
+            # CONF metrics
+            check_conf = 'conf_head' in config.MODEL.EXTRA.MODULES
+            if check_conf:
+                current_confusion_matrix_CONF = get_confusion_matrix_1ch(
+                    tcp > 0.5,
+                    conf,
+                    size,
+                    config.DATASET.NUM_CLASSES,
+                    config.TRAIN.IGNORE_LABEL)
+                val_mse = torch.mean((torch.sigmoid(conf[:, 0]) - tcp) ** 2).item()
+                c_avg_mse.update(val_mse)
+                confusion_matrix_CONF += current_confusion_matrix_CONF
+
+                c_pos = current_confusion_matrix_CONF.sum(1)  # ground truth label count
+                c_res = current_confusion_matrix_CONF.sum(0)  # prediction count
+                c_tp = np.diag(current_confusion_matrix_CONF)  # Intersection part
+
+                # mIoU (conf)
+                c_IoU_array = (c_tp / np.maximum(1.0, c_pos + c_res - c_tp))  # Union part
+                c_mean_IoU = c_IoU_array.mean()  # mean of the classes IoU
+                c_avg_mIoU.update(c_mean_IoU)
+
+                # mIoU_s (conf)
+                c_IoU_array_smooth = ((c_tp + smooth) / (c_pos + c_res - c_tp + smooth))  # Union part
+                c_mean_IoU_smooth = c_IoU_array_smooth.mean()  # mean of the classes IoU
+                c_avg_mIoU_s.update(c_mean_IoU_smooth)
+
+
+            # DET metrics
+            if det is not None:
+                det = det[:,0].cpu().numpy()
+            else:
+                det = np.max(pred[:,1].cpu().numpy(), axis=(1,2))
+            target_det = torch.count_nonzero(label * (label >= 0), (-1, -2)) > 3
+            target_det = target_det.cpu().numpy()
+            if np.any(target_det>0.5):
+                avg_det_tpr.update(np.sum(det[target_det>0.5]>0), np.sum(target_det>0.5))
+            if np.any(target_det<0.5):
+                avg_det_tnr.update(np.sum(det[target_det<0.5]<0), np.sum(target_det<0.5))
+
+
+    confusion_matrix = torch.from_numpy(confusion_matrix).cuda()
+    confusion_matrix = confusion_matrix.cpu().numpy()
+    
+    pos = confusion_matrix.sum(1)
+    res = confusion_matrix.sum(0)
+    tp = np.diag(confusion_matrix)
+    pixel_acc = tp.sum() / pos.sum()
+    IoU_array = (tp / np.maximum(1.0, pos + res - tp))
+    mean_IoU = IoU_array.mean()
+
+    IoU_array_smooth = ((tp + smooth)/ (pos + res - tp + smooth))  # Union part
+    mean_IoU_smooth = IoU_array_smooth.mean()                      # mean of the classes IoU      
+    
+    print_loss = avg_loss.average()
+    try:
+        bacc = (avg_det_tpr.average()+avg_det_tnr.average())/2
+    except:
+        bacc = 0
+    writer = writer_dict['writer']
+    global_steps = writer_dict['valid_global_steps']
+
+    metric_dict = {
+        'loss'              : print_loss,
+        'mIoU'              : mean_IoU,
+        'mIoU_smooth'       : mean_IoU_smooth,
+        'avg_mIoU'          : avg_mIoU.average(),
+        'avg_mIoU_smooth'   : avg_mIoU_s.average(),
+        'avg_det_tpr'       : avg_det_tpr.average(),
+        'avg_det_tnr'       : avg_det_tnr.average(),
+        'avg_det_bacc'      : bacc,
+        'avg_mse'           : avg_mse.average(),
+        'avg_IoU_1_smooth'  : avg_IoU_1_s.average(),
+        'avg_p-mIoU'        : avg_p_mIoU.average(),
+        'avg_p-mIoU_smooth' : avg_p_mIoU_s.average(),
+        'avg_p-F1'          : avg_p_F1.average(),
+        'avg_p-F1_smooth'   : avg_p_F1_s.average(),
+        'pixel_acc'         : pixel_acc,
+    }
+
+    if check_conf:
+        metric_dict['avg_mse_CONF']         = c_avg_mse.average()
+        metric_dict['avg_mIoU_CONF']        = c_avg_mIoU.average()
+        metric_dict['avg_mIoU_smooth_CONF'] = c_avg_mIoU_s.average()
+
+    for metric in metric_dict:
+        writer.add_scalar(valid_set + '_' + metric, metric_dict[metric], global_steps)
+
+    writer_dict['valid_global_steps'] = global_steps + 1
+
+    return metric_dict, IoU_array, confusion_matrix
+
+
+
+

TruFor_train_test/lib/models/DnCNN.py

@@ -0,0 +1,145 @@
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Copyright (c) 2023 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA').
+#
+# All rights reserved.
+# This work should only be used for nonprofit purposes.
+#
+# By downloading and/or using any of these files, you implicitly agree to all the
+# terms of the license, as specified in the document LICENSE.txt
+# (included in this package) and online at
+# http://www.grip.unina.it/download/LICENSE_OPEN.txt
+
+"""
+Created in September 2020
+@author: davide.cozzolino
+"""
+
+import math
+import torch.nn as nn
+
+def conv_with_padding(in_planes, out_planes, kernelsize, stride=1, dilation=1, bias=False, padding = None):
+    if padding is None:
+        padding = kernelsize//2
+    return nn.Conv2d(in_planes, out_planes, kernel_size=kernelsize, stride=stride, dilation=dilation, padding=padding, bias=bias)
+
+def conv_init(conv, act='linear'):
+    r"""
+    Reproduces conv initialization from DnCNN
+    """
+    n = conv.kernel_size[0] * conv.kernel_size[1] * conv.out_channels
+    conv.weight.data.normal_(0, math.sqrt(2. / n))
+
+def batchnorm_init(m, kernelsize=3):
+    r"""
+    Reproduces batchnorm initialization from DnCNN
+    """
+    n = kernelsize**2 * m.num_features
+    m.weight.data.normal_(0, math.sqrt(2. / (n)))
+    m.bias.data.zero_()
+
+def make_activation(act):
+    if act is None:
+        return None
+    elif act == 'relu':
+        return nn.ReLU(inplace=True)
+    elif act == 'tanh':
+        return nn.Tanh()
+    elif act == 'leaky_relu':
+        return nn.LeakyReLU(inplace=True)
+    elif act == 'softmax':
+        return nn.Softmax()
+    elif act == 'linear':
+        return None
+    else:
+        assert(False)
+
+def make_net(nplanes_in, kernels, features, bns, acts, dilats, bn_momentum = 0.1, padding=None):
+    r"""
+    :param nplanes_in: number of of input feature channels
+    :param kernels: list of kernel size for convolution layers
+    :param features: list of hidden layer feature channels
+    :param bns: list of whether to add batchnorm layers
+    :param acts: list of activations
+    :param dilats: list of dilation factors
+    :param bn_momentum: momentum of batchnorm
+    :param padding: integer for padding (None for same padding)
+    """
+
+    depth = len(features)
+    assert(len(features)==len(kernels))
+
+    layers = list()
+    for i in range(0,depth):
+        if i==0:
+            in_feats = nplanes_in
+        else:
+            in_feats = features[i-1]
+
+        elem = conv_with_padding(in_feats, features[i], kernelsize=kernels[i], dilation=dilats[i], padding=padding, bias=not(bns[i]))
+        conv_init(elem, act=acts[i])
+        layers.append(elem)
+
+        if bns[i]:
+            elem = nn.BatchNorm2d(features[i], momentum = bn_momentum)
+            batchnorm_init(elem, kernelsize=kernels[i])
+            layers.append(elem)
+
+        elem = make_activation(acts[i])
+        if elem is not None:
+            layers.append(elem)
+
+    return nn.Sequential(*layers)
+
+class DnCNN(nn.Module):
+    r"""
+    Implements a DnCNN network
+    """
+    def __init__(self, nplanes_in, nplanes_out, features, kernel, depth, activation, residual, bn, lastact=None, bn_momentum = 0.10, padding=None):
+        r"""
+        :param nplanes_in: number of of input feature channels
+        :param nplanes_out: number of of output feature channels
+        :param features: number of of hidden layer feature channels
+        :param kernel: kernel size of convolution layers
+        :param depth: number of convolution layers (minimum 2)
+        :param bn:  whether to add batchnorm layers
+        :param residual: whether to add a residual connection from input to output
+        :param bn_momentum: momentum of batchnorm
+        :param padding: inteteger for padding
+        """
+        super(DnCNN, self).__init__()
+
+        self.residual = residual
+        self.nplanes_out = nplanes_out
+        self.nplanes_in = nplanes_in
+
+        kernels = [kernel, ] * depth
+        features = [features, ] * (depth-1) + [nplanes_out, ]
+        bns = [False, ] + [bn,] * (depth - 2) + [False, ]
+        dilats = [1, ] * depth
+        acts = [activation, ] * (depth - 1) + [lastact, ]
+        self.layers = make_net(nplanes_in, kernels, features, bns, acts, dilats=dilats, bn_momentum = bn_momentum, padding=padding)
+
+
+    def forward(self, x):
+        shortcut = x
+
+        x = self.layers(x)
+
+        if self.residual:
+            nshortcut = min(self.nplanes_in, self.nplanes_out)
+            x[:, :nshortcut, :, :] = x[:, :nshortcut, :, :] + shortcut[:, :nshortcut, :, :]
+
+        return x
+
+
+def add_commandline_networkparams(parser, name, features, depth, kernel, activation, bn):
+    parser.add_argument("--{}.{}".format(name, "features"  ), type=int, default=features  )
+    parser.add_argument("--{}.{}".format(name, "depth"     ), type=int, default=depth     )
+    parser.add_argument("--{}.{}".format(name, "kernel"    ), type=int, default=kernel    )
+    parser.add_argument("--{}.{}".format(name, "activation"), type=str, default=activation)
+
+    bnarg = "{}.{}".format(name, "bn")
+    parser.add_argument("--"+bnarg   , action="store_true", dest=bnarg)
+    parser.add_argument("--{}.{}".format(name, "no-bn"), action="store_false", dest=bnarg)
+    parser.set_defaults(**{bnarg: bn})
+