final

app.py

@@ -18,8 +18,11 @@ Ajou Univ
 article = "<p style='text-align: center'><a href='https://github.com/' target='_blank'>ICDR</a></p>"
 
 #### Image,Prompts examples
-examples = [['input/00010.png', "I love this photo, could you remove the haze and more brighter?"],
-            ['input/00058.png', "I have to post an emotional shot on Instagram, but it was shot too foggy and too dark. Change it like a sunny day and brighten it up!"]]
+examples = [['input/00013.png', "Remove the rain as much as possible like the picture taken on a clear day."],
+            ['input/00010.png', "I love this photo, could you remove the haze and more brighter?"],
+            ['input/00058.png', "I have to post an emotional shot on Instagram, but it was shot too foggy and too dark. Change it like a sunny day and brighten it up!"],
+            ['input/00075.png', "Remove the rain from the video, remove the brightness and fog"],
+            ]
 
 css = """
     .image-frame img, .image-container img {

inference.ipynb

@@ -1,33 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install mmcv==2.2.0 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.4/index.html\n",
-    "!pip install git+https://github.com/openai/CLIP.git\n",
-    "!pip install scikit-video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from app.py import main\n",
-    "\n",
-    "main()"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

install.sh

@@ -1,2 +0,0 @@
-pip install --upgrade pip
-pip install mmcv==2.2.0 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.0/index.html

requirements_colab.txt

@@ -1,520 +0,0 @@
-absl-py==1.4.0
-accelerate==0.34.2
-addict==2.4.0
-aiohappyeyeballs==2.4.3
-aiohttp==3.10.10
-aiosignal==1.3.1
-alabaster==0.7.16
-albucore==0.0.16
-albumentations==1.4.15
-altair==4.2.2
-annotated-types==0.7.0
-anyio==3.7.1
-argon2-cffi==23.1.0
-argon2-cffi-bindings==21.2.0
-array_record==0.5.1
-arviz==0.19.0
-astropy==6.1.4
-astropy-iers-data==0.2024.10.7.0.32.46
-astunparse==1.6.3
-async-timeout==4.0.3
-atpublic==4.1.0
-attrs==24.2.0
-audioread==3.0.1
-autograd==1.7.0
-babel==2.16.0
-backcall==0.2.0
-beautifulsoup4==4.12.3
-bigframes==1.22.0
-bigquery-magics==0.4.0
-bleach==6.1.0
-blinker==1.4
-blis==0.7.11
-blosc2==2.0.0
-bokeh==3.4.3
-Bottleneck==1.4.0
-bqplot==0.12.43
-branca==0.8.0
-build==1.2.2.post1
-CacheControl==0.14.0
-cachetools==5.5.0
-catalogue==2.0.10
-certifi==2024.8.30
-cffi==1.17.1
-chardet==5.2.0
-charset-normalizer==3.4.0
-chex==0.1.87
-clarabel==0.9.0
-click==8.1.7
-clip @ git+https://github.com/openai/CLIP.git@dcba3cb2e2827b402d2701e7e1c7d9fed8a20ef1
-cloudpathlib==0.19.0
-cloudpickle==2.2.1
-cmake==3.30.4
-cmdstanpy==1.2.4
-colab-ssh==0.3.27
-colorcet==3.1.0
-colorlover==0.3.0
-colour==0.1.5
-community==1.0.0b1
-confection==0.1.5
-cons==0.4.6
-contextlib2==21.6.0
-contourpy==1.3.0
-cryptography==43.0.1
-cuda-python==12.2.1
-cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-24.6.1-cp310-cp310-manylinux_2_28_x86_64.whl
-cufflinks==0.17.3
-cupy-cuda12x==12.2.0
-cvxopt==1.3.2
-cvxpy==1.5.3
-cycler==0.12.1
-cymem==2.0.8
-Cython==3.0.11
-dask==2024.8.0
-datascience==0.17.6
-db-dtypes==1.3.0
-dbus-python==1.2.18
-debugpy==1.6.6
-decorator==4.4.2
-defusedxml==0.7.1
-Deprecated==1.2.14
-distributed==2024.8.0
-distro==1.7.0
-dlib==19.24.2
-dm-tree==0.1.8
-docstring_parser==0.16
-docutils==0.18.1
-dopamine_rl==4.0.9
-duckdb==1.1.1
-earthengine-api==1.0.0
-easydict==1.13
-ecos==2.0.14
-editdistance==0.8.1
-eerepr==0.0.4
-einops==0.8.0
-en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889
-entrypoints==0.4
-et-xmlfile==1.1.0
-etils==1.9.4
-etuples==0.3.9
-eval_type_backport==0.2.0
-exceptiongroup==1.2.2
-fastai==2.7.17
-fastcore==1.7.13
-fastdownload==0.0.7
-fastjsonschema==2.20.0
-fastprogress==1.0.3
-fastrlock==0.8.2
-filelock==3.16.1
-firebase-admin==6.5.0
-Flask==2.2.5
-flatbuffers==24.3.25
-flax==0.8.5
-folium==0.17.0
-fonttools==4.54.1
-frozendict==2.4.5
-frozenlist==1.4.1
-fsspec==2024.6.1
-ftfy==6.3.0
-future==1.0.0
-gast==0.6.0
-gcsfs==2024.6.1
-GDAL==3.6.4
-gdown==5.2.0
-geemap==0.34.5
-gensim==4.3.3
-geocoder==1.38.1
-geographiclib==2.0
-geopandas==1.0.1
-geopy==2.4.1
-gin-config==0.5.0
-glob2==0.7
-google==2.0.3
-google-ai-generativelanguage==0.6.6
-google-api-core==2.19.2
-google-api-python-client==2.137.0
-google-auth==2.27.0
-google-auth-httplib2==0.2.0
-google-auth-oauthlib==1.2.1
-google-cloud-aiplatform==1.70.0
-google-cloud-bigquery==3.25.0
-google-cloud-bigquery-connection==1.15.5
-google-cloud-bigquery-storage==2.26.0
-google-cloud-bigtable==2.26.0
-google-cloud-core==2.4.1
-google-cloud-datastore==2.19.0
-google-cloud-firestore==2.16.1
-google-cloud-functions==1.16.5
-google-cloud-iam==2.15.2
-google-cloud-language==2.13.4
-google-cloud-pubsub==2.25.0
-google-cloud-resource-manager==1.12.5
-google-cloud-storage==2.8.0
-google-cloud-translate==3.15.5
-google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz
-google-crc32c==1.6.0
-google-generativeai==0.7.2
-google-pasta==0.2.0
-google-resumable-media==2.7.2
-googleapis-common-protos==1.65.0
-googledrivedownloader==0.4
-graphviz==0.20.3
-greenlet==3.1.1
-grpc-google-iam-v1==0.13.1
-grpcio==1.64.1
-grpcio-status==1.48.2
-gspread==6.0.2
-gspread-dataframe==3.3.1
-gym==0.25.2
-gym-notices==0.0.8
-h5netcdf==1.4.0
-h5py==3.11.0
-holidays==0.58
-holoviews==1.19.1
-html5lib==1.1
-httpimport==1.4.0
-httplib2==0.22.0
-huggingface-hub==0.24.7
-humanize==4.10.0
-hyperopt==0.2.7
-ibis-framework==9.2.0
-idna==3.10
-imageio==2.35.1
-imageio-ffmpeg==0.5.1
-imagesize==1.4.1
-imbalanced-learn==0.12.4
-imgaug==0.4.0
-immutabledict==4.2.0
-importlib_metadata==8.5.0
-importlib_resources==6.4.5
-imutils==0.5.4
-inflect==7.4.0
-iniconfig==2.0.0
-intel-cmplr-lib-ur==2024.2.1
-intel-openmp==2024.2.1
-ipyevents==2.0.2
-ipyfilechooser==0.6.0
-ipykernel==5.5.6
-ipyleaflet==0.19.2
-ipyparallel==8.8.0
-ipython==7.34.0
-ipython-genutils==0.2.0
-ipython-sql==0.5.0
-ipytree==0.2.2
-ipywidgets==7.7.1
-itsdangerous==2.2.0
-jax==0.4.33
-jax-cuda12-pjrt==0.4.33
-jax-cuda12-plugin==0.4.33
-jaxlib==0.4.33
-jeepney==0.7.1
-jellyfish==1.1.0
-jieba==0.42.1
-Jinja2==3.1.4
-joblib==1.4.2
-jsonpickle==3.3.0
-jsonschema==4.23.0
-jsonschema-specifications==2024.10.1
-jupyter-client==6.1.12
-jupyter-console==6.1.0
-jupyter-leaflet==0.19.2
-jupyter-server==1.24.0
-jupyter_core==5.7.2
-jupyterlab_pygments==0.3.0
-jupyterlab_widgets==3.0.13
-kaggle==1.6.17
-kagglehub==0.3.1
-keras==3.4.1
-keyring==23.5.0
-kiwisolver==1.4.7
-langcodes==3.4.1
-language_data==1.2.0
-launchpadlib==1.10.16
-lazr.restfulclient==0.14.4
-lazr.uri==1.0.6
-lazy_loader==0.4
-libclang==18.1.1
-librosa==0.10.2.post1
-lightgbm==4.5.0
-linkify-it-py==2.0.3
-llvmlite==0.43.0
-locket==1.0.0
-logical-unification==0.4.6
-lxml==4.9.4
-marisa-trie==1.2.0
-Markdown==3.7
-markdown-it-py==3.0.0
-MarkupSafe==3.0.1
-matplotlib==3.7.1
-matplotlib-inline==0.1.7
-matplotlib-venn==1.1.1
-mdit-py-plugins==0.4.2
-mdurl==0.1.2
-miniKanren==1.0.3
-missingno==0.5.2
-mistune==0.8.4
-mizani==0.11.4
-mkl==2024.2.2
-ml-dtypes==0.4.1
-mlxtend==0.23.1
-mmcv==2.2.0
-mmengine==0.10.5
-more-itertools==10.5.0
-moviepy==1.0.3
-mpmath==1.3.0
-msgpack==1.0.8
-multidict==6.1.0
-multipledispatch==1.0.0
-multitasking==0.0.11
-murmurhash==1.0.10
-music21==9.1.0
-namex==0.0.8
-natsort==8.4.0
-nbclassic==1.1.0
-nbclient==0.10.0
-nbconvert==6.5.4
-nbformat==5.10.4
-nest-asyncio==1.6.0
-networkx==3.4
-nibabel==5.2.1
-nltk==3.8.1
-notebook==6.5.5
-notebook_shim==0.2.4
-numba==0.60.0
-numexpr==2.10.1
-numpy==1.26.4
-nvidia-cublas-cu12==12.6.3.3
-nvidia-cuda-cupti-cu12==12.6.80
-nvidia-cuda-nvcc-cu12==12.6.77
-nvidia-cuda-runtime-cu12==12.6.77
-nvidia-cudnn-cu12==9.5.0.50
-nvidia-cufft-cu12==11.3.0.4
-nvidia-cusolver-cu12==11.7.1.2
-nvidia-cusparse-cu12==12.5.4.2
-nvidia-nccl-cu12==2.23.4
-nvidia-nvjitlink-cu12==12.6.77
-nvtx==0.2.10
-oauth2client==4.1.3
-oauthlib==3.2.2
-opencv-contrib-python==4.10.0.84
-opencv-python==4.10.0.84
-opencv-python-headless==4.10.0.84
-openpyxl==3.1.5
-opentelemetry-api==1.16.0
-opentelemetry-sdk==1.16.0
-opentelemetry-semantic-conventions==0.37b0
-opt_einsum==3.4.0
-optax==0.2.3
-optree==0.13.0
-orbax-checkpoint==0.6.4
-ordered-set==4.1.0
-osqp==0.6.7.post0
-packaging==24.1
-pandas==2.2.2
-pandas-datareader==0.10.0
-pandas-gbq==0.23.2
-pandas-stubs==2.2.2.240909
-pandocfilters==1.5.1
-panel==1.4.5
-param==2.1.1
-parso==0.8.4
-parsy==2.1
-partd==1.4.2
-pathlib==1.0.1
-patsy==0.5.6
-peewee==3.17.6
-pexpect==4.9.0
-pickleshare==0.7.5
-pillow==10.4.0
-pip-tools==7.4.1
-platformdirs==4.3.6
-plotly==5.24.1
-plotnine==0.13.6
-pluggy==1.5.0
-polars==1.7.1
-pooch==1.8.2
-portpicker==1.5.2
-prefetch_generator==1.0.3
-preshed==3.0.9
-prettytable==3.11.0
-proglog==0.1.10
-progressbar2==4.5.0
-prometheus_client==0.21.0
-promise==2.3
-prompt_toolkit==3.0.48
-propcache==0.2.0
-prophet==1.1.6
-proto-plus==1.24.0
-protobuf==3.20.3
-psutil==5.9.5
-psycopg2==2.9.9
-ptyprocess==0.7.0
-py-cpuinfo==9.0.0
-py4j==0.10.9.7
-pyarrow==16.1.0
-pyarrow-hotfix==0.6
-pyasn1==0.6.1
-pyasn1_modules==0.4.1
-pycocotools==2.0.8
-pycparser==2.22
-pydantic==2.9.2
-pydantic_core==2.23.4
-pydata-google-auth==1.8.2
-pydot==3.0.2
-pydot-ng==2.0.0
-pydotplus==2.0.2
-PyDrive==1.3.1
-PyDrive2==1.20.0
-pyerfa==2.0.1.4
-pygame==2.6.1
-Pygments==2.18.0
-PyGObject==3.42.1
-PyJWT==2.9.0
-pymc==5.16.2
-pymystem3==0.2.0
-pynvjitlink-cu12==0.3.0
-pyogrio==0.10.0
-PyOpenGL==3.1.7
-pyOpenSSL==24.2.1
-pyparsing==3.1.4
-pyperclip==1.9.0
-pyproj==3.7.0
-pyproject_hooks==1.2.0
-pyshp==2.3.1
-PySocks==1.7.1
-pytensor==2.25.5
-pytest==7.4.4
-python-apt==0.0.0
-python-box==7.2.0
-python-dateutil==2.8.2
-python-louvain==0.16
-python-slugify==8.0.4
-python-utils==3.9.0
-pytz==2024.2
-pyviz_comms==3.0.3
-PyYAML==6.0.2
-pyzmq==24.0.1
-qdldl==0.1.7.post4
-ratelim==0.1.6
-referencing==0.35.1
-regex==2024.9.11
-requests==2.32.3
-requests-oauthlib==1.3.1
-requirements-parser==0.9.0
-rich==13.9.2
-rmm-cu12==24.6.0
-rpds-py==0.20.0
-rpy2==3.4.2
-rsa==4.9
-safetensors==0.4.5
-scikit-image==0.24.0
-scikit-learn==1.5.2
-scipy==1.13.1
-scooby==0.10.0
-scs==3.2.7
-seaborn==0.13.2
-SecretStorage==3.3.1
-Send2Trash==1.8.3
-sentencepiece==0.2.0
-shapely==2.0.6
-shellingham==1.5.4
-simple-parsing==0.1.6
-six==1.16.0
-sklearn-pandas==2.2.0
-smart-open==7.0.5
-sniffio==1.3.1
-snowballstemmer==2.2.0
-sortedcontainers==2.4.0
-soundfile==0.12.1
-soupsieve==2.6
-soxr==0.5.0.post1
-spacy==3.7.5
-spacy-legacy==3.0.12
-spacy-loggers==1.0.5
-Sphinx==5.0.2
-sphinxcontrib-applehelp==2.0.0
-sphinxcontrib-devhelp==2.0.0
-sphinxcontrib-htmlhelp==2.1.0
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==2.0.0
-sphinxcontrib-serializinghtml==2.0.0
-SQLAlchemy==2.0.35
-sqlglot==25.1.0
-sqlparse==0.5.1
-srsly==2.4.8
-ssh-import-id==5.11
-stanio==0.5.1
-statsmodels==0.14.4
-StrEnum==0.4.15
-sympy==1.13.3
-tables==3.8.0
-tabulate==0.9.0
-tbb==2021.13.1
-tblib==3.0.0
-tenacity==9.0.0
-tensorboard==2.17.0
-tensorboard-data-server==0.7.2
-tensorflow==2.17.0
-tensorflow-datasets==4.9.6
-tensorflow-hub==0.16.1
-tensorflow-io-gcs-filesystem==0.37.1
-tensorflow-metadata==1.16.1
-tensorflow-probability==0.24.0
-tensorstore==0.1.66
-termcolor==2.5.0
-terminado==0.18.1
-text-unidecode==1.3
-textblob==0.17.1
-tf-slim==1.1.0
-tf_keras==2.17.0
-thinc==8.2.5
-threadpoolctl==3.5.0
-tifffile==2024.9.20
-tinycss2==1.3.0
-tokenizers==0.19.1
-toml==0.10.2
-tomli==2.0.2
-toolz==0.12.1
-torch @ https://download.pytorch.org/whl/cu121_full/torch-2.4.1%2Bcu121-cp310-cp310-linux_x86_64.whl
-torchaudio @ https://download.pytorch.org/whl/cu121_full/torchaudio-2.4.1%2Bcu121-cp310-cp310-linux_x86_64.whl
-torchsummary==1.5.1
-torchvision @ https://download.pytorch.org/whl/cu121_full/torchvision-0.19.1%2Bcu121-cp310-cp310-linux_x86_64.whl
-tornado==6.3.3
-tqdm==4.66.5
-traitlets==5.7.1
-traittypes==0.2.1
-transformers==4.44.2
-tweepy==4.14.0
-typeguard==4.3.0
-typer==0.12.5
-types-pytz==2024.2.0.20241003
-types-setuptools==75.1.0.20241014
-typing_extensions==4.12.2
-tzdata==2024.2
-tzlocal==5.2
-uc-micro-py==1.0.3
-uritemplate==4.1.1
-urllib3==2.2.3
-vega-datasets==0.9.0
-wadllib==1.3.6
-wasabi==1.1.3
-wcwidth==0.2.13
-weasel==0.4.1
-webcolors==24.8.0
-webencodings==0.5.1
-websocket-client==1.8.0
-Werkzeug==3.0.4
-widgetsnbextension==3.6.9
-wordcloud==1.9.3
-wrapt==1.16.0
-xarray==2024.9.0
-xarray-einstats==0.8.0
-xgboost==2.1.1
-xlrd==2.0.1
-xyzservices==2024.9.0
-yapf==0.40.2
-yarl==1.14.0
-yellowbrick==1.5
-yfinance==0.2.44
-zict==3.0.0
-zipp==3.20.2

text_net/DGRN.py

@@ -3,7 +3,8 @@ import torch
 from .deform_conv import DCN_layer
 import clip
 
-clip_model, preprocess = clip.load("ViT-B/32", device='cpu')
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+clip_model, preprocess = clip.load("ViT-B/32", device=device)
 
 # 동적으로 텍스트 임베딩 차원 가져오기
 text_embed_dim = clip_model.text_projection.shape[1]
@@ -96,7 +97,7 @@ class SFT_layer(nn.Module):
         B, C, H, W = inter.shape #cross attention
         
 
-        text_tokens = clip.tokenize(text_prompt).to(x.device)  # Tokenize the text prompts (Batch size)
+        text_tokens = clip.tokenize(text_prompt).to(device)  # Tokenize the text prompts (Batch size)
         with torch.no_grad():
             text_embed = clip_model.encode_text(text_tokens)
 

text_net/moco.py

@@ -2,6 +2,7 @@
 import torch
 import torch.nn as nn
 
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 class MoCo(nn.Module):
     """
@@ -73,7 +74,7 @@ class MoCo(nn.Module):
         num_gpus = batch_size_all // batch_size_this
 
         # random shuffle index
-        idx_shuffle = torch.randperm(batch_size_all).to('cpu')
+        idx_shuffle = torch.randperm(batch_size_all).to(device)
 
         # broadcast to all gpus
         torch.distributed.broadcast(idx_shuffle, src=0)
@@ -140,7 +141,7 @@ class MoCo(nn.Module):
             logits /= self.T
 
             # labels: positive key indicators
-            labels = torch.zeros(logits.shape[0], dtype=torch.long).to('cpu')
+            labels = torch.zeros(logits.shape[0], dtype=torch.long).to(device)
             # dequeue and enqueue
             self._dequeue_and_enqueue(k)
 

utils/dataset_utils.py

@@ -1,309 +0,0 @@
-import os
-import random
-import copy
-from PIL import Image
-import numpy as np
-
-from torch.utils.data import Dataset
-from torchvision.transforms import ToPILImage, Compose, RandomCrop, ToTensor
-
-from utils.image_utils import random_augmentation, crop_img
-from utils.degradation_utils import Degradation
-
-
-class TrainDataset(Dataset):
-    def __init__(self, args):
-        super(TrainDataset, self).__init__()
-        self.args = args
-        self.rs_ids = []
-        self.hazy_ids = []
-        self.D = Degradation(args)
-        self.de_temp = 0
-        self.de_type = self.args.de_type
-        self.image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.gif']
-
-        self.de_dict = {'denoise_15': 0, 'denoise_25': 1, 'denoise_50': 2, 'derain': 3, 'dehaze': 4}
-
-        self._init_ids()
-
-        self.crop_transform = Compose([
-            ToPILImage(),
-            RandomCrop(args.patch_size),
-        ])
-
-        self.toTensor = ToTensor()
-
-    def _init_ids(self):
-        if 'denoise_15' in self.de_type or 'denoise_25' in self.de_type or 'denoise_50' in self.de_type:
-            self._init_clean_ids()
-        if 'derain' in self.de_type:
-            self._init_rs_ids()
-        if 'dehaze' in self.de_type:
-            self._init_hazy_ids()
-
-        random.shuffle(self.de_type)
-
-    def _init_clean_ids(self):
-        clean_ids = []
-        # 파일 목록 중 이미지 파일만 필터링
-        name_list = os.listdir(self.args.denoise_dir)
-        name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-
-        clean_ids += [self.args.denoise_dir + id_ for id_ in name_list]
-
-        if 'denoise_15' in self.de_type:
-            self.s15_ids = copy.deepcopy(clean_ids)
-            random.shuffle(self.s15_ids)
-            self.s15_counter = 0
-        if 'denoise_25' in self.de_type:
-            self.s25_ids = copy.deepcopy(clean_ids)
-            random.shuffle(self.s25_ids)
-            self.s25_counter = 0
-        if 'denoise_50' in self.de_type:
-            self.s50_ids = copy.deepcopy(clean_ids)
-            random.shuffle(self.s50_ids)
-            self.s50_counter = 0
-
-        # print(clean_ids)
-
-        self.num_clean = len(clean_ids)
-
-    def _init_hazy_ids(self):
-        # 파일 목록 중 이미지 파일만 필터링
-        dehaze_ids = []
-        name_list = os.listdir(self.args.dehaze_dir)
-        name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-        dehaze_ids += [self.args.dehaze_dir + id_ for id_ in name_list]
-        self.hazy_ids = dehaze_ids
-
-        self.hazy_counter = 0
-        self.num_hazy = len(self.hazy_ids)
-
-    def _init_rs_ids(self):
-        # 파일 목록 중 이미지 파일만 필터링
-        derain_ids = []
-        name_list = os.listdir(self.args.derain_dir)
-        name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-        derain_ids += [self.args.derain_dir + id_ for id_ in name_list]
-        self.rs_ids = derain_ids
-
-        self.rl_counter = 0
-        # print(derain_ids)
-
-        self.num_rl = len(self.rs_ids)
-
-    def _crop_patch(self, img_1, img_2):
-        H = img_1.shape[0]
-        W = img_1.shape[1]
-        ind_H = random.randint(0, H - self.args.patch_size)
-        ind_W = random.randint(0, W - self.args.patch_size)
-
-        patch_1 = img_1[ind_H:ind_H + self.args.patch_size, ind_W:ind_W + self.args.patch_size]
-        patch_2 = img_2[ind_H:ind_H + self.args.patch_size, ind_W:ind_W + self.args.patch_size]
-
-        return patch_1, patch_2
-
-    def _get_gt_name(self, rainy_name):
-        gt_name = 'data/' + 'Target/Derain/norain-' + rainy_name.split('rain-')[-1]
-        return gt_name
-
-    def _get_nonhazy_name(self, hazy_name):
-        gt_name = 'data/' + 'Target/Dehaze/nohaze-' + rainy_name.split('haze-')[-1]
-        return gt_name
-
-    def __getitem__(self, _):
-        de_id = self.de_dict[self.de_type[self.de_temp]]
-
-        if de_id < 3:
-            if de_id == 0:
-                clean_id = self.s15_ids[self.s15_counter]
-                self.s15_counter = (self.s15_counter + 1) % self.num_clean
-                if self.s15_counter == 0:
-                    random.shuffle(self.s15_ids)
-            elif de_id == 1:
-                clean_id = self.s25_ids[self.s25_counter]
-                self.s25_counter = (self.s25_counter + 1) % self.num_clean
-                if self.s25_counter == 0:
-                    random.shuffle(self.s25_ids)
-            elif de_id == 2:
-                clean_id = self.s50_ids[self.s50_counter]
-                self.s50_counter = (self.s50_counter + 1) % self.num_clean
-                if self.s50_counter == 0:
-                    random.shuffle(self.s50_ids)
-
-            # clean_id = random.randint(0, len(self.clean_ids) - 1)
-            clean_img = crop_img(np.array(Image.open(clean_id).convert('RGB')), base=16)
-            clean_patch_1, clean_patch_2 = self.crop_transform(clean_img), self.crop_transform(clean_img)
-            clean_patch_1, clean_patch_2 = np.array(clean_patch_1), np.array(clean_patch_2)
-
-            # clean_name = self.clean_ids[clean_id].split("/")[-1].split('.')[0]
-            clean_name = clean_id.split("/")[-1].split('.')[0]
-
-            clean_patch_1, clean_patch_2 = random_augmentation(clean_patch_1, clean_patch_2)
-            degrad_patch_1, degrad_patch_2 = self.D.degrade(clean_patch_1, clean_patch_2, de_id)
-        else:
-            if de_id == 3:
-                # Rain Streak Removal
-                # rl_id = random.randint(0, len(self.rl_ids) - 1)
-                degrad_img = crop_img(np.array(Image.open(self.rs_ids[self.rl_counter]).convert('RGB')), base=16)
-                clean_name = self._get_gt_name(self.rs_ids[self.rl_counter])
-                clean_img = crop_img(np.array(Image.open(clean_name).convert('RGB')), base=16)
-
-                self.rl_counter = (self.rl_counter + 1) % self.num_rl
-                if self.rl_counter == 0:
-                    random.shuffle(self.rs_ids)
-            elif de_id == 4:
-                # Dehazing with SOTS outdoor training set
-                # hazy_id = random.randint(0, len(self.hazy_ids) - 1)
-                degrad_img = crop_img(np.array(Image.open(self.hazy_ids[self.hazy_counter]).convert('RGB')), base=16)
-                clean_name = self._get_nonhazy_name(self.hazy_ids[self.hazy_counter])
-                clean_img = crop_img(np.array(Image.open(clean_name).convert('RGB')), base=16)
-
-                self.hazy_counter = (self.hazy_counter + 1) % self.num_hazy
-                if self.hazy_counter == 0:
-                    random.shuffle(self.hazy_ids)
-            degrad_patch_1, clean_patch_1 = random_augmentation(*self._crop_patch(degrad_img, clean_img))
-            degrad_patch_2, clean_patch_2 = random_augmentation(*self._crop_patch(degrad_img, clean_img))
-
-        clean_patch_1, clean_patch_2 = self.toTensor(clean_patch_1), self.toTensor(clean_patch_2)
-        degrad_patch_1, degrad_patch_2 = self.toTensor(degrad_patch_1), self.toTensor(degrad_patch_2)
-
-        self.de_temp = (self.de_temp + 1) % len(self.de_type)
-        if self.de_temp == 0:
-            random.shuffle(self.de_type)
-
-        return [clean_name, de_id], degrad_patch_1, degrad_patch_2, clean_patch_1, clean_patch_2
-
-    def __len__(self):
-        return 400 * len(self.args.de_type)
-
-
-class DenoiseTestDataset(Dataset):
-    def __init__(self, args):
-        super(DenoiseTestDataset, self).__init__()
-        self.args = args
-        self.clean_ids = []
-        self.sigma = 15
-        self.image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.gif']
-
-        self._init_clean_ids()
-
-        self.toTensor = ToTensor()
-
-    def _init_clean_ids(self):
-        clean_ids = []
-        # 파일 목록 중 이미지 파일만 필터링
-        name_list = os.listdir(self.args.denoise_path)
-        name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-        self.clean_ids += [self.args.denoise_path + id_ for id_ in name_list]
-
-        self.num_clean = len(self.clean_ids)
-
-    def _add_gaussian_noise(self, clean_patch):
-        noise = np.random.randn(*clean_patch.shape)
-        noisy_patch = np.clip(clean_patch + noise * self.sigma, 0, 255).astype(np.uint8)
-        return noisy_patch, clean_patch
-
-    def set_sigma(self, sigma):
-        self.sigma = sigma
-
-    def __getitem__(self, clean_id):
-        clean_img = crop_img(np.array(Image.open(self.clean_ids[clean_id]).convert('RGB')), base=16)
-        clean_name = self.clean_ids[clean_id].split("/")[-1].split('.')[0]
-
-        noisy_img, _ = self._add_gaussian_noise(clean_img)
-        clean_img, noisy_img = self.toTensor(clean_img), self.toTensor(noisy_img)
-
-        return [clean_name], noisy_img, clean_img
-
-    def __len__(self):
-        return self.num_clean
-
-
-class DerainDehazeDataset(Dataset):
-    def __init__(self, args, task="derain"):
-        super(DerainDehazeDataset, self).__init__()
-        self.ids = []
-        self.task_idx = 0
-        self.args = args
-        self.image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.gif']
-
-        self.task_dict = {'derain': 0, 'dehaze': 1}
-        self.toTensor = ToTensor()
-
-        self.set_dataset(task)
-
-    def _init_input_ids(self):
-        if self.task_idx == 0:
-            self.ids = []
-            # 파일 목록 중 이미지 파일만 필터링
-            name_list = os.listdir(self.args.derain_path + 'input/')
-            name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-            self.ids += [self.args.derain_path + 'input/' + id_ for id_ in name_list]
-        elif self.task_idx == 1:
-            self.ids = []
-            # 파일 목록 중 이미지 파일만 필터링
-            name_list = os.listdir(self.args.dehaze_path + 'input/')
-            name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-            self.ids += [self.args.dehaze_path + 'input/' + id_ for id_ in name_list]
-
-        self.length = len(self.ids)
-
-    def _get_gt_path(self, degraded_name):
-        if self.task_idx == 0:
-            gt_name = '/'.join(degraded_name.replace("input", "target").split('/')[:-1] + degraded_name.replace("input", "target").replace("rain", "norain").split('/')[-1:])
-            print(gt_name)
-        elif self.task_idx == 1:
-            dir_name = degraded_name.split("input")[0] + 'target/'
-            name = degraded_name.split('/')[-1].split('_')[0] + '.png'
-            gt_name = dir_name + name
-        return gt_name
-
-    def set_dataset(self, task):
-        self.task_idx = self.task_dict[task]
-        self._init_input_ids()
-
-    def __getitem__(self, idx):
-        degraded_path = self.ids[idx]
-        clean_path = self._get_gt_path(degraded_path)
-
-        degraded_img = crop_img(np.array(Image.open(degraded_path).convert('RGB')), base=16)
-        clean_img = crop_img(np.array(Image.open(clean_path).convert('RGB')), base=16)
-
-        clean_img, degraded_img = self.toTensor(clean_img), self.toTensor(degraded_img)
-        degraded_name = degraded_path.split('/')[-1][:-4]
-
-        return [degraded_name], degraded_img, clean_img
-
-    def __len__(self):
-        return self.length
-
-
-class TestSpecificDataset(Dataset):
-    def __init__(self, args):
-        super(TestSpecificDataset, self).__init__()
-        self.args = args
-        self.degraded_ids = []
-        self._init_clean_ids(args.test_path)
-
-        self.toTensor = ToTensor()
-
-    def _init_clean_ids(self, root):
-        degraded_ids = []
-        # 파일 목록 중 이미지 파일만 필터링
-        name_list = os.listdir(root)
-        name_list = [file for file in name_list if os.path.splitext(file)[1].lower() in self.image_extensions]
-        self.degraded_ids += [root + id_ for id_ in name_list]
-
-        self.num_img = len(self.degraded_ids)
-
-    def __getitem__(self, idx):
-        degraded_img = crop_img(np.array(Image.open(self.degraded_ids[idx]).convert('RGB')), base=16)
-        name = self.degraded_ids[idx].split('/')[-1][:-4]
-
-        degraded_img = self.toTensor(degraded_img)
-
-        return [name], degraded_img
-
-    def __len__(self):
-        return self.num_img