add other files

app.py

@@ -0,0 +1,107 @@
+import gradio as gr 
+from PIL import Image
+import os
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import torchvision
+import numpy as np
+import yaml
+from huggingface_hub import hf_hub_download
+
+from archs import Network_v3
+from options.options import parse
+
+path_opt = './options/test/LOLBlur.yml'
+
+opt = parse(path_opt)
+
+#define some auxiliary functions
+pil_to_tensor = transforms.ToTensor()
+    
+# define some parameters based on the run we want to make
+#selected network
+network = opt['network']['name']
+
+PATH_MODEL = opt['save']['path']
+
+model = Network_v3(img_channel=opt['network']['img_channels'], 
+                    width=opt['network']['width'], 
+                    middle_blk_num=opt['network']['middle_blk_num'], 
+                    enc_blk_nums=opt['network']['enc_blk_nums'],
+                    dec_blk_nums=opt['network']['dec_blk_nums'], 
+                    residual_layers=opt['network']['residual_layers'],
+                    dilations=opt['network']['dilations'])
+
+checkpoints = torch.load(opt['save']['best'])
+# print(checkpoints)
+model.load_state_dict(checkpoints['model_state_dict'])
+
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+model = model.to(device)
+
+def load_img (filename):
+    img = Image.open(filename).convert("RGB")
+    img_tensor = pil_to_tensor(img)
+    return img_tensor
+
+def process_img(image):
+    img = np.array(image)
+    img = img / 255.
+    img = img.astype(np.float32)
+    y = torch.tensor(img).permute(2,0,1).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        x_hat = model(y)
+
+    restored_img = x_hat.squeeze().permute(1,2,0).clamp_(0, 1).cpu().detach().numpy()
+    restored_img = np.clip(restored_img, 0. , 1.)
+
+    restored_img = (restored_img * 255.0).round().astype(np.uint8)  # float32 to uint8
+    return Image.fromarray(restored_img) #(image, Image.fromarray(restored_img))
+
+title = "Low-Light-Deblurring ✏️🖼️ 🤗"
+description = ''' ## [Low Light Image deblurring enhancement](https://github.com/cidautai/Net-Low-light-Deblurring)
+
+[Daniel Feijoo](https://github.com/danifei)
+
+Fundación Cidaut
+
+
+> **Disclaimer:** please remember this is not a product, thus, you will notice some limitations.
+**This demo expects an image with some degradations.**
+Due to the GPU memory limitations, the app might crash if you feed a high-resolution image (2K, 4K). <br>
+The model was trained using mostly synthetic data, thus it might not work great on real-world complex images. 
+
+<br>
+'''
+
+examples = [['examples/inputs/0010.png'],
+            ['examples/inputs/0060.png'], 
+            ['examples/inputs/0075.png'], 
+            ["examples/inputs/0087.png"], 
+            ["examples/inputs/0088.png"]]
+
+css = """
+    .image-frame img, .image-container img {
+        width: auto;
+        height: auto;
+        max-width: none;
+    }
+"""
+
+demo = gr.Interface(
+    fn = process_img,
+    inputs = [
+            gr.Image(type = 'pil', label = 'input')
+    ],
+    outputs = [gr.Image(type='pil', label = 'output')],
+    title = title,
+    description = description,
+    examples = examples,
+    css = css
+)
+
+if __name__ == '__main__':
+    demo.launch()

options/options.py

@@ -0,0 +1,44 @@
+import os
+import os.path as osp
+import logging
+import yaml
+from collections import OrderedDict
+try:
+    from yaml import CLoader as Loader, CDumper as Dumper
+except ImportError:
+    from yaml import Loader, Dumper
+
+
+def OrderedYaml():
+    '''yaml orderedDict support'''
+    _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
+
+    def dict_representer(dumper, data):
+        return dumper.represent_dict(data.items())
+
+    def dict_constructor(loader, node):
+        return OrderedDict(loader.construct_pairs(node))
+
+    Dumper.add_representer(OrderedDict, dict_representer)
+    Loader.add_constructor(_mapping_tag, dict_constructor)
+    return Loader, Dumper
+
+#-----------------------
+Loader, Dumper = OrderedYaml()
+
+def parse(opt_path):
+    with open(opt_path, mode='r') as f:
+        opt = yaml.load(f, Loader=Loader)
+    return opt
+
+
+
+if __name__ == '__main__':
+    
+    path_yaml = './train/NBDN.yml'
+    with open(path_yaml, mode='r') as f:
+        opt = yaml.load(f, Loader=Loader)
+    opt = parse(path_yaml)
+    # print(opt)
+    print(type(opt['network']['width']))
+    # print(opt['gpu'])

options/predict/LOLBlur.yml

@@ -0,0 +1,25 @@
+#### network structures
+network:
+  name: Network
+  img_channels: 3
+  width: 32
+  middle_blk_num: 3
+  enc_blk_nums: [1, 2, 3]
+  dec_blk_nums: [3, 1, 1]
+  enc_blk_nums_map: None
+  middle_blk_num_map: None
+  residual_layers: None
+  dilations: [1, 4, 9]
+  spatial: False
+  extra_depth_wise: True
+
+#### save model
+save:
+  best: ./models/bests/Network_no_attention_light_LOLBlur.pt
+
+LOLBlur:
+  inputs_path: ./inputs/synthetic
+  results_path: ./results/synthetic
+RealBlur:
+  inputs_path: ./inputs/real
+  results_path: ./results/real

options/train/All_LOL.yml

@@ -0,0 +1,82 @@
+#### general settings
+resume_training:
+  resume_training: True
+  resume: must
+  id: 2oceqxkt
+
+#### training devices
+device:
+  cuda: True
+  gpus: 0
+
+#### datasets
+datasets:
+  name: All_LOL
+  train:
+    train_path: /home/leadergpu/Datasets
+
+    n_workers: 4  # per GPU
+    batch_size_train: 12
+    cropsize: 256   # size you want to crop out as input sample.
+    flips: True
+    verbose: True
+    crop_type: Random
+  val:
+    test_path: /home/leadergpu/Datasets
+    batch_size_test: 1
+
+#### network structures
+network:
+  name: Network
+  img_channels: 3
+  width: 32
+  middle_blk_num: 3
+  enc_blk_nums: [1, 2, 3]
+  dec_blk_nums: [3, 1, 1]
+  enc_blk_nums_map: None
+  middle_blk_num_map: None
+  residual_layers: 2
+  dilations: [1, 4, 9]
+  spatial: None
+  extra_depth_wise: True
+
+
+#### training settings: learning rate scheme, loss
+train:
+  lr_initial: !!float 5e-4
+  lr_scheme: CosineAnnealing
+  betas: [0.9, 0.9]
+  epochs: 500
+  lr_gamma: 0.5
+  weight_decay: !!float 1e-3
+  eta_min: !!float 1e-6
+
+  pixel_criterion: l1
+  pixel_weight: 1.0
+
+  perceptual: True
+  perceptual_criterion: l1
+  perceptual_weight: 0.01
+  perceptual_reduction: mean
+
+  edge: True 
+  edge_criterion: l2
+  edge_weight: 50.0
+  edge_reduction: mean
+
+  frequency: True
+  frequency_criterion: l2
+  frequency_weight: 0.01
+  frequency_reduction: mean
+
+#### save model
+save:
+  path: ./models/Network_all_LOL.pt
+  best: ./models/bests/
+#### wandb:
+wandb:
+  init: True
+  project: LOLBlur
+  entity: cidautai
+  name: Network_all_LOL
+  save_code: True

options/train/LOL.yml

@@ -0,0 +1,79 @@
+#### general settings
+resume_training:
+  resume_training: True
+  resume: must
+  id: waye95l7
+
+
+#### datasets
+datasets:
+  name: LOL
+  train:
+    train_path: /mnt/valab-datasets/LOL/train
+
+    n_workers: 4  # per GPU
+    batch_size_train: 4
+    cropsize: 256   # size you want to crop out as input sample.
+    flips: True
+    verbose: True
+    crop_type: Random
+  val:
+    test_path: /mnt/valab-datasets/LOL/test
+    cropsize: 256
+    batch_size_test: 1
+
+#### network structures
+network:
+  name: Network_v3
+  img_channels: 3
+  width: 32
+  middle_blk_num: 3
+  enc_blk_nums: [1, 2, 3]
+  dec_blk_nums: [3, 1, 1]
+  enc_blk_nums_map: None
+  middle_blk_num_map: None
+  residual_layers: 1
+  dilations: [1, 4]
+  spatial: None
+  extra_depth_wise: False
+
+
+#### training settings: learning rate scheme, loss
+train:
+  lr_initial: !!float 5e-4
+  lr_scheme: CosineAnnealing
+  betas: [0.9, 0.9]
+  epochs: 500
+  lr_gamma: 0.5
+  weight_decay: !!float 1e-3
+  eta_min: !!float 1e-6
+
+  pixel_criterion: l1
+  pixel_weight: 1.0
+
+  perceptual: True
+  perceptual_criterion: l1
+  perceptual_weight: 0.01
+  perceptual_reduction: mean
+
+  edge: True 
+  edge_criterion: l2
+  edge_weight: 50.0
+  edge_reduction: mean
+
+  frequency: True
+  frequency_criterion: l2
+  frequency_weight: 0.01
+  frequency_reduction: mean
+
+#### save model
+save:
+  path: ./models/Network_v3_interpolate.pt
+  best: ./models/bests/
+#### wandb:
+wandb:
+  init: True
+  project: LOLBlur
+  entity: cidautai
+  name: Network_v3_interpolate_extraDW_LOLBlur
+  save_code: True

options/train/LOLBlur.yml

@@ -0,0 +1,85 @@
+#### general settings
+resume_training:
+  resume_training: True
+  resume: must
+  id: qa5ramgk
+
+#### training devices
+device:
+  cuda: True
+  gpus: 0
+
+#### datasets
+datasets:
+  name: LOLBlur
+  train:
+    train_path: /home/leadergpu/Datasets/LOLBlur_temp/train
+
+    n_workers: 4  # per GPU
+    batch_size_train: 12
+    cropsize: 256   # size you want to crop out as input sample.
+    flips: True
+    verbose: True
+    crop_type: Random
+  val:
+    test_path: /home/leadergpu/Datasets/LOLBlur_temp/test
+    batch_size_test: 1
+
+#### network structures
+network:
+  name: Network
+  img_channels: 3
+  width: 32
+  middle_blk_num: 3
+  enc_blk_nums: [1, 2, 3]
+  dec_blk_nums: [3, 1, 1]
+  enc_blk_nums_map: None
+  middle_blk_num_map: None
+  residual_layers: None
+  dilations: [1, 4]
+  spatial: None
+  extra_depth_wise: True
+
+
+#### training settings: learning rate scheme, loss
+train:
+  lr_initial: !!float 2e-5
+  lr_scheme: CosineAnnealing
+  betas: [0.9, 0.9]
+  epochs: 700
+  lr_gamma: 0.5
+  weight_decay: !!float 1e-3
+  eta_min: !!float 1e-6
+
+  pixel_criterion: l1
+  pixel_weight: 1.0
+
+  perceptual: True
+  perceptual_criterion: l1
+  perceptual_weight: 0.01
+  perceptual_reduction: mean
+
+  edge: True 
+  edge_criterion: l2
+  edge_weight: 50.0
+  edge_reduction: mean
+
+  frequency: True
+  frequency_criterion: l2
+  frequency_weight: 0.01
+  frequency_reduction: sum
+
+#### save model
+save:
+  path: ./models/Network_v3_new_network_noFPA_LOLBlur.pt
+  best: ./models/bests/
+  new: ./models/Network_v3_new_network_noFPA_LOLBlur_v2.pt 
+#### wandb:
+wandb:
+  init: True
+  project: LOLBlur
+  entity: cidautai
+  name: Network_v3_new_network_noFPA_LOLBlur
+  save_code: True
+
+

requirements.txt

@@ -0,0 +1,108 @@
+aiofiles==23.2.1
+altair==5.3.0
+annotated-types==0.7.0
+anyio==4.4.0
+attrs==23.2.0
+certifi==2024.6.2
+charset-normalizer==3.3.2
+click==8.1.7
+contourpy==1.2.1
+cycler==0.12.1
+dnspython==2.6.1
+docker-pycreds==0.4.0
+email_validator==2.2.0
+exceptiongroup==1.2.1
+fastapi==0.111.0
+fastapi-cli==0.0.4
+ffmpy==0.3.2
+filelock==3.15.3
+fonttools==4.53.0
+fsspec==2024.6.0
+gitdb==4.0.11
+GitPython==3.1.43
+gradio==4.36.1
+gradio_client==1.0.1
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.23.4
+idna==3.7
+importlib_resources==6.4.0
+Jinja2==3.1.4
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+kornia==0.7.2
+kornia_rs==0.1.3
+lpips==0.1.4
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.0
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.3
+numpy==2.0.0
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.20.5
+nvidia-nvjitlink-cu12==12.5.40
+nvidia-nvtx-cu12==12.1.105
+opencv-python==4.10.0.84
+orjson==3.10.5
+packaging==24.1
+pandas==2.2.2
+pillow==10.3.0
+platformdirs==4.2.2
+protobuf==5.27.1
+psutil==6.0.0
+ptflops==0.7.3
+pydantic==2.7.4
+pydantic_core==2.18.4
+pydub==0.25.1
+Pygments==2.18.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+pytorch-msssim==1.0.0
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.18.1
+ruff==0.4.10
+scipy==1.13.1
+semantic-version==2.10.0
+sentry-sdk==2.6.0
+setproctitle==1.3.3
+shellingham==1.5.4
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+starlette==0.37.2
+sympy==1.12.1
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.3.1
+torchvision==0.18.1
+tqdm==4.66.4
+triton==2.3.1
+typer==0.12.3
+typing_extensions==4.12.2
+tzdata==2024.1
+ujson==5.10.0
+urllib3==2.2.2
+uvicorn==0.30.1
+uvloop==0.19.0
+wandb==0.17.2
+watchfiles==0.22.0
+websockets==11.0.3