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1.000000 -0.937500 1.000000 1.000000 -0.968750 1.000000 1.000000 -1.000000 1.000000 1.000000 diff --git a/README.md b/README.md index 7316a58..2f024c9 100644 --- a/README.md +++ b/README.md @@ -29,4 +29,33 @@ Weiwen Chen [Project](https://cxh-research.github.io/DeVigNet/) | [Code](https://github.com/CXH-Research/DeVigNet) | [VigSet (Kaggle)](https://www.kaggle.com/datasets/xuhangc/vigset) --- -The code is scheduled for release around the date of the AAAI conference. +# Dataset +VigSet stands as the only large-scale high-resolution dataset that includes ground truth data specifically for the task of vignetting correction. + +# Usage + +## Training +You may download the dataset first, and then specify TRAIN_DIR, VAL_DIR and SAVE_DIR in the section TRAINING in `config.yml`. + +For single GPU training: +```bash +python train.py +``` +For multiple GPUs training: +```bash +accelerate config +accelerate launch train.py +``` + +If you have difficulties with the usage of `accelerate`, please refer to Accelerate. + +## Inference + +Please first specify TRAIN_DIR, VAL_DIR and SAVE_DIR in section TESTING in `config.yml`. +```bash +python infer.py +``` + +# Acknowledgement + +This work was supported in part by the Science and Technology Development Fund, Macau SAR, under Grant 0087/2020/A2 and Grant 0141/2023/RIA2, in part by the National Natural Science Foundations of China under Grant 62172403, in part by the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, in part by the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211. diff --git a/models/blocks.py b/models/blocks.py index b8db3ad..598bf30 100644 --- a/models/blocks.py +++ b/models/blocks.py @@ -4,15 +4,6 @@ import torch import torch.nn as nn import torch.nn.functional as F -from timm.models.layers import DropPath - -def discriminator_block(in_filters, out_filters, normalization=False): - """Returns downsampling layers of each discriminator block""" - layers = [nn.Conv2d(in_filters, out_filters, 3, stride=2, padding=1), nn.LeakyReLU(0.2)] - if normalization: - layers.append(nn.InstanceNorm2d(out_filters, affine=True)) - # layers.append(nn.BatchNorm2d(out_filters)) - return layers class ConvLayer(nn.Module): @@ -125,10 +116,6 @@ def forward(self, x): return self.fusion(torch.cat(out, dim=1)) - - - - class DAFT(nn.Module): def __init__(self, channels=16, num_blocks=[4, 3, 2, 1], num_heads=[8, 4, 2, 1]): @@ -259,7 +246,6 @@ def forward(self, inp): return out_rgb - class HCAM(nn.Module): """ Layer attention module""" @@ -442,7 +428,6 @@ def forward(self, x: torch.Tensor): return x - class TransformerBlock(nn.Module): def __init__(self, dim, num_heads=1, ffn_expansion_factor=2.66, bias=True, LayerNorm_type='WithBias'): super(TransformerBlock, self).__init__() @@ -564,6 +549,7 @@ def forward(self, inp): return y + x * self.gamma + class LBlock(nn.Module): def __init__(self): super(LBlock, self).__init__() @@ -573,6 +559,7 @@ def forward(self, inp): res = self.block(inp) return res + class HBlock(nn.Module): def __init__(self):