Closed BinuxLiu closed 5 months ago
Hi @BinuxLiu,
HEDNGAN generator was trained with small differences compared to CycleGAN generator:
no_antialias=True
and no_antialias_up=True
. By setting these options, the generator is built with learnable transposed convolutions in the upsampling part.You should achieve what you need with this snippet:
wget http://ptak.felk.cvut.cz/personal/jenicto2/download/iccv23_gan/hedngan_generator_X.pth
git clone https://github.com/taesungp/contrastive-unpaired-translation cut
import torch
import torch.nn as nn
import sys
sys.path.append("./cut")
from models.networks import ResnetGenerator
generator = ResnetGenerator(
input_nc=3,
output_nc=3,
ngf=64,
norm_layer=nn.BatchNorm2d,
use_dropout=False,
n_blocks=9,
padding_type='reflect',
no_antialias=True,
no_antialias_up=True,
opt=None
)
ckpt = torch.load("hedngan_generator_X.pth", map_location=lambda storage, loc: storage)
generator.load_state_dict(ckpt["model_state"])
You can also use our repository to load the checkpoints. By loading the models this way, all hyperparameter changes are already solved for you. Have a look in stages.
Thank you for your help! I have successfully tested it.
Given that our research interests align, you might be interested in the following experimental results, which were tested on the NightStreets dataset from my previous work. | Method | Dataset | FID | L2 | PSNR | SSIM |
---|---|---|---|---|---|---|
CycleGAN | Retrieval-SFM | 89.52 | 222.91 | 6.06 | -0.19 | |
HEDNGAN | Retrieval-SFM | 90.88 | 212.30 | 6.44 | -0.19 |
Additionally, I have also replicated the fair comparison experiment we discussed last time, using the model trained on Retrieval-SFM that you mentioned. | Method | Tokyo 24/7 Night | SVOX Night | MSLS Night |
---|---|---|---|---|
VGG16-GeM | 31.4 | 2.4 | 1.8 | |
VGG16-DSA | 42.9 | 8.3 | 3.6 |
Hello! @mohwald I successfully tested your trained cyclegan, but could not import Hedgan's checkpoints correctly in the open source CUT project. I use the default parameters in CUT's base_options.py