Problem using code to generate image from models
YoannRandon opened this issue · 6 comments
Hello,
I tried to generate image using the gen_single_image.py file in "/joliGAN/scripts/" but i got the following error of import on "networks" function from "model" package
So i check where the "networks" function is called in the code and it seems like it should be refered as gan_networks instead.
after swapping networks by gan_networks, i got another import error .on
I check in models and find out that cut_semantic_mask_model is more likely called re_cut_semantic_mask_model .
my current goal is to use a model loaded from the UI of joligan and use it on a sample of bdd100k images.
I would like to know if i am using the appropriate repository.
Thanks for your attention.
Hi,
Thanks for your interest in JoliGAN !
You're right, networks should now be refered as gan_networks, I will fix it in a coming PR.
The second import problem depends on your config file. Please copy your config file here, I think you are using an old one.
Moreover, I think there is a mistake in your commandline, you need to give the path to your generator weights not to your discriminator ones.
Using the UI won't solve your problem, you need to use a script. JoliGAN's documentation will be updated soon and will present how to do it.
I am using the current version of the code in github. I didn't modify anything concerning config file.
There is currently 6 config files in the models/configs/segformer/ folder
the first one look like this :
Sorry I'm talking about the training_config.json which needs to be in the same folder as your generator weights file latest_netG_A.pth. This json stores all the training parameters, some of them are needed to process inference.
The model i load from the UI Joligan contains 2 files:
the training_json file start with :
complete json file :
{
"D": {
"dropout": false,
"n_layers": 3,
"ndf": 64,
"netDs": [
"projected_d",
"basic",
"vision_aided"
],
"no_antialias": false,
"no_antialias_up": false,
"norm": "instance",
"proj_config_segformer": "models/configs/segformer/segformer_config_b0.py",
"proj_interp": 512,
"proj_network_type": "vitsmall",
"proj_weight_segformer": "models/configs/segformer/pretrain/segformer_mit-b0.pth",
"spectral": false,
"temporal_every": 4,
"temporal_frame_step": 30,
"temporal_num_common_char": -1,
"temporal_number_frames": 5,
"vision_aided_backbones": "clip+dino"
},
"G": {
"attn_nb_mask_attn": 10,
"attn_nb_mask_input": 1,
"backward_compatibility_twice_resnet_blocks": false,
"config_segformer": "models/configs/segformer/segformer_config_b0.py",
"dropout": false,
"netE": "resnet_512",
"netG": "segformer_attn_conv",
"ngf": 64,
"norm": "instance",
"padding_type": "reflect",
"spectral": false,
"stylegan2_num_downsampling": 1
},
"alg": {
"cut": {
"flip_equivariance": false,
"lambda_GAN": 1.0,
"lambda_NCE": 1.0,
"nce_T": 0.07,
"nce_idt": true,
"nce_includes_all_negatives_from_minibatch": false,
"nce_layers": "0,4,8,12,16",
"netF": "mlp_sample",
"netF_dropout": false,
"netF_nc": 256,
"netF_norm": "instance",
"num_patches": 256
},
"cyclegan": {},
"re": {
"P_lr": 0.0002,
"adversarial_loss_p": false,
"netP": "unet_128",
"no_train_P_fake_images": false,
"nuplet_size": 3,
"projection_threshold": 1.0
}
},
"data": {
"online_creation": {
"crop_delta_A": 64,
"crop_delta_B": 64,
"crop_size_A": 512,
"crop_size_B": 512,
"mask_delta_A": 0,
"mask_delta_B": 0,
"mask_square_A": false,
"mask_square_B": false
},
"crop_size": 512,
"dataset_mode": "unaligned_labeled_mask_online",
"direction": "AtoB",
"load_size": 512,
"max_dataset_size": 1000000000,
"num_threads": 4,
"online_context_pixels": 0,
"preprocess": "resize_and_crop",
"relative_paths": false,
"sanitize_paths": false,
"serial_batches": false
},
"f_s": {
"all_classes_as_one": false,
"class_weights": [
1,
10,
10,
1,
5,
5,
10,
10,
30,
50,
50
],
"config_segformer": "models/configs/segformer/segformer_config_b0.py",
"dropout": false,
"net": "segformer",
"nf": 64,
"semantic_nclasses": 11,
"semantic_threshold": 1.0,
"weight_segformer": ""
},
"output": {
"display": {
"G_attention_masks": false,
"diff_fake_real": false,
"env": "bdd100k_weather_det_clear2rainy_mm1",
"freq": 200,
"id": 1,
"ncols": 4,
"networks": false,
"port": 8097,
"server": "http://localhost",
"winsize": 256
},
"no_html": false,
"print_freq": 200,
"update_html_freq": 1000,
"verbose": false
},
"model": {
"init_gain": 0.02,
"init_type": "normal",
"input_nc": 3,
"multimodal": true,
"output_nc": 3
},
"train": {
"sem": {
"cls_B": false,
"cls_pretrained": false,
"cls_template": "basic",
"idt": true,
"l1_regression": false,
"lambda": 1.0,
"lr_f_s": 0.0002,
"net_output": false,
"regression": false,
"use_label_B": true
},
"mask": {
"charbonnier_eps": 1e-06,
"disjoint_f_s": false,
"f_s_B": true,
"for_removal": false,
"lambda_out_mask": 10.0,
"loss_out_mask": "L1",
"no_train_f_s_A": false,
"out_mask": false
},
"D_accuracy_every": 1000,
"D_lr": 0.0001,
"G_ema": true,
"G_ema_beta": 0.999,
"G_lr": 0.0002,
"batch_size": 2,
"beta1": 0.9,
"beta2": 0.999,
"compute_D_accuracy": false,
"compute_fid": false,
"compute_fid_val": false,
"continue": false,
"epoch": "latest",
"epoch_count": 1,
"fid_every": 1000,
"gan_mode": "lsgan",
"iter_size": 4,
"load_iter": 0,
"lr_decay_iters": 50,
"lr_policy": "linear",
"mm_lambda_z": 0.5,
"mm_nz": 16,
"n_epochs": 100,
"n_epochs_decay": 100,
"nb_img_max_fid": 1000000000,
"optim": "adam",
"pool_size": 50,
"save_by_iter": false,
"save_epoch_freq": 1,
"save_latest_freq": 5000,
"use_contrastive_loss_D": false
},
"dataaug": {
"APA": false,
"APA_every": 4,
"APA_nimg": 50,
"APA_p": 0,
"APA_target": 0.6,
"D_label_smooth": false,
"D_noise": 0.01,
"affine": 0.0,
"affine_scale_max": 1.2,
"affine_scale_min": 0.8,
"affine_shear": 45,
"affine_translate": 0.2,
"diff_aug_policy": "",
"diff_aug_proba": 0.5,
"imgaug": false,
"no_flip": false,
"no_rotate": true
},
"checkpoints_dir": "/data1/confiance_platform/checkpoints/",
"dataroot": "/data1/confiance/datasets/bdd100k_weather_det_clear2rainy/",
"ddp_port": "13456",
"gpu_ids": "1",
"model_type": "cut_semantic_mask",
"name": "bdd100k_weather_det_clear2rainy_mm1",
"phase": "train",
"suffix": "",
"warning_mode": false
}
Hi @YoannRandon
Initial issue is fixed by #289 now merged into master, thanks for the report.
The codebase is moving fast and the train_config.json needs a fix.
Change "model_type":"cut_semantic_mask" with "model_type":"cut" in the train_config.json. From there it works for me, image below.
