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ykdai / Flare7K

Official Implementation of "Flare7K: A Phenomenological Nighttime Flare Removal Dataset"
https://ykdai.github.io/projects/Flare7K
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Traning the model 7kpp baseline option #25

Open serna01 opened 2 weeks ago

serna01 commented 2 weeks ago

I am trying to reproduce the training model and after trying reducing num gpu, batch size per gpu and num work per gpu I got the following error at the end:

`$ python basicsr/train.py -opt options/uformer_flare7kpp_baseline_option.yml --debug Disable distributed. Path already exists. Rename it to /home/alejandro/Flare7K/experiments/debug_Uformer_flare7kpp_baseline_option.yml_archived_20240613_165148 2024-06-13 16:51:48,099 INFO:

Version Information: BasicSR: 1.4.2 PyTorch: 2.3.1+cu121 TorchVision: 0.18.1+cu121 2024-06-13 16:51:48,099 INFO: name: debug_Uformer_flare7kpp_baseline_option.yml model_type: DeflareModel scale: 1 num_gpu: 1 manual_seed: 0 datasets:[ train:[ name: Flare7Kpp type: Flare7kpp_Pair_Loader image_path: dataset/Flickr24K scattering_dict:[ Flare7k_scattering: dataset/Flare7Kpp/Flare7K/Scattering_Flare/Compound_Flare Real_scattering1: dataset/Flare7Kpp/Flare-R/Compound_Flare ] reflective_dict:[ Flare7k_reflective: dataset/Flare7Kpp/Flare7K/Reflective_Flare Real_reflective1: None ] light_dict:[ Flare7k_light: dataset/Flare7Kpp/Flare7K/Scattering_Flare/Light_Source Real_light1: dataset/Flare7Kpp/Flare-R/Light_Source ] data_ratio: [0.5, 0.5] transform_base:[ img_size: 512 ] transform_flare:[ scale_min: 0.7 scale_max: 1.2 translate: 100 shear: 20 ] mask_type: None use_shuffle: True num_worker_per_gpu: 2 batch_size_per_gpu: 1 dataset_enlarge_ratio: 1 prefetch_mode: None phase: train scale: 1 ] val:[ name: flare_test type: Image_Pair_Loader dataroot_gt: dataset/Flare7Kpp/val/gt dataroot_lq: dataset/Flare7Kpp/val/input gt_size: 512 phase: val scale: 1 ] ] network_g:[ type: Uformer img_size: 512 img_ch: 3 output_ch: 6 multi_stage: 1 ] path:[ pretrain_network_g: None strict_load_g: True resume_state: None experiments_root: /home/alejandro/Flare7K/experiments/debug_Uformer_flare7kpp_baseline_option.yml models: /home/alejandro/Flare7K/experiments/debug_Uformer_flare7kpp_baseline_option.yml/models training_states: /home/alejandro/Flare7K/experiments/debug_Uformer_flare7kpp_baseline_option.yml/training_states log: /home/alejandro/Flare7K/experiments/debug_Uformer_flare7kpp_baseline_option.yml visualization: /home/alejandro/Flare7K/experiments/debug_Uformer_flare7kpp_baseline_option.yml/visualization ] train:[ optim_g:[ type: Adam lr: 0.0001 weight_decay: 0 betas: [0.9, 0.99] ] scheduler:[ type: MultiStepLR milestones: [200000] gamma: 0.5 ] out_deflare: True ema_decay: 0.9 total_iter: 600000 warmup_iter: -1 l1_opt:[ type: L_Abs_pure loss_weight: 0.5 ] perceptual:[ type: L_percepture loss_weight: 0.5 ] ] val:[ val_freq: 8 save_img: True metrics:[ psnr:[ type: calculate_psnr crop_border: 0 test_y_channel: False ] ssim:[ type: calculate_ssim crop_border: 0 test_y_channel: False ] ] ] logger:[ print_freq: 1 save_checkpoint_freq: 8 use_tb_logger: True wandb:[ project: None resume_id: None ] ] dist_params:[ backend: nccl port: 29500 ] dist: False rank: 0 world_size: 1 auto_resume: False is_train: True root_path: /home/alejandro/Flare7K

Base Image Loaded with examples: 23949 Scattering Flare Image: Flare7k_scattering is loaded successfully with examples 5000 Now we have 1 scattering flare images Scattering Flare Image: Real_scattering1 is loaded successfully with examples 962 Now we have 2 scattering flare images Reflective Flare Image: Flare7k_reflective is loaded successfully with examples 2000 Now we have 1 refelctive flare images ERROR: reflective flare images are not loaded properly Now we have 2 refelctive flare images Light Source Image: Flare7k_light is loaded successfully with examples 5000 Now we have 1 light source images Light Source Image: Real_light1 is loaded successfully with examples 962 Now we have 2 light source images 2024-06-13 16:51:48,183 INFO: Dataset [Flare7kpp_Pair_Loader] - Flare7Kpp is built. 2024-06-13 16:51:48,183 INFO: Training statistics: Number of train images: 23949 Dataset enlarge ratio: 1 Batch size per gpu: 1 World size (gpu number): 1 Require iter number per epoch: 23949 Total epochs: 26; iters: 600000. 2024-06-13 16:51:48,183 INFO: Dataset [Image_Pair_Loader] - flare_test is built. 2024-06-13 16:51:48,183 INFO: Number of val images/folders in flare_test: 0 /home/alejandro/Flare7K/venv/lib/python3.10/site-packages/torch/functional.py:512: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3587.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] 2024-06-13 16:51:48,308 INFO: Network [Uformer] is created. 2024-06-13 16:51:48,382 INFO: Network: Uformer, with parameters: 20,473,888 2024-06-13 16:51:48,382 INFO: Uformer( embed_dim=32, token_projection=linear, token_mlp=ffn,win_size=8 (pos_drop): Dropout(p=0.0, inplace=False) (input_proj): InputProj( (proj): Sequential( (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.01, inplace=True) ) ) (output_proj): OutputProj( (proj): Sequential( (0): Conv2d(64, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (encoderlayer_0): BasicUformerLayer( dim=32, input_resolution=(512, 512), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=32, input_resolution=(512, 512), num_heads=1, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=32, win_size=(8, 8), num_heads=1 (qkv): LinearProjection( (to_q): Linear(in_features=32, out_features=32, bias=True) (to_kv): Linear(in_features=32, out_features=64, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=32, out_features=32, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): Identity() (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=32, out_features=128, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=128, out_features=32, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=32, input_resolution=(512, 512), num_heads=1, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=32, win_size=(8, 8), num_heads=1 (qkv): LinearProjection( (to_q): Linear(in_features=32, out_features=32, bias=True) (to_kv): Linear(in_features=32, out_features=64, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=32, out_features=32, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.014) (norm2): LayerNorm((32,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=32, out_features=128, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=128, out_features=32, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (dowsample_0): Downsample( (conv): Sequential( (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) ) (encoderlayer_1): BasicUformerLayer( dim=64, input_resolution=(256, 256), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=64, input_resolution=(256, 256), num_heads=2, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=64, win_size=(8, 8), num_heads=2 (qkv): LinearProjection( (to_q): Linear(in_features=64, out_features=64, bias=True) (to_kv): Linear(in_features=64, out_features=128, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=64, out_features=64, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.029) (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=64, out_features=256, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=256, out_features=64, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=64, input_resolution=(256, 256), num_heads=2, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=64, win_size=(8, 8), num_heads=2 (qkv): LinearProjection( (to_q): Linear(in_features=64, out_features=64, bias=True) (to_kv): Linear(in_features=64, out_features=128, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=64, out_features=64, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.043) (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=64, out_features=256, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=256, out_features=64, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (dowsample_1): Downsample( (conv): Sequential( (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) ) (encoderlayer_2): BasicUformerLayer( dim=128, input_resolution=(128, 128), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=128, input_resolution=(128, 128), num_heads=4, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=128, win_size=(8, 8), num_heads=4 (qkv): LinearProjection( (to_q): Linear(in_features=128, out_features=128, bias=True) (to_kv): Linear(in_features=128, out_features=256, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=128, out_features=128, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.057) (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=128, out_features=512, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=512, out_features=128, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=128, input_resolution=(128, 128), num_heads=4, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=128, win_size=(8, 8), num_heads=4 (qkv): LinearProjection( (to_q): Linear(in_features=128, out_features=128, bias=True) (to_kv): Linear(in_features=128, out_features=256, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=128, out_features=128, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.071) (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=128, out_features=512, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=512, out_features=128, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (dowsample_2): Downsample( (conv): Sequential( (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) ) (encoderlayer_3): BasicUformerLayer( dim=256, input_resolution=(64, 64), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=256, input_resolution=(64, 64), num_heads=8, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=256, win_size=(8, 8), num_heads=8 (qkv): LinearProjection( (to_q): Linear(in_features=256, out_features=256, bias=True) (to_kv): Linear(in_features=256, out_features=512, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=256, out_features=256, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.086) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=256, out_features=1024, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1024, out_features=256, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=256, input_resolution=(64, 64), num_heads=8, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=256, win_size=(8, 8), num_heads=8 (qkv): LinearProjection( (to_q): Linear(in_features=256, out_features=256, bias=True) (to_kv): Linear(in_features=256, out_features=512, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=256, out_features=256, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.100) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=256, out_features=1024, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1024, out_features=256, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (dowsample_3): Downsample( (conv): Sequential( (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) ) ) (conv): BasicUformerLayer( dim=512, input_resolution=(32, 32), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=512, input_resolution=(32, 32), num_heads=16, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=512, win_size=(8, 8), num_heads=16 (qkv): LinearProjection( (to_q): Linear(in_features=512, out_features=512, bias=True) (to_kv): Linear(in_features=512, out_features=1024, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=512, out_features=512, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.100) (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=512, out_features=2048, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=2048, out_features=512, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=512, input_resolution=(32, 32), num_heads=16, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=512, win_size=(8, 8), num_heads=16 (qkv): LinearProjection( (to_q): Linear(in_features=512, out_features=512, bias=True) (to_kv): Linear(in_features=512, out_features=1024, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=512, out_features=512, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.100) (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=512, out_features=2048, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=2048, out_features=512, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (upsample_0): Upsample( (deconv): Sequential( (0): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2)) ) ) (decoderlayer_0): BasicUformerLayer( dim=512, input_resolution=(64, 64), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=512, input_resolution=(64, 64), num_heads=16, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=512, win_size=(8, 8), num_heads=16 (qkv): LinearProjection( (to_q): Linear(in_features=512, out_features=512, bias=True) (to_kv): Linear(in_features=512, out_features=1024, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=512, out_features=512, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.100) (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=512, out_features=2048, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=2048, out_features=512, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=512, input_resolution=(64, 64), num_heads=16, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=512, win_size=(8, 8), num_heads=16 (qkv): LinearProjection( (to_q): Linear(in_features=512, out_features=512, bias=True) (to_kv): Linear(in_features=512, out_features=1024, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=512, out_features=512, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.086) (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=512, out_features=2048, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=2048, out_features=512, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (upsample_1): Upsample( (deconv): Sequential( (0): ConvTranspose2d(512, 128, kernel_size=(2, 2), stride=(2, 2)) ) ) (decoderlayer_1): BasicUformerLayer( dim=256, input_resolution=(128, 128), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=256, input_resolution=(128, 128), num_heads=8, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=256, win_size=(8, 8), num_heads=8 (qkv): LinearProjection( (to_q): Linear(in_features=256, out_features=256, bias=True) (to_kv): Linear(in_features=256, out_features=512, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=256, out_features=256, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.071) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=256, out_features=1024, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1024, out_features=256, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=256, input_resolution=(128, 128), num_heads=8, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=256, win_size=(8, 8), num_heads=8 (qkv): LinearProjection( (to_q): Linear(in_features=256, out_features=256, bias=True) (to_kv): Linear(in_features=256, out_features=512, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=256, out_features=256, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.057) (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=256, out_features=1024, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1024, out_features=256, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (upsample_2): Upsample( (deconv): Sequential( (0): ConvTranspose2d(256, 64, kernel_size=(2, 2), stride=(2, 2)) ) ) (decoderlayer_2): BasicUformerLayer( dim=128, input_resolution=(256, 256), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=128, input_resolution=(256, 256), num_heads=4, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=128, win_size=(8, 8), num_heads=4 (qkv): LinearProjection( (to_q): Linear(in_features=128, out_features=128, bias=True) (to_kv): Linear(in_features=128, out_features=256, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=128, out_features=128, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.043) (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=128, out_features=512, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=512, out_features=128, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=128, input_resolution=(256, 256), num_heads=4, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=128, win_size=(8, 8), num_heads=4 (qkv): LinearProjection( (to_q): Linear(in_features=128, out_features=128, bias=True) (to_kv): Linear(in_features=128, out_features=256, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=128, out_features=128, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.029) (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=128, out_features=512, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=512, out_features=128, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (upsample_3): Upsample( (deconv): Sequential( (0): ConvTranspose2d(128, 32, kernel_size=(2, 2), stride=(2, 2)) ) ) (decoderlayer_3): BasicUformerLayer( dim=64, input_resolution=(512, 512), depth=2 (blocks): ModuleList( (0): LeWinTransformerBlock( dim=64, input_resolution=(512, 512), num_heads=2, win_size=8, shift_size=0, mlp_ratio=4.0 (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=64, win_size=(8, 8), num_heads=2 (qkv): LinearProjection( (to_q): Linear(in_features=64, out_features=64, bias=True) (to_kv): Linear(in_features=64, out_features=128, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=64, out_features=64, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath(drop_prob=0.014) (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=64, out_features=256, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=256, out_features=64, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): LeWinTransformerBlock( dim=64, input_resolution=(512, 512), num_heads=2, win_size=8, shift_size=4, mlp_ratio=4.0 (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( dim=64, win_size=(8, 8), num_heads=2 (qkv): LinearProjection( (to_q): Linear(in_features=64, out_features=64, bias=True) (to_kv): Linear(in_features=64, out_features=128, bias=True) ) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=64, out_features=64, bias=True) (se_layer): Identity() (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): Identity() (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=64, out_features=256, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=256, out_features=64, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) (activation): Sequential( (0): Sigmoid() ) ) Output channel is: 6 Network contains 1 stages. 2024-06-13 16:51:48,383 INFO: Use Exponential Moving Average with decay: 0.9 2024-06-13 16:51:48,491 INFO: Network [Uformer] is created. 2024-06-13 16:51:48,516 INFO: Loss [L_Abs_pure] is created. /home/alejandro/Flare7K/venv/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. warnings.warn( /home/alejandro/Flare7K/venv/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or None for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing weights=VGG19_Weights.IMAGENET1K_V1. You can also use weights=VGG19_Weights.DEFAULT to get the most up-to-date weights. warnings.warn(msg) 2024-06-13 16:51:49,145 INFO: Loss [L_percepture] is created. 2024-06-13 16:51:49,146 INFO: Model [DeflareModel] is created. 2024-06-13 16:51:49,182 INFO: Start training from epoch: 0, iter: 0 2024-06-13 16:51:51,180 INFO: [debug..][epoch: 0, iter: 1, lr:(1.000e-04,)] [eta: 0:00:18, time (data): 1.997 (0.462)] l1_recons: 4.0594e-01 l1_flare: 1.6040e-01 l1_base: 1.7573e-01 l1: 7.4207e-01 l_vgg: 5.2195e+00 l_vgg_base: 4.1613e+00 l_vgg_flare: 1.0581e+00 2024-06-13 16:51:51,499 INFO: [debug..][epoch: 0, iter: 2, lr:(1.000e-04,)] [eta: 17:45:16, time (data): 1.158 (0.232)] l1_recons: 5.6692e-01 l1_flare: 2.1488e-01 l1_base: 2.0206e-01 l1: 9.8386e-01 l_vgg: 6.3416e+00 l_vgg_base: 3.2543e+00 l_vgg_flare: 3.0873e+00 2024-06-13 16:51:51,818 INFO: [debug..][epoch: 0, iter: 3, lr:(1.000e-04,)] [eta: 1 day, 2:36:36, time (data): 0.879 (0.155)] l1_recons: 3.8963e-01 l1_flare: 2.1506e-01 l1_base: 1.3989e-01 l1: 7.4458e-01 l_vgg: 5.3299e+00 l_vgg_base: 3.7483e+00 l_vgg_flare: 1.5816e+00 2024-06-13 16:51:52,138 INFO: [debug..][epoch: 0, iter: 4, lr:(1.000e-04,)] [eta: 1 day, 7:57:16, time (data): 0.739 (0.117)] l1_recons: 3.5855e-01 l1_flare: 2.2071e-01 l1_base: 9.5195e-02 l1: 6.7446e-01 l_vgg: 8.1121e+00 l_vgg_base: 5.9091e+00 l_vgg_flare: 2.2030e+00 2024-06-13 16:51:52,459 INFO: [debug..][epoch: 0, iter: 5, lr:(1.000e-04,)] [eta: 1 day, 11:32:46, time (data): 0.655 (0.094)] l1_recons: 2.4221e-01 l1_flare: 2.3469e-01 l1_base: 8.3441e-02 l1: 5.6034e-01 l_vgg: 6.0877e+00 l_vgg_base: 3.9796e+00 l_vgg_flare: 2.1080e+00 2024-06-13 16:51:52,777 INFO: [debug..][epoch: 0, iter: 6, lr:(1.000e-04,)] [eta: 1 day, 14:01:48, time (data): 0.599 (0.078)] l1_recons: 2.4650e-01 l1_flare: 1.0821e-01 l1_base: 1.0287e-01 l1: 4.5758e-01 l_vgg: 4.7867e+00 l_vgg_base: 3.2679e+00 l_vgg_flare: 1.5189e+00 2024-06-13 16:51:53,097 INFO: [debug..][epoch: 0, iter: 7, lr:(1.000e-04,)] [eta: 1 day, 15:56:33, time (data): 0.559 (0.067)] l1_recons: 2.5951e-01 l1_flare: 2.2821e-01 l1_base: 1.0263e-01 l1: 5.9036e-01 l_vgg: 5.8477e+00 l_vgg_base: 4.3610e+00 l_vgg_flare: 1.4867e+00 2024-06-13 16:51:53,416 INFO: [debug..][epoch: 0, iter: 8, lr:(1.000e-04,)] [eta: 1 day, 17:24:17, time (data): 0.529 (0.059)] l1_recons: 2.9199e-01 l1_flare: 1.8818e-01 l1_base: 1.0716e-01 l1: 5.8733e-01 l_vgg: 5.3248e+00 l_vgg_base: 3.5285e+00 l_vgg_flare: 1.7962e+00 2024-06-13 16:51:53,416 INFO: Saving models and training states. Traceback (most recent call last): File "/home/alejandro/Flare7K/basicsr/train.py", line 215, in train_pipeline(root_path) File "/home/alejandro/Flare7K/basicsr/train.py", line 193, in train_pipeline model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) File "/home/alejandro/Flare7K/basicsr/models/base_model.py", line 48, in validation self.nondist_validation(dataloader, current_iter, tb_logger, save_img) File "/home/alejandro/Flare7K/basicsr/models/deflare_model.py", line 190, in nondist_validation self.metric_results[metric] /= (idx + 1) UnboundLocalError: local variable 'idx' referenced before assignment`

ykdai commented 2 weeks ago

This error may be caused by failing to load the validation set. If you do not prepare the validation set by yourself, you may try to test change the config of dataroot_lq: dataset/Flare7Kpp/val/input to dataroot_lq: dataset/Flare7Kpp/test/input as same as the dataroot_gt.