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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| model: | ||
| base_learning_rate: 1e-5 | ||
| target: vidtok.models.autoencoder.AutoencodingEngine | ||
| params: | ||
| monitor: val/rec_loss | ||
| mode: min | ||
| # ckpt_path: checkpoints/fsq_causal_41616_262144.ckpt # train from existing checkpoint | ||
| ignore_keys: [] | ||
| # ema_decay: 0.999 | ||
|
|
||
| encoder_config: | ||
| target: vidtok.modules.model_3dcausal.EncoderCausal3DPadding | ||
| params: | ||
| double_z: false | ||
| z_channels: 6 | ||
| in_channels: 3 | ||
| out_ch: 3 | ||
| ch: 128 | ||
| ch_mult: [1, 2, 4, 4, 4] | ||
| time_downsample_factor: 4 | ||
| num_res_blocks: 2 | ||
| dropout: 0.0 | ||
| use_checkpoint: true | ||
| init_pad_mode: replicate | ||
| norm_type: layernorm # layernorm, groupnorm | ||
| fix_encoder: false # if True, fix it without updating params | ||
| fix_decoder: false # if True, fix it without updating params | ||
|
|
||
| decoder_config: | ||
| target: vidtok.modules.model_3dcausal.DecoderCausal3DPadding | ||
| params: ${model.params.encoder_config.params} | ||
|
|
||
| regularizer_config: | ||
| target: vidtok.modules.regularizers.FSQRegularizer | ||
| params: | ||
| levels: [8, 8, 8, 8, 8, 8] # codebook size: 8*8*8*8*8*8=262144 | ||
| entropy_loss_weight: 0.1 | ||
| entropy_loss_annealing_steps: 2000 | ||
| entropy_loss_annealing_factor: 3 | ||
| commitment_loss_weight: 0.25 | ||
|
|
||
| loss_config: | ||
| target: vidtok.modules.losses.GeneralLPIPSWithDiscriminator | ||
| params: | ||
| dims: 3 # video - [t,h,w] | ||
| perceptual_weight: 1.0 | ||
| disc_start: 20001 | ||
| disc_weight: 0.2 | ||
| disc_type: 2d # 2d, 3d | ||
| learn_logvar: true | ||
| gen_loss_cross_entropy: true | ||
| lecam_loss_weight: 0.005 | ||
| regularization_weights: {'aux_loss': 1.0, 'kl_loss': 0.000001} | ||
|
|
||
| data: | ||
| target: vidtok.data.datamodule.DataModuleFromConfig | ||
| params: | ||
| batch_size: 2 | ||
| num_workers: 12 | ||
|
|
||
| train: | ||
| target: vidtok.data.vidtok.VidTokDataset | ||
| params: | ||
| data_dir: DATA_DIR_1 # DATA_DIR for training data | ||
| meta_path: META_PATH_1 # path to the .csv meta file of training data | ||
| video_params: | ||
| input_height: INPUT_HEIGHT_1 | ||
| input_width: INPUT_WIDTH_1 | ||
| sample_num_frames: 17 | ||
| sample_fps: 3 | ||
|
|
||
| validation: | ||
| target: vidtok.data.vidtok.VidTokDataset | ||
| params: | ||
| data_dir: DATA_DIR_2 # DATA_DIR for validation data | ||
| meta_path: META_PATH_2 # path to the .csv meta file of validation data | ||
| video_params: | ||
| input_height: INPUT_HEIGHT_2 | ||
| input_width: INPUT_WIDTH_2 | ||
| sample_num_frames: 17 | ||
| sample_fps: 8 | ||
| start_index: 0 | ||
|
|
||
| lightning: | ||
| strategy: | ||
| target: lightning.pytorch.strategies.DDPStrategy | ||
| params: | ||
| find_unused_parameters: true | ||
|
|
||
| modelcheckpoint: | ||
| params: | ||
| every_n_train_steps: 5000 | ||
|
|
||
| callbacks: | ||
| image_logger: | ||
| target: vidtok.modules.logger.ImageVideoLogger | ||
| params: | ||
| disabled: false | ||
| rescale: true | ||
| enable_autocast: false | ||
| batch_frequency: 5000 | ||
| max_samples: 2 | ||
| increase_log_steps: false | ||
| log_first_step: false | ||
| log_before_first_step: false | ||
| log_images_kwargs: | ||
| n_rows: 17 | ||
|
|
||
| trainer: | ||
| precision: bf16-mixed | ||
| devices: auto | ||
| num_nodes: 1 | ||
| benchmark: true | ||
| num_sanity_val_steps: 10 | ||
| val_check_interval: 2000 | ||
| check_val_every_n_epoch: null # default: 1 | ||
| accumulate_grad_batches: 1 | ||
| max_epochs: 1000 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| model: | ||
| base_learning_rate: 1e-5 | ||
| target: vidtok.models.autoencoder.AutoencodingEngine | ||
| params: | ||
| monitor: val/rec_loss | ||
| mode: min | ||
| # ckpt_path: checkpoints/fsq_causal_488_262144.ckpt # train from existing checkpoint | ||
| ignore_keys: [] | ||
| # ema_decay: 0.999 | ||
|
|
||
| encoder_config: | ||
| target: vidtok.modules.model_3dcausal.EncoderCausal3DPadding | ||
| params: | ||
| double_z: false | ||
| z_channels: 6 | ||
| in_channels: 3 | ||
| out_ch: 3 | ||
| ch: 128 | ||
| ch_mult: [1, 2, 4, 4] | ||
| time_downsample_factor: 4 | ||
| num_res_blocks: 2 | ||
| dropout: 0.0 | ||
| use_checkpoint: true | ||
| init_pad_mode: replicate | ||
| norm_type: layernorm # layernorm, groupnorm | ||
| fix_encoder: false # if True, fix it without updating params | ||
| fix_decoder: false # if True, fix it without updating params | ||
|
|
||
| decoder_config: | ||
| target: vidtok.modules.model_3dcausal.DecoderCausal3DPadding | ||
| params: ${model.params.encoder_config.params} | ||
|
|
||
| regularizer_config: | ||
| target: vidtok.modules.regularizers.FSQRegularizer | ||
| params: | ||
| levels: [8, 8, 8, 8, 8, 8] # codebook size: 8*8*8*8*8*8=262144 | ||
| entropy_loss_weight: 0.1 | ||
| entropy_loss_annealing_steps: 2000 | ||
| entropy_loss_annealing_factor: 3 | ||
| commitment_loss_weight: 0.25 | ||
|
|
||
| loss_config: | ||
| target: vidtok.modules.losses.GeneralLPIPSWithDiscriminator | ||
| params: | ||
| dims: 3 # video - [t,h,w] | ||
| perceptual_weight: 1.0 | ||
| disc_start: 20001 | ||
| disc_weight: 0.2 | ||
| disc_type: 2d # 2d, 3d | ||
| learn_logvar: true | ||
| gen_loss_cross_entropy: true | ||
| lecam_loss_weight: 0.005 | ||
| regularization_weights: {'aux_loss': 1.0, 'kl_loss': 0.000001} | ||
|
|
||
| data: | ||
| target: vidtok.data.datamodule.DataModuleFromConfig | ||
| params: | ||
| batch_size: 2 | ||
| num_workers: 12 | ||
|
|
||
| train: | ||
| target: vidtok.data.vidtok.VidTokDataset | ||
| params: | ||
| data_dir: DATA_DIR_1 # DATA_DIR for training data | ||
| meta_path: META_PATH_1 # path to the .csv meta file of training data | ||
| video_params: | ||
| input_height: INPUT_HEIGHT_1 | ||
| input_width: INPUT_WIDTH_1 | ||
| sample_num_frames: 17 | ||
| sample_fps: 3 | ||
|
|
||
| validation: | ||
| target: vidtok.data.vidtok.VidTokDataset | ||
| params: | ||
| data_dir: DATA_DIR_2 # DATA_DIR for validation data | ||
| meta_path: META_PATH_2 # path to the .csv meta file of validation data | ||
| video_params: | ||
| input_height: INPUT_HEIGHT_2 | ||
| input_width: INPUT_WIDTH_2 | ||
| sample_num_frames: 17 | ||
| sample_fps: 8 | ||
| start_index: 0 | ||
|
|
||
| lightning: | ||
| strategy: | ||
| target: lightning.pytorch.strategies.DDPStrategy | ||
| params: | ||
| find_unused_parameters: true | ||
|
|
||
| modelcheckpoint: | ||
| params: | ||
| every_n_train_steps: 5000 | ||
|
|
||
| callbacks: | ||
| image_logger: | ||
| target: vidtok.modules.logger.ImageVideoLogger | ||
| params: | ||
| disabled: false | ||
| rescale: true | ||
| enable_autocast: false | ||
| batch_frequency: 5000 | ||
| max_samples: 2 | ||
| increase_log_steps: false | ||
| log_first_step: false | ||
| log_before_first_step: false | ||
| log_images_kwargs: | ||
| n_rows: 17 | ||
|
|
||
| trainer: | ||
| precision: bf16-mixed | ||
| devices: auto | ||
| num_nodes: 1 | ||
| benchmark: true | ||
| num_sanity_val_steps: 10 | ||
| val_check_interval: 2000 | ||
| check_val_every_n_epoch: null # default: 1 | ||
| accumulate_grad_batches: 1 | ||
| max_epochs: 1000 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| model: | ||
| base_learning_rate: 1e-5 | ||
| target: vidtok.models.autoencoder.AutoencodingEngine | ||
| params: | ||
| monitor: val/rec_loss | ||
| mode: min | ||
| # ckpt_path: checkpoints/fsq_causal_488_32768.ckpt # train from existing checkpoint | ||
| ignore_keys: [] | ||
| # ema_decay: 0.999 | ||
|
|
||
| encoder_config: | ||
| target: vidtok.modules.model_3dcausal.EncoderCausal3DPadding | ||
| params: | ||
| double_z: false | ||
| z_channels: 5 | ||
| in_channels: 3 | ||
| out_ch: 3 | ||
| ch: 128 | ||
| ch_mult: [1, 2, 4, 4] | ||
| time_downsample_factor: 4 | ||
| num_res_blocks: 2 | ||
| dropout: 0.0 | ||
| use_checkpoint: true | ||
| init_pad_mode: replicate | ||
| norm_type: layernorm # layernorm, groupnorm | ||
| fix_encoder: false # if True, fix it without updating params | ||
| fix_decoder: false # if True, fix it without updating params | ||
|
|
||
| decoder_config: | ||
| target: vidtok.modules.model_3dcausal.DecoderCausal3DPadding | ||
| params: ${model.params.encoder_config.params} | ||
|
|
||
| regularizer_config: | ||
| target: vidtok.modules.regularizers.FSQRegularizer | ||
| params: | ||
| levels: [8, 8, 8, 8, 8] # codebook size: 8*8*8*8*8=32768 | ||
| entropy_loss_weight: 0.1 | ||
| entropy_loss_annealing_steps: 2000 | ||
| entropy_loss_annealing_factor: 3 | ||
| commitment_loss_weight: 0.25 | ||
|
|
||
| loss_config: | ||
| target: vidtok.modules.losses.GeneralLPIPSWithDiscriminator | ||
| params: | ||
| dims: 3 # video - [t,h,w] | ||
| perceptual_weight: 1.0 | ||
| disc_start: 20001 | ||
| disc_weight: 0.2 | ||
| disc_type: 2d # 2d, 3d | ||
| learn_logvar: true | ||
| gen_loss_cross_entropy: true | ||
| lecam_loss_weight: 0.005 | ||
| regularization_weights: {'aux_loss': 1.0, 'kl_loss': 0.000001} | ||
|
|
||
| data: | ||
| target: vidtok.data.datamodule.DataModuleFromConfig | ||
| params: | ||
| batch_size: 2 | ||
| num_workers: 12 | ||
|
|
||
| train: | ||
| target: vidtok.data.vidtok.VidTokDataset | ||
| params: | ||
| data_dir: DATA_DIR_1 # DATA_DIR for training data | ||
| meta_path: META_PATH_1 # path to the .csv meta file of training data | ||
| video_params: | ||
| input_height: INPUT_HEIGHT_1 | ||
| input_width: INPUT_WIDTH_1 | ||
| sample_num_frames: 17 | ||
| sample_fps: 3 | ||
|
|
||
| validation: | ||
| target: vidtok.data.vidtok.VidTokDataset | ||
| params: | ||
| data_dir: DATA_DIR_2 # DATA_DIR for validation data | ||
| meta_path: META_PATH_2 # path to the .csv meta file of validation data | ||
| video_params: | ||
| input_height: INPUT_HEIGHT_2 | ||
| input_width: INPUT_WIDTH_2 | ||
| sample_num_frames: 17 | ||
| sample_fps: 8 | ||
| start_index: 0 | ||
|
|
||
| lightning: | ||
| strategy: | ||
| target: lightning.pytorch.strategies.DDPStrategy | ||
| params: | ||
| find_unused_parameters: true | ||
|
|
||
| modelcheckpoint: | ||
| params: | ||
| every_n_train_steps: 5000 | ||
|
|
||
| callbacks: | ||
| image_logger: | ||
| target: vidtok.modules.logger.ImageVideoLogger | ||
| params: | ||
| disabled: false | ||
| rescale: true | ||
| enable_autocast: false | ||
| batch_frequency: 5000 | ||
| max_samples: 2 | ||
| increase_log_steps: false | ||
| log_first_step: false | ||
| log_before_first_step: false | ||
| log_images_kwargs: | ||
| n_rows: 17 | ||
|
|
||
| trainer: | ||
| precision: bf16-mixed | ||
| devices: auto | ||
| num_nodes: 1 | ||
| benchmark: true | ||
| num_sanity_val_steps: 10 | ||
| val_check_interval: 2000 | ||
| check_val_every_n_epoch: null # default: 1 | ||
| accumulate_grad_batches: 1 | ||
| max_epochs: 1000 |
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