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| # Dataset parameters | |
| dataset_params: | |
| # Path to data, data can be stored in several formats: .mp4 or .gif videos, stacked .png images or folders with frames. | |
| root_dir: data/taichi-png | |
| # Image shape, needed for staked .png format. | |
| frame_shape: [256, 256, 3] | |
| # In case of TaiChi single video can be splitted in many chunks, or the maybe several videos for single person. | |
| # In this case epoch can be a pass over different videos (if id_sampling=True) or over different chunks (if id_sampling=False) | |
| # If the name of the video '12335#adsbf.mp4' the id is assumed to be 12335 | |
| id_sampling: True | |
| # List with pairs for animation, None for random pairs | |
| pairs_list: data/taichi256.csv | |
| # Augmentation parameters see augmentation.py for all posible augmentations | |
| augmentation_params: | |
| flip_param: | |
| horizontal_flip: True | |
| time_flip: True | |
| jitter_param: | |
| brightness: 0.1 | |
| contrast: 0.1 | |
| saturation: 0.1 | |
| hue: 0.1 | |
| # Defines model architecture | |
| model_params: | |
| common_params: | |
| # Number of keypoint | |
| num_kp: 10 | |
| # Number of channels per image | |
| num_channels: 3 | |
| # Using first or zero order model | |
| estimate_jacobian: True | |
| kp_detector_params: | |
| # Softmax temperature for keypoint heatmaps | |
| temperature: 0.1 | |
| # Number of features mutliplier | |
| block_expansion: 32 | |
| # Maximum allowed number of features | |
| max_features: 1024 | |
| # Number of block in Unet. Can be increased or decreased depending or resolution. | |
| num_blocks: 5 | |
| # Keypioint is predicted on smaller images for better performance, | |
| # scale_factor=0.25 means that 256x256 image will be resized to 64x64 | |
| scale_factor: 0.25 | |
| generator_params: | |
| # Number of features mutliplier | |
| block_expansion: 64 | |
| # Maximum allowed number of features | |
| max_features: 512 | |
| # Number of downsampling blocks in Jonson architecture. | |
| # Can be increased or decreased depending or resolution. | |
| num_down_blocks: 2 | |
| # Number of ResBlocks in Jonson architecture. | |
| num_bottleneck_blocks: 6 | |
| # Use occlusion map or not | |
| estimate_occlusion_map: True | |
| dense_motion_params: | |
| # Number of features mutliplier | |
| block_expansion: 64 | |
| # Maximum allowed number of features | |
| max_features: 1024 | |
| # Number of block in Unet. Can be increased or decreased depending or resolution. | |
| num_blocks: 5 | |
| # Dense motion is predicted on smaller images for better performance, | |
| # scale_factor=0.25 means that 256x256 image will be resized to 64x64 | |
| scale_factor: 0.25 | |
| discriminator_params: | |
| # Discriminator can be multiscale, if you want 2 discriminator on original | |
| # resolution and half of the original, specify scales: [1, 0.5] | |
| scales: [1] | |
| # Number of features mutliplier | |
| block_expansion: 32 | |
| # Maximum allowed number of features | |
| max_features: 512 | |
| # Number of blocks. Can be increased or decreased depending or resolution. | |
| num_blocks: 4 | |
| use_kp: True | |
| # Parameters of training | |
| train_params: | |
| # Number of training epochs | |
| num_epochs: 150 | |
| # For better i/o performance when number of videos is small number of epochs can be multiplied by this number. | |
| # Thus effectivlly with num_repeats=100 each epoch is 100 times larger. | |
| num_repeats: 150 | |
| # Drop learning rate by 10 times after this epochs | |
| epoch_milestones: [] | |
| # Initial learing rate for all modules | |
| lr_generator: 2.0e-4 | |
| lr_discriminator: 2.0e-4 | |
| lr_kp_detector: 0 | |
| batch_size: 27 | |
| # Scales for perceptual pyramide loss. If scales = [1, 0.5, 0.25, 0.125] and image resolution is 256x256, | |
| # than the loss will be computer on resolutions 256x256, 128x128, 64x64, 32x32. | |
| scales: [1, 0.5, 0.25, 0.125] | |
| # Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs. | |
| checkpoint_freq: 50 | |
| # Parameters of transform for equivariance loss | |
| transform_params: | |
| # Sigma for affine part | |
| sigma_affine: 0.05 | |
| # Sigma for deformation part | |
| sigma_tps: 0.005 | |
| # Number of point in the deformation grid | |
| points_tps: 5 | |
| loss_weights: | |
| # Weight for LSGAN loss in generator | |
| generator_gan: 1 | |
| # Weight for LSGAN loss in discriminator | |
| discriminator_gan: 1 | |
| # Weights for feature matching loss, the number should be the same as number of blocks in discriminator. | |
| feature_matching: [10, 10, 10, 10] | |
| # Weights for perceptual loss. | |
| perceptual: [10, 10, 10, 10, 10] | |
| # Weights for value equivariance. | |
| equivariance_value: 10 | |
| # Weights for jacobian equivariance. | |
| equivariance_jacobian: 10 | |
| # Parameters of reconstruction | |
| reconstruction_params: | |
| # Maximum number of videos for reconstruction | |
| num_videos: 1000 | |
| # Format for visualization, note that results will be also stored in staked .png. | |
| format: '.mp4' | |
| # Parameters of animation | |
| animate_params: | |
| # Maximum number of pairs for animation, the pairs will be either taken from pairs_list or random. | |
| num_pairs: 50 | |
| # Format for visualization, note that results will be also stored in staked .png. | |
| format: '.mp4' | |
| # Normalization of diriving keypoints | |
| normalization_params: | |
| # Increase or decrease relative movement scale depending on the size of the object | |
| adapt_movement_scale: False | |
| # Apply only relative displacement of the keypoint | |
| use_relative_movement: True | |
| # Apply only relative change in jacobian | |
| use_relative_jacobian: True | |
| # Visualization parameters | |
| visualizer_params: | |
| # Draw keypoints of this size, increase or decrease depending on resolution | |
| kp_size: 5 | |
| # Draw white border around images | |
| draw_border: True | |
| # Color map for keypoints | |
| colormap: 'gist_rainbow' | |