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