You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
158 lines
6.0 KiB
158 lines
6.0 KiB
# Dataset parameters
|
|
# Each dataset should contain 2 folders train and test
|
|
# Each video can be represented as:
|
|
# - an image of concatenated frames
|
|
# - '.mp4' or '.gif'
|
|
# - folder with all frames from a specific video
|
|
# In case of Taichi. Same (youtube) video can be splitted in many parts (chunks). Each part has a following
|
|
# format (id)#other#info.mp4. For example '12335#adsbf.mp4' has an id 12335. In case of TaiChi id stands for youtube
|
|
# video id.
|
|
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
|
|
|
|
# Parameters of training
|
|
train_params:
|
|
# Number of training epochs
|
|
num_epochs: 100
|
|
# 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: [60, 90]
|
|
# Initial learing rate for all modules
|
|
lr_generator: 2.0e-4
|
|
lr_discriminator: 2.0e-4
|
|
lr_kp_detector: 2.0e-4
|
|
batch_size: 30
|
|
# 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, 0 for no adversarial loss.
|
|
generator_gan: 0
|
|
# 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'
|