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common_setups.py
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import copy, munch, numbers, collections.abc
import lib.scalar_schedule as sSchedule
#https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth
def _CSETUPS_update_dict_recursive(d, u, deepcopy=False, new_key='KEEP'):
if deepcopy:
d = copy.deepcopy(d)
for k, v in u.items():
if not k in d:
if new_key.upper()=='ERROR':
raise KeyError("Update key {} does not exisit in base dictionary.".format(k))
elif new_key.upper()=='DISCARD':
continue
elif new_key.upper()=='KEEP':
pass
else:
raise ValueError("Unknown policy for new key: {}".format(new_key))
if isinstance(v, collections.abc.Mapping):
if k in d and not isinstance(d[k], collections.abc.Mapping):
# if something that is not a Mapping is updated with a Mapping
#e.g. a default constant schedule (int, float, list) with a complex one (dict {"type":<type>, ...})
if isinstance(d[k], (numbers.Number, list)) and "type" in v and isinstance(v["type"], str) and v["type"].upper() in ['SCHEDULE', 'CONST','LINEAR','EXPONENTIAL','ROOT_DECAY'] and "start" in v:
d[k] = sSchedule._get_base_schedule()
else:
d[k] = {}
d[k] = _CSETUPS_update_dict_recursive(d.get(k, {}), v, deepcopy=deepcopy, new_key=new_key)
else:
if deepcopy:
d[k] = copy.deepcopy(v)
else:
d[k] = v
return d
#######################
# --- Setup Modules ---
#######################
#crop and main-opt growth with new loss balaning (after scale fixes)
RECONSTRUCT_SEQUENCE_SETUP_BASE = {
'title':'seq_test',
'desc':'sequence reconstruction run description',
'paths':{
'base':"./runs",
'group':"sequence_recon_test",
},
'rendering':{
'monochrome':False,
'luma':[0.2126,0.7152,0.0722], #[0.299,0.587,0.144] #https://en.wikipedia.org/wiki/Luma_(video)
'filter_mode':'LINEAR', #NEAREST, LINEAR
'mip':{
'mode':'LINEAR', #NONE, NEAREST, LINEAR
'level':4,
'bias':0.0,
},
'blend_mode':'BEER_LAMBERT', #BEER_LAMBERT, ALPHA, ADDITIVE
'sample_gradients':True,
'boundary': None,
'SDF_threshold': 0.02,
'steps_per_cycle':24,
'num_images': 24,
'main_camera':{
'base_resolution':[256,1920,1080], #z(depth), y(height), x(width)
'resolution_scale':1./3., # only for xy
'fov':40,
'near':0.3,
'distance':0.8,
'far':1.3,
},
'target_cameras':{
'calibration_file':"scalaFlow_cameras.json",
'camera_ids':[2,1,0,4,3],
'crop_frustum':False, # crop frustum grid to AABB of vidual hull. for performance
'crop_frustum_pad':2, # view space cells
},
'allow_static_cameras':False,
'allow_fused_rendering':True,
'background':{
'type':'COLOR', #'CAM', 'COLOR', 'NONE'; only for vis-rendering, not used in optimization
'color': [0,0.5,1.0], #[0,0.5,0],
},
'lighting':{
#'scattering_ratio':1.,
'ambient_intensity':0.64,
'initial_intensity':0.85,
'shadow_resolution':[256,196,196], #DHW
},
"velocity_scale":1024,
"synthetic_target":{
'filter_mode':'LINEAR',
'blend_mode':'BEER_LAMBERT',
'ambient_intensity':0.64,
'initial_intensity':0.85,
}
},#rendering
'data':{
"rand_seed_global": 460585320,
'run_dirs':['./runs/test-and-debug','./runs/sequence_recon_test'],
'grid_size':128, #x and z resolution/grid size, y is ceil(this*scale_y) from callibration (scale_y ~ 1.77)
'y_scale': 2, #"SF"
'clip_grid':False,
'clip_grid_pad':6,
'crop_grid':True,
'hull':'TARGETS', #ALL, TARGETS, ROT, [<ids>, ], empty==ALL
'simulation':0,
'start':140,
'stop':142, #exclusive
'step':1,
'scalarFlow_frame_offset':-11,
"SDF": False,
'MS_volume':False,
'MS_images':False,
'density':{
'scale': 1,
#'initial_value':'HULL_TIGHT',#"CONST", "ESTIMATE", "HULL" or path to an npz file '[RUNID:000000-000000]frame_{frame:06d}/density_pre-opt.npz'
'initial_value':'data/scalarFlow/sim_{sim:06d}/reconstruction/density_{frame:06d}.npz',
'density_type': "SF", #OWN SF MANTA
'min':0.0,
'max':256.0,
'target_type': "RAW", #PREPROC, SYNTHETIC
'render_targets': False,
'target': 'data/scalarFlow/sim_{sim:06d}/input/cam/imgsUnproc_{frame:06d}.npz', # 'data/scalarFlow/sim_{:06d}/input/cam/imgsUnproc_{:06d}.npz',
'target_preproc': 'data/scalarFlow/sim_{sim:06d}/input/postprocessed/imgs_{frame:06d}.npz',
'target_flip_y': False,
'target_cam_ids':"ALL",#[0,1,2,3,4], #which ScalarFlow camera targets to use. 0:center, 1:center-left, 2:left, 3:right, 4:center-right (CW starting from center)
'target_threshold':4e-2, #only used for disc dataset
'target_scale': 1.5,
'hull_image_blur_std':1.0,
'hull_volume_blur_std':0.5,
'hull_smooth_blur_std':0.0,
'hull_threshold':4e-2,
'inflow':{
'active':True,
'hull_height':10,
'height':'MAX',
},
'scalarFlow_reconstruction':'data/scalarFlow/sim_{sim:06d}/reconstruction/density_{frame:06d}.npz',
'synthetic_target_density_scale':1.,
},
'velocity':{
'initial_value':'data/scalarFlow/sim_{sim:06d}/reconstruction/velocity_{frame:06d}.npz', #'RAND',
'load_step':1,
'init_std':0.1,
'init_mask':'HULL_TIGHT_NEXT', #NONE HULL, HULL_TIGHT
'boundary':'CLAMP', #BORDER (closed 0 bounds), CLAMP (open bounds)
'scalarFlow_reconstruction':'data/scalarFlow/sim_{sim:06d}/reconstruction/velocity_{frame:06d}.npz',
},
'initial_buoyancy':[0.,0.,0.],
'discriminator':{
'simulations':[0,6],
'frames':[45,145, 1],
'target_type': "RAW", #PREPROC, (SYNTHETIC not supported)
'initial_value':'data/scalarFlow/sim_{sim:06d}/reconstruction/density_{frame:06d}.npz', #path to density. needed for target rendering
'density_type': "SF", #OWN SF MANTA
'render_targets': False,
'density_type': "SF", #OWN SF MANTA
'target': 'data/scalarFlow/sim_{sim:06d}/input/cam/imgsUnproc_{frame:06d}.npz', # 'data/scalarFlow/sim_{:06d}/input/cam/imgsUnproc_{:06d}.npz',
'target_preproc': 'data/scalarFlow/sim_{sim:06d}/input/postprocessed/imgs_{frame:06d}.npz',
#augmentation
'crop_size':[96,96], #HW, input size to disc
'scale_input_to_crop':False, #resize all discriminator input to its base input resolution (crop_size) before augmentation
'real_res_down': 4,
'scale_real_to_cam':True, #scale real data resolution to current discriminator fake-sample camera (to match resolution growth)
'scale_range':[0.85,1.15],
'rotation_mode': "90",
# 'resolution_fake':[256,1920/4,1080/4],
# 'resolution_scales_fake':[1, 1/2],
# 'resolution_loss':[256,1920/8,1080/8],
'scale_real':[0.8, 1.8], #range for random intensity scale on real samples
'scale_fake':[0.7, 1.4],
# 'scale_loss':[0.8, 1.2],
'gamma_real':[0.5,2], #range for random gamma correction on real samples (value here is inverse gamma)
'gamma_fake':[0.5,2], #range for random gamma correction on fake samples (value here is inverse gamma)
# 'gamma_loss':[0.5,2], #range for random gamma correction applied to input when evaluating the disc as loss (for density) (value here is inverse gamma)
},
'load_sequence':None, #only for rendering without optimization
'load_sequence_pre_opt':False,
'synth_shapes':{ #dataset of randomly generated shapes
'active':True, #overrides other data loader
'max_translation': 0.4, #
'shape_types': 0, # single type-id or list of type-ids. 0=smooth sphere, 1=cube, 2=cube edges, 3=cube vertices, 4=sphere
'init_center': True,
},
'resource_device': '/cpu:0',#'/cpu:0'
},#data
'training':{
'iterations':5000,
'start_iteration': 0,
'frame_order':'FWD', #FWD, BWD, RAND
'sequence_length': sSchedule.setup_constant_schedule(start=-1), #shedule for sequence length. cast to int, -1 to use full length
'resource_device':'/cpu:0', #'/cpu:0', '/gpu:0'
#'loss':'L2',
'train_res_down':6,
'loss_active_eps':1e-18, #minimum absolute value of a loss scale for the loss to be considered active (to prevent losses scaled to (almost) 0 from being evaluated)
'randomization':{
'transform':False,
'sequence_length':False,
'grid_size_relative': 1.0, # minimum relative grid size, 1 to diable grid size randomizaton
'grid_size_min': 3, # minimum absolute grid size
'grow_mode': None, # None, "RAND", "ITERATE", "RANDINTERVAL"
# range of random intensity scale for images (inputs and targets)
'scale_images_min':1,
'scale_images_max':1,
# range of random density scale for velocity training
'scale_density_min':1,
'scale_density_max':1,
'inputs':True,
'input_weights':None, #relative weights for targets, none for uniform
'min_inputs':1,
'max_inputs':5,
'num_inputs_weights':None, #relative weights for number of targets, none for uniform
'targets':False,
'target_weights':None, #relative weights for targets, none for uniform
'min_targets':1,
'max_targets':5,
'num_targets_weights':None, #relative weights for number of targets, none for uniform
},
'density':{
'optim_beta':0.9,
'use_hull':True,
'warp_clamp':"MC_SMOOTH",
'camera_jitter':False,
'scale_render_grads_sharpness':0.0,
'error_functions':{
'raw_target_loss':'SE',
'preprocessed_target_loss':'SE',
'volume_target_loss':'SE',
'volume_proxy_loss':'SE',
'target_depth_smoothness_loss':'SE',
'hull':'SE',
'negative':'SE',
'smoothness_loss':'SE',
'smoothness_loss_2':'SE',
'temporal_smoothness_loss':'SE',
'warp_loss':'SE',
'center_loss':'SE',
'SDF_pos_loss':'AE',
},
'pre_optimization':True, #whether pre-optim will run for density, affects forward propagation/advection of state and optimization
# to only have fwd advection without optimization set iterations to 0
'pre_opt':{
'first':{ #settings for first frame
'iterations':0,#30000,
'learning_rate':sSchedule.setup_exponential_schedule(start=3.0, base=0.5, scale=2/30000), #{'type':'exponential', 'start':3.0, 'base':0.5, 'scale':2/30000},
'raw_target_loss':8.7e-7 *20,
'preprocessed_target_loss':0.,
'volume_target_loss':0.,
'target_depth_smoothness_loss':0.,
'hull':0.,
'negative':0.,
'smoothness_loss':0.0,#6.8e-14,
'smoothness_neighbours':3,
'smoothness_loss_2':0.0,#8.2e-14,
'temporal_smoothness_loss':0.0,#0.2
'discriminator_loss':0.0,
'warp_loss':0.0,
'center_loss':0.0,
'SDF_pos_loss':0.0,
'regularization':0.0001,
},
#settings for remaining frames
'iterations':2400,#5000,
'seq_init':"WARP", #WARP, COPY, BASE
'learning_rate':sSchedule.setup_exponential_schedule(start=3.0, base=0.5, scale=1/3000), #{'type':'exponential', 'start':0.00005, 'base':0.5, 'scale':2/30000},
'raw_target_loss':8.7e-7 *20,
'preprocessed_target_loss':0.,
'volume_target_loss':0.,
'target_depth_smoothness_loss':0.,
'hull':0.,
'negative':0.,
'smoothness_loss':0.0,#6.8e-14,
'smoothness_neighbours':3,
'smoothness_loss_2':0.0,#8.2e-14,
'temporal_smoothness_loss':0.0,#0.2
'discriminator_loss':0.0,
'warp_loss':0.0,
'center_loss':0.0,
'SDF_pos_loss':0.0,
'regularization':0.0001,
'inspect_gradients':1,
'grow':{
"factor":1.2,#2. factor per interval, max down-scaling is factor^len(intervals)
'intervals':[],
},
},
'grow_lifting_skip': None,
'grow_lifting_train': None,
'grow_lifting_lr': None,
'grow_lifting_residual': None,
'grow_volenc_residual': None,
'learning_rate':sSchedule.setup_exponential_schedule(start=2.45, base=0.5, scale=2/30000),#0.00015, #[0.00001,0.00001,0.0001, 0.00009/20000, 4000],
'raw_target_loss':8.7e-7 *20,#for AE; for SE *40; for Huber *80
'preprocessed_target_loss':0.,
'volume_target_loss':0.,
'volume_proxy_loss':0.,
'target_depth_smoothness_loss':0.,
'hull':0.,#1e-12,
'negative':0.,#1e-12,
'smoothness_loss':0.0,
'smoothness_neighbours':3, # the kind of neighbourhood to consider in the edge filter (e.g. wether to use diagonals), NOT the kernel size.
'smoothness_loss_2':0.0,
'temporal_smoothness_loss':0.0,#0.2
'discriminator_loss':1.5e-5,
'warp_loss':[6.7e-11 *4,6.7e-11 *4,13.4e-11 *4, 6.7e-11 *4/2000, 2000],#for AE; for SE *8, for Huber *24
'center_loss':0.0,
'SDF_pos_loss':0.0,#for AE; for SE *8, for Huber *24
'regularization':0.0001,
'main_warp_fwd':False,
'warp_gradients':{
'weight':sSchedule.setup_constant_schedule(start=1.0),
'active':False,
'decay':sSchedule.setup_constant_schedule(start=0.9), #[0,1], lower is faster decay
'update_first_only':False,
},
"view_interpolation":{
"steps":0,
},
'grow':{
"factor":1.2,#2. factor per interval, max down-scaling is factor^len(intervals)
"pre_grow_actions":[],# "WARP", list, unused
"post_grow_actions":[],# "WARP", list
#iterations for each grow step, empty to disable
'intervals':[],
},
},
'velocity':{
'optim_beta':0.9,
'warp_order':2,
'warp_clamp':"MC_SMOOTH",
'error_functions':{
'volume_target_loss':'SE',
'density_warp_loss':'SE',
'density_proxy_warp_loss':'SE',
'density_target_warp_loss':'SE',
'velocity_warp_loss':'SE',
'divergence_loss':'SE',
'magnitude_loss':'SE',
'CFL_loss':'SE',
'MS_coherence_loss':'SE',
},
'pre_optimization':True, #whether pre-optim will run for velocity, affects forward propagation/advection of state and optimization
'pre_opt':{
'first':{ #settings for first frame
'iterations':0,
'learning_rate':0.04,
'volume_target_loss':0.,
'density_warp_loss':8.2e-11 *5,
'density_proxy_warp_loss':0,
'density_target_warp_loss':8.2e-11 *5,
'velocity_warp_loss':0.0,
'smoothness_loss':0.0,
'smoothness_neighbours':3,
'cossim_loss':0.0,
'divergence_loss':sSchedule.setup_exponential_schedule(start=4.3e-10 *2, base=1.2, scale=1/10000), #sSchedule.setup_linear_schedule_2(start=0, end=4.3e-10, steps=7000),#
#'divergence_normalize':0.0,
#adjust according to data.step, large values can lead to NaN.
'magnitude_loss':0.0,#{'type':'exponential', 'start':4e-11, 'base':0.10, 'scale':1/5000},
'CFL_loss':0.0,
'MS_coherence_loss':0.0,
'regularization':0.0001,
'grow':{
"factor":1.2,
"scale_magnitude":True,
'intervals':[200, 260, 330, 400, 460, 530, 600, 660, 800, 930, 1060, 1260], #7490
},
},
#settings for remaining frames
'iterations':1200,
'seq_init':"WARP", #WARP, COPY, BASE
'learning_rate':0.02,
'volume_target_loss':0.,
'density_warp_loss':8.2e-11 *5,
'density_proxy_warp_loss':0,
'density_target_warp_loss':8.2e-11 *5,
'velocity_warp_loss':0.0,
'smoothness_loss':0.0,
'smoothness_neighbours':3,
'cossim_loss':0.0,
'divergence_loss':4.3e-10 *6,
#'divergence_normalize':0.0,
'magnitude_loss':0.0,#4e-12,
'CFL_loss':0.0,
'MS_coherence_loss':0.0,
'regularization':0.0001,
'grow':{
"factor":1.2,#2. factor per interval, max down-scaling is factor^len(intervals)
"scale_magnitude":True,
'intervals':[],
},
},
'noise_std':sSchedule.setup_constant_schedule(start=0.0),
'learning_rate':sSchedule.setup_exponential_schedule(start=0.02, base=0.5, scale=2/30000),#{'type':'exponential', 'max':0.02, 'start':0.02, 'base':0.5, 'scale':2/30000, 'offset':0},
# 'lr_decay':0.00,
# 'loss':'L2',
'volume_target_loss':0.,
'density_warp_loss':8.2e-11 *5,#for AE; for SE *10; for Huber *25 #influence of loss(A(dt, vt), dt+1) on velocity, can be a schedule
'density_warp_loss_MS_weighting':sSchedule.setup_constant_schedule(start=1.0),
'density_proxy_warp_loss':0,
'density_target_warp_loss':8.2e-11 *5,#for AE; for SE *10; for Huber *25 #influence of loss(A(dt, vt), dt+1) on velocity, can be a schedule
'density_target_warp_loss_MS_weighting':sSchedule.setup_constant_schedule(start=1.0),
'velocity_warp_loss':sSchedule.setup_linear_schedule_2(start=1.35e-11 *3, end=1.35e-11 *6, steps=5000), #2.7e-12 *5,#for AE; for SE *10; for Huber *20 #influence of loss(A(vt, vt), vt+1) on velocity, can be a schedule
# 'velocity_warp_loss_MS_weighting':sSchedule.setup_constant_schedule(start=1.0),
'smoothness_loss':0.0,
'smoothness_neighbours':3, # the kind of neighbourhood to consider in the edge filter (e.g. wether to use diagonals), NOT the kernel size.
'cossim_loss':0.0,
'divergence_loss':sSchedule.setup_exponential_schedule(start=4.3e-10 *6, base=1.2, scale=1/500),
'divergence_loss_MS_weighting':sSchedule.setup_constant_schedule(start=1.0),
'divergence_normalize':0.0,
'magnitude_loss':0.0,#1e-12,
'CFL_loss':0.0,
'CFL_loss_MS_weighting':sSchedule.setup_constant_schedule(start=1.0),
'MS_coherence_loss':0.0,
'MS_coherence_loss_MS_weighting':sSchedule.setup_constant_schedule(start=1.0),
'regularization':0.0001,
'warp_gradients':{
'weight':sSchedule.setup_constant_schedule(start=1.0), #affects warp gradients for velocity from backward dens warp, even if vel- warp gradients are inactive
'active':False,
'decay':sSchedule.setup_constant_schedule(start=0.9), #[0,1], lower is faster decay
},
'grow':{
"factor":1.2,#2. factor per interval, max down-scaling is factor^len(intervals)
"scale_magnitude":True,
'intervals':[],
},
},
"MS_weighting": sSchedule.setup_constant_schedule(start=1.0), #level-relative loss weighting for multi-scale losses. here the "iteration" is the level index, which starts with 0 at the COARSEST resolution.
"allow_MS_losses": True,
'optimize_buoyancy':False,
"light":{
"optimize":False,
'optim_beta':0.9,
"min":0.01,
"max":6.0,
"learning_rate":{'type':'exponential', 'start':0.001, 'base':1, 'scale':0},
},
# "scattering":{
# "optimize":False,
# 'optim_beta':0.9,
# "min":0.01,
# "max":1.0,
# "learning_rate":{'type':'exponential', 'start':0.001, 'base':1, 'scale':0},
# },
'discriminator':{
'active':False,
'model':None,#'[RUNID:200227-162722]disc_model.h5',#
'loss_type':"RaLSGAN", #"SGAN", "RpSGAN", "RpLSGAN", "RaSGAN", "RaLSGAN"
'target_label':1.0,#0.9, #0.9 for label smoothing, 1.0 for LS-GAN
# l4s
'layers':[16,16,24,24,32,32,32,64,64,64,16, 4],
'stride':[ 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1],
'kernel_size':4,
'padding':'MIRROR', #NONE(=valid), ZERO(=same), MIRROR(=tf.pad(REFLECT))
'activation':'lrelu', # relu or lrelu
'activation_alpha':0.2, # leak for lrelu
'use_fc': None, # list of filters, usually makes disc stronger
'noise_std':0.0,
'start_delay':0, #50,
'pre_opt':{
'first':{
'train':False,
'learning_rate':{'type':'exponential', 'start':4e-4, 'base':0.5, 'scale':4/30000},
# 'steps':1,
'regularization':0.002,
},
'train':False,
'learning_rate':1.6e-4,
# 'steps':1,
'regularization':0.002,
},
'train':True,
'learning_rate':2e-4,
'steps':1,
'regularization':0.002,
'optim_beta':0.5,
'grow':{ # not yet used
"factor":2.,#2. factor per interval, max down-scaling is factor^len(intervals)
#iterations for each grow step, empty to disable
'intervals':[]
},
'conditional_hull':False,
'temporal_input':{
'active':False,
'step_range':(-3,4,1), #-3 to 3 inclusive, 0 will be removed
},
'num_real':4,
'cam_res_down':6,
'num_fake':3,
'fake_camera_jitter':False,
"history":{
'load':None,
'samples':4, #use older samples as fake samples as well. 0 to disable
'size':800, #
'keep_chance':0.01, # chance to put a rendered sample in the history buffer
'save':False,
'sequence_reuse':True,
'reset_on_density_grow':True,
# 'reset_on_discriminator_grow':False,
},
# 'sequence_reuse':True,
},#discriminator
'summary_interval':100,
'checkpoint_interval':500,
},#training
'validation':{
'output_interval':100,
'stats':True,
'cmp_vol_targets':False,
'cmp_scalarFlow':False,
'cmp_scalarFlow_render':False,
'warp_test':["BASE", "NO_INFLOW", "ONLY_INFLOW"],
'warp_test_render':False,
'render_cycle':False,
'render_cycle_steps':8,
'render_density':True,
'render_shadow':True,
'render_target':True,
'render_velocity':True,
'render_MS':False,
# if data.synth_shapes.active
'synth_data_seed': 1802168824,
'synth_data_shape_types':0, #for training mode
'synth_data_eval_setup':"SPHERE", #pre-made test-cases for evaluation mode # SF, SPHERE, CUBE, ROTCUBE
},
'debug':{
'print_weight_grad_stats':False,
'target_dump_samples':False,
'disc_dump_samples':False,
},
}
# RECONSTRUCT_SEQUENCE_SETUP_GROW = {
# "training":{
# 'iterations':7000,
# "density":{
# 'pre_opt':{
# 'first':{
# 'iterations':400,
# },
# 'iterations':400,
# },
# 'grow':{
# "factor":1.2,
# "pre_grow_actions":[],
# "post_grow_actions":[],
# 'intervals':[200,300,400,500,500,600,700,800], #4000
# #'intervals':[400,400,400,400,400,400,400,400], #3200
# },
# },
# 'velocity':{
# 'pre_opt':{
# 'first':{
# 'iterations':5000,
# 'divergence_loss':sSchedule.setup_exponential_schedule(start=4.3e-10 *2, base=1.2, scale=1/1000),
# 'grow':{
# "factor":1.2,
# "scale_magnitude":True,
# 'intervals':[660, 860, 1100, 1300], #3920
# },
# },
# 'iterations':400,
# 'divergence_loss':4.3e-10 *6,
# },
# 'divergence_loss':sSchedule.setup_exponential_schedule(start=4.3e-10 *6, base=1.2, scale=1/700),
# 'grow':{
# "factor":1.2,
# "scale_magnitude":True,
# 'intervals':[200,300,400,500,500,600,700,800], #4000
# },
# },
# },
# }
# RECONSTRUCT_SEQUENCE_SETUP_GROW = _CSETUPS_update_dict_recursive(RECONSTRUCT_SEQUENCE_SETUP_BASE, RECONSTRUCT_SEQUENCE_SETUP_GROW, deepcopy=True, new_key='ERROR')
# RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP = {
# "training":{
# 'frame_order':'BWD',
# "density":{
# 'main_warp_fwd':True,
# 'warp_gradients':{
# 'weight':1.0,
# 'active':True,
# 'decay':0.9,
# 'update_first_only':True,
# },
# },
# 'velocity':{
# 'warp_gradients':{
# 'weight':1.0,
# 'active':False,
# 'decay':0.9,
# },
# },
# },
# }
# RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP = _CSETUPS_update_dict_recursive(RECONSTRUCT_SEQUENCE_SETUP_GROW, RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP, deepcopy=True, new_key='ERROR')
# RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP_FRONT = {
# "desc":"front-loaded training. 50% longer pre-training, linear growth intervals (longer low res, shorter high res)",
# "training":{
# 'iterations':4200,
# "density":{
# 'pre_opt':{
# 'first':{
# 'iterations':600,
# },
# 'iterations':600,
# },
# 'grow':{
# "factor":1.2,
# "pre_grow_actions":[],
# "post_grow_actions":[],
# #'intervals':[200,300,400,500,500,600,700,800], #4000
# 'intervals':[400,400,400,400,400,400,400,400], #3200
# },
# },
# 'velocity':{
# 'pre_opt':{
# 'first':{
# 'iterations':6000,
# 'divergence_loss':sSchedule.setup_exponential_schedule(start=4.3e-10 *2, base=1.2, scale=1/1000),
# 'grow':{
# "factor":1.2,
# "scale_magnitude":True,
# 'intervals':[1000, 1000, 1000, 1000], #4000
# },
# },
# 'iterations':600,
# 'divergence_loss':4.3e-10 *6,
# },
# 'divergence_loss':sSchedule.setup_exponential_schedule(start=4.3e-10 *6, base=1.2, scale=1/400),
# 'grow':{
# "factor":1.2,
# "scale_magnitude":True,
# 'intervals':[400,400,400,400,400,400,400,400], #3200
# },
# },
# },
# }
# RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP_FRONT = _CSETUPS_update_dict_recursive(RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP, RECONSTRUCT_SEQUENCE_SETUP_GROW_DGRADWARP_FRONT, deepcopy=True, new_key='ERROR')
"""
def __init__(self, dimension=2, num_levels=3, level_scale_factor=2, input_channels=3, \
input_levels=2, create_inputs=True, input_blocks=None, share_input_layer=True, \
input_conv_filters=1, input_conv_kernel_size=1, \
down_mode="STRIDED", down_conv_filters=None, down_conv_kernel_size=4, share_down_layer=True, \
encoder_filters=[[1]], encoder_kernel_sizes=[[3]], \
encoder_resblocks=None, share_encoder=False, \
decoder_filters=[[1]], decoder_kernel_sizes=[[3]], \
decoder_resblocks=None, share_decoder=False, \
up_mode="NNSAMPLE", up_conv_filters=1, up_conv_kernel_size=4, share_up_layer=True, \
skip_merge_mode="CONCAT", \
output_levels=1, output_blocks=None, share_output_layer=True, output_activation="none", \
output_channels=1, output_conv_kernel_size=1, output_mode="SINGLE", \
conv_activation="relu", alpha=0.2, conv_padding="ZERO", \
name="GrowingUNet", normalization="NONE", **kwargs)
"""
GROWINGUNET_CONFIG = {
#"dimension": 2,
#"num_levels": 3,
"level_scale_factor": 2,
#"input_channels": 3,
"input_levels": 1,
"create_inputs": True,
"input_blocks": ["C:1-1"],
#"input_conv_filters": 1,
#"input_conv_kernel_size": 1,
"share_input_layer": False,
"down_mode": "STRIDED", # STRIDED, NONE
"down_conv_filters": None,
"down_conv_kernel_size": 4,
"share_down_layer": True,
"encoder_resblocks": ["RB:1-1_1-1"],
"share_encoder": False,
"decoder_resblocks": ["RB:1-1_1-1"],
"share_decoder": False,
"up_mode": "NNSAMPLE_CONV",
"up_conv_filters": 1,
"up_conv_kernel_size": 4,
"share_up_layer": True,
"skip_merge_mode": "CONCAT", # CONCAT, WSUM, SUM
#"output_levels": 1,
"output_blocks": None,
#"output_channels": 1,
"share_output_layer": True,
"output_activation": "none", # none, relu, lrelu
"output_conv_kernel_size": 1,
"output_mode": "SINGLE", # SINGLE, RESIDUAL
# global settings
"conv_activation": "relu", # none, relu, lrelu
"alpha": 0.2, # leak for lrelu
"conv_padding": "ZERO", # ZERO, MIRRIR
"normalization": "LAYER", # NONE, LAYER
}
RECONSTRUCT_SEQUENCE_SETUP_NEURAL_DENSITY = {
"desc":"train neural networks to generate density directly from the targets.",
"data":{
'grid_size':48, #128, #resolution
'y_scale': 2, # "SF" ~1.77 (16/9)
'clip_grid':False,
'clip_grid_pad':4,
'crop_grid':True,
'res_down_factor':128*5, #target data down scale factor is 6 (i.e. 1/6 of raw image resolution) at 128 base grid resolution
'start':20,#56,#40,
'stop':141,#68,#52, #exclusive
'step':2,#2,
#
"randomize":64,
"batch_size":4,
"sims":list(range(20)),
"sequence_step":1,
"sequence_length":1,
"density":{
'target_type': "PREPROC", #RAW, PREPROC, SYNTHETIC
'scale':1,
'inflow':{
'active':False,
'hull_height':4,
'height':'MAX',
},
},
},
"training":{
'iterations':0,
"view_encoder":{
#"active":True, active= density.decoder.active or velocity.decoder.active
'encoder': ['L'], #NETWORK, NONE
# 'layers':[4,8,16,16,16,4],
# 'stride':1,
# 'skip_connections':None,
# 'skip_mode':"CONCAT",
# 'kernel_size':4,
# 'padding':'MIRROR', #NONE(=valid), ZERO(=same), MIRROR(=tf.pad(REFLECT))
# 'activation':'lrelu', # relu or lrelu
# 'activation_alpha':0.2, # leak for lrelu
# "load_encoder":None,
"lifting":"UNPROJECT", #UNPROJECT ;2D->3D method
#"load_lifting":None, #lifting could be NN
"merge":"MEAN", # SUM, MEAN ;method to merge 3D embeddings of multiple views
#"load_merge":None, #merging could be NN
"model":{
"num_levels": "VARIABLE",
"level_scale_factor": 2,
"input_levels": 1,
"create_inputs": True,
"input_blocks": ["C:1-1"],
#"input_conv_filters": 1,
#"input_conv_kernel_size": 1,
"share_input_layer": False,
"down_mode": "STRIDED", # STRIDED, NONE
"down_conv_filters": None,
"down_conv_kernel_size": 4,
"share_down_layer": True,
"encoder_resblocks": ["RB:1-1_1-1"],
"share_encoder": False,
"decoder_resblocks": ["RB:1-1_1-1"],
"share_decoder": False,
"up_mode": "NNSAMPLE_CONV",
"up_conv_filters": 1,
"up_conv_kernel_size": 4,
"share_up_layer": True,
"skip_merge_mode": "CONCAT", # CONCAT, WSUM, SUM
#"output_levels": 1,
"output_blocks": None,
"output_channels": 1,
"share_output_layer": True,
"output_activation": "none", # none, relu, lrelu
"output_conv_kernel_size": 1,
"output_mode": "SINGLE", # SINGLE, RESIDUAL
# global settings
"conv_activation": "relu", # none, relu, lrelu
"alpha": 0.2, # leak for lrelu
"conv_padding": "ZERO", # ZERO, MIRRIR
"normalization": "LAYER", # NONE, LAYER
}, # or path to model file
"start_level": 0,
"min_grid_res": 8,
"train_mode": "ALL", #"ALL", "TOP", "TOP_DEC"
"train_mode_schedule": sSchedule.setup_boolean_schedule(start=True, offset=0),
"skip_merge_weight_schedule": sSchedule.setup_linear_schedule_2(start=1., end=1.0, steps=650, offset=250),
"grow_intervals": [],
},
"volume_encoder":{
"active":False,
#"model":"decoder_model",
"model":{
"num_levels": "VARIABLE",
"level_scale_factor": 2,
"input_levels": 1,
"create_inputs": True,
"input_blocks": ["C:1-1"],
#"input_conv_filters": 1,
#"input_conv_kernel_size": 1,
"share_input_layer": False,
"down_mode": "STRIDED", # STRIDED, NONE
"down_conv_filters": None,
"down_conv_kernel_size": 4,
"share_down_layer": True,
"encoder_resblocks": ["RB:1-1_1-1"],
"share_encoder": False,
"decoder_resblocks": ["RB:1-1_1-1"],
"share_decoder": False,
"up_mode": "NNSAMPLE_CONV",
"up_conv_filters": 1,
"up_conv_kernel_size": 4,
"share_up_layer": True,
"skip_merge_mode": "CONCAT", # CONCAT, WSUM, SUM
#"output_levels": 1,
"output_blocks": None,
"output_channels": 1,
"share_output_layer": True,
"output_activation": "none", # none, relu, lrelu
"output_conv_kernel_size": 1,
"output_mode": "SINGLE", # SINGLE, RESIDUAL
# global settings
"conv_activation": "relu", # none, relu, lrelu
"alpha": 0.2, # leak for lrelu
"conv_padding": "ZERO", # ZERO, MIRRIR
"normalization": "LAYER", # NONE, LAYER
}, # or path to model file
"start_level": 0,
"min_grid_res": 8,
"train_mode": "ALL", #"ALL", "TOP", "TOP_DEC"
"train_mode_schedule": sSchedule.setup_boolean_schedule(start=True, offset=0),
"skip_merge_weight_schedule": sSchedule.setup_linear_schedule_2(start=1., end=1.0, steps=650, offset=250),
"grow_intervals": [],
},
"lifting_network":{
"active":False,
#"model":"decoder_model",
"model":{
"num_levels": "VARIABLE",
"level_scale_factor": 2,
"input_levels": 1,
"create_inputs": True,
"input_blocks": ["C:1-1"],
#"input_conv_filters": 1,
#"input_conv_kernel_size": 1,
"share_input_layer": False,
"down_mode": "STRIDED", # STRIDED, NONE
"down_conv_filters": None,
"down_conv_kernel_size": 4,
"share_down_layer": True,
"encoder_resblocks": ["RB:1-1_1-1"],
"share_encoder": False,
"decoder_resblocks": ["RB:1-1_1-1"],
"share_decoder": False,
"up_mode": "NNSAMPLE_CONV",
"up_conv_filters": 1,
"up_conv_kernel_size": 4,
"share_up_layer": True,
"skip_merge_mode": "CONCAT", # CONCAT, WSUM, SUM
#"output_levels": 1,
"output_blocks": None,
"output_channels": 1,
"share_output_layer": True,
"output_activation": "none", # none, relu, lrelu
"output_conv_kernel_size": 1,
"output_mode": "SINGLE", # SINGLE, RESIDUAL
# global settings
"conv_activation": "relu", # none, relu, lrelu
"alpha": 0.2, # leak for lrelu
"conv_padding": "ZERO", # ZERO, MIRRIR
"normalization": "LAYER", # NONE, LAYER
}, # or path to model file
"start_level": 0,
"min_grid_res": 8,
"train_mode": "ALL", #"ALL", "TOP", "TOP_DEC"
"train_mode_schedule": sSchedule.setup_boolean_schedule(start=True, offset=0),
"skip_merge_weight_schedule": sSchedule.setup_linear_schedule_2(start=1., end=1.0, steps=650, offset=250),
"grow_intervals": [],
},
"frame_merge_network":{
"active":False,
#"model":"decoder_model",
"model":{
"num_levels": "VARIABLE",
"level_scale_factor": 2,
"input_levels": 1,
"create_inputs": True,
"input_blocks": ["C:1-1"],
#"input_conv_filters": 1,
#"input_conv_kernel_size": 1,
"share_input_layer": False,
"down_mode": "STRIDED", # STRIDED, NONE
"down_conv_filters": None,
"down_conv_kernel_size": 4,
"share_down_layer": True,
"encoder_resblocks": ["RB:1-1_1-1"],
"share_encoder": False,
"decoder_resblocks": ["RB:1-1_1-1"],
"share_decoder": False,
"up_mode": "NNSAMPLE_CONV",
"up_conv_filters": 1,
"up_conv_kernel_size": 4,
"share_up_layer": True,
"skip_merge_mode": "CONCAT", # CONCAT, WSUM, SUM
#"output_levels": 1,
"output_blocks": None,
"share_output_layer": True,
"output_activation": "none", # none, relu, lrelu
"output_conv_kernel_size": 1,
"output_mode": "SINGLE", # SINGLE, RESIDUAL
# global settings
"conv_activation": "relu", # none, relu, lrelu
"alpha": 0.2, # leak for lrelu
"conv_padding": "ZERO", # ZERO, MIRRIR
"normalization": "LAYER", # NONE, LAYER
}, # or path to model file
"start_level": 0,
"min_grid_res": 8,
"train_mode": "ALL", #"ALL", "TOP", "TOP_DEC"
"train_mode_schedule": sSchedule.setup_boolean_schedule(start=True, offset=0),
"skip_merge_weight_schedule": sSchedule.setup_linear_schedule_2(start=1., end=1.0, steps=650, offset=250),
"grow_intervals": [],