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evolution_one_case.py
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'''Test script for experiments in paper Sec. 4.2, Supplement Sec. 3, reconstruction from laplacian.
'''
# Enable import from parent package
import os
import sys
import pandas as pd
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import naisr.modules
import naisr_meshing
import naisr
import naisr.workspace as ws
import argparse
import torch.utils.data as data_utils
from utils import cond_mkdir
from naisr import *
from visualizer import *
if __name__ == "__main__":
arg_parser = argparse.ArgumentParser(description="Testing a DeepSDF autodecoder")
arg_parser.add_argument(
"--networksetting",
"-e",
dest="networksetting",
default='examples/pediatric_airway/naivf_deepnaigsr.json',
help="The experiment directory. This directory should include "
+ "experiment specifications in 'specs.json', and logging will be "
+ "done in this directory as well.",
)
arg_parser.add_argument(
"--backbone",
"-b",
dest="backbone",
default='siren',
help="mlp or siren",
)
arg_parser.add_argument(
"--dim",
"-d",
dest="dimension",
default=3,
type=int,
help="shape ellipsoid or torus",
)
args = arg_parser.parse_args()
specs = ws.load_experiment_specifications(args.networksetting)
'''
read network setting and IO settings
'''
backbone = args.backbone
in_features = int(args.dimension)
experiment_name = specs["ExperimentName"]
print(experiment_name)
template_attributes = specs["TemplateAttributes"]
attributes = specs["Attributes"]
split_file = specs["Split"]
num_samp_per_scene = specs["SamplesPerScene"]
device = specs['Device']
latent_size = specs["CodeLength"]
root_path = os.path.join(specs['LoggingRoot'], specs['ExperimentName'])
cond_mkdir(root_path)
'''
load dataset
'''
data_source = specs["DataSource"]
# load model
latent_vectors = ws.load_latent_vectors(root_path, 'epoch_3000', torch.device(device)).to(device)
# load model
model = eval(specs['Network'])(
template_attributes=specs['TemplateAttributes'],
in_features=specs['InFeatures'],
hidden_features=specs['HiddenFeatures'],
hidden_layers=specs['HidenLayers'],
out_features=specs['OutFeatures'],
device=specs['Device'],
backbone=specs['Backbone'],
outermost_linear=False,
pos_enc=specs['PosEnc'],
latent_size=specs["CodeLength"])
checkpoint_path = os.path.join(root_path, 'checkpoints', 'epoch_3000.pth')
print(checkpoint_path)
model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device(device))["model_state_dict"])
model.to(specs['Device'])
model.eval()
# evaluate testing
savepath_evo = os.path.join(root_path, 'evolution')
cond_mkdir(savepath_evo)
list_metrics = []
'''
load a sample
'''
training_cases = naisr.get_ids(specs["Split"], split='test')
import pandas as pd
df_data = pd.read_csv(specs["DataSource"], header=0)
arr_samples_specific, attributes_specific, gt_specific = naisr.get_data_for_id('1379',
df_data,
training_cases,
specs["Attributes"])
attributes_specific = {key: value.to(device) for key, value in attributes_specific.items()}
# average_latent_code = latent_vectors[3][None, None, :] # [indices]
codes_dir = os.path.join(root_path, ws.transport_general_subdir, ws.transport_general_codes_subdir, )
codes_with_cov_dir = os.path.join(root_path, ws.transport_subdir, ws.transport_codes_subdir)
recons_mesh_dir_withcov = os.path.join(root_path, ws.transport_subdir, ws.transport_meshes_subdir, 'all', '1379', )
recons_mesh_dir = os.path.join(root_path, ws.transport_general_subdir, ws.transport_general_meshes_subdir, 'all','1379', )
savepath_specifc = os.path.join(savepath_evo,'1379')
cond_mkdir(savepath_specifc)
# gt
visualize_a_case(os.path.join(savepath_specifc, 'gt'), gt_specific['pvgt_path'][0], colors=None, colormap='rgb')
# without cov
#inferred_attributes_specific = load_inferred_covariates(codes_dir, '1193', device)
average_latent_code = load_transport_vectors(codes_dir, '1379', device)
current_color = naisr_meshing.revert_points_to_template(model,
average_latent_code,
attributes_specific,
recons_mesh_dir,
device)
visualize_a_case(os.path.join(savepath_specifc, 'pred_wo_cov'), os.path.join(recons_mesh_dir, 'surface.stl'), colors=current_color, colormap='rgb', normalize=True)
# with cov
average_latent_code_withcov = load_transport_vectors(codes_with_cov_dir,'1379', device)
current_color = naisr_meshing.revert_points_to_template(model,
average_latent_code_withcov,
attributes_specific,
recons_mesh_dir_withcov,
device)
visualize_a_case(os.path.join(savepath_specifc, 'pred_with_cov'), os.path.join(recons_mesh_dir_withcov,'surface.stl'), colors=current_color, colormap='rgb', normalize=True)
print(attributes_specific)
# reading data
model_input = arr_samples_specific.to(device)
for key, value in gt_specific.items():
if isinstance(value, torch.Tensor):
gt_specific[key] = value.to(device)
gt_specific['sdf'] = torch.clamp(gt_specific['sdf'], -1., 1.).float()
subj_name = str(gt_specific['id'][0])
savepath_interp_subj = os.path.join(savepath_evo, subj_name)
cond_mkdir(savepath_interp_subj)
#indices = indices[[0], None].repeat(1, num_samp_per_scene).to(device)
#batch_lat = average_latent_code[indices]
'''
evolution
'''
list_of_evolution_age = []
list_text_age = []
list_color_age = []
for age in [attributes_specific['age']]:
attributes_specific = {key: torch.zeros_like(value).to(device).float()[[0], ...] for key, value in
attributes_specific.items()}
attributes_specific['age'] = torch.zeros_like(attributes_specific['age']) + age
# attributes['weight'] = torch.zeros_like(attributes['weight']) + age
savedir_age = os.path.join(savepath_interp_subj, 'age_' + str(age))
cond_mkdir(savedir_age)
savepath = naisr_meshing.create_mesh_3dairway_evolution(model,
average_latent_code,
attributes_specific,
gt_specific,
savedir_age,
which_attribute='age',
output_type='model_out',
N=256, device=specs['Device'], EVALUATE=False)
current_color = naisr_meshing.revert_points_to_template_for_one_cov(model, average_latent_code, attributes_specific, savedir_age, device, 'age')
list_of_evolution_age.append(savepath)
list_color_age.append(current_color)
list_text_age.append('')
#plotter_evolution(list_of_evolution_age, os.path.join(savepath_interp_subj, 'age', ), list_text=None,
# list_colors=list_color_age)
visualize_a_case(os.path.join(savepath_specifc, 'age_disentangle'), savepath, colors=current_color, colormap='rgb', normalize=True)
list_of_evolution_weight = []
list_text_weight = []
list_color_weight = []
# for weight in [attributes['weight']-0.2, attributes['weight']-0.1, attributes['weight'], attributes['weight']+0.1, attributes['weight']+0.2]:
for weight in [attributes_specific['weight']]:
attributes_specific = {key: torch.zeros_like(value).to(device).float()[[0], ...] for key, value in
attributes_specific.items()}
attributes_specific['weight'] = torch.zeros_like(attributes_specific['weight']) + weight
# attributes['weight'] = torch.zeros_like(attributes['weight']) + age
savedir_weight = os.path.join(savepath_interp_subj, 'weight_' + str(weight))
cond_mkdir(savedir_weight)
# savepath = os.path.join(savedir_weight, 'weight')
savepath = naisr_meshing.create_mesh_3dairway_evolution(model,
average_latent_code,
attributes_specific,
gt_specific,
savedir_weight,
which_attribute='weight',
output_type='model_out',
N=256, device=specs['Device'], EVALUATE=False)
current_color = naisr_meshing.revert_points_to_template_for_one_cov(model, average_latent_code, attributes_specific, savedir_weight, device, 'weight')
list_of_evolution_weight.append(savepath)
list_color_weight.append(current_color)
list_text_weight.append('')
visualize_a_case(os.path.join(savepath_specifc, 'weight_disentangle'), savepath, colors=current_color, colormap='rgb', normalize=True)
#plotter_evolution(list_of_evolution_weight, os.path.join(savepath_interp_subj, 'weight'), list_text=None,
# list_colors=list_color_weight)
'''
evolution
'''
list_of_evolution_age = []
list_text_age = []
list_color_age = []
# for age in [attributes['age']-0.2, attributes['age']-0.1, attributes['age'], attributes['age']+0.1, attributes['age']+0.2]:
for age in np.linspace(-0.8, 0.8, 7):
attributes_specific = {key: torch.zeros_like(value).to(device).float()[[0], ...] for key, value in
attributes_specific.items()}
attributes_specific['age'] = torch.zeros_like(attributes_specific['age']) + age
# attributes['weight'] = torch.zeros_like(attributes['weight']) + age
savedir_age = os.path.join(savepath_interp_subj, 'age_' + str(age))
cond_mkdir(savedir_age)
savepath = naisr_meshing.create_mesh_3dairway_evolution(model,
average_latent_code,
attributes_specific,
gt_specific,
savedir_age,
which_attribute='age',
output_type='model_out',
N=256, device=specs['Device'], EVALUATE=False)
current_color = naisr_meshing.revert_points_to_template_for_one_cov(model, average_latent_code, attributes_specific, savedir_age, device, 'age')
list_of_evolution_age.append(savepath)
list_color_age.append(current_color)
list_text_age.append('')
plotter_evolution(list_of_evolution_age, os.path.join(savepath_interp_subj, 'age', ), list_text=None,
list_colors=list_color_age)
'''
'''
list_of_evolution_weight = []
list_text_weight = []
list_color_weight = []
# for weight in [attributes['weight']-0.2, attributes['weight']-0.1, attributes['weight'], attributes['weight']+0.1, attributes['weight']+0.2]:
for weight in np.linspace(-0.8, 0.8, 7):
attributes_specific = {key: torch.zeros_like(value).to(device).float()[[0], ...] for key, value in
attributes_specific.items()}
attributes_specific['weight'] = torch.zeros_like(attributes_specific['weight']) + weight
# attributes['weight'] = torch.zeros_like(attributes['weight']) + age
savedir_weight = os.path.join(savepath_interp_subj, 'weight_' + str(weight))
cond_mkdir(savedir_weight)
# savepath = os.path.join(savedir_weight, 'weight')
savepath = naisr_meshing.create_mesh_3dairway_evolution(model,
average_latent_code,
attributes_specific,
gt_specific,
savedir_weight,
which_attribute='weight',
output_type='model_out',
N=256, device=specs['Device'], EVALUATE=False)
current_color = naisr_meshing.revert_points_to_template_for_one_cov(model, average_latent_code, attributes_specific, savedir_weight, device, 'weight')
list_of_evolution_weight.append(savepath)
list_color_weight.append(current_color)
list_text_weight.append('')
plotter_evolution(list_of_evolution_weight, os.path.join(savepath_interp_subj, 'weight'), list_text=None,
list_colors=list_color_weight)