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evolution_template_shape_trajectory.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
import visualizer
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 *
dict_list_cov = {}
dict_list_cov['Airway'] = {'age': np.linspace(0, 240, 20), 'weight': np.linspace(0, 160, 20).tolist(), 'sex': np.linspace(0, 1, 20).tolist()}
dict_list_cov['starman'] = {'cov_1': np.linspace(-1., 1., 20), 'cov_2': np.linspace(-1., 1., 20).tolist()}
dict_list_cov['ADNI'] = {'age': np.linspace(50, 100, 20), 'AD': np.linspace(0, 1, 20).tolist(), 'edu': np.linspace(0, 30, 20).tolist(), 'sex': np.linspace(0, 1, 20).tolist()}
def extract_latent_vector(latent_vectors, idx):
average_latent_code = torch.mean(latent_vectors, dim=-2)[None, None, :]
if idx == 'mean':
return average_latent_code
elif idx == 'zero':
return torch.zeros_like(average_latent_code)
else:
try:
return latent_vectors[int(idx)][None, None, :]
except:
print("Wrong index of latent vectors. Return average latent vector")
return average_latent_code
if __name__ == "__main__":
arg_parser = argparse.ArgumentParser(description="Testing a DeepSDF autodecoder")
arg_parser.add_argument(
"--networksetting",
"-e",
dest="networksetting",
default="examples/hippocampus/naigsr_0920_base.json", #'examples/pediatric_airway/naigsr_0920_nodisentinv.json', # #"examples/hyperpar_gridsearch/subset.json",#'examples/pediatric_airway/naigsr_0920_nodisentinv.json', #"examples/hippocampus/naigsr_0920_base.json",# 'examples/pediatric_airway/naigsr_0920_nodisentinv.json', #"examples/hippocampus/naigsr_0920_base.json",#"examples/hippocampus/asdf_0919.json",#"examples/hippocampus/naigsr_0920_base.json",#'examples/pediatric_airway/naigsr_0920_nodisentinv.json', #='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(
"--checkpoint",
"-c",
dest="checkpoint",
default="latest",
help="The checkpoint weights to use. This can be a number indicated an epoch "
+ "or 'latest' for the latest weights (this is the default)",
)
args = arg_parser.parse_args()
specs = ws.load_experiment_specifications(args.networksetting)
'''
read network setting and IO settings
'''
backbone = args.backbone
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, 'latest', torch.device(device)).to(device)
average_latent_code = torch.zeros_like(torch.mean(latent_vectors, dim=-2)[None, None, :])
# loading dataset
shapetype = specs["Class"]
filename_dataset = specs["DataSource"]
num_dim = specs["InFeatures"]
if shapetype == 'Airway':
cases = naisr.get_airway_ids(specs["Split"], split='test')
training_cases = naisr.get_airway_ids(specs["Split"], split='train')
load_one_case = naisr.get_airway_data_for_id
df_data = pd.read_csv(filename_dataset)
load_mean_normals_of_covariates = naisr.airway_dataset.get_mean_normals_of_covariates
elif shapetype == 'starman':
cases = naisr.get_starman_ids(filename_dataset, 'test')
training_cases = naisr.get_starman_ids(filename_dataset, 'train')
load_one_case = naisr.get_starman_data_for_id
df_data = pd.read_csv(filename_dataset['test'])
load_mean_normals_of_covariates = naisr.starman_dataset.get_mean_normals_of_covariates
#load_mean_normals_of_covariates = naisr.adni_dataset.get_mean_normals_of_covariates
elif shapetype == 'ADNI':
cases = naisr.get_adni_ids(specs["Split"], split='test')
training_cases = naisr.get_adni_ids(specs["Split"], split='train')
load_one_case = naisr.get_adni_data_for_id
df_data = pd.read_csv(filename_dataset)
load_mean_normals_of_covariates = naisr.adni_dataset.get_mean_normals_of_covariates
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, ws.model_params_subdir, args.checkpoint + '.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, 'TemplateShapeEvolution')
cond_mkdir(savepath_evo)
savepath_evo_type = os.path.join(savepath_evo, 'average')
cond_mkdir(savepath_evo_type)
covariante_names = specs['Attributes'] #[specs['Attributes'][0], specs['Attributes'][1]]
attributes_mean, attributes_std = load_mean_normals_of_covariates(df_data, training_cases, covariante_names)
for ith_cov_name in covariante_names: # current covariate name
ith_shape = 0
for current_cov_value in dict_list_cov[shapetype][ith_cov_name]: # current covariate value
attributes = specs["TemplateAttributes"].copy()
attributes[ith_cov_name] = (current_cov_value - attributes_mean[ith_cov_name]) / attributes_std[ith_cov_name]
attributes = {key: torch.from_numpy(np.array([value])[None, :]).to(device).float()[[0], ...] for key, value in attributes.items()}
savedir = os.path.join(savepath_evo_type, ith_cov_name)
cond_mkdir(savedir)
dict_savepath = naisr_meshing.create_mesh_reconstruction(shapetype)(model,
average_latent_code,
attributes,
{},
savedir,
output_type='model_out',
dim=num_dim,
shapetype=shapetype,
N=256,
device=specs['Device'],
EVALUATE=False,
MAKE_GT=False,
MAKE_TEMPLATE=True)
visualizer.plotter_evolution_traj(shapetype)(dict_savepath, savepath=os.path.join(savedir, str(int(ith_shape)).zfill(6)) + '.png')
ith_shape += 1
print(1)