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reconstruct_atlas_with_cov.py
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reconstruct_atlas_with_cov.py
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#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import argparse
import json
import logging
import os
import random
import time
import torch
import naisr
import naisr.workspace as ws
from utils import cond_mkdir
import pandas as pd
import naisr_meshing
from naisr import loss_pointcloud_sdf
from naisr import *
def reconstruct(
decoder,
num_iterations,
latent_size,
test_sdf,
attributes,
stat,
clamp_dist,
lr=5e-4,
l2reg=False,
):
def adjust_learning_rate(
initial_lr, optimizer, num_iterations, decreased_by, adjust_lr_every
):
lr = initial_lr * ((1 / decreased_by) ** (num_iterations // adjust_lr_every))
for param_group in optimizer.param_groups:
param_group["lr"] = lr
decreased_by = 10
adjust_lr_every = int(num_iterations / 2)
if type(stat) == type(0.1):
latent = torch.ones(1, latent_size).normal_(mean=0, std=stat).to(device)
else:
latent = torch.normal(stat[0].detach(), stat[1].detach()).to(device)
latent.requires_grad = True
# define attributes
optimizer_lat = torch.optim.Adam([latent], lr=lr)
loss_num = 0
#loss_l1 = torch.nn.L1Loss()
loss_fn = loss_pointcloud_sdf
for e in range(num_iterations):
decoder.eval()
xyz = test_sdf#[:, :, 0:3]
adjust_learning_rate(lr, optimizer_lat, e, decreased_by, adjust_lr_every)
optimizer_lat.zero_grad()
latent_inputs = latent.expand(xyz.shape[-2], -1)
model_output = decoder(xyz, attributes, latent_inputs[None, :, :], training=False, testing=False)
gt['sdf'] = torch.clamp(gt['sdf'], -1., 1.)
specs['Loss']["whether_icon"] = False
specs['Loss']['whether_disentangle'] = False
specs['Loss']['whether_inv'] = False
specs['Loss']["whether_inv_sampling"] = False
losses = loss_fn(model_output, gt, latent_inputs, epoch=e, dict_losses=specs['Loss'])
loss = 0
for name, il in losses.items():
if 'code_reg' in name:
loss += (il*10)
else:
loss += il
loss.backward()
optimizer_lat.step()
if e % 50 == 0:
logging.info(loss.cpu().data.numpy())
logging.info(e)
logging.info(latent.norm())
loss_num = loss.cpu().data.numpy()
return loss_num, latent
if __name__ == "__main__":
arg_parser = argparse.ArgumentParser(
description="Use a trained DeepSDF decoder to reconstruct a shape given SDF "
+ "samples."
)
arg_parser.add_argument(
"--experiment",
"-e",
dest="experiment_directory",
default='examples/pediatric_airway/naivf_deepnaisr.json',
required=False,
help="The experiment directory which includes specifications and saved model "
+ "files to use for reconstruction",
)
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)",
)
arg_parser.add_argument(
"--iters",
"-i",
dest="iterations",
default=800,
help="The number of iterations of latent code optimization to perform.",
)
arg_parser.add_argument(
"--skip",
dest="skip",
default=True,
help="Skip meshes which have already been reconstructed.",
)
naisr.add_common_args(arg_parser)
args = arg_parser.parse_args()
naisr.configure_logging(args)
def empirical_stat(latent_vecs, indices):
lat_mat = torch.zeros(0).to(device)
for ind in indices:
lat_mat = torch.cat([lat_mat, latent_vecs[ind]], 0)
mean = torch.mean(lat_mat, 0)
var = torch.var(lat_mat, 0)
return mean, var
specs_filename = args.experiment_directory
if not os.path.isfile(specs_filename):
raise Exception(
'The experiment directory does not include specifications file "specs.json"'
)
specs = json.load(open(specs_filename))
device = specs['Device']
num_dim = specs["InFeatures"]
root_path = os.path.join(specs['LoggingRoot'], specs['ExperimentName'])
decoder = 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"])
latent_size = specs["CodeLength"]
#decoder = arch.Decoder(num_atc_parts=specs["NumAtcParts"], do_sup_with_part=specs["TrainWithParts"])
#decoder = torch.nn.DataParallel(decoder)
saved_model_state = torch.load(
os.path.join(
root_path, ws.model_params_subdir, args.checkpoint + ".pth"
), map_location=torch.device(device)
)
saved_model_epoch = saved_model_state["epoch"]
decoder.load_state_dict(saved_model_state["model_state_dict"])
decoder = decoder.to(device)
logging.info(decoder)
err_sum = 0.0
repeat = 1
save_latvec_only = False
rerun = 0
reconstruction_withcov_dir = os.path.join(root_path, ws.reconstructions_withcov_subdir)
if not os.path.isdir(reconstruction_withcov_dir):
os.makedirs(reconstruction_withcov_dir)
reconstruction_withcov_meshes_dir = os.path.join(reconstruction_withcov_dir, ws.reconstruction_withcov_meshes_subdir)
if not os.path.isdir(reconstruction_withcov_meshes_dir):
os.makedirs(reconstruction_withcov_meshes_dir)
reconstruction_withcov_codes_dir = os.path.join(reconstruction_withcov_dir, ws.reconstruction_withcov_codes_subdir)
if not os.path.isdir(reconstruction_withcov_codes_dir):
os.makedirs(reconstruction_withcov_codes_dir)
# loading dataset
shapetype = specs["Class"]
filename_dataset = specs["DataSource"]
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)
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'])
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)
list_metrics = []
for ii, test_idx in enumerate(cases):
arr_samples, attributes, gt = load_one_case(test_idx, df_data, training_cases, specs["Attributes"], stage='test')
arr_samples = arr_samples.to(device)
attributes = {key: value.to(device).float() for key, value in attributes.items()}
for key, value in gt.items():
if isinstance(value, torch.Tensor):
gt[key] = value.to(device)
reconstruction_withcov_meshes_dir_subj = os.path.join(reconstruction_withcov_meshes_dir, str(test_idx))
cond_mkdir(reconstruction_withcov_meshes_dir_subj)
if args.skip:
if shapetype != 'starman':
stl_path = os.path.join(reconstruction_withcov_meshes_dir_subj, 'surface.stl')
if os.path.exists(stl_path):
continue
else:
stl_path = os.path.join(reconstruction_withcov_meshes_dir_subj, 'surface.vtk')
if os.path.exists(stl_path):
continue
#reconstruction_codes_dir_subj = os.path.join(reconstruction_codes_dir, str(gt['id'][0]))
#cond_mkdir(reconstruction_codes_dir_subj)
#print( str(gt['id']) + '-----')
for k in range(repeat):
logging.info("reconstructing {}".format(test_idx))
start = time.time()
err, latent = reconstruct(
decoder,
int(args.iterations),
latent_size,
arr_samples,
attributes,
0.01, # [emp_mean,emp_var],
0.1,
lr=5e-3,
l2reg=True,
)
logging.info("reconstruct time: {}".format(time.time() - start))
err_sum += err
logging.info("current_error avg: {}".format((err_sum / (ii + 1))))
print('attributes: ' + str(attributes))
if not save_latvec_only:
latent_filename = os.path.join(reconstruction_withcov_codes_dir, str(test_idx) + ".pth")
#print(latent.shape)
start = time.time()
with torch.no_grad():
#print(reconstruction_meshes_dir_subj)
dict_metrics = naisr_meshing.create_mesh_reconstruction(shapetype)(decoder,
latent[None, :, ],
attributes,
gt,
reconstruction_withcov_meshes_dir_subj,
output_type='model_out',
dim=num_dim,
N=256,
shapetype=shapetype,
device=specs['Device'],
EVALUATE=True)
#naisr_meshing.save_to_ply(decoder, latent[None, :, ], attributes, reconstruction_withcov_meshes_dir_subj, device)
if not os.path.exists(os.path.dirname(latent_filename)):
os.makedirs(os.path.dirname(latent_filename))
torch.save(latent.unsqueeze(0), latent_filename)
inferred_covariate_filename = os.path.join(reconstruction_withcov_codes_dir,
str(test_idx) + "_covariate.pth")
list_metrics.append(dict_metrics)
# save
pd.DataFrame.from_records(list_metrics).to_csv(os.path.join(reconstruction_withcov_meshes_dir_subj, 'metrics.csv'))