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train_dmsr.py
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train_dmsr.py
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import os
import torch
import numpy as np
from networks.render import dm_nerf
from config import initial, create_nerf
from networks.tester import render_test
from datasets.loader_dmsr import load_data
from networks.penalizer import ins_penalizer
from networks.helpers import get_select_full, z_val_sample
from networks.evaluator import ins_criterion, img2mse, mse2psnr
np.random.seed(0)
torch.cuda.manual_seed(3)
def train():
model_fine.train()
model_coarse.train()
N_iters = 500000 + 1
z_val_coarse = z_val_sample(args.N_train, args.near, args.far, args.N_samples)
args.N_ins = None
for i in range(0, N_iters):
img_i = np.random.choice(i_train)
gt_rgb = images[img_i].to(args.device)
pose = poses[img_i, :3, :4].to(args.device)
gt_label = gt_labels[img_i].to(args.device)
target_c, target_i, batch_rays = get_select_full(gt_rgb, pose, K, gt_label, args.N_train)
all_info = dm_nerf(batch_rays, position_embedder, view_embedder, model_coarse, model_fine, z_val_coarse, args)
# coarse losses
rgb_loss_coarse = img2mse(all_info['rgb_coarse'], target_c)
psnr_coarse = mse2psnr(rgb_loss_coarse)
ins_loss_coarse, valid_ce_coarse, invalid_ce_coarse, valid_siou_coarse = \
ins_criterion(all_info['ins_coarse'], target_i, args.ins_num)
# fine losses
rgb_loss_fine = img2mse(all_info['rgb_fine'], target_c)
psnr_fine = mse2psnr(rgb_loss_fine)
ins_loss_fine, valid_ce_fine, invalid_ce_fine, valid_siou_fine = \
ins_criterion(all_info['ins_fine'], target_i, args.ins_num)
# without penalize loss
ins_loss = ins_loss_fine + ins_loss_coarse
rgb_loss = rgb_loss_fine + rgb_loss_coarse
total_loss = ins_loss + rgb_loss
# use penalize
if args.penalize:
emptiness_coarse = ins_penalizer(all_info['raw_coarse'], all_info['z_vals_coarse'],
all_info['depth_coarse'], batch_rays[1], args)
emptiness_fine = ins_penalizer(all_info['raw_fine'], all_info['z_vals_fine'],
all_info['depth_fine'], batch_rays[1], args)
emptiness_loss = emptiness_fine + emptiness_coarse
total_loss = total_loss + emptiness_loss
# optimizing
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# losses decay
### update learning rate ###
decay_rate = 0.1
decay_steps = args.lrate_decay * 1000
new_lrate = args.lrate * (decay_rate ** ((i) / decay_steps))
for param_group in optimizer.param_groups:
param_group['lr'] = new_lrate
###################################
if i % args.i_print == 0:
print(f"[TRAIN] Iter: {i} PSNR: {psnr_fine.item()} Total_Loss: {total_loss.item()} RGB_Loss: {rgb_loss.item()} Ins_Loss: {ins_loss.item()}")
if i % args.i_save == 0:
path = os.path.join(args.basedir, args.expname, args.log_time, '{:06d}.tar'.format(i))
save_model = {
'iteration': i,
'network_coarse_state_dict': model_coarse.state_dict(),
'network_fine_state_dict': model_fine.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(save_model, path)
if i % args.i_test == 0:
model_coarse.eval()
model_fine.eval()
args.is_train = False
selected_indices = np.random.choice(len(i_test), size=[10], replace=False)
selected_i_test = i_test[selected_indices]
testsavedir = os.path.join(args.basedir, args.expname, args.log_time, 'testset_{:06d}'.format(i))
matched_file = os.path.join(testsavedir, 'matching_log.txt')
os.makedirs(testsavedir, exist_ok=True)
with torch.no_grad():
test_poses = torch.Tensor(poses[selected_i_test].to(args.device))
test_imgs = images[selected_i_test]
test_gt_labels = gt_labels[selected_i_test].to(args.device)
render_test(position_embedder, view_embedder, model_coarse, model_fine, test_poses, hwk, args,
gt_imgs=test_imgs, gt_labels=test_gt_labels, ins_rgbs=ins_rgbs, savedir=testsavedir,
matched_file=matched_file)
print('Training model saved!')
args.is_train = True
model_coarse.train()
model_fine.train()
if __name__ == '__main__':
args = initial()
# load data
images, poses, hwk, i_split, gt_labels, ins_rgbs, args.ins_num, objs, view_poses, ins_map = load_data(args)
print('Load data from', args.datadir)
i_train, i_test = i_split
H, W, K = hwk
# Create nerf model
position_embedder, view_embedder, model_coarse, model_fine, args = create_nerf(args)
# Create optimizer
grad_vars = list(model_coarse.parameters()) + list(model_fine.parameters())
optimizer = torch.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999))
# move data to gpu
images = torch.Tensor(images).cpu()
gt_labels = torch.Tensor(gt_labels).type(torch.int16).cpu()
poses = torch.Tensor(poses).cpu()
train()