from tqdm import tqdm
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
from numpy.linalg import inv
from lib.options import BaseOptions
from lib.mesh_util import save_obj_mesh_with_color, reconstruction
from lib.data import EvalWPoseDataset, EvalDataset
from lib.model import HGPIFuNetwNML, HGPIFuMRNet
from lib.geometry import index
from PIL import Image
parser = BaseOptions()
def gen_mesh(res,
net,
cuda,
data,
save_path,
thresh=0.5,
use_octree=True,
components=False):
image_tensor_global = data['img_512'].to(device=cuda)
image_tensor = data['img'].to(device=cuda)
calib_tensor = data['calib'].to(device=cuda)
net.filter_global(image_tensor_global)
import torch
from torch.utils.data import DataLoader
from lib.options import BaseOptions
from lib.mesh_util import *
from lib.sample_util import *
from lib.train_util import *
from lib.model import *
from PIL import Image
import torchvision.transforms as transforms
import glob
import tqdm
# get options
opt = BaseOptions().parse()
class Evaluator:
def __init__(self, opt, projection_mode='orthogonal'):
self.opt = opt
self.load_size = self.opt.loadSize
self.to_tensor = transforms.Compose([
transforms.Resize(self.load_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# set cuda
cuda = torch.device('cuda:%d' % opt.gpu_id)
# create net
# visual sanity check
if visual_demo_flag:
print("saving demo for visualCheck...")
# the input 512x512x3 image
os.system("cp %s ./sample_images/" % (save_path_png))
# the estimated mesh .obj
os.system("cp %s ./sample_images/" % (save_path_obj))
# the gt high-resolution mesh .obj
gtMesh = read_and_canonize_gt_mesh(preFix=frameIdx[1],
args=args,
withTexture=True)
ObjIO.save_obj_data_color(
gtMesh, "./sample_images/%06d_meshGT.obj" % (frameIdx[1]))
# the gt low-resolution mesh. obj
os.system("mv %s ./sample_images/" % (save_path_gt_obj))
if __name__ == '__main__':
# parse args.
args = BaseOptions().parse()
# main function
main(args=args)
def train(opt):
# set cuda
device_ids = [int(i) for i in opt.gpu_ids.split(",")]
cuda = torch.device('cuda:%d' % device_ids[0])
tb.initWriter(opt)
train_dataset = TrainDataset(opt, phase='train')
test_dataset = TrainDataset(opt, phase='test')
print("Loading training data...")
coll = MultiViewCollator(opt)
# create data loader
train_data_loader = DataLoader(train_dataset,
batch_size=opt.batch_size,
shuffle=not opt.serial_batches,
collate_fn=coll,
num_workers=opt.num_threads,
pin_memory=opt.pin_memory)
print('train data size: ', len(train_data_loader))
# NOTE: batch size should be 1 and use all the points for evaluation
#test_data_loader = DataLoader(test_dataset,
# batch_size=1, shuffle=True, collate_fn=coll,
# num_workers=opt.num_threads, pin_memory=opt.pin_memory)
print('test data size: ', len(test_dataset))
# create net
print("Num GPUs: " + str(torch.cuda.device_count()))
if len(device_ids) > 1:
netG = MyDataParallel(HGPIFuNet(opt, train_dataset.projection_mode),
device_ids=device_ids).to(device=cuda)
else:
netG = HGPIFuNet(opt, train_dataset.projection_mode).to(device=cuda)
#netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
#optimizerG = torch.optim.Adam(netG.parameters(), lr=opt.learning_rate)
optimizerG = torch.optim.RMSprop(netG.parameters(),
lr=opt.learning_rate,
momentum=0,
weight_decay=0)
lr = opt.learning_rate
print('Using Network: ', netG.name)
def set_train():
netG.train()
def set_eval():
netG.eval()
# load checkpoints
if opt.load_netG_checkpoint_path is not None:
print('loading for net G ...', opt.load_netG_checkpoint_path)
netG.load_state_dict(
torch.load(opt.load_netG_checkpoint_path, map_location=cuda))
if opt.continue_train:
if opt.resume_epoch < 0:
model_path = '%s/%s/netG_latest' % (opt.checkpoints_path, opt.name)
else:
model_path = '%s/%s/netG_epoch_%d' % (opt.checkpoints_path,
opt.name, opt.resume_epoch)
print('Resuming from ', model_path)
netG.load_state_dict(torch.load(model_path, map_location=cuda))
if opt.decoder_base != "":
model_opt = BaseOptions().loadOptFromFile(name=opt.decoder_base)
model_with_decoder = HGPIFuNet(
model_opt, train_dataset.projection_mode).to(device=cuda)
model_path = '%s/%s/netG_latest' % (opt.checkpoints_path,
opt.decoder_base)
model_with_decoder.load_state_dict(
torch.load(model_path, map_location=cuda))
netG.surface_classifier = model_with_decoder.surface_classifier
for param in netG.surface_classifier.parameters():
param.requires_grad = False
print("Loaded decoder from {0} and froze parameters...".format(
opt.decoder_base))
os.makedirs(opt.checkpoints_path, exist_ok=True)
os.makedirs(opt.results_path, exist_ok=True)
os.makedirs('%s/%s' % (opt.checkpoints_path, opt.name), exist_ok=True)
os.makedirs('%s/%s/training' % (opt.results_path, opt.name), exist_ok=True)
os.makedirs('%s/%s/test' % (opt.results_path, opt.name), exist_ok=True)
BaseOptions().saveOptToFile(opt)
scaler = torch.cuda.amp.GradScaler()
# training
start_epoch = 0 if not opt.continue_train else max(opt.resume_epoch, 0)
for epoch in range(start_epoch, opt.num_epoch):
epoch_start_time = time.time()
set_train()
iter_data_time = time.time()
for train_idx, train_data in enumerate(train_data_loader):
iter_start_time = time.time()
train_data = move_to_gpu(train_data, cuda)
pred, nmls, _, error = netG.forward(
train_data['images'],
train_data['samples'],
train_data['calib'],
imgSizes=train_data['size'],
labels=train_data['labels'],
points_surface=train_data['samples_normals'],
labels_nml=train_data['normals'],
labels_edges=train_data['edges'])
scaler.scale(error['Err(cmb)'].mean()).backward()
scaler.step(optimizerG)
scaler.update()
optimizerG.zero_grad()
netG.zero_grad()
#optimizerG.step()
iter_net_time = time.time()
eta = ((iter_net_time - epoch_start_time) /
(train_idx + 1)) * len(train_data_loader) - (
iter_net_time - epoch_start_time)
if train_idx % opt.freq_plot == 0:
normal_loss = 0
edge_loss = 0
if opt.use_normal_loss:
normal_loss = error['Err(nml)'].mean().item()
if opt.use_edge_loss:
edge_loss = error['Err(edges)'].mean().item()
print(
'Name: {0} | Epoch: {1} | {2}/{3} | Err (Cmb): {4:.06f} | Err(Occ): {5:.06f} | Err(Nml): {6:.06f} | Err(edges): {7:.06f} | LR: {8:.06f} | dataT: {9:.05f} | netT: {10:.05f} | ETA: {11:02d}:{12:02d}'
.format(opt.name, epoch, train_idx, len(train_data_loader),
error['Err(cmb)'].mean().item(),
error['Err(occ)'].mean().item(), normal_loss,
edge_loss, lr, iter_start_time - iter_data_time,
iter_net_time - iter_start_time, int(eta // 60),
int(eta - 60 * (eta // 60))))
if not opt.debug and train_idx % opt.freq_save == 0 and train_idx != 0:
torch.save(
netG.state_dict(),
'%s/%s/netG_latest' % (opt.checkpoints_path, opt.name))
torch.save(
netG.state_dict(), '%s/%s/netG_epoch_%d' %
(opt.checkpoints_path, opt.name, epoch))
if train_idx % opt.freq_save_ply == 0:
save_path = '%s/%s/pred.ply' % (opt.results_path, opt.name)
r = pred[0].cpu()
points = train_data['samples'][0].transpose(0, 1).cpu()
save_samples_truncted_prob(save_path,
points.detach().numpy(),
r.detach().numpy())
nml_source = pred[0] if opt.predict_normal else nmls[0]
gt_labels_rgb = (
train_data['normals'][0].cpu().detach().numpy() + 1) * 0.5
pred_labels_rgb = (nml_source.cpu().detach().numpy() + 1) * 0.5
save_samples_rgb(
'%s/%s/normals_gt.ply',
train_data['samples_normals'][0].cpu().detach().numpy().T,
gt_labels_rgb.T)
save_samples_rgb(
'%s/%s/normals_pred.ply',
train_data['samples_normals'][0].cpu().detach().numpy().T,
pred_labels_rgb.T)
iter_data_time = time.time()
# update learning rate
if isinstance(optimizerG, torch.optim.RMSprop):
lr = adjust_learning_rate(optimizerG, epoch, lr, opt.schedule,
opt.gamma)
if len(device_ids) > 1:
netG.device_ids = [device_ids[0]]
#### test
with torch.no_grad():
set_eval()
if not opt.no_num_eval:
test_losses = {}
print('calc error (test) ...')
test_errors = calc_error(coll, netG, cuda, test_dataset, 100)
print(
'eval test OCC: {0:06f} NML: {1:06f} EDGES: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'
.format(*test_errors))
occ, nml, edges, IOU, prec, recall = test_errors
test_losses['OCC(test)'] = occ
test_losses['NML(test)'] = nml
test_losses['EDGES(test'] = edges
test_losses['IOU(test)'] = IOU
test_losses['prec(test)'] = prec
test_losses['recall(test)'] = recall
print('calc error (train) ...')
train_dataset.is_train = False
train_errors = calc_error(coll, netG, cuda, train_dataset, 100)
train_dataset.is_train = True
print(
'eval train OCC: {0:06f} NML: {1:06f} EDGES: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'
.format(*train_errors))
occ, nml, edges, IOU, prec, recall = train_errors
test_losses['OCC(train)'] = occ
test_losses['NML(train)'] = nml
test_losses['EDGES(train'] = edges
test_losses['IOU(train)'] = IOU
test_losses['prec(train)'] = prec
test_losses['recall(train)'] = recall
if not opt.no_gen_mesh:
print('generate mesh (test) ...')
test_data = None
train_data = None
for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
test_data = test_dataset[random.randint(
0,
len(test_dataset) - 1)]
#test_data = test_dataset[6]
test_data_batched = coll([test_data])
test_data_batched = move_to_gpu(test_data_batched, cuda)
save_path = '%s/%s/test/test_eval_epoch%d_%s.obj' % (
opt.results_path, opt.name, epoch, test_data['name'])
mesh_test = gen_mesh(opt, netG, cuda, test_data_batched,
save_path)
print('generate mesh (train) ...')
train_dataset.is_train = False
for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
train_data = train_dataset[random.randint(
0,
len(test_dataset) - 1)]
train_data_batched = coll([train_data])
train_data_batched = move_to_gpu(train_data_batched, cuda)
save_path = '%s/%s/training/train_eval_epoch%d_%s.obj' % (
opt.results_path, opt.name, epoch, train_data['name'])
mesh_train = gen_mesh(opt, netG, cuda, train_data_batched,
save_path)
train_dataset.is_train = True
tb.updateAfterEpoch(epoch, train_errors, test_errors,
train_data['img'], test_data['img'])
if len(device_ids) > 1:
netG.device_ids = device_ids
def train(opt):
# set cuda
device_ids = [int(i) for i in opt.gpu_ids.split(",")]
cuda = torch.device('cuda:%d' % device_ids[0])
tb.initWriter(opt)
train_dataset = TrainDataset(opt, phase='train')
test_dataset = TrainDataset(opt, phase='test')
print("Loading training data...")
coll = MultiViewCollator(opt)
# create data loader
train_data_loader = DataLoader(train_dataset,
batch_size=opt.batch_size, shuffle=not opt.serial_batches,collate_fn=coll,
num_workers=opt.num_threads, pin_memory=opt.pin_memory)
print('train data size: ', len(train_data_loader))
# NOTE: batch size should be 1 and use all the points for evaluation
#test_data_loader = DataLoader(test_dataset,
# batch_size=1, shuffle=True, collate_fn=coll,
# num_workers=opt.num_threads, pin_memory=opt.pin_memory)
print('test data size: ', len(test_dataset))
# create net
print("Num GPUs: " + str(torch.cuda.device_count()))
name = "multiview_pifu_OCC_hg_bp__256_15000_nml_loss_edge_loss__mlp"
model_opt = BaseOptions().loadOptFromFile(name= name)
model_opt.use_edge_loss = False
if len(device_ids) > 1:
netG = MyDataParallel(HGPIFuNet(model_opt, train_dataset.projection_mode), device_ids=device_ids).to(device=cuda)
else:
netG = HGPIFuNet(model_opt, train_dataset.projection_mode).to(device=cuda)
model_path = '%s/%s/netG_epoch_%d' % (opt.checkpoints_path, name, 20)
netG.load_state_dict(torch.load(model_path, map_location=cuda))
optimizerG = torch.optim.RMSprop(netG.parameters(), lr=opt.learning_rate, momentum=0, weight_decay=0)
lr = opt.learning_rate
print('Using Network: ', netG.name)
def set_train():
netG.train()
def set_eval():
netG.eval()
os.makedirs(opt.checkpoints_path, exist_ok=True)
os.makedirs(opt.results_path, exist_ok=True)
os.makedirs('%s/%s' % (opt.checkpoints_path, opt.name), exist_ok=True)
os.makedirs('%s/%s/training' % (opt.results_path, opt.name), exist_ok=True)
os.makedirs('%s/%s/test' % (opt.results_path, opt.name), exist_ok=True)
BaseOptions().saveOptToFile(opt)
vgg_loss = LossNetwork.VGGPerceptualLoss(False).to(device=cuda)
scaler = torch.cuda.amp.GradScaler()
# training
start_epoch = 0 if not opt.continue_train else max(opt.resume_epoch,0)
for epoch in range(start_epoch, opt.num_epoch):
epoch_start_time = time.time()
set_train()
iter_data_time = time.time()
for train_idx, train_data in enumerate(train_data_loader):
iter_start_time = time.time()
train_data = move_to_gpu(train_data, cuda)
image_idx = 0
features = netG.filter(train_data['images'])
sampled_points = 0
num_points = 50000
points = train_data['samples_depth'][image_idx]
total_points = points.shape[2]
current_normals_list = []
while False and sampled_points < total_points:
num_points = min(num_points, total_points - sampled_points)
slice = points[:,:,sampled_points:num_points+sampled_points]
pred = netG.query(slice, train_data['calib'], train_data['size'])
new_nmls = torch.autograd.grad(outputs=pred, inputs=[slice[0:1,:,:]], grad_outputs=torch.ones_like(slice[0:1,:,:]),create_graph=True, retain_graph=True)
#new_nmls = netG.forward(reshape_sample_tensor(slice, opt.num_views), train_data['calib'],train_data['size'], fd_type='forward')
points_query = reshape_sample_tensor(slice, opt.num_views)
#new_nmls, _ = netG.calc_normal_edges(features, points_query, train_data['calib'], train_data['size'], fd_type='central')
current_normals_list.append(new_nmls[image_idx::opt.num_views, :, :])
sampled_points += num_points
points_query = reshape_sample_tensor(points, opt.num_views)
gt_normals_img = train_data['normal_images'][image_idx].to(dtype=torch.float32)
gt_normals_img_mask = (gt_normals_img > 0).to(dtype=torch.float32)
sdf = netG.calc_pred(features, points_query, train_data['calib'], train_data['size'])
_, normals, _, errorPred = netG.forward(train_data['images'], train_data['samples'], train_data['calib'], train_data['size'],
labels=train_data['labels'], points_surface=points_query, labels_nml=None)
#normals = torch.autograd.grad(outputs=train_data['samples'], inputs=sdf,
# grad_outputs=torch.ones_like(train_data['samples']), create_graph=True,
# retain_graph=True)
img_orig_shape = train_data['normal_images'][image_idx].shape
pred_normals_img = torch.reshape(sdf, (-1, 1, img_orig_shape[2], img_orig_shape[3]))
pred_normals_img = gt_normals_img - (pred_normals_img-0.5)
#pred_normals_img = torch.reshape(normals, (-1, 3, img_orig_shape[2], img_orig_shape[3]))
#pred_normals_img = F.normalize(pred_normals_img, dim=1, eps=1e-8)
#print(torch.min(pred_normals_img), torch.max(pred_normals_img))
#pred_normals_img = (pred_normals_img + 1) * 0.5
pred_normals_img *= gt_normals_img_mask
#plt.imshow(gt_normals_img[0].detach().cpu().numpy().transpose(1,2,0))
#plt.show()
#preview = 0.5 - pred_normals_img[0].detach().cpu().numpy().transpose(1,2,0)
preview = np.concatenate([pred_normals_img[0].detach().cpu().numpy().transpose(1,2,0), gt_normals_img[0].detach().cpu().numpy().transpose(1,2,0)], axis=1)
cv2.imshow("prediction", preview)
cv2.waitKey(1)
#plt.imshow(pred_normals_img[0].detach().cpu().numpy().transpose(1,2,0))
#plt.show()
#mse = torch.nn.MSELoss()
error = 0
#error = mse(pred_normals_img, gt_normals_img) + errorPred['Err(cmb)'].mean()
error = vgg_loss(pred_normals_img, gt_normals_img) + errorPred['Err(cmb)'].mean()*10
scaler.scale(error.mean()).backward()
scaler.step(optimizerG)
scaler.update()
optimizerG.zero_grad()
netG.zero_grad()
iter_net_time = time.time()
eta = ((iter_net_time - epoch_start_time) / (train_idx + 1)) * len(train_data_loader) - (
iter_net_time - epoch_start_time)
if train_idx % opt.freq_plot == 0:
print(
'Name: {0} | Epoch: {1} | {2}/{3} | Err(VGG): {4:.06f} | LR: {5:.06f} | dataT: {6:.05f} | netT: {7:.05f} | ETA: {8:02d}:{9:02d}'.format(
opt.name, epoch, train_idx, len(train_data_loader), error, lr, iter_start_time - iter_data_time, iter_net_time - iter_start_time, int(eta // 60),int(eta - 60 * (eta // 60))))
if not opt.debug and train_idx % opt.freq_save == 0 and train_idx != 0:
torch.save(netG.state_dict(), '%s/%s/netG_latest' % (opt.checkpoints_path, opt.name))
torch.save(netG.state_dict(), '%s/%s/netG_epoch_%d' % (opt.checkpoints_path, opt.name, epoch))
if train_idx % opt.freq_save_ply == 0:
save_path = '%s/%s/nml.ply' % (opt.results_path, opt.name)
normals = normals.detach().cpu().numpy()
points = points.detach().cpu().numpy()
save_samples_rgb(save_path, points[0].T, (normals[0].T + 1) / 2)
iter_data_time = time.time()
# update learning rate
if isinstance(optimizerG, torch.optim.RMSprop):
lr = adjust_learning_rate(optimizerG, epoch, lr, opt.schedule, opt.gamma)
if len(device_ids) > 1:
netG.device_ids = [device_ids[0]]
#### test
with torch.no_grad():
set_eval()
if not opt.no_gen_mesh:
print('generate mesh (test) ...')
test_data = None
train_data = None
for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
#test_data = test_dataset[random.randint(0, len(test_dataset) - 1)]
test_data = test_dataset[6]
test_data_batched = coll([test_data])
test_data_batched = move_to_gpu(test_data_batched, cuda)
save_path = '%s/%s/test/test_eval_epoch%d_%s.obj' % (opt.results_path, opt.name, epoch, test_data['name'])
mesh_test = gen_mesh(opt, netG, cuda, test_data_batched, save_path)
print('generate mesh (train) ...')
train_dataset.is_train = False
for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
train_data = train_dataset[random.randint(0, len(test_dataset) - 1)]
train_data_batched = coll([train_data])
train_data_batched = move_to_gpu(train_data_batched, cuda)
save_path = '%s/%s/training/train_eval_epoch%d_%s.obj' % (opt.results_path, opt.name, epoch, train_data['name'])
mesh_train = gen_mesh(opt, netG, cuda, train_data_batched, save_path)
train_dataset.is_train = True
#tb.updateAfterEpoch(epoch, train_errors, test_errors, train_data['img'], test_data['img'])
if len(device_ids) > 1:
netG.device_ids = device_ids
