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import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
import time
import json
import numpy as np
import cv2
import random
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm
from lib.options import BaseOptions
from lib.mesh_util import *
from lib.sample_util import *
from lib.train_util import *
from lib.data import *
from lib.model import *
from lib.geometry import index
# get options
opt = BaseOptions().parse()
def train_color(opt):
# set cuda
cuda = torch.device('cuda:%d' % opt.gpu_id)
train_dataset = TrainDataset(opt, phase='train')
test_dataset = TrainDataset(opt, phase='test')
projection_mode = train_dataset.projection_mode
# create data loader
train_data_loader = DataLoader(train_dataset,
batch_size=opt.batch_size, shuffle=not opt.serial_batches,
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=False,
num_workers=opt.num_threads, pin_memory=opt.pin_memory)
print('test data size: ', len(test_data_loader))
# create net
netG = HGPIFuNet(opt, projection_mode).to(device=cuda)
lr = opt.learning_rate
# Always use resnet for color regression
netC = ResBlkPIFuNet(opt).to(device=cuda)
optimizerC = torch.optim.Adam(netC.parameters(), lr=opt.learning_rate)
def set_train():
netG.eval()
netC.train()
def set_eval():
netG.eval()
netC.eval()
print('Using NetworkG: ', netG.name, 'networkC: ', netC.name)
# 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))
else:
model_path_G = '%s/%s/netG_latest' % (opt.checkpoints_path, opt.name)
print('loading for net G ...', model_path_G)
netG.load_state_dict(torch.load(model_path_G, map_location=cuda))
if opt.load_netC_checkpoint_path is not None:
print('loading for net C ...', opt.load_netC_checkpoint_path)
netC.load_state_dict(torch.load(opt.load_netC_checkpoint_path, map_location=cuda))
if opt.continue_train:
if opt.resume_epoch < 0:
model_path_C = '%s/%s/netC_latest' % (opt.checkpoints_path, opt.name)
else:
model_path_C = '%s/%s/netC_epoch_%d' % (opt.checkpoints_path, opt.name, opt.resume_epoch)
print('Resuming from ', model_path_C)
netC.load_state_dict(torch.load(model_path_C, map_location=cuda))
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' % (opt.results_path, opt.name), exist_ok=True)
opt_log = os.path.join(opt.results_path, opt.name, 'opt.txt')
with open(opt_log, 'w') as outfile:
outfile.write(json.dumps(vars(opt), indent=2))
# 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()
# retrieve the data
image_tensor = train_data['img'].to(device=cuda)
calib_tensor = train_data['calib'].to(device=cuda)
color_sample_tensor = train_data['color_samples'].to(device=cuda)
image_tensor, calib_tensor = reshape_multiview_tensors(image_tensor, calib_tensor)
if opt.num_views > 1:
color_sample_tensor = reshape_sample_tensor(color_sample_tensor, opt.num_views)
rgb_tensor = train_data['rgbs'].to(device=cuda)
with torch.no_grad():
netG.filter(image_tensor)
resC, error = netC.forward(image_tensor, netG.get_im_feat(), color_sample_tensor, calib_tensor, labels=rgb_tensor)
optimizerC.zero_grad()
error.backward()
optimizerC.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:
print(
'Name: {0} | Epoch: {1} | {2}/{3} | Err: {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.item(),
lr,
iter_start_time - iter_data_time,
iter_net_time - iter_start_time, int(eta // 60),
int(eta - 60 * (eta // 60))))
if train_idx % opt.freq_save == 0 and train_idx != 0:
torch.save(netC.state_dict(), '%s/%s/netC_latest' % (opt.checkpoints_path, opt.name))
torch.save(netC.state_dict(), '%s/%s/netC_epoch_%d' % (opt.checkpoints_path, opt.name, epoch))
if train_idx % opt.freq_save_ply == 0:
save_path = '%s/%s/pred_col.ply' % (opt.results_path, opt.name)
rgb = resC[0].transpose(0, 1).cpu() * 0.5 + 0.5
points = color_sample_tensor[0].transpose(0, 1).cpu()
save_samples_rgb(save_path, points.detach().numpy(), rgb.detach().numpy())
iter_data_time = time.time()
#### test
with torch.no_grad():
set_eval()
if not opt.no_num_eval:
test_losses = {}
print('calc error (test) ...')
test_color_error = calc_error_color(opt, netG, netC, cuda, test_dataset, 100)
print('eval test | color error:', test_color_error)
test_losses['test_color'] = test_color_error
print('calc error (train) ...')
train_dataset.is_train = False
train_color_error = calc_error_color(opt, netG, netC, cuda, train_dataset, 100)
train_dataset.is_train = True
print('eval train | color error:', train_color_error)
test_losses['train_color'] = train_color_error
if not opt.no_gen_mesh:
print('generate mesh (test) ...')
for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
test_data = random.choice(test_dataset)
save_path = '%s/%s/test_eval_epoch%d_%s.obj' % (
opt.results_path, opt.name, epoch, test_data['name'])
gen_mesh_color(opt, netG, netC, cuda, test_data, save_path)
print('generate mesh (train) ...')
train_dataset.is_train = False
for gen_idx in tqdm(range(opt.num_gen_mesh_test)):
train_data = random.choice(train_dataset)
save_path = '%s/%s/train_eval_epoch%d_%s.obj' % (
opt.results_path, opt.name, epoch, train_data['name'])
gen_mesh_color(opt, netG, netC, cuda, train_data, save_path)
train_dataset.is_train = True
if __name__ == '__main__':
train_color(opt) |