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 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(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) 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)) 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) sample_tensor = train_data['samples'].to(device=cuda) image_tensor, calib_tensor = reshape_multiview_tensors(image_tensor, calib_tensor) if opt.num_views > 1: sample_tensor = reshape_sample_tensor(sample_tensor, opt.num_views) label_tensor = train_data['labels'].to(device=cuda) res, error = netG.forward(image_tensor, sample_tensor, calib_tensor, labels=label_tensor) optimizerG.zero_grad() error.backward() 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: print( 'Name: {0} | Epoch: {1} | {2}/{3} | Err: {4:.06f} | LR: {5:.06f} | Sigma: {6:.02f} | dataT: {7:.05f} | netT: {8:.05f} | ETA: {9:02d}:{10:02d}'.format( opt.name, epoch, train_idx, len(train_data_loader), error.item(), lr, opt.sigma, 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(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 = res[0].cpu() points = sample_tensor[0].transpose(0, 1).cpu() save_samples_truncted_prob(save_path, points.detach().numpy(), r.detach().numpy()) iter_data_time = time.time() # update learning rate lr = adjust_learning_rate(optimizerG, epoch, lr, opt.schedule, opt.gamma) #### test with torch.no_grad(): set_eval() if not opt.no_num_eval: test_losses = {} print('calc error (test) ...') test_errors = calc_error(opt, netG, cuda, test_dataset, 100) print('eval test MSE: {0:06f} IOU: {1:06f} prec: {2:06f} recall: {3:06f}'.format(*test_errors)) MSE, IOU, prec, recall = test_errors test_losses['MSE(test)'] = MSE 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(opt, netG, cuda, train_dataset, 100) train_dataset.is_train = True print('eval train MSE: {0:06f} IOU: {1:06f} prec: {2:06f} recall: {3:06f}'.format(*train_errors)) MSE, IOU, prec, recall = train_errors test_losses['MSE(train)'] = MSE 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) ...') 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(opt, netG, 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(opt, netG, cuda, train_data, save_path) train_dataset.is_train = True if __name__ == '__main__': train(opt)