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)