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| import os | |
| import cv2 | |
| import time | |
| import yaml | |
| import torch | |
| import datetime | |
| from tensorboardX import SummaryWriter | |
| import torchvision.transforms as tvf | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from nets.geom import getK, getWarp, _grid_positions, getWarpNoValidate | |
| from nets.loss import make_detector_loss | |
| from nets.score import extract_kpts | |
| from nets.sampler import NghSampler2 | |
| from nets.reliability_loss import ReliabilityLoss | |
| from datasets.noise_simulator import NoiseSimulator | |
| from nets.l2net import Quad_L2Net | |
| class SingleTrainer: | |
| def __init__(self, config, device, loader, job_name, start_cnt): | |
| self.config = config | |
| self.device = device | |
| self.loader = loader | |
| # tensorboard writer construction | |
| os.makedirs('./runs/', exist_ok=True) | |
| if job_name != '': | |
| self.log_dir = f'runs/{job_name}' | |
| else: | |
| self.log_dir = f'runs/{datetime.datetime.now().strftime("%m-%d-%H%M%S")}' | |
| self.writer = SummaryWriter(self.log_dir) | |
| with open(f'{self.log_dir}/config.yaml', 'w') as f: | |
| yaml.dump(config, f) | |
| if config['network']['input_type'] == 'gray' or config['network']['input_type'] == 'raw-gray': | |
| self.model = eval(f'{config["network"]["model"]}(inchan=1)').to(device) | |
| elif config['network']['input_type'] == 'rgb' or config['network']['input_type'] == 'raw-demosaic': | |
| self.model = eval(f'{config["network"]["model"]}(inchan=3)').to(device) | |
| elif config['network']['input_type'] == 'raw': | |
| self.model = eval(f'{config["network"]["model"]}(inchan=4)').to(device) | |
| else: | |
| raise NotImplementedError() | |
| # noise maker | |
| self.noise_maker = NoiseSimulator(device) | |
| # load model | |
| self.cnt = 0 | |
| if start_cnt != 0: | |
| self.model.load_state_dict(torch.load(f'{self.log_dir}/model_{start_cnt:06d}.pth')) | |
| self.cnt = start_cnt + 1 | |
| # sampler | |
| sampler = NghSampler2(ngh=7, subq=-8, subd=1, pos_d=3, neg_d=5, border=16, | |
| subd_neg=-8,maxpool_pos=True).to(device) | |
| self.reliability_loss = ReliabilityLoss(sampler, base=0.3, nq=20).to(device) | |
| # reliability map conv | |
| self.model.clf = nn.Conv2d(128, 2, kernel_size=1).cuda() | |
| # optimizer and scheduler | |
| if self.config['training']['optimizer'] == 'SGD': | |
| self.optimizer = torch.optim.SGD( | |
| [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], | |
| lr=self.config['training']['lr'], | |
| momentum=self.config['training']['momentum'], | |
| weight_decay=self.config['training']['weight_decay'], | |
| ) | |
| elif self.config['training']['optimizer'] == 'Adam': | |
| self.optimizer = torch.optim.Adam( | |
| [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], | |
| lr=self.config['training']['lr'], | |
| weight_decay=self.config['training']['weight_decay'] | |
| ) | |
| else: | |
| raise NotImplementedError() | |
| self.lr_scheduler = torch.optim.lr_scheduler.StepLR( | |
| self.optimizer, | |
| step_size=self.config['training']['lr_step'], | |
| gamma=self.config['training']['lr_gamma'], | |
| last_epoch=start_cnt | |
| ) | |
| for param_tensor in self.model.state_dict(): | |
| print(param_tensor, "\t", self.model.state_dict()[param_tensor].size()) | |
| def save(self, iter_num): | |
| torch.save(self.model.state_dict(), f'{self.log_dir}/model_{iter_num:06d}.pth') | |
| def load(self, path): | |
| self.model.load_state_dict(torch.load(path)) | |
| def train(self): | |
| self.model.train() | |
| for epoch in range(2): | |
| for batch_idx, inputs in enumerate(self.loader): | |
| self.optimizer.zero_grad() | |
| t = time.time() | |
| # preprocess and add noise | |
| img0_ori, noise_img0_ori = self.preprocess_noise_pair(inputs['img0'], self.cnt) | |
| img1_ori, noise_img1_ori = self.preprocess_noise_pair(inputs['img1'], self.cnt) | |
| img0 = img0_ori.permute(0, 3, 1, 2).float().to(self.device) | |
| img1 = img1_ori.permute(0, 3, 1, 2).float().to(self.device) | |
| if self.config['network']['input_type'] == 'rgb': | |
| # 3-channel rgb | |
| RGB_mean = [0.485, 0.456, 0.406] | |
| RGB_std = [0.229, 0.224, 0.225] | |
| norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std) | |
| img0 = norm_RGB(img0) | |
| img1 = norm_RGB(img1) | |
| noise_img0 = norm_RGB(noise_img0) | |
| noise_img1 = norm_RGB(noise_img1) | |
| elif self.config['network']['input_type'] == 'gray': | |
| # 1-channel | |
| img0 = torch.mean(img0, dim=1, keepdim=True) | |
| img1 = torch.mean(img1, dim=1, keepdim=True) | |
| noise_img0 = torch.mean(noise_img0, dim=1, keepdim=True) | |
| noise_img1 = torch.mean(noise_img1, dim=1, keepdim=True) | |
| norm_gray0 = tvf.Normalize(mean=img0.mean(), std=img0.std()) | |
| norm_gray1 = tvf.Normalize(mean=img1.mean(), std=img1.std()) | |
| img0 = norm_gray0(img0) | |
| img1 = norm_gray1(img1) | |
| noise_img0 = norm_gray0(noise_img0) | |
| noise_img1 = norm_gray1(noise_img1) | |
| elif self.config['network']['input_type'] == 'raw': | |
| # 4-channel | |
| pass | |
| elif self.config['network']['input_type'] == 'raw-demosaic': | |
| # 3-channel | |
| pass | |
| else: | |
| raise NotImplementedError() | |
| desc0, score_map0, _, _ = self.model(img0) | |
| desc1, score_map1, _, _ = self.model(img1) | |
| cur_feat_size0 = torch.tensor(score_map0.shape[2:]) | |
| cur_feat_size1 = torch.tensor(score_map1.shape[2:]) | |
| conf0 = F.softmax(self.model.clf(torch.abs(desc0)**2.0), dim=1)[:,1:2] | |
| conf1 = F.softmax(self.model.clf(torch.abs(desc1)**2.0), dim=1)[:,1:2] | |
| desc0 = desc0.permute(0, 2, 3, 1) | |
| desc1 = desc1.permute(0, 2, 3, 1) | |
| score_map0 = score_map0.permute(0, 2, 3, 1) | |
| score_map1 = score_map1.permute(0, 2, 3, 1) | |
| conf0 = conf0.permute(0, 2, 3, 1) | |
| conf1 = conf1.permute(0, 2, 3, 1) | |
| r_K0 = getK(inputs['ori_img_size0'], cur_feat_size0, inputs['K0']).to(self.device) | |
| r_K1 = getK(inputs['ori_img_size1'], cur_feat_size1, inputs['K1']).to(self.device) | |
| pos0 = _grid_positions( | |
| cur_feat_size0[0], cur_feat_size0[1], img0.shape[0]).to(self.device) | |
| pos0_for_rel, pos1_for_rel, _ = getWarpNoValidate( | |
| pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), | |
| r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) | |
| pos0, pos1, _ = getWarp( | |
| pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), | |
| r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) | |
| reliab_loss = self.reliability_loss(desc0, desc1, conf0, conf1, pos0_for_rel, pos1_for_rel, img0.shape[0], img0.shape[2], img0.shape[3]) | |
| det_structured_loss, det_accuracy = make_detector_loss( | |
| pos0, pos1, desc0, desc1, | |
| score_map0, score_map1, img0.shape[0], | |
| self.config['network']['use_corr_n'], | |
| self.config['network']['loss_type'], | |
| self.config | |
| ) | |
| total_loss = det_structured_loss | |
| self.writer.add_scalar("loss/det_loss_normal", det_structured_loss, self.cnt) | |
| total_loss += reliab_loss | |
| self.writer.add_scalar("acc/normal_acc", det_accuracy, self.cnt) | |
| self.writer.add_scalar("loss/total_loss", total_loss, self.cnt) | |
| self.writer.add_scalar("loss/reliab_loss", reliab_loss, self.cnt) | |
| print('iter={},\tloss={:.4f},\tacc={:.4f},\t{:.4f}s/iter'.format(self.cnt, total_loss, det_accuracy, time.time()-t)) | |
| if det_structured_loss != 0: | |
| total_loss.backward() | |
| self.optimizer.step() | |
| self.lr_scheduler.step() | |
| if self.cnt % 100 == 0: | |
| indices0, scores0 = extract_kpts( | |
| score_map0.permute(0, 3, 1, 2), | |
| k=self.config['network']['det']['kpt_n'], | |
| score_thld=self.config['network']['det']['score_thld'], | |
| nms_size=self.config['network']['det']['nms_size'], | |
| eof_size=self.config['network']['det']['eof_size'], | |
| edge_thld=self.config['network']['det']['edge_thld'] | |
| ) | |
| indices1, scores1 = extract_kpts( | |
| score_map1.permute(0, 3, 1, 2), | |
| k=self.config['network']['det']['kpt_n'], | |
| score_thld=self.config['network']['det']['score_thld'], | |
| nms_size=self.config['network']['det']['nms_size'], | |
| eof_size=self.config['network']['det']['eof_size'], | |
| edge_thld=self.config['network']['det']['edge_thld'] | |
| ) | |
| if self.config['network']['input_type'] == 'raw': | |
| kpt_img0 = self.showKeyPoints(img0_ori[0][..., :3] * 255., indices0[0]) | |
| kpt_img1 = self.showKeyPoints(img1_ori[0][..., :3] * 255., indices1[0]) | |
| else: | |
| kpt_img0 = self.showKeyPoints(img0_ori[0] * 255., indices0[0]) | |
| kpt_img1 = self.showKeyPoints(img1_ori[0] * 255., indices1[0]) | |
| self.writer.add_image('img0/kpts', kpt_img0, self.cnt, dataformats='HWC') | |
| self.writer.add_image('img1/kpts', kpt_img1, self.cnt, dataformats='HWC') | |
| self.writer.add_image('img0/score_map', score_map0[0], self.cnt, dataformats='HWC') | |
| self.writer.add_image('img1/score_map', score_map1[0], self.cnt, dataformats='HWC') | |
| self.writer.add_image('img0/conf', conf0[0], self.cnt, dataformats='HWC') | |
| self.writer.add_image('img1/conf', conf1[0], self.cnt, dataformats='HWC') | |
| if self.cnt % 10000 == 0: | |
| self.save(self.cnt) | |
| self.cnt += 1 | |
| def showKeyPoints(self, img, indices): | |
| key_points = cv2.KeyPoint_convert(indices.cpu().float().numpy()[:, ::-1]) | |
| img = img.numpy().astype('uint8') | |
| img = cv2.drawKeypoints(img, key_points, None, color=(0, 255, 0)) | |
| return img | |
| def preprocess(self, img, iter_idx): | |
| if not self.config['network']['noise'] and 'raw' not in self.config['network']['input_type']: | |
| return img | |
| raw = self.noise_maker.rgb2raw(img, batched=True) | |
| if self.config['network']['noise']: | |
| ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] | |
| raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) | |
| if self.config['network']['input_type'] == 'raw': | |
| return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)) | |
| if self.config['network']['input_type'] == 'raw-demosaic': | |
| return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)) | |
| rgb = self.noise_maker.raw2rgb(raw, batched=True) | |
| if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': | |
| return torch.tensor(rgb) | |
| raise NotImplementedError() | |
| def preprocess_noise_pair(self, img, iter_idx): | |
| assert self.config['network']['noise'] | |
| raw = self.noise_maker.rgb2raw(img, batched=True) | |
| ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] | |
| noise_raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) | |
| if self.config['network']['input_type'] == 'raw': | |
| return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)), \ | |
| torch.tensor(self.noise_maker.raw2packedRaw(noise_raw, batched=True)) | |
| if self.config['network']['input_type'] == 'raw-demosaic': | |
| return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)), \ | |
| torch.tensor(self.noise_maker.raw2demosaicRaw(noise_raw, batched=True)) | |
| if self.config['network']['input_type'] == 'raw-gray': | |
| factor = torch.tensor([0.299, 0.587, 0.114]).double() | |
| return torch.matmul(torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)), factor).unsqueeze(-1), \ | |
| torch.matmul(torch.tensor(self.noise_maker.raw2demosaicRaw(noise_raw, batched=True)), factor).unsqueeze(-1) | |
| noise_rgb = self.noise_maker.raw2rgb(noise_raw, batched=True) | |
| if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': | |
| return img, torch.tensor(noise_rgb) | |
| raise NotImplementedError() | |