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import os |
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import cv2 |
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import time |
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import yaml |
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import torch |
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import datetime |
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from tensorboardX import SummaryWriter |
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import torchvision.transforms as tvf |
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import torch.nn as nn |
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import torch.nn.functional as F |
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|
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from nets.geom import getK, getWarp, _grid_positions, getWarpNoValidate |
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from nets.loss import make_detector_loss, make_noise_score_map_loss |
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from nets.score import extract_kpts |
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from nets.multi_sampler import MultiSampler |
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from nets.noise_reliability_loss import MultiPixelAPLoss |
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from datasets.noise_simulator import NoiseSimulator |
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from nets.l2net import Quad_L2Net |
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class Trainer: |
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def __init__(self, config, device, loader, job_name, start_cnt): |
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self.config = config |
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self.device = device |
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self.loader = loader |
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os.makedirs("./runs/", exist_ok=True) |
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if job_name != "": |
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self.log_dir = f"runs/{job_name}" |
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else: |
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self.log_dir = f'runs/{datetime.datetime.now().strftime("%m-%d-%H%M%S")}' |
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self.writer = SummaryWriter(self.log_dir) |
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with open(f"{self.log_dir}/config.yaml", "w") as f: |
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yaml.dump(config, f) |
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|
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if config["network"]["input_type"] == "gray": |
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self.model = eval(f'{config["network"]["model"]}(inchan=1)').to(device) |
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elif ( |
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config["network"]["input_type"] == "rgb" |
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or config["network"]["input_type"] == "raw-demosaic" |
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): |
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self.model = eval(f'{config["network"]["model"]}(inchan=3)').to(device) |
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elif config["network"]["input_type"] == "raw": |
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self.model = eval(f'{config["network"]["model"]}(inchan=4)').to(device) |
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else: |
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raise NotImplementedError() |
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self.noise_maker = NoiseSimulator(device) |
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self.model.clf = nn.Conv2d(128, 2, kernel_size=1).cuda() |
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self.cnt = 0 |
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if start_cnt != 0: |
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self.model.load_state_dict( |
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torch.load( |
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f"{self.log_dir}/model_{start_cnt:06d}.pth", map_location=device |
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) |
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) |
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self.cnt = start_cnt + 1 |
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sampler = MultiSampler( |
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ngh=7, |
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subq=-8, |
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subd=1, |
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pos_d=3, |
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neg_d=5, |
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border=16, |
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subd_neg=-8, |
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maxpool_pos=True, |
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).to(device) |
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self.reliability_relitive_loss = MultiPixelAPLoss(sampler, nq=20).to(device) |
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if self.config["training"]["optimizer"] == "SGD": |
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self.optimizer = torch.optim.SGD( |
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[ |
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{ |
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"params": self.model.parameters(), |
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"initial_lr": self.config["training"]["lr"], |
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} |
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], |
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lr=self.config["training"]["lr"], |
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momentum=self.config["training"]["momentum"], |
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weight_decay=self.config["training"]["weight_decay"], |
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) |
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elif self.config["training"]["optimizer"] == "Adam": |
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self.optimizer = torch.optim.Adam( |
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[ |
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{ |
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"params": self.model.parameters(), |
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"initial_lr": self.config["training"]["lr"], |
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} |
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], |
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lr=self.config["training"]["lr"], |
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weight_decay=self.config["training"]["weight_decay"], |
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) |
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else: |
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raise NotImplementedError() |
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self.lr_scheduler = torch.optim.lr_scheduler.StepLR( |
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self.optimizer, |
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step_size=self.config["training"]["lr_step"], |
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gamma=self.config["training"]["lr_gamma"], |
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last_epoch=start_cnt, |
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) |
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for param_tensor in self.model.state_dict(): |
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print(param_tensor, "\t", self.model.state_dict()[param_tensor].size()) |
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|
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def save(self, iter_num): |
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torch.save(self.model.state_dict(), f"{self.log_dir}/model_{iter_num:06d}.pth") |
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def load(self, path): |
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self.model.load_state_dict(torch.load(path)) |
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def train(self): |
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self.model.train() |
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for epoch in range(2): |
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for batch_idx, inputs in enumerate(self.loader): |
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self.optimizer.zero_grad() |
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t = time.time() |
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img0_ori, noise_img0_ori = self.preprocess_noise_pair( |
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inputs["img0"], self.cnt |
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) |
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img1_ori, noise_img1_ori = self.preprocess_noise_pair( |
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inputs["img1"], self.cnt |
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) |
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img0 = img0_ori.permute(0, 3, 1, 2).float().to(self.device) |
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img1 = img1_ori.permute(0, 3, 1, 2).float().to(self.device) |
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noise_img0 = noise_img0_ori.permute(0, 3, 1, 2).float().to(self.device) |
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noise_img1 = noise_img1_ori.permute(0, 3, 1, 2).float().to(self.device) |
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if self.config["network"]["input_type"] == "rgb": |
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|
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RGB_mean = [0.485, 0.456, 0.406] |
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RGB_std = [0.229, 0.224, 0.225] |
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norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std) |
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img0 = norm_RGB(img0) |
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img1 = norm_RGB(img1) |
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noise_img0 = norm_RGB(noise_img0) |
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noise_img1 = norm_RGB(noise_img1) |
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|
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elif self.config["network"]["input_type"] == "gray": |
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img0 = torch.mean(img0, dim=1, keepdim=True) |
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img1 = torch.mean(img1, dim=1, keepdim=True) |
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noise_img0 = torch.mean(noise_img0, dim=1, keepdim=True) |
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noise_img1 = torch.mean(noise_img1, dim=1, keepdim=True) |
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norm_gray0 = tvf.Normalize(mean=img0.mean(), std=img0.std()) |
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norm_gray1 = tvf.Normalize(mean=img1.mean(), std=img1.std()) |
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img0 = norm_gray0(img0) |
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img1 = norm_gray1(img1) |
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noise_img0 = norm_gray0(noise_img0) |
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noise_img1 = norm_gray1(noise_img1) |
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|
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elif self.config["network"]["input_type"] == "raw": |
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pass |
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elif self.config["network"]["input_type"] == "raw-demosaic": |
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pass |
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else: |
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raise NotImplementedError() |
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desc0, score_map0, _, _ = self.model(img0) |
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desc1, score_map1, _, _ = self.model(img1) |
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conf0 = F.softmax(self.model.clf(torch.abs(desc0) ** 2.0), dim=1)[ |
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:, 1:2 |
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] |
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conf1 = F.softmax(self.model.clf(torch.abs(desc1) ** 2.0), dim=1)[ |
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:, 1:2 |
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] |
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noise_desc0, noise_score_map0, noise_at0, noise_att0 = self.model( |
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noise_img0 |
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) |
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noise_desc1, noise_score_map1, noise_at1, noise_att1 = self.model( |
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noise_img1 |
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) |
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noise_conf0 = F.softmax( |
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self.model.clf(torch.abs(noise_desc0) ** 2.0), dim=1 |
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)[:, 1:2] |
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noise_conf1 = F.softmax( |
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self.model.clf(torch.abs(noise_desc1) ** 2.0), dim=1 |
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)[:, 1:2] |
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cur_feat_size0 = torch.tensor(score_map0.shape[2:]) |
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cur_feat_size1 = torch.tensor(score_map1.shape[2:]) |
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|
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desc0 = desc0.permute(0, 2, 3, 1) |
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desc1 = desc1.permute(0, 2, 3, 1) |
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score_map0 = score_map0.permute(0, 2, 3, 1) |
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score_map1 = score_map1.permute(0, 2, 3, 1) |
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noise_desc0 = noise_desc0.permute(0, 2, 3, 1) |
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noise_desc1 = noise_desc1.permute(0, 2, 3, 1) |
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noise_score_map0 = noise_score_map0.permute(0, 2, 3, 1) |
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noise_score_map1 = noise_score_map1.permute(0, 2, 3, 1) |
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conf0 = conf0.permute(0, 2, 3, 1) |
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conf1 = conf1.permute(0, 2, 3, 1) |
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noise_conf0 = noise_conf0.permute(0, 2, 3, 1) |
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noise_conf1 = noise_conf1.permute(0, 2, 3, 1) |
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|
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r_K0 = getK(inputs["ori_img_size0"], cur_feat_size0, inputs["K0"]).to( |
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self.device |
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) |
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r_K1 = getK(inputs["ori_img_size1"], cur_feat_size1, inputs["K1"]).to( |
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self.device |
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) |
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pos0 = _grid_positions( |
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cur_feat_size0[0], cur_feat_size0[1], img0.shape[0] |
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).to(self.device) |
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pos0_for_rel, pos1_for_rel, _ = getWarpNoValidate( |
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pos0, |
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inputs["rel_pose"].to(self.device), |
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inputs["depth0"].to(self.device), |
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r_K0, |
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inputs["depth1"].to(self.device), |
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r_K1, |
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img0.shape[0], |
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) |
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pos0, pos1, _ = getWarp( |
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pos0, |
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inputs["rel_pose"].to(self.device), |
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inputs["depth0"].to(self.device), |
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r_K0, |
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inputs["depth1"].to(self.device), |
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r_K1, |
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img0.shape[0], |
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) |
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reliab_loss_relative = self.reliability_relitive_loss( |
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desc0, |
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desc1, |
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noise_desc0, |
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noise_desc1, |
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conf0, |
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conf1, |
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noise_conf0, |
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noise_conf1, |
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pos0_for_rel, |
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pos1_for_rel, |
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img0.shape[0], |
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img0.shape[2], |
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img0.shape[3], |
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) |
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det_structured_loss, det_accuracy = make_detector_loss( |
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pos0, |
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pos1, |
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desc0, |
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desc1, |
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score_map0, |
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score_map1, |
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img0.shape[0], |
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self.config["network"]["use_corr_n"], |
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self.config["network"]["loss_type"], |
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self.config, |
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) |
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det_structured_loss_noise, det_accuracy_noise = make_detector_loss( |
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pos0, |
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pos1, |
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noise_desc0, |
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noise_desc1, |
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noise_score_map0, |
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noise_score_map1, |
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img0.shape[0], |
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self.config["network"]["use_corr_n"], |
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self.config["network"]["loss_type"], |
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self.config, |
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) |
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indices0, scores0 = extract_kpts( |
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score_map0.permute(0, 3, 1, 2), |
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k=self.config["network"]["det"]["kpt_n"], |
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score_thld=self.config["network"]["det"]["score_thld"], |
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nms_size=self.config["network"]["det"]["nms_size"], |
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eof_size=self.config["network"]["det"]["eof_size"], |
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edge_thld=self.config["network"]["det"]["edge_thld"], |
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) |
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indices1, scores1 = extract_kpts( |
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score_map1.permute(0, 3, 1, 2), |
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k=self.config["network"]["det"]["kpt_n"], |
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score_thld=self.config["network"]["det"]["score_thld"], |
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nms_size=self.config["network"]["det"]["nms_size"], |
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eof_size=self.config["network"]["det"]["eof_size"], |
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edge_thld=self.config["network"]["det"]["edge_thld"], |
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) |
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noise_score_loss0, mask0 = make_noise_score_map_loss( |
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score_map0, noise_score_map0, indices0, img0.shape[0], thld=0.1 |
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) |
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noise_score_loss1, mask1 = make_noise_score_map_loss( |
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score_map1, noise_score_map1, indices1, img1.shape[0], thld=0.1 |
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) |
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total_loss = det_structured_loss + det_structured_loss_noise |
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total_loss += noise_score_loss0 / 2.0 * 1.0 |
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total_loss += noise_score_loss1 / 2.0 * 1.0 |
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total_loss += reliab_loss_relative[0] / 2.0 * 0.5 |
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total_loss += reliab_loss_relative[1] / 2.0 * 0.5 |
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|
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self.writer.add_scalar("acc/normal_acc", det_accuracy, self.cnt) |
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self.writer.add_scalar("acc/noise_acc", det_accuracy_noise, self.cnt) |
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self.writer.add_scalar("loss/total_loss", total_loss, self.cnt) |
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self.writer.add_scalar( |
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"loss/noise_score_loss", |
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(noise_score_loss0 + noise_score_loss1) / 2.0, |
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self.cnt, |
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) |
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self.writer.add_scalar( |
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"loss/det_loss_normal", det_structured_loss, self.cnt |
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) |
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self.writer.add_scalar( |
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"loss/det_loss_noise", det_structured_loss_noise, self.cnt |
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) |
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print( |
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"iter={},\tloss={:.4f},\tacc={:.4f},\t{:.4f}s/iter".format( |
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self.cnt, total_loss, det_accuracy, time.time() - t |
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) |
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) |
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|
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if det_structured_loss != 0: |
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total_loss.backward() |
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self.optimizer.step() |
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self.lr_scheduler.step() |
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|
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if self.cnt % 100 == 0: |
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noise_indices0, noise_scores0 = extract_kpts( |
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noise_score_map0.permute(0, 3, 1, 2), |
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k=self.config["network"]["det"]["kpt_n"], |
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score_thld=self.config["network"]["det"]["score_thld"], |
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nms_size=self.config["network"]["det"]["nms_size"], |
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eof_size=self.config["network"]["det"]["eof_size"], |
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edge_thld=self.config["network"]["det"]["edge_thld"], |
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) |
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noise_indices1, noise_scores1 = extract_kpts( |
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noise_score_map1.permute(0, 3, 1, 2), |
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k=self.config["network"]["det"]["kpt_n"], |
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score_thld=self.config["network"]["det"]["score_thld"], |
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nms_size=self.config["network"]["det"]["nms_size"], |
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eof_size=self.config["network"]["det"]["eof_size"], |
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edge_thld=self.config["network"]["det"]["edge_thld"], |
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) |
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if self.config["network"]["input_type"] == "raw": |
|
kpt_img0 = self.showKeyPoints( |
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img0_ori[0][..., :3] * 255.0, indices0[0] |
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) |
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kpt_img1 = self.showKeyPoints( |
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img1_ori[0][..., :3] * 255.0, indices1[0] |
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) |
|
noise_kpt_img0 = self.showKeyPoints( |
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noise_img0_ori[0][..., :3] * 255.0, noise_indices0[0] |
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) |
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noise_kpt_img1 = self.showKeyPoints( |
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noise_img1_ori[0][..., :3] * 255.0, noise_indices1[0] |
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) |
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else: |
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kpt_img0 = self.showKeyPoints(img0_ori[0] * 255.0, indices0[0]) |
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kpt_img1 = self.showKeyPoints(img1_ori[0] * 255.0, indices1[0]) |
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noise_kpt_img0 = self.showKeyPoints( |
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noise_img0_ori[0] * 255.0, noise_indices0[0] |
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) |
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noise_kpt_img1 = self.showKeyPoints( |
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noise_img1_ori[0] * 255.0, noise_indices1[0] |
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) |
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|
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self.writer.add_image( |
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"img0/kpts", kpt_img0, self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
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"img1/kpts", kpt_img1, self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
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"img0/noise_kpts", noise_kpt_img0, self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
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"img1/noise_kpts", noise_kpt_img1, self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
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"img0/score_map", score_map0[0], self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
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"img1/score_map", score_map1[0], self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
|
"img0/noise_score_map", |
|
noise_score_map0[0], |
|
self.cnt, |
|
dataformats="HWC", |
|
) |
|
self.writer.add_image( |
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"img1/noise_score_map", |
|
noise_score_map1[0], |
|
self.cnt, |
|
dataformats="HWC", |
|
) |
|
self.writer.add_image( |
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"img0/kpt_mask", mask0.unsqueeze(2), self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
|
"img1/kpt_mask", mask1.unsqueeze(2), self.cnt, dataformats="HWC" |
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) |
|
self.writer.add_image( |
|
"img0/conf", conf0[0], self.cnt, dataformats="HWC" |
|
) |
|
self.writer.add_image( |
|
"img1/conf", conf1[0], self.cnt, dataformats="HWC" |
|
) |
|
self.writer.add_image( |
|
"img0/noise_conf", noise_conf0[0], self.cnt, dataformats="HWC" |
|
) |
|
self.writer.add_image( |
|
"img1/noise_conf", noise_conf1[0], self.cnt, dataformats="HWC" |
|
) |
|
|
|
if self.cnt % 5000 == 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)) |
|
|
|
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() |
|
|
