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419 lines
14 KiB
419 lines
14 KiB
import os
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import sys
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import time
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import logging
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from collections import namedtuple
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import yaml
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# from tensorboardX import SummaryWriter
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from nets import Model
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# from dataset import CREStereoDataset
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from dataset import CREStereoDataset, CTDDataset
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.utils.data import DataLoader
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import wandb
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import numpy as np
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import cv2
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def normalize_and_colormap(img):
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ret = (img - img.min()) / (img.max() - img.min()) * 255.0
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if isinstance(ret, torch.Tensor):
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ret = ret.cpu().detach().numpy()
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ret = ret.astype("uint8")
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ret = cv2.applyColorMap(ret, cv2.COLORMAP_INFERNO)
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return ret
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def inference(left, right, gt_disp, mask, model, epoch, n_iter=20, wandb_log=True):
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print("Model Forwarding...")
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left = left.cpu().detach().numpy()
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imgL = left
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imgR = right.cpu().detach().numpy()
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imgL = np.ascontiguousarray(imgL[None, :, :, :])
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imgR = np.ascontiguousarray(imgR[None, :, :, :])
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# chosen for convenience
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device = torch.device('cuda:0')
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imgL = torch.tensor(imgL.astype("float32")).to(device)
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imgR = torch.tensor(imgR.astype("float32")).to(device)
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imgL = imgL.transpose(2,3).transpose(1,2)
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if imgL.shape != imgR.shape:
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imgR = imgR.transpose(2,3).transpose(1,2)
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imgL_dw2 = F.interpolate(
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imgL,
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size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
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mode="bilinear",
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align_corners=True,
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)
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imgR_dw2 = F.interpolate(
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imgR,
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size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
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mode="bilinear",
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align_corners=True,
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)
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with torch.inference_mode():
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pred_flow_dw2 = model(image1=imgL_dw2, image2=imgR_dw2, iters=n_iter, flow_init=None)
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pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
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if not wandb_log:
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return torch.squeeze(pred_flow[:, 0, :, :]).cpu().detach().numpy()
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log = {}
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in_h, in_w = left.shape[:2]
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# Resize image in case the GPU memory overflows
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eval_h, eval_w = (in_h,in_w)
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for i, (pf, pf_dw2) in enumerate(zip(pred_flow, pred_flow_dw2)):
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pred_disp = torch.squeeze(pf[:, 0, :, :]).cpu().detach().numpy()
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pred_disp_dw2 = torch.squeeze(pf_dw2[:, 0, :, :]).cpu().detach().numpy()
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pred_disp_norm = cv2.normalize(pred_disp, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
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pred_disp_dw2_norm = cv2.normalize(pred_disp_dw2, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
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if i == n_iter-1:
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t = float(in_w) / float(eval_w)
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disp = cv2.resize(pred_disp, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
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log[f'disp_vis'] = wandb.Image(
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normalize_and_colormap(disp),
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caption=f"Disparity \n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
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)
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log[f'pred_{i}'] = wandb.Image(
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np.array([pred_disp.reshape(480, 640)]),
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caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
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)
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log[f'pred_norm_{i}'] = wandb.Image(
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np.array([pred_disp_norm.reshape(480, 640)]),
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caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
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)
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log[f'pred_dw2_{i}'] = wandb.Image(
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np.array([pred_disp_dw2.reshape(240, 320)]),
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caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}",
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)
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log[f'pred_dw2_norm_{i}'] = wandb.Image(
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np.array([pred_disp_dw2_norm.reshape(240, 320)]),
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caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}",
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)
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log['input_left'] = wandb.Image(left.astype('uint8'), caption="Input Left")
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log['input_right'] = wandb.Image(right.cpu().detach().numpy().transpose(1,2,0).astype('uint8'), caption="Input Right")
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log['gt_disp'] = wandb.Image(gt_disp, caption=f"GT Disparity\n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}")
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disp_error = gt_disp - disp
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log['disp_error'] = wandb.Image(
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normalize_and_colormap(disp_error.abs()),
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caption=f"Disp. Error\n{disp_error.min():.{2}f}/{disp_error.max():.{2}f}\n{disp_error.abs().mean():.{2}f}",
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)
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log[f'gt_disp_vis'] = wandb.Image(
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normalize_and_colormap(gt_disp),
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caption=f"GT Disp Vis \n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}",
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)
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wandb.log(log)
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def parse_yaml(file_path: str) -> namedtuple:
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"""Parse yaml configuration file and return the object in `namedtuple`."""
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with open(file_path, "rb") as f:
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cfg: dict = yaml.safe_load(f)
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args = namedtuple("train_args", cfg.keys())(*cfg.values())
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return args
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def format_time(elapse):
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elapse = int(elapse)
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hour = elapse // 3600
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minute = elapse % 3600 // 60
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seconds = elapse % 60
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return "{:02d}:{:02d}:{:02d}".format(hour, minute, seconds)
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def ensure_dir(path):
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if not os.path.exists(path):
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os.makedirs(path, exist_ok=True)
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def adjust_learning_rate(optimizer, epoch):
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warm_up = 0.02
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const_range = 0.6
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min_lr_rate = 0.05
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if epoch <= args.n_total_epoch * warm_up:
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lr = (1 - min_lr_rate) * args.base_lr / (
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args.n_total_epoch * warm_up
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) * epoch + min_lr_rate * args.base_lr
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elif args.n_total_epoch * warm_up < epoch <= args.n_total_epoch * const_range:
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lr = args.base_lr
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else:
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lr = (min_lr_rate - 1) * args.base_lr / (
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(1 - const_range) * args.n_total_epoch
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) * epoch + (1 - min_lr_rate * const_range) / (1 - const_range) * args.base_lr
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8):
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'''
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valid: (2, 384, 512) (B, H, W) -> (B, 1, H, W)
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flow_preds[0]: (B, 2, H, W)
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flow_gt: (B, 2, H, W)
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'''
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n_predictions = len(flow_preds)
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flow_loss = 0.0
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# TEST
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flow_gt = torch.squeeze(flow_gt, dim=-1)
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for i in range(n_predictions):
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i_weight = gamma ** (n_predictions - i - 1)
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i_loss = torch.abs(flow_preds[i] - flow_gt)
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flow_loss += i_weight * (valid.unsqueeze(1) * i_loss).mean()
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return flow_loss
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def main(args):
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# initial info
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torch.manual_seed(args.seed)
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torch.cuda.manual_seed(args.seed)
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# rank, world_size = dist.get_rank(), dist.get_world_size()
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world_size = torch.cuda.device_count() # number of GPU(s)
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# directory check
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log_model_dir = os.path.join(args.log_dir, "models")
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ensure_dir(log_model_dir)
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# model / optimizer
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model = Model(
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max_disp=args.max_disp, mixed_precision=args.mixed_precision, test_mode=False
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)
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model = nn.DataParallel(model,device_ids=[i for i in range(world_size)])
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model.cuda()
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optimizer = optim.Adam(model.parameters(), lr=0.1, betas=(0.9, 0.999))
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# model = nn.DataParallel(model,device_ids=[0])
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# tb_log = SummaryWriter(os.path.join(args.log_dir, "train.events"))
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wandb.watch(model)
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metrics = {}
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# worklog
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logging.basicConfig(level=eval(args.log_level))
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worklog = logging.getLogger("train_logger")
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worklog.propagate = False
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fileHandler = logging.FileHandler(
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os.path.join(args.log_dir, "worklog.txt"), mode="a", encoding="utf8"
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)
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formatter = logging.Formatter(
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fmt="%(asctime)s %(message)s", datefmt="%Y/%m/%d %H:%M:%S"
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)
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fileHandler.setFormatter(formatter)
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consoleHandler = logging.StreamHandler(sys.stdout)
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formatter = logging.Formatter(
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fmt="\x1b[32m%(asctime)s\x1b[0m %(message)s", datefmt="%Y/%m/%d %H:%M:%S"
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)
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consoleHandler.setFormatter(formatter)
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worklog.handlers = [fileHandler, consoleHandler]
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# params stat
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worklog.info(f"Use {world_size} GPU(s)")
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worklog.info("Params: %s" % sum([p.numel() for p in model.parameters()]))
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# load pretrained model if exist
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chk_path = os.path.join(log_model_dir, "latest.pth")
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if args.loadmodel is not None:
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chk_path = args.loadmodel
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elif not os.path.exists(chk_path):
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chk_path = None
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if chk_path is not None:
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# if rank == 0:
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worklog.info(f"loading model: {chk_path}")
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state_dict = torch.load(chk_path)
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model.load_state_dict(state_dict['state_dict'])
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optimizer.load_state_dict(state_dict['optim_state_dict'])
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resume_epoch_idx = state_dict["epoch"]
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resume_iters = state_dict["iters"]
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start_epoch_idx = resume_epoch_idx + 1
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start_iters = resume_iters
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else:
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start_epoch_idx = 1
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start_iters = 0
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# pattern_path = '/home/nils/kinect_reference_high_res_scaled_down.png'
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pattern_path = '/home/nils/kinect_reference_cropped.png'
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# pattern_path = '/home/nils/orig_ctd/connecting_the_dots/data/kinect_pattern.png'
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# datasets
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# dataset = CREStereoDataset(args.training_data_path)
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dataset = CTDDataset(args.training_data_path, pattern_path=pattern_path)
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# if rank == 0:
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worklog.info(f"Dataset size: {len(dataset)}")
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dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
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num_workers=0, drop_last=True, persistent_workers=False, pin_memory=True)
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# counter
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cur_iters = start_iters
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total_iters = args.minibatch_per_epoch * args.n_total_epoch
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t0 = time.perf_counter()
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for epoch_idx in range(start_epoch_idx, args.n_total_epoch + 1):
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# adjust learning rate
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epoch_total_train_loss = 0
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adjust_learning_rate(optimizer, epoch_idx)
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model.train()
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t1 = time.perf_counter()
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# batch_idx = 0
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# for mini_batch_data in dataloader:
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for batch_idx, mini_batch_data in enumerate(dataloader):
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if batch_idx % args.minibatch_per_epoch == 0 and batch_idx != 0:
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break
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# batch_idx += 1
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cur_iters += 1
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# parse data
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left, right, gt_disp, valid_mask = (
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mini_batch_data["left"],
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mini_batch_data["right"],
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mini_batch_data["disparity"].cuda(),
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mini_batch_data["mask"].cuda(),
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)
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t2 = time.perf_counter()
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optimizer.zero_grad()
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# pre-process
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gt_disp = torch.unsqueeze(gt_disp, dim=1) # [2, 384, 512] -> [2, 1, 384, 512]
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gt_flow = torch.cat([gt_disp, gt_disp * 0], dim=1) # [2, 2, 384, 512]
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# forward
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# left = left.transpose(1, 2).transpose(2, 3)
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left = left.transpose(1, 3).transpose(2, 3)
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right = right.transpose(2, 3).transpose(2, 3)#.transpose(1, 2)
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flow_predictions = model(left.cuda(), right.cuda())
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# loss & backword
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loss = sequence_loss(
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flow_predictions, gt_flow, valid_mask, gamma=0.8
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)
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if batch_idx % 128 == 0:
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inference(
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mini_batch_data['left'][0],
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mini_batch_data['right'][0],
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mini_batch_data['disparity'][0],
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mini_batch_data['mask'][0],
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model,
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batch_idx,
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)
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# loss stats
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loss_item = loss.data.item()
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epoch_total_train_loss += loss_item
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loss.backward()
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optimizer.step()
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t3 = time.perf_counter()
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if cur_iters % 10 == 0:
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tdata = t2 - t1
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time_train_passed = t3 - t0
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time_iter_passed = t3 - t1
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step_passed = cur_iters - start_iters
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eta = (
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(total_iters - cur_iters)
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/ max(step_passed, 1e-7)
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* time_train_passed
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)
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meta_info = list()
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meta_info.append("{:.2g} b/s".format(1.0 / time_iter_passed))
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meta_info.append("passed:{}".format(format_time(time_train_passed)))
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meta_info.append("eta:{}".format(format_time(eta)))
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meta_info.append(
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"data_time:{:.2g}".format(tdata / time_iter_passed)
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)
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meta_info.append(
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"lr:{:.5g}".format(optimizer.param_groups[0]["lr"])
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)
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meta_info.append(
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"[{}/{}:{}/{}]".format(
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epoch_idx,
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args.n_total_epoch,
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batch_idx,
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args.minibatch_per_epoch,
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)
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)
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loss_info = [" ==> {}:{:.4g}".format("loss", loss_item)]
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# exp_name = ['\n' + os.path.basename(os.getcwd())]
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info = [",".join(meta_info)] + loss_info
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worklog.info("".join(info))
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# minibatch loss
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# tb_log.add_scalar("train/loss_batch", loss_item, cur_iters)
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metrics['train/loss_batch'] = loss_item
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# tb_log.add_scalar(
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# "train/lr", optimizer.param_groups[0]["lr"], cur_iters
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# )
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metrics['train/lr'] = optimizer.param_groups[0]["lr"]
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# tb_log.flush()
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wandb.log(metrics)
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t1 = time.perf_counter()
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# tb_log.add_scalar(
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# "train/loss",
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# epoch_total_train_loss / args.minibatch_per_epoch,
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# epoch_idx,
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# )
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metrics['train/loss'] = epoch_total_train_loss / args.minibatch_per_epoch
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# tb_log.flush()
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wandb.log(metrics)
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# save model params
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ckp_data = {
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"epoch": epoch_idx,
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"iters": cur_iters,
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"batch_size": args.batch_size,
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"epoch_size": args.minibatch_per_epoch,
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"train_loss": epoch_total_train_loss / args.minibatch_per_epoch,
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"state_dict": model.state_dict(),
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"optim_state_dict": optimizer.state_dict(),
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}
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torch.save(ckp_data, os.path.join(log_model_dir, "latest.pth"))
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if epoch_idx % args.model_save_freq_epoch == 0:
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save_path = os.path.join(log_model_dir, "epoch-%d.pth" % epoch_idx)
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worklog.info(f"Model params saved: {save_path}")
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torch.save(ckp_data, save_path)
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worklog.info("Training is done, exit.")
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if __name__ == "__main__":
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# train configuration
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args = parse_yaml("cfgs/train.yaml")
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wandb.init(project="crestereo", entity="cpt-captain")
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wandb.config.update(args._asdict())
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main(args)
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