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train.py
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train.py
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import numpy as np
import time
import logging
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.utils.tensorboard import SummaryWriter
from options import opts
import torch.distributed as dist
import os
import json
from utils import *
from kitti_utils import *
from layers import *
import datasets
from networks import *
import copy
import shutil
import warnings
warnings.filterwarnings("ignore")
class Trainer:
def __init__(self, options):
self.opt = options
self.log_path = os.path.join(self.opt.log_dir, self.opt.exp_name)
if self.opt.global_rank == 0:
if not os.path.exists(self.log_path):
os.makedirs(self.log_path)
self.save_opts()
if not os.path.exists(os.path.join(self.log_path, 'ckpt.pth')):
setup_logging(os.path.join(self.log_path, 'logger.log'), rank=self.opt.global_rank)
logging.info("Experiment is named: %s", self.opt.exp_name)
logging.info("Saving to: %s", os.path.abspath(self.log_path))
logging.info("GPU numbers: %d", self.opt.world_size)
logging.info("Training dataset: %s", self.opt.dataset)
else:
setup_logging(os.path.join(self.log_path, 'logger.log'), filemode='a', rank=self.opt.global_rank)
self.writers = {}
for mode in ["train"]:
self.writers[mode] = SummaryWriter(os.path.join(self.log_path, "tensorboard", mode))
if self.opt.world_size > 1:
dist.barrier()
self.device = torch.device('cuda', self.opt.local_rank)
if self.opt.seed > 0:
self.set_seed(self.opt.seed)
else:
cudnn.benchmark = True
# checking height and width are multiples of 32
assert self.opt.height % 32 == 0, "'height' must be a multiple of 32"
assert self.opt.width % 32 == 0, "'width' must be a multiple of 32"
self.models = {}
self.parameters_to_train = []
self.num_input_frames = len(self.opt.frame_ids)
self.num_pose_frames = 2
self.ep_start = 0
self.batch_start = 0
self.step = 0
assert self.opt.frame_ids[0] == 0, "frame_ids must start with 0"
self.use_pose_net = not (self.opt.use_stereo and self.opt.frame_ids == [0])
if self.opt.use_stereo:
self.opt.frame_ids.append("s")
# data
datasets_dict = {"kitti": datasets.KITTIRAWDataset,
"kitti_odom": datasets.KITTIOdomDataset,
"cityscapes": datasets.CityscapesDataset,
"nyuv2": datasets.NYUDataset}
self.dataset = datasets_dict[self.opt.dataset]
if self.opt.dataset == "kitti":
fpath = os.path.join(os.path.dirname(__file__), "splits/kitti", self.opt.split, "{}_files.txt")
fpath_test = os.path.join(os.path.dirname(__file__), "splits/kitti", self.opt.eval_split, "{}_files.txt")
elif self.opt.dataset == "kitti_odom":
fpath = os.path.join(os.path.dirname(__file__), "splits/kitti", "odom", "{}_files.txt")
fpath_test = os.path.join(os.path.dirname(__file__), "splits/kitti", "odom", "{}_files_09.txt")
elif self.opt.dataset == "nyuv2":
fpath = os.path.join(os.path.dirname(__file__), "splits/nyuv2", "{}_files.txt")
fpath_test = os.path.join(os.path.dirname(__file__), "splits/nyuv2", "{}_files.txt")
elif self.opt.dataset == "cityscapes":
fpath = os.path.join(os.path.dirname(__file__), "splits/cityscapes", "{}_files.txt")
fpath_test = os.path.join(os.path.dirname(__file__), "splits/cityscapes", "{}_files.txt")
else:
pass
train_filenames = readlines(fpath.format("train"))
test_filenames = readlines(fpath_test.format("test"))
img_ext = '.jpg' if self.opt.jpg else '.png'
num_train_samples = len(train_filenames)
self.num_steps_per_epoch = num_train_samples // self.opt.world_size // self.opt.batch_size
self.num_total_steps = self.num_steps_per_epoch * self.opt.num_epochs
if self.opt.dataset == "cityscapes":
train_dataset = self.dataset(
self.opt.data_path_pre, train_filenames, self.opt.height, self.opt.width, self.opt.frame_ids, self.opt.num_scales, self.opt.use_affine, is_train=True, img_ext=img_ext)
else:
train_dataset = self.dataset(
self.opt.data_path, train_filenames, self.opt.height, self.opt.width, self.opt.frame_ids, self.opt.num_scales, self.opt.use_affine, is_train=True, img_ext=img_ext)
if self.opt.world_size > 1:
self.sampler = datasets.CustomDistributedSampler(train_dataset, self.opt.seed)
else:
self.sampler = datasets.CustomSampler(train_dataset, self.opt.seed)
self.train_loader = DataLoader(
train_dataset, self.opt.batch_size, shuffle=False, sampler=self.sampler, num_workers=self.opt.num_workers, pin_memory=True, drop_last=True)
# for testing the model at the end of each epoch
test_dataset = self.dataset(
self.opt.data_path, test_filenames, self.opt.height, self.opt.width,
[0,-1,1], self.opt.num_scales, is_train=False, img_ext=img_ext)
self.test_loader = DataLoader(
test_dataset, self.opt.batch_size, shuffle=False,
num_workers=self.opt.num_workers, pin_memory=True, drop_last=False)
if self.opt.dataset == "kitti":
gt_path = os.path.join(os.path.dirname(__file__), "splits/kitti", self.opt.eval_split, "gt_depths.npz")
self.gt_depths = np.load(gt_path, fix_imports=True, encoding='latin1', allow_pickle=True)["data"]
elif self.opt.dataset == "cityscapes":
gt_path = os.path.join(os.path.dirname(__file__), "splits", "cityscapes", "gt_depths")
self.gt_depths = []
for i in range(len(test_dataset)):
gt_depth = np.load(os.path.join(gt_path, str(i).zfill(3) + '_depth.npy'))
self.gt_depths.append(gt_depth)
else:
pass
# create models
if self.opt.backbone == "ResNet18":
self.models["encoder"] = monodepth2.DepthEncoder(
18, self.opt.weights_init == "pretrained")
self.models["depth"] = monodepth2.DepthDecoder(
self.models["encoder"].num_ch_enc, range(self.opt.num_scales))
elif self.opt.backbone == "ResNet50":
self.models["encoder"] = monodepth2.DepthEncoder(
50, self.opt.weights_init == "pretrained")
self.models["depth"] = monodepth2.DepthDecoder(
self.models["encoder"].num_ch_enc, range(self.opt.num_scales))
elif self.opt.backbone == "DHRNet":
self.models["encoder"] = DHRNet.DepthEncoder(
18, self.opt.weights_init == "pretrained")
self.models["depth"] = DHRNet.DepthDecoder(
self.models["encoder"].num_ch_enc, range(self.opt.num_scales))
elif self.opt.backbone == "LiteMono":
self.models["encoder"] = LiteMono.DepthEncoder(model='lite-mono',
drop_path_rate=0.2,
width=self.opt.width, height=self.opt.height)
model_dict = self.models["encoder"].state_dict()
pretrained_dict = torch.load("./weights/lite-mono-pretrain.pth")['model']
pretrained_dict = {k: v for k, v in pretrained_dict.items() if (k in model_dict and not k.startswith('norm'))}
model_dict.update(pretrained_dict)
self.models["encoder"].load_state_dict(model_dict)
self.models["depth"] = LiteMono.DepthDecoder(
self.models["encoder"].num_ch_enc, range(self.opt.num_scales))
## create multi-frame depth model
if self.opt.fuse_model_type == "shared_all":
self.models["encoder_mf"] = self.models["encoder"]
self.models["depth_mf"] = self.models["depth"]
elif self.opt.fuse_model_type == "shared_encoder":
self.models["encoder_mf"] = self.models["encoder"]
self.models["depth_mf"] = copy.deepcopy(self.models["depth"])
elif self.opt.fuse_model_type == "separate_all":
self.models["encoder_mf"] = copy.deepcopy(self.models["encoder"])
self.models["depth_mf"] = copy.deepcopy(self.models["depth"])
self.models["fusion_module"] = FusionModule(self.opt, self.models["encoder_mf"].num_ch_enc)
if self.use_pose_net:
self.models["pose_encoder"] = posenet.ResnetEncoder(
self.opt.num_layers,
self.opt.weights_init == "pretrained",
num_input_images=self.num_pose_frames)
self.models["pose"] = posenet.PoseDecoder(
self.models["pose_encoder"].num_ch_enc,
num_input_features=1,
num_frames_to_predict_for=2)
if self.opt.pretrained_path:
if not self.opt.resume:
self.load_pretrained_model()
elif not os.path.exists(os.path.join(self.log_path, 'ckpt.pth')):
self.load_pretrained_model()
for k in self.models.keys():
self.models[k].to(self.device)
self.parameters_to_train += list(self.models[k].parameters())
if self.opt.resume:
checkpoint = self.load_ckpt()
if self.opt.world_size > 1:
for k in self.models.keys():
self.models[k] = nn.SyncBatchNorm.convert_sync_batchnorm(self.models[k])
self.models[k] = nn.parallel.DistributedDataParallel(self.models[k], device_ids=[self.opt.local_rank], output_device=self.opt.local_rank, find_unused_parameters=True)
self.model_vfi_train = IFRNet(scale="large")
self.model_vfi_test = IFRNet(scale="small")
if self.opt.dataset == "kitti":
self.model_vfi_train.load_state_dict(torch.load("./weights/IFRNet_L_KITTI.pth")["VFI"])
self.model_vfi_test.load_state_dict(torch.load("./weights/IFRNet_S_KITTI.pth")["VFI"])
elif self.opt.dataset == "cityscapes":
self.model_vfi_train.load_state_dict(torch.load("./weights/IFRNet_L_CS.pth")["VFI"])
self.model_vfi_test.load_state_dict(torch.load("./weights/IFRNet_S_CS.pth")["VFI"])
else:
pass
self.model_vfi_train.to(self.device).eval()
self.model_vfi_test.to(self.device).eval()
if self.opt.world_size > 1:
self.model_vfi_train = nn.SyncBatchNorm.convert_sync_batchnorm(self.model_vfi_train)
self.model_vfi_train = nn.parallel.DistributedDataParallel(self.model_vfi_train, device_ids=[self.opt.local_rank], output_device=self.opt.local_rank, find_unused_parameters=True)
self.model_vfi_test = nn.SyncBatchNorm.convert_sync_batchnorm(self.model_vfi_test)
self.model_vfi_test = nn.parallel.DistributedDataParallel(self.model_vfi_test, device_ids=[self.opt.local_rank], output_device=self.opt.local_rank, find_unused_parameters=True)
# optimizer settings
if self.opt.optimizer == 'adamw':
self.model_optimizer = torch.optim.AdamW(self.parameters_to_train, lr=self.opt.learning_rate, betas=(self.opt.beta1, self.opt.beta2),weight_decay=self.opt.weight_decay)
elif self.opt.optimizer == 'adam':
self.model_optimizer = torch.optim.Adam(self.parameters_to_train, lr=self.opt.learning_rate, betas=(self.opt.beta1, self.opt.beta2))
elif self.opt.optimizer == 'sgd':
self.model_optimizer = torch.optim.SGD(self.parameters_to_train, lr=self.opt.learning_rate, momentum=self.opt.momentum)
else:
logging.error("Optimizer '%s' not defined. Use (adamw|adam|sgd) instead", self.opt.optimizer)
if self.opt.lr_sche_type == 'cos':
self.model_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.model_optimizer, T_max=self.num_total_steps, eta_min=self.opt.eta_min)
elif self.opt.lr_sche_type == 'step':
self.model_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(self.model_optimizer, self.opt.decay_step, self.opt.decay_rate)
if checkpoint:
self.model_optimizer.load_state_dict(checkpoint["optimizer"])
self.model_lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
del checkpoint
if not self.opt.no_ssim:
self.ssim = SSIM()
self.ssim.to(self.device)
self.backproject_depth = BackprojectDepth(self.opt.batch_size, self.opt.height, self.opt.width)
self.backproject_depth.to(self.device)
self.project_3d = Project3D(self.opt.batch_size, self.opt.height, self.opt.width)
self.project_3d.to(self.device)
if self.opt.dataset == "kitti":
logging.info("Using split: %s", self.opt.split)
logging.info("There are {:d} training items and {:d} test items\n".format(len(train_dataset), len(test_dataset)))
if self.opt.world_size > 1:
dist.barrier()
def set_seed(self, seed=1234):
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def set_train(self):
"""Convert all models to training mode
"""
for m in self.models.values():
m.train()
def set_eval(self):
"""Convert all models to testing/evaluation mode
"""
for m in self.models.values():
m.eval()
def train(self):
"""Run the entire training pipeline
"""
for self.epoch in range(self.ep_start, self.opt.num_epochs):
self.run_epoch()
if self.opt.lr_sche_type == "step":
self.model_lr_scheduler.step()
with torch.no_grad():
if self.opt.dataset == "kitti":
self.test_kitti()
self.test_kitti_mf()
elif self.opt.dataset == "cityscapes":
self.test_cityscapes()
self.test_cityscapes_mf()
elif self.opt.dataset == "nyuv2":
self.test_nyuv2()
else:
pass
if self.opt.global_rank == 0:
self.save_model(ep_end=True)
def test_nyuv2(self):
logging.info(" ")
logging.info("Test the model at epoch {} \n".format(self.epoch))
self.set_eval()
pred_depths = []
gt_depths = []
for idx, (color, depth) in enumerate(self.test_loader):
input_color = color.to(self.device)
output = self.models["depth"](self.models["encoder"](input_color))
pred_disp, _ = disp_to_depth(output[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
pred_disp = pred_disp[:, 0]
gt_depth = depth
_, h, w = gt_depth.shape
pred_depth = 1 / pred_disp
pred_depth = F.interpolate(pred_depth.unsqueeze(0), (h, w), mode="nearest")[0]
pred_depths.append(pred_depth)
gt_depths.append(gt_depth)
pred_depths = torch.cat(pred_depths, dim=0)
gt_depths = torch.cat(gt_depths, dim=0).to(self.device)
errors = []
ratios = []
for i in range(pred_depths.shape[0]):
pred_depth = pred_depths[i]
gt_depth = gt_depths[i]
mask = (gt_depth > 0) & (gt_depth < 10)
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
ratio = torch.median(gt_depth) / torch.median(pred_depth)
ratios.append(ratio)
pred_depth *= ratio
pred_depth[pred_depth > 10] = 10
errors.append(compute_depth_errors(gt_depth, pred_depth))
ratios = torch.tensor(ratios)
med = torch.median(ratios)
std = torch.std(ratios / med)
logging.info(" Mono evaluation - using median scaling")
logging.info(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, std))
mean_errors = torch.tensor(errors).mean(0)
logging.info(("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
logging.info(("{: 8.3f} | " * 7 + "\n").format(*mean_errors.tolist()))
self.set_train()
def test_cityscapes(self):
logging.info(" ")
logging.info("Test the model at epoch {} \n".format(self.epoch))
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
STEREO_SCALE_FACTOR = 5.4
self.set_eval()
pred_disps = []
for idx, data in enumerate(self.test_loader):
input_color = data[("color", 0, 0)].to(self.device)
output = self.models["depth"](self.models["encoder"](input_color))
pred_disp, _ = disp_to_depth(output[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
pred_disps.append(pred_disp[:, 0])
pred_disps = torch.cat(pred_disps, dim=0)
errors = []
ratios = []
for i in range(pred_disps.shape[0]):
gt_depth = torch.from_numpy(self.gt_depths[i]).cuda()
gt_height, gt_width = gt_depth.shape[:2]
# crop ground truth to remove ego car -> this has happened in the dataloader for inputs
gt_height = int(round(gt_height * 0.75))
gt_depth = gt_depth[:gt_height]
pred_disp = pred_disps[i:i+1].unsqueeze(0)
pred_disp = F.interpolate(pred_disp, (gt_height, gt_width), mode="bilinear", align_corners=True)
pred_depth = 1 / pred_disp[0, 0, :]
# when evaluating cityscapes, we centre crop to the middle 50% of the image.
# Bottom 25% has already been removed - so crop the sides and the top here
gt_depth = gt_depth[256:, 192:1856]
pred_depth = pred_depth[256:, 192:1856]
mask = (gt_depth > MIN_DEPTH) & (gt_depth < MAX_DEPTH)
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
if self.opt.use_stereo:
pred_depth *= STEREO_SCALE_FACTOR
else:
ratio = torch.median(gt_depth) / torch.median(pred_depth)
ratios.append(ratio)
pred_depth *= ratio
pred_depth = torch.clamp(pred_depth, MIN_DEPTH, MAX_DEPTH)
errors.append(compute_depth_errors(gt_depth, pred_depth))
if self.opt.use_stereo:
logging.info(" Stereo evaluation - disabling median scaling")
logging.info(" Scaling by {}".format(STEREO_SCALE_FACTOR))
else:
ratios = torch.tensor(ratios)
med = torch.median(ratios)
std = torch.std(ratios / med)
logging.info(" Mono evaluation - using median scaling")
logging.info(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, std))
mean_errors = torch.tensor(errors).mean(0)
logging.info(("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
logging.info(("{: 8.3f} | " * 7 + "\n").format(*mean_errors.tolist()))
self.set_train()
def test_kitti(self):
"""Test the model on a single minibatch
"""
logging.info(" ")
logging.info("Test the model at epoch {} \n".format(self.epoch))
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
# Models which were trained with stereo supervision were trained with a nominal baseline of 0.1 units. The KITTI rig has a baseline of 54cm. Therefore, to convert our stereo predictions to real-world scale we multiply our depths by 5.4.
STEREO_SCALE_FACTOR = 5.4
self.set_eval()
pred_disps = []
for idx, data in enumerate(self.test_loader):
input_color = data[("color", 0, 0)].to(self.device)
output = self.models["depth"](self.models["encoder"](input_color))
pred_disp, _ = disp_to_depth(output[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
pred_disps.append(pred_disp[:, 0])
pred_disps = torch.cat(pred_disps, dim=0)
errors = []
ratios = []
for i in range(pred_disps.shape[0]):
gt_depth = torch.from_numpy(self.gt_depths[i]).cuda()
gt_height, gt_width = gt_depth.shape[:2]
pred_disp = pred_disps[i:i+1].unsqueeze(0)
pred_disp = F.interpolate(pred_disp, (gt_height, gt_width), mode="bilinear", align_corners=False)
pred_depth = 1 / pred_disp[0, 0, :]
if self.opt.eval_split == "eigen":
mask = (gt_depth > MIN_DEPTH) & (gt_depth < MAX_DEPTH)
crop_mask = torch.zeros_like(mask)
crop_mask[
int(0.40810811 * gt_height):int(0.99189189 * gt_height),
int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
mask = mask * crop_mask
else:
mask = gt_depth > 0
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
if self.opt.use_stereo:
pred_depth *= STEREO_SCALE_FACTOR
else:
ratio = torch.median(gt_depth) / torch.median(pred_depth)
ratios.append(ratio)
pred_depth *= ratio
pred_depth = torch.clamp(pred_depth, MIN_DEPTH, MAX_DEPTH)
errors.append(compute_depth_errors(gt_depth, pred_depth))
if self.opt.use_stereo:
logging.info(" Stereo evaluation - disabling median scaling")
logging.info(" Scaling by {}".format(STEREO_SCALE_FACTOR))
else:
ratios = torch.tensor(ratios)
med = torch.median(ratios)
std = torch.std(ratios / med)
logging.info(" Mono evaluation - using median scaling")
logging.info(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, std))
mean_errors = torch.tensor(errors).mean(0)
logging.info(("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
logging.info(("{: 8.3f} | " * 7 + "\n").format(*mean_errors.tolist()))
self.set_train()
def test_cityscapes_mf(self):
logging.info(" ")
logging.info("Test the model at epoch {} \n".format(self.epoch))
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
STEREO_SCALE_FACTOR = 5.4
self.set_eval()
pred_disps = []
for idx, data in enumerate(self.test_loader):
img_n1 = data[("color", -1, 0)].to(self.device)
img_p1 = data[("color", 1, 0)].to(self.device)
img_0 = data[("color", 0, 0)].to(self.device)
embt = torch.tensor(0.5).view(1, 1, 1, 1).float().to(self.device)
embt = embt.repeat(img_n1.shape[0], 1, 1, 1)
flow_0_n1, flow_0_p1, merge_mask_01 = self.model_vfi_test(img_n1, img_p1, embt, onlyFlow=True)
feats_n1 = self.models["encoder_mf"](img_n1)
feats_p1 = self.models["encoder_mf"](img_p1)
feats_0 = self.models["encoder_mf"](img_0)
feats = [feats_n1, feats_0, feats_p1]
flows = [flow_0_n1, flow_0_p1]
feats = self.models["fusion_module"](feats, flows, merge_mask_01)
output = self.models["depth_mf"](feats)
pred_disp, _ = disp_to_depth(output[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
pred_disps.append(pred_disp[:, 0])
pred_disps = torch.cat(pred_disps, dim=0)
errors = []
ratios = []
for i in range(pred_disps.shape[0]):
gt_depth = torch.from_numpy(self.gt_depths[i]).cuda()
gt_height, gt_width = gt_depth.shape[:2]
# crop ground truth to remove ego car -> this has happened in the dataloader for inputs
gt_height = int(round(gt_height * 0.75))
gt_depth = gt_depth[:gt_height]
pred_disp = pred_disps[i:i+1].unsqueeze(0)
pred_disp = F.interpolate(pred_disp, (gt_height, gt_width), mode="bilinear", align_corners=True)
pred_depth = 1 / pred_disp[0, 0, :]
# when evaluating cityscapes, we centre crop to the middle 50% of the image.
# Bottom 25% has already been removed - so crop the sides and the top here
gt_depth = gt_depth[256:, 192:1856]
pred_depth = pred_depth[256:, 192:1856]
mask = (gt_depth > MIN_DEPTH) & (gt_depth < MAX_DEPTH)
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
if self.opt.use_stereo:
pred_depth *= STEREO_SCALE_FACTOR
else:
ratio = torch.median(gt_depth) / torch.median(pred_depth)
ratios.append(ratio)
pred_depth *= ratio
pred_depth = torch.clamp(pred_depth, MIN_DEPTH, MAX_DEPTH)
errors.append(compute_depth_errors(gt_depth, pred_depth))
if self.opt.use_stereo:
logging.info(" Stereo evaluation - disabling median scaling")
logging.info(" Scaling by {}".format(STEREO_SCALE_FACTOR))
else:
ratios = torch.tensor(ratios)
med = torch.median(ratios)
std = torch.std(ratios / med)
logging.info(" Mono evaluation - using median scaling")
logging.info(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, std))
mean_errors = torch.tensor(errors).mean(0)
logging.info(("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
logging.info(("{: 8.3f} | " * 7 + "\n").format(*mean_errors.tolist()))
self.set_train()
def test_kitti_mf(self):
"""Test the model on a single minibatch
"""
logging.info(" ")
logging.info("Test the model at epoch {} \n".format(self.epoch))
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
# Models which were trained with stereo supervision were trained with a nominal baseline of 0.1 units. The KITTI rig has a baseline of 54cm. Therefore, to convert our stereo predictions to real-world scale we multiply our depths by 5.4.
STEREO_SCALE_FACTOR = 5.4
self.set_eval()
embt = torch.tensor(0.5).view(1, 1, 1, 1).float().cuda()
pred_disps = []
for idx, data in enumerate(self.test_loader):
img_n1 = data[("color", -1, 0)].to(self.device)
img_p1 = data[("color", 1, 0)].to(self.device)
img_0 = data[("color", 0, 0)].to(self.device)
embt = torch.tensor(0.5).view(1, 1, 1, 1).float().to(self.device)
embt = embt.repeat(img_n1.shape[0], 1, 1, 1)
flow_0_n1, flow_0_p1, merge_mask_01 = self.model_vfi_test(img_n1, img_p1, embt, onlyFlow=True)
feats_n1 = self.models["encoder_mf"](img_n1)
feats_p1 = self.models["encoder_mf"](img_p1)
feats_0 = self.models["encoder_mf"](img_0)
feats = [feats_n1, feats_0, feats_p1]
flows = [flow_0_n1, flow_0_p1]
feats = self.models["fusion_module"](feats, flows, merge_mask_01)
output = self.models["depth_mf"](feats)
pred_disp, _ = disp_to_depth(output[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
pred_disps.append(pred_disp[:, 0])
pred_disps = torch.cat(pred_disps, dim=0)
errors = []
ratios = []
for i in range(pred_disps.shape[0]):
gt_depth = torch.from_numpy(self.gt_depths[i]).cuda()
gt_height, gt_width = gt_depth.shape[:2]
pred_disp = pred_disps[i:i+1].unsqueeze(0)
pred_disp = F.interpolate(pred_disp, (gt_height, gt_width), mode="bilinear", align_corners=False)
pred_depth = 1 / pred_disp[0, 0, :]
if self.opt.eval_split == "eigen":
mask = (gt_depth > MIN_DEPTH) & (gt_depth < MAX_DEPTH)
crop_mask = torch.zeros_like(mask)
crop_mask[
int(0.40810811 * gt_height):int(0.99189189 * gt_height),
int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
mask = mask * crop_mask
else:
mask = gt_depth > 0
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
if self.opt.use_stereo:
pred_depth *= STEREO_SCALE_FACTOR
else:
ratio = torch.median(gt_depth) / torch.median(pred_depth)
ratios.append(ratio)
pred_depth *= ratio
pred_depth = torch.clamp(pred_depth, MIN_DEPTH, MAX_DEPTH)
errors.append(compute_depth_errors(gt_depth, pred_depth))
if self.opt.use_stereo:
logging.info(" Stereo evaluation - disabling median scaling")
logging.info(" Scaling by {}".format(STEREO_SCALE_FACTOR))
else:
ratios = torch.tensor(ratios)
med = torch.median(ratios)
std = torch.std(ratios / med)
logging.info(" Mono evaluation - using median scaling")
logging.info(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, std))
mean_errors = torch.tensor(errors).mean(0)
logging.info(("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
logging.info(("{: 8.3f} | " * 7 + "\n").format(*mean_errors.tolist()))
self.set_train()
def run_epoch(self):
"""Run a single epoch of training and validation
"""
logging.info("Training epoch {}\n".format(self.epoch))
self.sampler.set_epoch(self.epoch)
self.sampler.set_start_iter(self.batch_start*self.opt.batch_size)
self.set_train()
if self.opt.world_size > 1:
dist.barrier()
start_data_time = time.time()
for batch_idx, inputs in enumerate(self.train_loader):
self.step += 1
start_fp_time = time.time()
outputs, losses = self.process_batch(inputs)
start_bp_time = time.time()
self.model_optimizer.zero_grad()
losses["loss"].backward()
if self.opt.clip_grad != -1:
for params in self.model_optimizer.param_groups:
params = params['params']
nn.utils.clip_grad_norm_(params, max_norm=self.opt.clip_grad)
self.model_optimizer.step()
if self.opt.lr_sche_type == "cos":
self.model_lr_scheduler.step()
# compute the process time
data_time = start_fp_time - start_data_time
fp_time = start_bp_time - start_fp_time
bp_time = time.time() - start_bp_time
# logging
if ((batch_idx+self.batch_start) % self.opt.log_frequency == 0):
if self.opt.world_size > 1:
dist.barrier()
for k in losses.keys():
dist.all_reduce(losses[k], op=dist.ReduceOp.SUM)
losses[k] /= self.opt.world_size
dist.barrier()
if self.opt.global_rank == 0:
self.log_time(batch_idx+self.batch_start, data_time, fp_time,bp_time, losses["loss"].cpu().data)
self.log_tensorboard("train", losses)
# save ckpt
if ((batch_idx+self.batch_start)>0 and (batch_idx+self.batch_start) % self.opt.save_frequency == 0):
if self.opt.global_rank == 0:
self.save_model(batch_idx=batch_idx+self.batch_start+1)
if self.opt.world_size > 1:
dist.barrier()
start_data_time = time.time()
self.batch_start = 0
def process_batch(self, inputs):
"""Pass a minibatch through the network and generate images and losses
"""
for key, ipt in inputs.items():
try:
inputs[key] = ipt.to(self.device)
except:
pass
embt = torch.tensor(0.5).view(1, 1, 1, 1).float().to(self.device)
embt = embt.repeat(self.opt.batch_size, 1, 1, 1)
img_n1 = inputs[("color", -1, 0)]
img_p1 = inputs[("color", 1, 0)]
img_0 = inputs[("color", 0, 0)]
## n1 denotes -1 (negative), nt denotes -t
## p1 denotes +1 (positive), pt denotes +t
with torch.no_grad():
img_nt, flow_nt_n1, flow_nt_0, merge_mask_nt = self.model_vfi_train(img_n1, img_0, embt)
img_pt, flow_pt_0, flow_pt_p1, merge_mask_pt = self.model_vfi_train(img_0, img_p1, embt)
flow_0_n1, flow_0_p1, merge_mask_01 = self.model_vfi_train(img_n1, img_p1, embt, onlyFlow=True)
K = inputs[("K", 0)]
inv_K = inputs[("inv_K", 0)]
losses = {}
losses["loss_base"] = torch.tensor(0.0).to(self.device)
losses["loss_dc"] = torch.tensor(0.0).to(self.device)
pose_n1_0, pose_0_n1 = self.predict_poses(inputs[("color_aug", -1, 0)], inputs[("color_aug", 0, 0)])
pose_0_p1, pose_p1_0 = self.predict_poses(inputs[("color_aug", 0, 0)], inputs[("color_aug", 1, 0)])
pose_n1_nt, pose_nt_n1 = self.predict_poses(img_n1, img_nt)
pose_nt_p1, pose_p1_nt = self.predict_poses(img_nt, img_p1)
pose_n1_pt, pose_pt_n1 = self.predict_poses(img_n1, img_pt)
pose_pt_p1, pose_p1_pt = self.predict_poses(img_pt, img_p1)
## predict single-frame depths
feats_0 = self.models["encoder"](inputs[("color_aug", 0, 0)])
feats_nt = self.models["encoder"](img_nt)
feats_pt = self.models["encoder"](img_pt)
disp_0 = self.models["depth"](feats_0)
disp_pt = self.models["depth"](feats_pt)
disp_nt = self.models["depth"](feats_nt)
_, depth_0 = disp_to_depth(disp_0[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
_, depth_pt = disp_to_depth(disp_pt[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
_, depth_nt = disp_to_depth(disp_nt[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
## calculate the self-supervised losses on single-frame depths
img_n1_00 = self.generate_images_pred(disp_0, pose_0_n1, img_n1, K, inv_K)
img_p1_00 = self.generate_images_pred(disp_0, pose_0_p1, img_p1, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_0, img_0, [img_n1_00, img_p1_00], [img_n1, img_p1])
losses["loss_base"] += loss_base
img_n1_pt = self.generate_images_pred(disp_pt, pose_pt_n1, img_n1, K, inv_K)
img_p1_pt = self.generate_images_pred(disp_pt, pose_pt_p1, img_p1, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_pt, img_pt, [img_n1_pt, img_p1_pt], [img_n1, img_p1])
losses["loss_base"] += loss_base
img_n1_nt = self.generate_images_pred(disp_nt, pose_nt_n1, img_n1, K, inv_K)
img_p1_nt = self.generate_images_pred(disp_nt, pose_nt_p1, img_p1, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_nt, img_nt, [img_n1_nt, img_p1_nt], [img_n1, img_p1])
losses["loss_base"] += loss_base
## predict multi-frame depths
if self.opt.fuse_model_type == "separate_all":
feats_0 = self.models["encoder_mf"](inputs[("color_aug", 0, 0)])
feats_nt = self.models["encoder_mf"](img_nt)
feats_pt = self.models["encoder_mf"](img_pt)
feats_n1 = self.models["encoder_mf"](inputs[("color_aug", -1, 0)])
feats_p1 = self.models["encoder_mf"](inputs[("color_aug", 1, 0)])
else:
feats_n1 = self.models["encoder"](inputs[("color_aug", -1, 0)])
feats_p1 = self.models["encoder"](inputs[("color_aug", 1, 0)])
feats = [feats_n1, feats_0, feats_p1]
flows = [flow_0_n1, flow_0_p1]
feats = self.models["fusion_module"](feats, flows, merge_mask_01)
disp_0_fuse = self.models["depth_mf"](feats)
_, depth_0_fuse = disp_to_depth(disp_0_fuse[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
feats = [feats_n1, feats_nt, feats_0]
flows = [flow_nt_n1, flow_nt_0]
feats = self.models["fusion_module"](feats, flows, merge_mask_nt)
disp_nt_fuse = self.models["depth_mf"](feats)
_, depth_nt_fuse = disp_to_depth(disp_nt_fuse[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
feats = [feats_0, feats_pt, feats_p1]
flows = [flow_pt_0, flow_pt_p1]
feats = self.models["fusion_module"](feats, flows, merge_mask_pt)
disp_pt_fuse = self.models["depth_mf"](feats)
_, depth_pt_fuse = disp_to_depth(disp_pt_fuse[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
## calculate the self-supervised losses on multi-frame depths
## and the depth consistency losses (SVDC)
img_n1_0 = self.generate_images_pred(disp_0_fuse, pose_0_n1, img_n1, K, inv_K)
img_p1_0 = self.generate_images_pred(disp_0_fuse, pose_0_p1, img_p1, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_0_fuse, img_0, [img_n1_0, img_p1_0], [img_n1, img_p1])
losses["loss_base"] += loss_base
loss_dc = self.compute_SI_log_depth_loss(depth_0, depth_0_fuse)
losses["loss_dc"] += loss_dc
img_n1_nt = self.generate_images_pred(disp_nt_fuse, pose_nt_n1, img_n1, K, inv_K)
img_p1_nt = self.generate_images_pred(disp_nt_fuse, pose_nt_p1, img_p1, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_nt_fuse, img_nt, [img_n1_nt, img_p1_nt], [img_n1, img_p1])
losses["loss_base"] += loss_base
loss_dc = self.compute_SI_log_depth_loss(depth_nt, depth_nt_fuse)
losses["loss_dc"] += loss_dc
img_n1_pt = self.generate_images_pred(disp_pt_fuse, pose_pt_n1, img_n1, K, inv_K)
img_p1_pt = self.generate_images_pred(disp_pt_fuse, pose_pt_p1, img_p1, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_pt_fuse, img_pt, [img_n1_pt, img_p1_pt], [img_n1, img_p1])
losses["loss_base"] += loss_base
loss_dc = self.compute_SI_log_depth_loss(depth_pt, depth_pt_fuse)
losses["loss_dc"] += loss_dc
## losses relevant to affine augmentation
if self.opt.use_affine:
## for img_0
disp_0_affine = self.models["depth"](self.models["encoder"](inputs[("color_affine_aug", 0, 0)]))
_, depth_0_affine = disp_to_depth(disp_0_affine[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
Rc = inputs[("Rc")]
Rt_Rc = torch.zeros_like(pose_0_n1).to(self.device)
Rt_Rc[:, :3, :3] = torch.matmul(Rc, torch.matmul(pose_0_n1[:, :3, :3], torch.inverse(Rc)))
Rt_Rc[:, :3, 3:4] = torch.matmul(Rc, pose_0_n1[:, :3, 3:4])
pose_0_n1_affine = Rt_Rc
Rt_Rc = torch.zeros_like(pose_0_p1).to(self.device)
Rt_Rc[:, :3, :3] = torch.matmul(Rc, torch.matmul(pose_0_p1[:, :3, :3], torch.inverse(Rc)))
Rt_Rc[:, :3, 3:4] = torch.matmul(Rc, pose_0_p1[:, :3, 3:4])
pose_0_p1_affine = Rt_Rc
img_n1_affine = inputs[("color_affine", -1, 0)]
img_p1_affine = inputs[("color_affine", 1, 0)]
img_0_affine = inputs[("color_affine", 0, 0)]
mask_rec = inputs[("valid_mask_rec")]
img_n1_0_affine = self.generate_images_pred(disp_0_affine, pose_0_n1_affine, img_n1_affine, K, inv_K)
img_p1_0_affine = self.generate_images_pred(disp_0_affine, pose_0_p1_affine, img_p1_affine, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_0_affine, img_0_affine, [img_n1_0_affine, img_p1_0_affine], [img_n1_affine, img_p1_affine], mask_rec)
## calculate the self-supervised loss on augmented single-frame depth
losses["loss_base"] += loss_base
## calculate two scale-aware depth consistency losses (SADC)
losses["loss_dc"] += self.compute_depth_consistency_loss_affine(depth_0_affine, depth_0, depth_0_fuse, inputs)
## for img_nt
img_nt_affine = self.affine_transform(img_nt, inputs)
disp_nt_affine = self.models["depth"](self.models["encoder"](img_nt_affine))
_, depth_nt_affine = disp_to_depth(disp_nt_affine[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
Rc = inputs[("Rc")]
Rt_Rc = torch.zeros_like(pose_nt_n1).to(self.device)
Rt_Rc[:, :3, :3] = torch.matmul(Rc, torch.matmul(pose_nt_n1[:, :3, :3], torch.inverse(Rc)))
Rt_Rc[:, :3, 3:4] = torch.matmul(Rc, pose_nt_n1[:, :3, 3:4])
pose_nt_n1_affine = Rt_Rc
Rt_Rc = torch.zeros_like(pose_nt_p1).to(self.device)
Rt_Rc[:, :3, :3] = torch.matmul(Rc, torch.matmul(pose_nt_p1[:, :3, :3], torch.inverse(Rc)))
Rt_Rc[:, :3, 3:4] = torch.matmul(Rc, pose_nt_p1[:, :3, 3:4])
pose_nt_p1_affine = Rt_Rc
img_n1_nt_affine = self.generate_images_pred(disp_nt_affine, pose_nt_n1_affine, img_n1_affine, K, inv_K)
img_p1_nt_affine = self.generate_images_pred(disp_nt_affine, pose_nt_p1_affine, img_p1_affine, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_nt_affine, img_nt_affine, [img_n1_nt_affine, img_p1_nt_affine], [img_n1_affine, img_p1_affine], mask_rec)
losses["loss_base"] += loss_base
losses["loss_dc"] += self.compute_depth_consistency_loss_affine(depth_nt_affine, depth_nt, depth_nt_fuse, inputs)
## for img_pt
img_pt_affine = self.affine_transform(img_pt, inputs)
disp_pt_affine = self.models["depth"](self.models["encoder"](img_pt_affine))
_, depth_pt_affine = disp_to_depth(disp_pt_affine[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
Rc = inputs[("Rc")]
Rt_Rc = torch.zeros_like(pose_pt_n1).to(self.device)
Rt_Rc[:, :3, :3] = torch.matmul(Rc, torch.matmul(pose_pt_n1[:, :3, :3], torch.inverse(Rc)))
Rt_Rc[:, :3, 3:4] = torch.matmul(Rc, pose_pt_n1[:, :3, 3:4])
pose_pt_n1_affine = Rt_Rc
Rt_Rc = torch.zeros_like(pose_pt_p1).to(self.device)
Rt_Rc[:, :3, :3] = torch.matmul(Rc, torch.matmul(pose_pt_p1[:, :3, :3], torch.inverse(Rc)))
Rt_Rc[:, :3, 3:4] = torch.matmul(Rc, pose_pt_p1[:, :3, 3:4])
pose_pt_p1_affine = Rt_Rc
img_n1_pt_affine = self.generate_images_pred(disp_pt_affine, pose_pt_n1_affine, img_n1_affine, K, inv_K)
img_p1_pt_affine = self.generate_images_pred(disp_pt_affine, pose_pt_p1_affine, img_p1_affine, K, inv_K)
loss_base, _ = self.compute_losses_base(disp_pt_affine, img_pt_affine, [img_n1_pt_affine, img_p1_pt_affine], [img_n1_affine, img_p1_affine], mask_rec)
losses["loss_base"] += loss_base
losses["loss_dc"] += self.compute_depth_consistency_loss_affine(depth_pt_affine, depth_pt, depth_pt_fuse, inputs)
losses["loss"] = losses["loss_base"] + self.opt.lamda * losses["loss_dc"]
return None, losses
def affine_transform(self, img, inputs):
# img: tensor [B, 3, H, W]
img_affine = []
for b in range(self.opt.batch_size):
angle = inputs[("angle")][b][0].item()
x0 = inputs[("box")][b, 0].item()
y0 = inputs[("box")][b, 1].item()
w = inputs[("box")][b, 2].item()
h = inputs[("box")][b, 3].item()
img_b = img[b].unsqueeze(0)
img_b = transforms.functional.rotate(img_b, angle=angle, interpolation=2)
img_b = img_b[:, :, y0:y0+h, x0:x0+w]
img_b = torch.nn.functional.interpolate(img_b, [self.opt.height, self.opt.width], mode="bilinear", align_corners=False)
img_affine.append(img_b)
return torch.cat(img_affine, 0)
def compute_depth_consistency_loss_affine(self, depth_affine, depth, depth_fuse, inputs):
loss_dc_affine = 0
for b in range(self.opt.batch_size):
angle = inputs[("angle")][b][0].item()
x0 = inputs[("box")][b, 0].item()
y0 = inputs[("box")][b, 1].item()
w = inputs[("box")][b, 2].item()
h = inputs[("box")][b, 3].item()
tmp = F.interpolate(depth_affine[b].unsqueeze(0), [h, w], mode="bilinear", align_corners=False)
depth_restore = torch.zeros((1, 1, self.opt.height, self.opt.width)).to(self.device)
depth_restore[:, :, y0:y0+h, x0:x0+w] = tmp
depth_restore = transforms.functional.rotate(depth_restore, angle=-angle, interpolation=2)
depth_restore *= inputs[("ratio_local")][b, 0]
depth_origin_fuse = depth_fuse[b].unsqueeze(0)
depth_origin = depth[b].unsqueeze(0)
loss_dc_affine += self.compute_SI_log_depth_loss(depth_restore, depth_origin_fuse, inputs[("valid_mask_cons")][b].unsqueeze(0))
loss_dc_affine += self.compute_SI_log_depth_loss(depth_restore, depth_origin, inputs[("valid_mask_cons")][b].unsqueeze(0))
loss_dc_affine /= self.opt.batch_size
return loss_dc_affine
def compute_SI_log_depth_loss(self, pred, target, mask=None, beta=0.5):
# B*1*H*W -> B*H*W
if mask is None:
mask = torch.ones_like(pred).to(self.device)
mask = mask[:, 0]
log_pred = torch.log(pred[:, 0]+1e-7) * mask
log_tgt = torch.log(target[:, 0]+1e-7) * mask
log_diff = log_pred - log_tgt
valid_num = mask.sum(1).sum(1) + 1e-8
log_diff_squre_sum = (log_diff ** 2).sum(1).sum(1)
log_diff_sum_squre = (log_diff.sum(1).sum(1)) ** 2
loss = log_diff_squre_sum/valid_num - beta*log_diff_sum_squre/(valid_num**2)
loss = loss.mean()
return loss
def predict_poses(self, img_0, img1):
"""Predict poses between input frames for monocular sequences.
"""
pose_inputs = [img_0, img1]
pose_inputs = [self.models["pose_encoder"](torch.cat(pose_inputs, 1))]
axisangle, translation = self.models["pose"](pose_inputs)
pose = transformation_from_parameters(axisangle[:, 0], translation[:, 0], invert=False)
pose_inv = transformation_from_parameters(axisangle[:, 0], translation[:, 0], invert=True)
return pose, pose_inv
def generate_images_pred(self, disp_tgt, pose_tgt_src, img_src, K, inv_K):
"""Generate the warped (reprojected) color images for a minibatch.
Generated images are saved into the `outputs` dictionary.
"""
disp = disp_tgt[("disp", 0)]
_, depth = disp_to_depth(disp, self.opt.min_depth, self.opt.max_depth)
cam_points = self.backproject_depth(depth, inv_K)
pix_coords = self.project_3d(cam_points, K, pose_tgt_src)
img_src_tgt = F.grid_sample(
img_src,
pix_coords,
padding_mode="border", align_corners=True)
return img_src_tgt
def compute_reprojection_loss(self, pred, target):
"""Computes reprojection loss between a batch of predicted and target images
"""
abs_diff = torch.abs(target - pred)
l1_loss = abs_diff.mean(1, True)
if self.opt.no_ssim:
reprojection_loss = l1_loss
else:
ssim_loss = self.ssim(pred, target).mean(1, True)
reprojection_loss = 0.85 * ssim_loss + 0.15 * l1_loss
return reprojection_loss
def compute_losses_base(self, disp_tgt, img_tgt, imgs_src_tgt, imgs_src, mask_rec=None):
"""Compute the reprojection and smoothness losses for a minibatch
"""
loss = 0
reprojection_losses = []
disp = disp_tgt[("disp", 0)]
for i in range(len(imgs_src_tgt)):
pred = imgs_src_tgt[i]
reprojection_losses.append(self.compute_reprojection_loss(pred, img_tgt))
reprojection_losses = torch.cat(reprojection_losses, 1)