train_depth_mv.py

# Lint as: python3
"""Train depth and pose on the carla dataset.
"""
import cv2
import os
import distro
import numpy as np
# Pytorch Imports
import torch
from progress.bar import Bar
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

# from data_readers.carla_reader import CarlaReader
from data_readers.habitat_data_neuray_ft_mv import HabitatImageGeneratorFTMultiView
from data_readers.habitat_data_neuray_ft_lmdb_mv import HabitatImageGeneratorFTMultiView_LMDB
# from lmdb_rw.habitat_data_neuray_ft_lmdb import HabitatImageGeneratorFT_LMDB
from helpers import my_torch_helpers
from helpers.torch_checkpoint_manager import CheckpointManager
from models import loss_lib
from network.omni_mvsnet.pipeline3_model import FullPipeline
import numpy as np
import random
import argparse

from utils.base_utils import load_cfg
from utils.seed import setup_seed, seed_everything

    
class App:
  """Main app class"""
  default_cfg={
    "script_mode": "train_depth_pose",
    "model_name": "",
    "checkpoints_dir": "",
    "batch_size": 2,
    "epochs": 999999,#useless
    "learning_rate":0.00006,
    "width": 512,
    "height": 256,
    "opt_beta1": 0.5,
    "opt_beta2": 0.999,
    "train_tensorboard_interval": 0,
    "validation_interval": 200,
    "checkpoint_interval":200,
    "smoothness_loss_lambda": 1,
    "checkpoint_count": 3,
    "point_radius": 0.004,
    "device": "cuda",
    "verbose": True,
    "patch_loss_patch_size": 5,
    "patch_loss_stride": 1,
    "patch_loss_stride_dist": 3,
    "inpaint_use_residual": False,
    "inpaint_wrap_padding": False,
    "loss": "l1",
    "inpaint_use_batchnorm":True,
    "upscale_point_cloud": True,
    "inpaint_one_conv": False,
    "interpolation_mode": "bilinear",
    "add_depth_noise": 0,
    "depth_input_uv": False,
    "normalize_depth": False, 
    "predict_zdepth": False,
    "cost_volume": "",
    "clip_grad_value": 0,
    "model_use_v_input": False,
    "debug_mode": False,
    "dataset": "m3d",
    "carla_min_dist": 2, 
    "carla_max_dist": 100,
    "min_depth": 0.1,
    "max_depth": 10,
    "turbo_cmap_min": 2,
    "m3d_dist": 1,
    "depth_type": "one_over",
    "depth_loss": "l1",
    "load_prepared_test_data": False,
    "test_sample_num": 200,
    "save_datadir": "/home/chenzheng/nas/PanoNVS/somsi_data/test_m3d",
    "total_iter": 100000,
    #unifuse
    "num_layers": 18, # choices=[2, 18, 34, 50, 101]
    "imagenet_pretrained": False,
    # ablation settings
    "mono_net": "UniFuse",#choices=["UniFuse", "Equi", "ERP+TP", "TP", "Cube", "OmniSyn"]
    "fusion":"cee", #choices=["cee", "cat", "biproj"]
    "se_in_fusion": False,
    "num_workers": 2,
    # Multi_view matching hyper_parameters

    "MAGNET_sampling_range": 3,
    "MAGNET_num_samples": 5,
    "MAGNET_mvs_weighting": "CW5",
    "DNET_ckpt": None,
    "contain_dnet": False,
    "use_wrap_padding": False,
    "stereo_out_type": "disparity",
    "dnet_out_type": "depth",
    #fusemodel output type
    "out_type": "depth",
    "stereonet_ckpt": None,
    "fuse_type": "simple", #simple, geometry_emb_only, geometry_emb_scaled, geometry_emb_scaled_masked etc
    "patchsize": (128, 128),
    "fov": 80,
    "nrow": 4,#3,4,5,6
    "lr_decay": False, 
    "lrate_decay": 250,
    "aug": False,
    "wo_hdh": False,
    "use_lmdb": False,
    "use_depth_sampling": False,
    "show_stats": False,
    "relaxation_factor": 1,
    "mvs_uncertainty": False,
    "revise_range": False,
    "uncert_tune": False,
    "change_input": False,
    "wo_mono_feat": False,
    "with_sin": False,
    "wo_lowres_loss": False,
    "mono_uncert_tune": False,
    "basic_sigma": 0,
    "std_uncert_tune": False,

  }  
  

  def __init__(self):
    self.model = None
    self.optimizer = None
    self.checkpoint_manager = None
    self.args = None
    self.writer = None

    # Attributes to hold training data.
    self.train_data = None
    self.train_data_loader = None

    # Attributes to hold validation data.
    self.val_data = None
    self.val_data_indices = None
    self.input_panos_val = None
    self.input_depths_val = None
    self.input_rots_val = None
    self.input_trans_val = None

  def start(self, flags):
    """Starts the training."""

    try:
      cfg=load_cfg(flags.cfg)
      self.cfg={**self.default_cfg,**cfg}           
      args = self.cfg
      self.args = args
      if args['use_lmdb']:
        self.full_width = args["mono_width"]
        self.full_height = args["mono_height"]

      else:
        self.full_width = args["width"]
        self.full_height = args["height"]

      seed = 2022
      setup_seed(seed)
      
      
      self.load_training_data()
      self.load_validation_data()
      self.setup_model()
      self.setup_checkpoints()
      step = self.checkpoint_manager.step
      if args["script_mode"] == "train_depth_pose":
        total_params = self.model.get_total_params()
        with open(args["checkpoints_dir"]+"/params.txt", "w") as fp:
          fp.write("params:"+str(total_params))
        self.run_training_loop()
        # args["checkpoints_dir"]
      elif args["script_mode"] == "eval_depth_pose":
        total_params = self.model.get_total_params()
        print("Total parameters:", total_params)
        self.eval_on_training_data()
        self.eval_on_validation_data()

      elif args["script_mode"] == "eval_depth_test":
        total_params = self.model.get_total_params()
        print("Total parameters:", total_params)
        
        self.eval_on_validation_data()
      else:
        raise ValueError("Unknown script mode: " + str(args["script_mode"]))

    except KeyboardInterrupt:
      print("Terminating script")
      self.writer.close()

  def setup_model(self):
    """Sets up the model."""
    args = self.args
    model = FullPipeline(args,
                         width=args["width"],
                         height=args["height"],
                         layers=5,
                         raster_resolution=args["width"],
                         depth_input_images=1,
                         depth_output_channels=1,
                         include_poseestimator=True,
                         verbose=args["verbose"],
                         input_uv=args["depth_input_uv"],
                         interpolation_mode=args["interpolation_mode"],
                         cost_volume=args["cost_volume"],
                         use_v_input=args["model_use_v_input"],
                         ).to(args["device"])
    if args["uncert_tune"]:
      
      from network.omni_mvsnet.uncert_wrapper import UncertWrapper
      model = UncertWrapper(args, model)
    
    if args["std_uncert_tune"]:
      from network.omni_mvsnet.std_uncert_wrapper import StdUncertWrapper
      model = StdUncertWrapper(args, model)

    optimizer = torch.optim.Adam(model.parameters(),
                                 lr=args["learning_rate"],
                                 betas=(args["opt_beta1"], args["opt_beta2"]))
    self.model = model
    self.optimizer = optimizer

  def setup_checkpoints(self):
    """Sets up the checkpoint manager."""
    args = self.args
    model = self.model
    optimizer = self.optimizer
    checkpoint_manager = CheckpointManager(args["checkpoints_dir"],
                                           max_to_keep=args["checkpoint_count"])
                                          
    latest_checkpoint = checkpoint_manager.load_latest_checkpoint()
    if latest_checkpoint is not None:
      model.load_state_dict(latest_checkpoint['model_state_dict'])
      optimizer.load_state_dict(latest_checkpoint['optimizer_state_dict'])
    else:
      print("without any pretrained model for entire model!")
      

    writer = SummaryWriter(log_dir=os.path.join(args["checkpoints_dir"], "logs"))

    self.checkpoint_manager = checkpoint_manager
    self.writer = writer

  def load_training_data(self):
    """Loads training data."""
    args = self.args

    # Prepare dataset loaders for train and validation datasets.
    # if args["dataset"] == "carla":
    #   train_data = CarlaReader(
    #     args["carla_path"],
    #     width=self.full_width,
    #     height=self.full_height,
    #     towns=["Town01", "Town02", "Town03", "Town04"],
    #     min_dist=args["carla_min_dist"],
    #     max_dist=args["carla_max_dist"],
    #     seq_len=2,
    #     reference_idx=1,
    #     use_meters_depth=True,
    #     interpolation_mode=args["interpolation_mode"],
    #     sampling_method="dense")
    #   print("Size of training set: %d" % (len(train_data),))
    #   train_dataloader = DataLoader(
    #     train_data,
    #     batch_size=args["batch_size"],
    #     shuffle=True,
    #     num_workers=4)
    if args["dataset"] == "m3d":
        # args=cfg,
        # split=mode,
        # seq_len=seq_len,
        # reference_idx=reference_idx,
        # full_width=full_width,
        # full_height=full_height,
        # m3d_dist=m3d_dist
      if args["use_lmdb"]:
        train_data = HabitatImageGeneratorFTMultiView_LMDB(
          args,
          split="train",
          seq_len = args["seq_len"],
          reference_idx = args["reference_idx"],
          full_width=self.full_width,
          full_height=self.full_height,
          m3d_dist=args["m3d_dist"],
          aug=args["aug"]
        )
        train_dataloader = DataLoader(
          dataset=train_data,
          num_workers=args["num_workers"],
          batch_size=args["batch_size"],
          shuffle=True,
          drop_last=True,
          pin_memory=True,
          prefetch_factor=1,
        )

      else:
      # for multi-view
        train_data = HabitatImageGeneratorFTMultiView(
          args,
          split="train",
          seq_len = args["seq_len"],
          reference_idx = args["reference_idx"],
          full_width=self.full_width,
          full_height=self.full_height,
          m3d_dist=args["m3d_dist"],
          aug=args["aug"]
        )      
        train_dataloader = DataLoader(
          dataset=train_data,
          num_workers=args["num_workers"],
          batch_size=args["batch_size"],
          shuffle=False,
          drop_last=True,
          pin_memory=True
        )

    train_data.cache_depth_to_dist(args["height"], args["width"])

    self.train_data = train_data
    self.train_data_loader = train_dataloader

  def load_validation_data(self):
    """Loads validation data."""
    args = self.args
    # if args["dataset"] == "carla":
    #   towns = ["Town05"]
    #   if args["script_mode"] == "eval_depth_test":
    #     towns = ["Town06"]
    #   val_data = CarlaReader(
    #     args["carla_path"],
    #     width=self.full_width,
    #     height=self.full_height,
    #     towns=towns,
    #     min_dist=args["carla_min_dist"],
    #     max_dist=args["carla_max_dist"],
    #     seq_len=2,
    #     reference_idx=1,
    #     use_meters_depth=True,
    #     interpolation_mode=args["interpolation_mode"])
    if args["dataset"] == "m3d":
      if args["use_lmdb"]: #  == "eval_depth_test":
        if "eval_mode" in args:
          eval_mode=args["eval_mode"]
        else:
          eval_mode= "val"
        
        val_data = HabitatImageGeneratorFTMultiView_LMDB(
          args,
          eval_mode,
          seq_len = args["seq_len"],
          reference_idx = args["reference_idx"],
          full_width=self.full_width,
          full_height=self.full_height,
          m3d_dist=args["m3d_dist"])
      else:
        if "eval_mode" in args:
          eval_mode=args["eval_mode"]
        else:
          eval_mode= "val"
        val_data = HabitatImageGeneratorFTMultiView(
          args,
          eval_mode,
          seq_len = args["seq_len"],
          reference_idx = args["reference_idx"],
          full_width=self.full_width,
          full_height=self.full_height,
          m3d_dist=args["m3d_dist"])

    # Load a single batch of validation data.
    # # val_data_indices = [20, 40, 60, 80, 100]
    # val_data_indices = [0, 40, 80, 120]
    # val_data_all = tuple(val_data[i] for i in val_data_indices)
    # input_panos_val = np.stack(tuple(
    #   v_data["rgb_panos"] for v_data in val_data_all),
    #   axis=0)
    # input_panos_val = torch.tensor(input_panos_val,
    #                                dtype=torch.float32,
    #                                device=args["device"])
    # input_depths_val = np.stack(tuple(
    #   v_data["depth_panos"] for v_data in val_data_all),
    #   axis=0)
    # input_depths_val = torch.tensor(input_depths_val,
    #                                 dtype=torch.float32,
    #                                 device=args["device"])
    # input_rots_val = np.stack(
    #   tuple(v_data["rots"] for v_data in val_data_all),
    #   axis=0)
    # input_rots_val = torch.tensor(input_rots_val,
    #                               dtype=torch.float32,
    #                               device=args["device"])
    # input_trans_val = np.stack(tuple(
    #   v_data["trans"] for v_data in val_data_all),
    #   axis=0)
    # input_trans_val = torch.tensor(input_trans_val,
    #                                dtype=torch.float32,
    #                                device=args["device"])

    self.val_data = val_data
    # self.val_data_indices = val_data_indices
    # self.input_panos_val = input_panos_val
    # self.input_depths_val = input_depths_val
    # self.input_rots_val = input_rots_val
    # self.input_trans_val = input_trans_val

  def run_depth_pose_carla(self, step, panos, depths, rots, trans, curr_idx):
    """Does a single run and returns results.
    Args:
      step: Current step.
      panos: Input panoramas.
      depths: GT depths.
      rots: GT rotations.
      trans: GT translations.

    Returns:
      Dictionary containing all outputs.

    """

    args = self.args
    height = args["height"]
    width = args["width"]
    model = self.model
    train_data = self.train_data
    batch_size, seq_len = panos.shape[:2]
    panos_small = panos.reshape(
      (batch_size * seq_len, self.full_height, self.full_width, 3))
    panos_small = my_torch_helpers.resize_torch_images(
      panos_small, (args["height"], args["width"]), mode=args["interpolation_mode"])
    panos_small = panos_small.reshape(batch_size, seq_len, height, width, 3)

    depths_height, depths_width = depths.shape[2:4]
    depths_small = depths.reshape(
      (batch_size * seq_len, depths_height, depths_width, 1))
    depths_small = my_torch_helpers.resize_torch_images(
      depths_small, (args["height"], args["width"]), mode=args["interpolation_mode"])
    depths_small = depths_small.reshape(batch_size, seq_len, height, width, 1)

    # rots_pred, trans_pred = model.estimate_pose(panos_small[:, :2, :, :, :])
    # if args["cost_volume"]:
    outputs = model.estimate_depth_using_cost_volume_v3_erp_multiview(panos_small, rots, trans, curr_idx, 
                                                      min_depth=args["min_depth"],
                                                      max_depth=args["max_depth"])

    # outputs = model.estimate_depth_using_cost_volume(panos_small, rots, trans,
    #                                                   min_depth=args["min_depth"],
    #                                                   max_depth=args["max_depth"])
    depths_pred = outputs["depth"]

    # rgb_ = np.uint8(panos[0, 1].data.cpu().numpy()*255)
    # depth_ = depths_pred[0, 0]
    # # for only depth debug
    # import cv2
    # os.makedirs("./erp_debug", exist_ok=True)
    # cv2.imwrite("./erp_debug/rgb.jpg", rgb_)
    # # def depth_norm(depth_np):
    # #   d_min = depth_np.min()
    # #   d_max = depth_np.max()
    # #   d_norm = (depth_np-d_min)/(d_max-d_min)
    # #   d_gray = np.uint8(d_norm*255)
    # #   d_rgb = cv2.applyColorMap(d_gray, cv2.COLORMAP_JET)
    # #   return d_rgb    
    # def normalize_depth(depth):
    #   d_min = depth.min()
    #   d_max = depth.max()
    #   d_norm = np.uint8((depth-d_min)/(d_max-d_min)*255)
    #   d_rgb = cv2.applyColorMap(d_norm, cv2.COLORMAP_JET)
    #   return d_rgb
    # d_rgb = normalize_depth(depth_.data.cpu().numpy())
    # cv2.imwrite("./erp_debug/d_rgb.jpg", d_rgb)


    # if args["predict_zdepth"]:
    #   depths_pred = train_data.zdepth_to_distance_torch(depths_pred)
    depths_pred = depths_pred.reshape(
      (batch_size, 1, height, width, depths_pred.shape[3]))     
    assert torch.isfinite(depths_pred).all(), "Nan in depths_pred"
    
    # disp_c1 = None
    # depths_c1 = None
    # zdepths_small_1 = None
    # rect_gt_depth = None
    # rect_gt_disp = None
    # disp_pred_c1 = None
    # if args["cost_volume"] == "v1" or \
    #     args["cost_volume"] == "v2" or \
    #     args["cost_volume"] == "v3":
      
    #   rect_gt_depth = my_torch_helpers.rotate_equirectangular_image(
    #     depths_small[:, 1], outputs["rect_rots"][:, 1])
      
    #   # rect_gt_disp = model.erp_depth_to_disparity(
    #   #   rect_gt_depth.permute((0, 3, 1, 2)), outputs["trans_norm"])
    #   # rect_gt_disp = rect_gt_disp.permute((0, 2, 3, 1))
    #   # unrect_gt_disp = model.unrectify_image(rect_gt_disp,
    #   #                                        outputs["rect_rots"][:, 1])
    #   # assert torch.isfinite(rect_gt_disp).all(), "Nan in rect_gt_disp"
    #   assert torch.isfinite(
    #     outputs["raw_image_features"]).all(), "Nan in raw image features"
    
    
    if args["loss"] == "l1_cost_volume_erp":
      assert torch.isfinite(depths_small).all(), "Nan in depths_small"
      one_over_gt_depth = my_torch_helpers.safe_divide(1.0, depths_small[:, curr_idx]) #todo
      if args["std_uncert_tune"]:
        gt_dmap = depths_small[:, curr_idx].reshape(batch_size, 1, height, width).clone()
        # gt_dmap[gt_dmap > args["max_depth"]+1] = args["max_depth"] + 1
        pred = outputs["depth"].permute(((0, 3, 1, 2)))
        sigma_pred = outputs["mvs_std"]
        # depths_pred, gt_dmap, sigma_pred, torch.gt(gt_dmap, 0.1)
        depth_loss = loss_lib.new_compute_gaussian_loss(
          pred,
          gt_dmap,
          sigma_pred,
          torch.gt(gt_dmap, 0.1)
        )
        loss1 = depth_loss
      elif args["mvs_uncertainty"] or args["uncert_tune"]:
        gt_dmap = depths_small[:, curr_idx].reshape(batch_size, 1, height, width).clone()
        # gt_dmap[gt_dmap > args["max_depth"]+1] = args["max_depth"] + 1
        pred = outputs["pred_final"]
        if "new_uncert_tune" in self.cfg and self.cfg["new_uncert_tune"]:
          depth_loss = loss_lib.new_loss_uncertainty(
            pred,
            gt_dmap,
            torch.gt(gt_dmap, 0.1)
          )
        else:
          depth_loss = loss_lib.loss_uncertainty(
            pred,
            gt_dmap,
            torch.gt(gt_dmap, 0.1),
            sphere=args["sphere"]
          )
        
        #visualize gt_dmap


        depths_pred = torch.clamp(pred[:, :1, ...], min=0.1).unsqueeze(4)

        # if args["out_type"]=="disparity":
        #   # loss1 = loss_lib.compute_l1_sphere_loss(
        #   #   outputs['raw_image_features'],
        #   #   one_over_gt_depth,
        #   #   mask=torch.gt(depths_small[:, 1], 0.1))
        #   loss1 = depth_loss + 0.5 * loss_lib.compute_l1_sphere_loss(
        #     outputs['raw_image_features_d1'],
        #     one_over_gt_depth,
        #     mask=torch.gt(depths_small[:, 1], 0.1))
        # elif args["out_type"]=="depth":
        #   # loss1 = loss_lib.compute_l1_sphere_loss(
        #   #   outputs['raw_image_features'],
        #   #   depths_small[:, 1],
        #   #   mask=torch.gt(depths_small[:, 1], 0.1))          
        #   loss1 = depth_loss + 0.5 * loss_lib.compute_l1_sphere_loss(
        #     outputs['raw_image_features_d1'],
        #     depths_small[:, 1],
        #     mask=torch.gt(depths_small[:, 1], 0.1))
        loss1 = depth_loss
      else:
        if args["out_type"]=="disparity":
          loss1 = loss_lib.compute_l1_sphere_loss(
            outputs['raw_image_features'],
            one_over_gt_depth,
            mask=torch.gt(depths_small[:, curr_idx], 0.1))
          if not args["wo_lowres_loss"]:
            loss1 = loss1 + 0.5 * loss_lib.compute_l1_sphere_loss(
              outputs['raw_image_features_d1'],
              one_over_gt_depth,
              mask=torch.gt(depths_small[:, curr_idx], 0.1))
        elif args["out_type"]=="depth":
          loss1 = loss_lib.compute_l1_sphere_loss(
            outputs['raw_image_features'],
            depths_small[:, curr_idx],
            mask=torch.gt(depths_small[:, curr_idx], 0.1))          
          if not args["wo_lowres_loss"]:
            loss1 = loss1 + 0.5 * loss_lib.compute_l1_sphere_loss(
              outputs['raw_image_features_d1'],
              depths_small[:, curr_idx],
              mask=torch.gt(depths_small[:, curr_idx], 0.1))   
    else:
      raise ValueError("Loss not found: %s" % (args["loss"],))

    # rot_loss = torch.mean(torch.abs(rots_pred _ rots[:, 0]))
    # trans_loss = torch.mean(torch.abs(trans_pred _ trans[:, 0, :]))
    final_loss = loss1 #
    depths_pred = torch.clamp(depths_pred, min=0.1)

    assert torch.isfinite(loss1).all(), "Nan in depth final_loss"
    assert torch.isfinite(final_loss).all(), "Nan in final_loss function"

    return {
      # "error_flag":error_flag,
      "loss1": loss1,
      "final_loss": final_loss,
      # "rot_loss": rot_loss,
      # "trans_loss": trans_loss,
      "depths_pred": depths_pred,
      "panos_small": panos_small,
      "depths_small": depths_small,
      "outputs": outputs,
      # "rect_gt_depth": rect_gt_depth,
      # "rect_gt_disp": rect_gt_disp,
      # "depth_smoothness_loss": depth_smoothness_loss,
      # "disp_c1": disp_c1,
      # "disp_c1_pred": disp_pred_c1,
      # "depths_c1": depths_c1,
      # "zdepths_small_1": zdepths_small_1,
      # "rots_pred": rots_pred,
      # "trans_pred": trans_pred
    }

  def do_validation_run(self, step):    
    """Does a validation run.
    Args:
      step: Current step.
    Returns:
      None.
    """
    args = self.args
    model = self.model
    writer = self.writer
    if step == 1 or \
        args["validation_interval"] == 0 or \
        step % args["validation_interval"] == 0:
      # Calculate validation final_loss.
      with torch.no_grad():
        panos = self.input_panos_val
        depths = self.input_depths_val
        rots = self.input_rots_val
        trans = self.input_trans_val
        batch_size, seq_len = panos.shape[:2]
        curr_idx=0
        run_outputs = self.run_depth_pose_carla(step, panos, depths, rots,
                                                trans, curr_idx=curr_idx)
        final_loss = run_outputs["final_loss"]
        depths_pred = run_outputs["depths_pred"]
        depths_small = run_outputs["depths_small"]
        panos_small = run_outputs["panos_small"]

        writer.add_scalar("val_loss", final_loss.item(), step)
        writer.add_scalar("val_image_loss", run_outputs["loss1"].item(), step)
        # writer.add_scalar("val_rot_loss", run_outputs["rot_loss"].item(), step)
        # writer.add_scalar("val_trans_loss", run_outputs["trans_loss"].item(),
                          # step)

        
        depths_turbo = my_torch_helpers.depth_to_turbo_colormap(
          depths_small[:, curr_idx], min_depth=args["turbo_cmap_min"])
        normalized_depth_pred = depths_pred[:, 0]
        if args["normalize_depth"]:
          std, mean = torch.std_mean(depths_small[:, curr_idx],
                                      dim=(1, 2),
                                      keepdim=True)
          normalized_depth_pred = loss_lib.normalize_depth(
            normalized_depth_pred, new_std=std, new_mean=mean)
        depths_pred_turbo = my_torch_helpers.depth_to_turbo_colormap(
          normalized_depth_pred, min_depth=args["turbo_cmap_min"])

        # back_warped_1 = model.backwards_warping(panos[:, 0],
        #                                         depths_small[:, 1, :, :, 0],
        #                                         run_outputs["rots_pred"],
        #                                         run_outputs["trans_pred"],
        #                                         inv_rot=False)

        depth_abs_error_img = torch.abs(depths_small[:, curr_idx] -
                                        normalized_depth_pred)
        depth_abs_error_img = depth_abs_error_img.expand(
          (batch_size, args["height"], args["width"], 3))
        depth_abs_error_img_stacked = torch.cat(
          (panos_small[:, curr_idx], depths_turbo, depths_pred_turbo,
            depth_abs_error_img),
          dim=2)
        depth_mae = torch.mean(torch.abs(depths_small[:, curr_idx] -
                                          normalized_depth_pred),
                                dim=(1, 2, 3))
        depth_mse = torch.mean(torch.pow(
          depths_small[:, curr_idx] - normalized_depth_pred, 2.0),
          dim=(1, 2, 3))

        # y_stacked = torch.cat((panos_small[:, 0], panos_small[:, 1],
        #                         depths_turbo, depths_pred_turbo),
        #                       dim=2)
        # for j in range(len(self.val_data_indices)):
        #   writer.add_image("80_val_image_%02d" % j,
        #                     y_stacked[j].clamp(0, 1),
        #                     step,
        #                     dataformats="HWC")
        #   writer.add_image("82_val_depth_ae_%02d" % j,
        #                     depth_abs_error_img_stacked[j].clamp(0, 1),
        #                     step,
        #                     dataformats="HWC")
        #   writer.add_scalar("84_val_depth_mae_%02d" % j, depth_mae[j], step)
        #   writer.add_scalar("86_val_depth_mse_%02d" % j, depth_mse[j], step)

  def log_training_to_tensorboard(self, step, run_outputs, curr_idx):
    """Logs training to tensorboard.

    Args:
      step: Current step.
      run_outputs: Outputs of the training step.

    Returns:
      None.

    """
    args = self.args
    model = self.model
    writer = self.writer

    depths_pred = run_outputs["depths_pred"]
    depths_small = run_outputs["depths_small"]
    outputs = run_outputs["outputs"]
    # rect_gt_depth = run_outputs["rect_gt_depth"]
    # rect_gt_disp = run_outputs["rect_gt_disp"]
    panos_small = run_outputs["panos_small"]

    final_loss = run_outputs["final_loss"]
    loss_np = final_loss.detach().cpu().numpy()
    average_depth_np = torch.mean(depths_pred).detach().cpu().numpy()

    writer.add_scalar("train_loss", loss_np, step)
    writer.add_scalar("train_depth", average_depth_np, step)
    writer.add_scalar("train_image_loss", run_outputs["loss1"].item(), step)
    # writer.add_scalar("train_depth_smoothness_loss",
    #                   run_outputs["depth_smoothness_loss"].item(), step)
    # writer.add_scalar("train_rot_loss", run_outputs["rot_loss"].item(), step)
    # writer.add_scalar("train_trans_loss", run_outputs["trans_loss"].item(),
    #                   step)

    if step == 1 or \
        args["train_tensorboard_interval"] == 0 or \
        step % args["train_tensorboard_interval"] == 0:
      with torch.no_grad():
        depths_small_turbo = my_torch_helpers.depth_to_turbo_colormap(
          depths_small[:, curr_idx], min_depth=args["turbo_cmap_min"])
        depths_scale_factor = 1
        normalized_depth_pred = depths_pred[:, 0]
        if args["normalize_depth"]:
          std, mean = torch.std_mean(depths_small[:, curr_idx], dim=(1, 2),
                                     keepdim=True)
          normalized_depth_pred = loss_lib.normalize_depth(
            normalized_depth_pred,
            new_std=std,
            new_mean=mean)
        depths_pred_turbo = my_torch_helpers.depth_to_turbo_colormap(
          normalized_depth_pred, min_depth=args["turbo_cmap_min"])
        
        stacked_input_panos = torch.cat((panos_small[:, 0], panos_small[:, 1]),
                                        dim=1)
        writer.add_images("00_train_inputs",
                          stacked_input_panos,
                          step,
                          dataformats="NHWC")
        
        writer.add_images("05_train_depths_gt",
                          depths_small_turbo,
                          step,
                          dataformats="NHWC")
        
        writer.add_images("10_train_pred_depths",
                          depths_pred_turbo,
                          step,
                          dataformats="NHWC")
        if self.args["contain_dnet"]:
          mono_depth_ref = outputs["mono_depth_ref"]
          mono_depth_ref_turbo = my_torch_helpers.depth_to_turbo_colormap(
            mono_depth_ref[:, 0].unsqueeze(3), min_depth=args["turbo_cmap_min"])

          writer.add_images("105_mono_depth_ref",
                    mono_depth_ref_turbo,
                    step,
                    dataformats="NHWC")

        y_pred = depths_pred[:, 0]
        y_true = depths_small[:, curr_idx]
        if args["normalize_depth"]:
          y_pred = loss_lib.normalize_depth(y_pred)
          y_true = loss_lib.normalize_depth(y_true)
        depth_loss_image = torch.abs(y_true - y_pred)
        writer.add_images("11_train_l1_loss_image",
                          depth_loss_image.clamp(0, 1),
                          step,
                          dataformats="NHWC")

  def save_checkpoint(self, step):
    """Saves a checkpoint.

    Args:
      step: Current step.

    Returns:
      None.

    """
    args = self.args

    if args["checkpoint_interval"] == 0 or step % args["checkpoint_interval"] == 0:
      # Save a checkpoint
      self.checkpoint_manager.save_checkpoint({
        "model_state_dict": self.model.state_dict(),
        "optimizer_state_dict": self.optimizer.state_dict()
      })
      self.writer.flush()

  def run_training_loop(self):
    args = self.args
    train_dataloader = self.train_data_loader
    optimizer = self.optimizer
    checkpoint_manager = self.checkpoint_manager
    model = self.model
    pbar = tqdm(total=args["total_iter"], bar_format='{r_bar}')
    for epoch in range(args["epochs"]):
      for i, data in enumerate(train_dataloader):
        optimizer.zero_grad()
        step = checkpoint_manager.increment_step()
        # if i%100==0:
        print("step:", step)      
        if step >= args["total_iter"]:
          # print("evaluation...")
          # self.do_validation_run(step)
          self.save_checkpoint(step)
          self.eval_on_validation_data(step)
          # self.val_data.close()
          pbar.close()
          exit()

        if step % args["validation_interval"]==0 and step>0:
          # self.do_validation_run(step)
          self.save_checkpoint(step)          
          self.eval_on_validation_data(step)
          # self.val_data.close()

        if args["debug_mode"]:
          assert distro.linux_distribution()[0] == "Ubuntu", "Debug mode is on"
          panos = self.input_panos_val
          depths = self.input_depths_val
          rots = self.input_rots_val
          trans = self.input_trans_val
        else:
          assert distro is not None
          panos = data["rgb_panos"].to(args["device"])
          depths = data["depth_panos"].to(args["device"])
          rots = data["rots"].to(args["device"])
          trans = data["trans"].to(args["device"])
          # curr_idx = data["curr_idx"]

        curr_idx = np.random.randint(0, args["reference_idx"])#args['seq_len']-1)
        # print("panos", panos.dtype, depths.dtype, rots.dtype, trans.dtype)
        # print("maxmin", torch.max(panos), torch.min(panos))

        run_outputs = self.run_depth_pose_carla(step, panos, depths,
                                                rots,
                                                trans, curr_idx)
        # if run_outputs["error_flag"]:
        #   #args["checkpoints_dir"]
        #   data0 = run_outputs["i0"]
        #   data1 = run_outputs["i1"]    
        #   import cv2      
        #   cv2.imwrite(args["checkpoints_dir"]+"/step_"+str(step)+"_0.jpg", data0)
        #   cv2.imwrite(args["checkpoints_dir"]+"/step_"+str(step)+"_1.jpg", data1)
        #   continue
        self.log_training_to_tensorboard(step, run_outputs, curr_idx)

        final_loss = run_outputs["final_loss"]
        depths_pred = run_outputs["depths_pred"]

        final_loss.backward()
        if args["clip_grad_value"] > 1e-10:
          # print("Clipping gradients to %f" % args["clip_grad_value"])
          torch.nn.utils.clip_grad_value_(model.parameters(),
                                          args["clip_grad_value"])

        optimizer.step()

        # self.do_validation_run(step)
        self.save_checkpoint(step)

        if i%100==0:
          loss_np = final_loss.detach().cpu().numpy()
          average_depth_np = torch.mean(depths_pred).detach().cpu().numpy()
          print("Step: %d [%d:%d] Loss: %f, average depth %f" %
                (step, epoch, i, loss_np, average_depth_np))
        if args["lr_decay"]:
          # NOTE: IMPORTANT!
          ###   update learning rate   ###
          decay_rate = 0.1
          #250*1000=250000
          decay_steps = args["lrate_decay"] * 1000 # iteration: 20w->10w, 1000->500
          new_lrate = args["learning_rate"] * (decay_rate ** (step / decay_steps))
          for param_group in self.optimizer.param_groups:
              param_group['lr'] = new_lrate
        pbar.update(1)


    if args["use_lmdb"]:
      self.train_data.env.close()
  def eval_on_training_data(self):
    """Performs evaluation on the whole evaluation dataset.

    Returns:
      None
    """
    args = self.args
    train_dataloader = DataLoader(self.train_data,
                                  batch_size=args["batch_size"],
                                  shuffle=False,
                                  num_workers=4)

    self.model.eval()

    num_iterations = 10
    bar = Bar('Eval on training data',
              max=num_iterations)

    train_iterator = iter(train_dataloader)

    min_pred_depth = 9999.9
    max_pred_depth = 0.0
    min_gt_depth = 999.9
    max_gt_depth = 0.0
    l1_errors = []
    l2_errors = []
    wl1_errors = []
    wl2_errors = []
    with torch.no_grad():
      step = 100000
      weight = (torch.arange(0, args["height"], device=args["device"],
                             dtype=torch.float32) + 0.5) * np.pi / args["height"]
      weight = torch.sin(weight).view(1, args["height"], 1, 1)
      for i in range(num_iterations):
        data = next(train_iterator)
        panos = data["rgb_panos"].to(args["device"])
        depths = data["depth_panos"].to(args["device"])
        rots = data["rots"].to(args["device"])
        trans = data["trans"].to(args["device"])
        curr_idx = data["curr_idx"]

        run_outputs = self.run_depth_pose_carla(step, panos, depths,
                                                rots,
                                                trans, curr_idx)

        m_weight = weight.expand(
          panos.shape[0], args["height"], args["width"], 1)

        depths_small = run_outputs["depths_small"][:, curr_idx]
        depths_pred = run_outputs["depths_pred"][:, 0]
        depths_pred = torch.clamp_min(depths_pred, 0.0)

        wl1_error = torch.abs(depths_small - depths_pred) * m_weight
        wl1_error = torch.sum(wl1_error, dim=(1, 2, 3)) / torch.sum(m_weight,
                                                                    dim=(
                                                                      1, 2, 3))
        wl1_errors.append(wl1_error.cpu().numpy())

        wl2_error = torch.pow(depths_small - depths_pred, 2.0) * m_weight
        wl2_error = torch.sum(wl2_error, dim=(1, 2, 3)) / torch.sum(m_weight,
                                                                    dim=(
                                                                      1, 2, 3))
        wl2_errors.append(wl2_error.cpu().numpy())
        l1_error = torch.mean(torch.abs(depths_small - depths_pred),
                              dim=(1, 2, 3))
        l1_errors.append(l1_error.cpu().numpy())
        l2_error = torch.mean(torch.pow(depths_small - depths_pred, 2.0),
                              dim=(1, 2, 3))
        l2_errors.append(l2_error.cpu().numpy())
        min_pred_depth = min(min_pred_depth, torch.min(depths_pred).item())
        max_pred_depth = max(max_pred_depth, torch.max(depths_pred).item())
        min_gt_depth = min(min_gt_depth, torch.min(depths_small).item())
        max_gt_depth = max(max_gt_depth, torch.max(depths_small).item())
        bar.next()
    total_l1_errors = np.mean(np.stack(l1_errors))
    total_l2_errors = np.mean(np.stack(l2_errors))
    total_wl1_errors = np.mean(np.stack(wl1_errors))
    total_wl2_errors = np.mean(np.stack(wl2_errors))
    bar.finish()

    print("Evaluation on training data:")
    print("Total l1 error:", total_l1_errors, "Weighted:", total_wl1_errors)
    print("Total l2 error:", total_l2_errors, "Weighted:", total_wl2_errors)
    print("True depth range", min_gt_depth, max_gt_depth)
    print("Pred depth range", min_pred_depth, max_pred_depth)

  def eval_on_validation_data(self, step=-1):
    """Performs evaluation on the whole evaluation dataset.

    Returns:
      None
    """
    args = self.args

    self.model.eval()
    self.load_validation_data()
    val_dataloader = DataLoader(self.val_data,
                                batch_size=1,    
                                shuffle=False,
                                num_workers=0,
                                pin_memory=True)
    max_examples = len(self.val_data)
    if args["dataset"] == 'm3d':
      max_examples = min(len(self.val_data), args["test_sample_num"])
    if args["script_mode"] == 'eval_depth_test':
      results_file = os.path.join(
        args["checkpoints_dir"],
        "test_eval_results_%02f_%02f.txt" % (
          args["carla_min_dist"], args["carla_max_dist"]))
      if args["dataset"] == 'm3d':
        if step==-1:
          results_file = os.path.join(
            args["checkpoints_dir"],
            "test_eval_results_%02f.txt" % (
              args["m3d_dist"]))
        else:
          results_file = os.path.join(
            args["checkpoints_dir"],
            "test_eval_results_%02f_%06d.txt" % (
              args["m3d_dist"], step))
      results_file = open(results_file, "w")
    else:
      results_file = os.path.join(
        args["checkpoints_dir"],
        "val_eval_results_%02f_%02f.txt" % (
          args["carla_min_dist"], args["carla_max_dist"]))
      if args["dataset"] == 'm3d':
        if step==-1:

          results_file = os.path.join(
            args["checkpoints_dir"],
            "val_eval_results_%02f.txt" % (
              args["m3d_dist"]))
        else:
          results_file = os.path.join(
            args["checkpoints_dir"],
            "val_eval_results_%02f_%06d.txt" % (
              args["m3d_dist"], step))
      results_file = open(results_file, "w")

    min_pred_depth = 9999.9
    max_pred_depth = 0.0
    min_gt_depth = 999.9
    max_gt_depth = 0.0
    all_errors = {}
    max_depth_list = []
    if args["show_stats"]:
      max_depth = -1
      mean_depth = 0

    with torch.no_grad():
      # step = 100000
      weight = (torch.arange(0, args["height"], device=args["device"],
                             dtype=torch.float32) + 0.5) * np.pi / args["height"]
      weight = torch.sin(weight).view(1, args["height"], 1, 1)
      def load_data(args, idx):
        # np.savez(args["save_datadir"]+'/data_'+str(i)+'.npz', panos = panos.data.cpu().numpy(), rots=rots.data.cpu().numpy(), trans=trans.data.cpu().numpy(), depths=depths.data.cpu().numpy())
        data = np.load(args["save_datadir"]+'/data_'+str(idx)+'.npz')  
        return data


    #todo
      #args["checkpoints_dir"]
      
      if step != -1:
        test_imgs_dir=os.path.join(args["checkpoints_dir"], "test_images_"+str(step)+"_"+str(args["m3d_dist"]))
      else:
        test_imgs_dir=os.path.join(args["checkpoints_dir"], "test_images"+"_"+str(args["m3d_dist"]))
      # test_imgs_dir=os.path.join(args["checkpoints_dir"], "test_images")
      os.makedirs(test_imgs_dir, exist_ok=True)


      if args["load_prepared_test_data"]:
        print("load_prepared_test_data:")
        #for mono depth: evaluation 1_th(not zero)
        for i in range(args["test_sample_num"]):
          print("evaluate i:", i)
          data = load_data(args, i)
         
          panos = torch.from_numpy(data['panos'])[:, :, ...].to(args["device"])
          rots = torch.from_numpy(data['rots'])[:, :, ...].to(args["device"])
          trans = torch.from_numpy(data['trans'])[:, :, ...].to(args["device"])
          depths = torch.from_numpy(data['depths'])[:, :, ...].to(args["device"])
          curr_idx = 0 #data["curr_idx"]

          run_outputs = self.run_depth_pose_carla(step, panos, depths,
                                                  rots,
                                                  trans, curr_idx)

          m_weight = weight.expand(
            panos.shape[0], args["height"], args["width"], 1)

          depths_small = run_outputs["depths_small"][:, curr_idx]
          depths_pred = run_outputs["depths_pred"][:, 0]
          depths_pred = torch.clamp_min(depths_pred, 0.0)
          #visualize
          
          # test_imgs_dir
          # import pdb;pdb.set_trace()
          # print("panos.shape:", panos.shape)
          rgb = np.uint8(panos[:, curr_idx][0].data.cpu().numpy()*255)
          cv2.imwrite(test_imgs_dir+"/"+str(i)+"_rgb.jpg", rgb)

          d_pred = depths_pred[0].data.cpu().numpy()
          d_gt = depths_small[0].data.cpu().numpy()
          def normalize_depth(depth):
              d_min = depth.min()
              d_max = depth.max()
              d_norm = np.uint8((depth-d_min)/(d_max-d_min)*255)
              d_rgb = cv2.applyColorMap(d_norm, cv2.COLORMAP_JET)
              return d_rgb

          d_pred = normalize_depth(d_pred)
          d_gt = normalize_depth(d_gt)
          cv2.imwrite(test_imgs_dir+"/"+str(i)+"_depth_pred.jpg", d_pred)
          cv2.imwrite(test_imgs_dir+"/"+str(i)+"_depth_gt.jpg", d_gt)
          
          erp_errors = self.compute_erp_depth_results(
            gt_depth=depths_small,
            pred_depth=depths_pred,
            m_weight=m_weight
          )

          cube_errors = self.compute_zdepth_results(
            gt_depth=depths[:, curr_idx, :, :, None],
            pred_depth=depths_pred
          )

          for k, v in erp_errors.items():
            if k not in all_errors:
              all_errors[k] = []
            all_errors[k].append(v.detach().cpu().numpy())

          for k, v in cube_errors.items():
            if k not in all_errors:
              all_errors[k] = []
            all_errors[k].append(v.detach().cpu().numpy())

      else:
        print("in val_dataloader:")

        for i, data in enumerate(val_dataloader):
          print("evaluate i:", i)

          if i >= max_examples:
            break
          if args["seq_len"] >= 3:
            # reference_idx=2
            panos = data["rgb_panos"].to(args["device"])
            depths = data["depth_panos"].to(args["device"])
            rots = data["rots"].to(args["device"])
            trans = data["trans"].to(args["device"])
          else:
            panos = data["rgb_panos"].to(args["device"])
            depths = data["depth_panos"].to(args["device"])
            rots = data["rots"].to(args["device"])
            trans = data["trans"].to(args["device"])
          curr_idx = 0 #data["curr_idx"]
          run_outputs = self.run_depth_pose_carla(step, panos, depths, rots,
                                                  trans, curr_idx)
          
          m_weight = weight.expand(
            panos.shape[0], args["height"], args["width"], 1)
          depths_small = run_outputs["depths_small"][:, curr_idx]
          if args["show_stats"]:
            # if depths_small.max()> max_depth:
              # max_depth = depths_small.max()
            max_depth_list.append(depths_small.max().data.cpu().numpy())
            mean_depth+= depths_small.mean()/max_examples

          depths_pred = run_outputs["depths_pred"][:, 0]
          depths_pred = torch.clamp_min(depths_pred, 0.0)

          #vis
          rgb = np.uint8(panos[:, curr_idx][0].data.cpu().numpy()*255)
          cv2.imwrite(test_imgs_dir+"/"+str(i)+"_rgb.jpg", rgb)
          d_pred = depths_pred[0].data.cpu().numpy()
          d_gt = depths_small[0].data.cpu().numpy()
          def normalize_depth(depth, depth_gt):
              d_min = min(depth.min(), depth_gt.min())
              d_max = max(depth.max(), depth_gt.max())
              d_norm = np.uint8((depth-d_min)/(d_max-d_min)*255)

              d_rgb = cv2.applyColorMap(d_norm, cv2.COLORMAP_JET)

              d_gt_norm = np.uint8((depth_gt - d_min)/(d_max-d_min)*255)
              d_gt_rgb = cv2.applyColorMap(d_gt_norm, cv2.COLORMAP_JET)
              return d_rgb, d_gt_rgb
          np.savez(test_imgs_dir+"/"+str(i)+"_depths.npz", pred=d_pred, gt=d_gt)

          d_pred, d_gt = normalize_depth(d_pred, d_gt)
          # d_gt = normalize_depth(d_gt)

          cv2.imwrite(test_imgs_dir+"/"+str(i)+"_depth_pred.jpg", d_pred)
          cv2.imwrite(test_imgs_dir+"/"+str(i)+"_depth_gt.jpg", d_gt)
          
          def normalize_sigma(sigma_pred):
            s_min = sigma_pred.min()
            s_max = sigma_pred.max()
            s_pred_norm = np.uint8((sigma_pred-s_min)/(s_max-s_min)*255)
            pred_rgb = cv2.applyColorMap(s_pred_norm, cv2.COLORMAP_PINK)
            return pred_rgb
          if args["std_uncert_tune"]:
            sig_pred = run_outputs["outputs"]["mvs_std"][0, 0].data.cpu().numpy()
            sig_vis = normalize_sigma(sig_pred)
            cv2.imwrite(test_imgs_dir+"/"+str(i)+"_sigma.jpg", sig_vis)
          if args["uncert_tune"]:
            sig_pred = run_outputs["outputs"]["pred_final"][0, 1].data.cpu().numpy()
            sig_vis = normalize_sigma(sig_pred)
            cv2.imwrite(test_imgs_dir+"/"+str(i)+"_sigma.jpg", sig_vis)


          erp_errors = self.compute_erp_depth_results(
            gt_depth=depths_small,
            pred_depth=depths_pred,
            m_weight=m_weight
          )

          cube_errors = self.compute_zdepth_results(
            gt_depth=depths[:, curr_idx, :, :, None],
            pred_depth=depths_pred
          )

          for k, v in erp_errors.items():
            if k not in all_errors:
              all_errors[k] = []
            all_errors[k].append(v.detach().cpu().numpy())

          for k, v in cube_errors.items():
            if k not in all_errors:
              all_errors[k] = []
            all_errors[k].append(v.detach().cpu().numpy())

    all_errors_concatenated = {}
    for k, v in all_errors.items():
      all_errors_concatenated[k] = np.mean(np.concatenate(v))

    for k, v in all_errors_concatenated.items():
      results_file.write("%s: %0.5f\n" % (k, v))
    results_file.close()
    if args["show_stats"]:
      print("mean_depth:", mean_depth)
      # print("max_depth_list:", )
      max_depth = np.array(max_depth_list).max()
      # max_depth_list = [ max_depth_list[idx] for idx in range(len(max_depth_list))]
      print("max_depth:", max_depth)
    print("Evaluation done")
    if args["use_lmdb"]:
      self.val_data.env.close()

  def compute_erp_depth_results(self, gt_depth, pred_depth, m_weight):
    """Computes and returns results.

    Args:
      gt_depth: ERP GT depth.
      pred_depth: ERP predicted euclidean depth.

    Returns:
      Dictionary of torch tensors.

    """
    args = self.args
    valid_regions = torch.logical_and(torch.gt(gt_depth, 0.1), #
                                      torch.lt(gt_depth, args["max_depth"]))
    valid_regions_sum = torch.sum(valid_regions, dim=(1, 2, 3))
    m_weight = m_weight * valid_regions
    m_weight_sum = torch.sum(m_weight, dim=(1, 2, 3))
    one_over_gt_depth = my_torch_helpers.safe_divide(1.0, gt_depth)
    one_over_pred_depth = my_torch_helpers.safe_divide(1.0, pred_depth)

    print("Min depth, max depth", torch.min(gt_depth), torch.max(gt_depth))
    print("Min pred depth, max pred depth", torch.min(pred_depth),
          torch.max(pred_depth))

    imae_error = torch.abs(
      one_over_gt_depth - one_over_pred_depth) * valid_regions
    if torch.any(imae_error > 100.0):
      print("max imae", torch.max(imae_error))
      big_error = (imae_error > 100.0).float()
      for i in range(pred_depth.shape[0]):
        my_torch_helpers.save_torch_image(
          os.path.join(args["checkpoints_dir"], "pred_depth_%d.png" % i),
          my_torch_helpers.depth_to_turbo_colormap(
            pred_depth[i:(i + 1)],
            min_depth=args["turbo_cmap_min"]
          )
        )
        my_torch_helpers.save_torch_image(
          os.path.join(args["checkpoints_dir"], "gt_depth_%d.png" % i),
          my_torch_helpers.depth_to_turbo_colormap(
            gt_depth[i:(i + 1)],
            min_depth=args["turbo_cmap_min"]
          )
        )
        my_torch_helpers.save_torch_image(
          os.path.join(args["checkpoints_dir"], "error_depth_%d.png" % i),
          big_error[i:(i + 1)].expand((_1, _1, _1, 3))
        )
      raise ValueError("Error")

    imae_error = torch.abs(
      one_over_gt_depth - one_over_pred_depth) * valid_regions
    imae_error = torch.sum(imae_error, dim=(1, 2, 3)) / valid_regions_sum

    irmse_error = torch.pow(
      one_over_gt_depth - one_over_pred_depth, 2.0) * valid_regions
    irmse_error = torch.sum(irmse_error, dim=(1, 2, 3)) / valid_regions_sum
    irmse_error = torch.sqrt(irmse_error)

    l1_error = torch.abs(gt_depth - pred_depth) * valid_regions
    l1_error = torch.sum(l1_error, dim=(1, 2, 3)) / valid_regions_sum

    l2_error = torch.pow(gt_depth - pred_depth, 2.0)
    l2_error = torch.sum(l2_error, dim=(1, 2, 3)) / valid_regions_sum
    rmse_error = torch.sqrt(l2_error)

    wl1_error = torch.abs(gt_depth - pred_depth) * m_weight
    wl1_error = torch.sum(wl1_error, dim=(1, 2, 3)) / m_weight_sum

    wl2_error = torch.pow(gt_depth - pred_depth, 2.0) * m_weight
    wl2_error = torch.sum(wl2_error, dim=(1, 2, 3)) / m_weight_sum

    wrmse_error = torch.sqrt(wl2_error)

    relative_error = (torch.abs(
      gt_depth - pred_depth) / gt_depth) * valid_regions
    relative_105 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.05 - 1)).float(),
      dim=(1, 2, 3)) / valid_regions_sum
    relative_110 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.10 - 1)).float(),
      dim=(1, 2, 3)) / valid_regions_sum
    relative_125 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.25 - 1)).float(),
      dim=(1, 2, 3)) / valid_regions_sum
    relative_125_2 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.25 ** 2 - 1)).float(),
      dim=(1, 2, 3)) / valid_regions_sum
    relative_125_3 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.25 ** 3 - 1)).float(),
      dim=(1, 2, 3)) / valid_regions_sum

    return {
      'l1_error': l1_error,
      'l2_error': l2_error,
      'rmse_error': rmse_error,
      'wl1_error': wl1_error,
      'wl2_error': wl2_error,
      'wrmse_error': wrmse_error,
      'relative_105': relative_105,
      'relative_110': relative_110,
      'relative_125': relative_125,
      'relative_125_2': relative_125_2,
      'relative_125_3': relative_125_3,
      'imae_error': imae_error,
      'irmse_error': irmse_error
    }

  def compute_zdepth_results(self, gt_depth, pred_depth,
                             cubemap_sides=(2, 3, 4, 5)):
    """Computes z_depth results on the 4 cubemap sides.

    Args:
      gt_depth: ERP euclidean depth.
      pred_depth: Predicted depth.
      cubemap_sides: Which sides of the cubemap.

    Returns:
      Dictionary of torch tensors.

    """
    args = self.args
    pred_depth_cube = []
    gt_depth_cube = []
    pred_zdepth = self.train_data.distance_to_zdepth_torch(pred_depth)
    gt_zdepth = self.train_data.distance_to_zdepth_torch(gt_depth)
    for side in cubemap_sides:
      pred_depth_cube.append(
        my_torch_helpers.equirectangular_to_cubemap(
          pred_zdepth, side=side))
      gt_depth_cube.append(
        my_torch_helpers.equirectangular_to_cubemap(
          gt_zdepth, side=side))
    pred_zdepth_cube = torch.stack(pred_depth_cube, dim=1)
    gt_zdepth_cube = torch.stack(gt_depth_cube, dim=1)

    # for i in range(pred_zdepth_cube.shape[1]):
    #   my_torch_helpers.save_torch_image(
    #     os.path.join(self.args["checkpoints_dir"], "depth_%d.png" % i),
    #     my_torch_helpers.depth_to_turbo_colormap(
    #       gt_zdepth_cube[:, i], min_depth=self.args["turbo_cmap_min"]
    #     )
    #   )
    #   my_torch_helpers.save_torch_image(
    #     os.path.join(self.args["checkpoints_dir"], "depth__%d.png" % i),
    #     my_torch_helpers.depth_to_turbo_colormap(
    #       pred_zdepth_cube[:, i], min_depth=self.args["turbo_cmap_min"]
    #     )
    #   )

    valid_regions = torch.logical_and(torch.gt(gt_zdepth_cube, 0.1),
                                      torch.lt(gt_zdepth_cube, args["max_depth"]))
    valid_regions_sum = torch.sum(valid_regions, dim=(1, 2, 3, 4))
    one_over_gt = my_torch_helpers.safe_divide(1.0, gt_zdepth_cube)
    one_over_pred = my_torch_helpers.safe_divide(1.0, pred_zdepth_cube)

    imae_error = torch.abs(one_over_gt - one_over_pred) * valid_regions
    imae_error = torch.sum(imae_error, dim=(1, 2, 3, 4)) / valid_regions_sum

    irmse_error = torch.pow(one_over_gt - one_over_pred, 2.0) * valid_regions
    irmse_error = torch.sum(irmse_error, dim=(1, 2, 3, 4)) / valid_regions_sum
    irmse_error = torch.sqrt(irmse_error)

    l1_error = torch.abs(gt_zdepth_cube - pred_zdepth_cube) * valid_regions
    l1_error = torch.sum(l1_error, dim=(1, 2, 3, 4)) / valid_regions_sum

    l2_error = torch.pow(gt_zdepth_cube - pred_zdepth_cube, 2.0)
    l2_error = torch.sum(l2_error, dim=(1, 2, 3, 4)) / valid_regions_sum
    rmse_error = torch.sqrt(l2_error)

    relative_error = (torch.abs(
      gt_zdepth_cube - pred_zdepth_cube) / gt_zdepth_cube) * valid_regions
    relative_105 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.05 - 1)).float(),
      dim=(1, 2, 3, 4)) / valid_regions_sum
    relative_110 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.10 - 1)).float(),
      dim=(1, 2, 3, 4)) / valid_regions_sum
    relative_125 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.25 - 1)).float(),
      dim=(1, 2, 3, 4)) / valid_regions_sum
    relative_125_2 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.25 ** 2 - 1)).float(),
      dim=(1, 2, 3, 4)) / valid_regions_sum
    relative_125_3 = torch.sum(
      torch.logical_and(valid_regions,
                        torch.lt(relative_error, 1.25 ** 3 - 1)).float(),
      dim=(1, 2, 3, 4)) / valid_regions_sum

    return {
      'cube_l1_error': l1_error,
      'cube_l2_error': l2_error,
      'cube_rmse_error': rmse_error,
      'cube_relative_105': relative_105,
      'cube_relative_110': relative_110,
      'cube_relative_125': relative_125,
      'cube_relative_125_2': relative_125_2,
      'cube_relative_125_3': relative_125_3,
      'cube_imae_error': imae_error,
      'cube_irmse_error': irmse_error
    }


if __name__ == "__main__":
  parser = argparse.ArgumentParser()
  parser.add_argument('--cfg', type=str, default='configs/train/gen/neuray_gen_depth_train.yaml')
  flags = parser.parse_args()

  app = App()
  app.start(flags)