trainer_end_to_end.py

from __future__ import absolute_import, division, print_function

import time
import json
import datasets
import models.encoders as encoders
import models.decoders as decoders
import models.endodac as endodac
import numpy as np
import torch.optim as optim

from utils.utils import *
from utils.layers import *
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter

splits_dir = os.path.join(os.path.dirname(__file__), "splits")

class Trainer:
    def __init__(self, options):
        self.opt = options
        self.log_path = os.path.join(self.opt.log_dir, self.opt.model_name)

        # 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 = {}  # 字典
        self.parameters_to_train = []  # 列表
        self.parameters_to_train_0 = []

        self.device = torch.device("cpu" if self.opt.no_cuda else "cuda")

        self.num_scales = len(self.opt.scales)  # 4
        self.num_input_frames = len(self.opt.frame_ids)  # 3
        self.num_pose_frames = 2 if self.opt.pose_model_input == "pairs" else self.num_input_frames  # 2

        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")

        self.models["depth_model"] = endodac.endodac(
            backbone_size = "base", r=self.opt.lora_rank, lora_type=self.opt.lora_type,
            image_shape=(224,280), pretrained_path=self.opt.pretrained_path,
            residual_block_indexes=self.opt.residual_block_indexes,
            include_cls_token=self.opt.include_cls_token)
        self.models["depth_model"].to(self.device)
        self.parameters_to_train += list(filter(lambda p: p.requires_grad, self.models["depth_model"].parameters()))

        self.models["position_encoder"] = encoders.ResnetEncoder(
            self.opt.num_layers, self.opt.weights_init == "pretrained", num_input_images=2)  # 18
        self.models["position_encoder"].to(self.device)
        self.parameters_to_train_0 += list(self.models["position_encoder"].parameters())

        self.models["position"] = decoders.PositionDecoder(
            self.models["position_encoder"].num_ch_enc, self.opt.scales)
        self.models["position"].to(self.device)
        self.parameters_to_train_0 += list(self.models["position"].parameters())

        self.models["transform_encoder"] = encoders.ResnetEncoder(
            self.opt.num_layers, self.opt.weights_init == "pretrained", num_input_images=2)  # 18
        self.models["transform_encoder"].to(self.device)
        self.parameters_to_train += list(self.models["transform_encoder"].parameters())

        self.models["transform"] = decoders.TransformDecoder(
            self.models["transform_encoder"].num_ch_enc, self.opt.scales)
        self.models["transform"].to(self.device)
        self.parameters_to_train += list(self.models["transform"].parameters())

        if self.use_pose_net:

            if self.opt.pose_model_type == "separate_resnet":
                self.models["pose_encoder"] = encoders.ResnetEncoder(
                    self.opt.num_layers,
                    self.opt.weights_init == "pretrained",
                    num_input_images=self.num_pose_frames)
                self.models["pose_encoder"].to(self.device)
                self.parameters_to_train += list(self.models["pose_encoder"].parameters())

                self.models["pose"] = decoders.PoseDecoder(
                    self.models["pose_encoder"].num_ch_enc,
                    num_input_features=1,
                    num_frames_to_predict_for=2)

            elif self.opt.pose_model_type == "shared":
                self.models["pose"] = decoders.PoseDecoder(
                    self.models["encoder"].num_ch_enc, self.num_pose_frames)

            elif self.opt.pose_model_type == "posecnn":
                self.models["pose"] = decoders.PoseCNN(
                    self.num_input_frames if self.opt.pose_model_input == "all" else 2)

            self.models["pose"].to(self.device)
            self.parameters_to_train += list(self.models["pose"].parameters())
            
            if self.opt.learn_intrinsics:
                self.models['intrinsics_head'] = decoders.IntrinsicsHead(self.models["pose_encoder"].num_ch_enc)
                self.models['intrinsics_head'].to(self.device)
                self.parameters_to_train += list(self.models['intrinsics_head'].parameters())

        if self.opt.predictive_mask:
            assert self.opt.disable_automasking, \
                "When using predictive_mask, please disable automasking with --disable_automasking"

            # Our implementation of the predictive masking baseline has the the same architecture
            # as our depth decoder. We predict a separate mask for each source frame.
            self.models["predictive_mask"] = decoders.DepthDecoder(
                self.models["encoder"].num_ch_enc, self.opt.scales,
                num_output_channels=(len(self.opt.frame_ids) - 1))
            self.models["predictive_mask"].to(self.device)
            self.parameters_to_train += list(self.models["predictive_mask"].parameters())

        self.model_optimizer = optim.Adam(self.parameters_to_train, self.opt.learning_rate)
        self.model_lr_scheduler = optim.lr_scheduler.StepLR(
            self.model_optimizer, self.opt.scheduler_step_size, 0.1)
        self.model_optimizer_0 = optim.Adam(self.parameters_to_train_0, 1e-4)
        self.model_lr_scheduler_0 = optim.lr_scheduler.StepLR(
            self.model_optimizer_0, self.opt.scheduler_step_size, 0.1)

        if self.opt.load_weights_folder is not None:
            self.load_model()

        print("Training model named:\n  ", self.opt.model_name)
        print("Models and tensorboard events files are saved to:\n  ", self.opt.log_dir)
        print("Training is using:\n  ", self.device)

        # data
        datasets_dict = {"endovis": datasets.SCAREDRAWDataset}
        self.dataset = datasets_dict[self.opt.dataset]

        fpath = os.path.join(os.path.dirname(__file__), "splits", self.opt.split, "{}_files.txt")
        train_filenames = readlines(fpath.format("train"))
        val_filenames = readlines(fpath.format("val"))
        test_filenames = readlines(fpath.format("test"))
        img_ext = '.png'  

        num_train_samples = len(train_filenames)
        self.num_total_steps = num_train_samples // self.opt.batch_size * self.opt.num_epochs

        train_dataset = self.dataset(
            self.opt.data_path, train_filenames, self.opt.height, self.opt.width,
            self.opt.frame_ids, 4, is_train=True, img_ext=img_ext)
        self.train_loader = DataLoader(
            train_dataset, self.opt.batch_size, True,
            num_workers=self.opt.num_workers, pin_memory=True, drop_last=True)
        val_dataset = self.dataset(
            self.opt.data_path, val_filenames, self.opt.height, self.opt.width,
            self.opt.frame_ids, 4, is_train=False, img_ext=img_ext)
        self.val_loader = DataLoader(
            val_dataset, self.opt.batch_size, False,
            num_workers=1, pin_memory=True, drop_last=True)
        test_dataset = self.dataset(
            self.opt.data_path, test_filenames, self.opt.height, self.opt.width,
            self.opt.frame_ids, 4, is_train=False, img_ext=img_ext)
        self.test_loader = DataLoader(
            test_dataset, 1, False,
            num_workers=1, pin_memory=True, drop_last=True)
        self.val_iter = iter(self.val_loader)

        self.writers = {}
        for mode in ["train", "val"]:
            self.writers[mode] = SummaryWriter(os.path.join(self.log_path, mode))

        if not self.opt.no_ssim:
            self.ssim = SSIM()
            self.ssim.to(self.device)

        self.spatial_transform = SpatialTransformer((self.opt.height, self.opt.width))
        self.spatial_transform.to(self.device)

        self.get_occu_mask_backward = get_occu_mask_backward((self.opt.height, self.opt.width))
        self.get_occu_mask_backward.to(self.device)

        self.get_occu_mask_bidirection = get_occu_mask_bidirection((self.opt.height, self.opt.width))
        self.get_occu_mask_bidirection.to(self.device)

        self.backproject_depth = {}
        self.project_3d = {}
        self.position_depth = {}
        
        for scale in self.opt.scales:
            h = self.opt.height // (2 ** scale)
            w = self.opt.width // (2 ** scale)

            self.backproject_depth[scale] = BackprojectDepth(self.opt.batch_size, h, w)
            self.backproject_depth[scale].to(self.device)

            self.project_3d[scale] = Project3D(self.opt.batch_size, h, w)
            self.project_3d[scale].to(self.device)

            self.position_depth[scale] = optical_flow((h, w), self.opt.batch_size, h, w)
            self.position_depth[scale].to(self.device)

        self.depth_metric_names = [
            "de/abs_rel", "de/sq_rel", "de/rmse", "de/log_rmse", "da/a1", "da/a2", "da/a3"]

        gt_path = os.path.join(splits_dir, self.opt.eval_split, "gt_depths.npz")
        self.gt_depths = np.load(gt_path, fix_imports=True, encoding='latin1')["data"]
        
        print("Using split:\n  ", self.opt.split)
        print("There are {:d} training items, {:d} validation items and {:d} testing items\n".format(
            len(train_dataset), len(val_dataset), len(test_dataset)))

        self.save_opts()
        Total_params = 0
        Trainable_params = 0
        NonTrainable_params = 0
        for name, param in self.models["depth_model"].named_parameters():
            mulValue = np.prod(param.size())
            Total_params += mulValue
            if param.requires_grad == False:
                NonTrainable_params += mulValue
                # print(name)
            else:
                Trainable_params += mulValue


        print(f'Total params: {Total_params}')
        print(f'Trainable params: {Trainable_params}')
        print(f'Non-trainable params: {NonTrainable_params}')
        print(f'Trainable params ratio: {100 * Trainable_params / Total_params}%')

    def set_train_0(self):
        """Convert all models to training mode
        """
        for param in self.models["position_encoder"].parameters():
            param.requires_grad = True
        for param in self.models["position"].parameters():
            param.requires_grad = True

        for param in self.models["depth_model"].parameters():
            param.requires_grad = False
        for param in self.models["pose_encoder"].parameters():
            param.requires_grad = False
        for param in self.models["pose"].parameters():
            param.requires_grad = False
        for param in self.models["transform_encoder"].parameters():
            param.requires_grad = False
        for param in self.models["transform"].parameters():
            param.requires_grad = False
        if self.opt.learn_intrinsics:
            for param in self.models["intrinsics_head"].parameters():
                param.requires_grad = False
            
        self.models["position_encoder"].train()
        self.models["position"].train()

        self.models["depth_model"].eval()
        self.models["pose_encoder"].eval()
        self.models["pose"].eval()
        self.models["transform_encoder"].eval()
        self.models["transform"].eval()
        if self.opt.learn_intrinsics:
            self.models["intrinsics_head"].eval()

    def set_train(self):
        """Convert all models to training mode
        """
        for param in self.models["position_encoder"].parameters():
            param.requires_grad = False
        for param in self.models["position"].parameters():
            param.requires_grad = False

        for name, param in self.models["depth_model"].named_parameters():
            if "seed_" not in name:
                param.requires_grad = True
        if self.step < self.opt.warm_up_step:
            warm_up = True
        else:
            warm_up = False
        endodac.mark_only_part_as_trainable(self.models["depth_model"], warm_up=warm_up)
        for param in self.models["pose_encoder"].parameters():
            param.requires_grad = True
        for param in self.models["pose"].parameters():
            param.requires_grad = True
        for param in self.models["transform_encoder"].parameters():
            param.requires_grad = True
        for param in self.models["transform"].parameters():
            param.requires_grad = True
        if self.opt.learn_intrinsics:
            for param in self.models["intrinsics_head"].parameters():
                param.requires_grad = True

        self.models["position_encoder"].eval()
        self.models["position"].eval()

        self.models["depth_model"].train()
        self.models["pose_encoder"].train()
        self.models["pose"].train()
        self.models["transform_encoder"].train()
        self.models["transform"].train()
        if self.opt.learn_intrinsics:
            self.models["intrinsics_head"].train()

    def set_eval(self):
        """Convert all models to testing/evaluation mode
        """
        self.models["depth_model"].eval()
        self.models["transform_encoder"].eval()
        self.models["transform"].eval()
        self.models["pose_encoder"].eval()
        self.models["pose"].eval()
        if self.opt.learn_intrinsics:
            self.models["intrinsics_head"].eval()

    def train(self):
        """Run the entire training pipeline
        """
        self.epoch = 0
        self.step = 0
        self.start_time = time.time()
        for self.epoch in range(self.opt.num_epochs):
            self.run_epoch()
            if self.epoch == 0:
                rmse, a1 = self.run_epoch_eval()
                self.save_model(mode='epoch')
            else:
                rmse_new, a1_new = self.run_epoch_eval()
                if rmse_new < rmse:
                    rmse = rmse_new
                    # a1 = a1_new
                    self.save_model(mode='epoch')
            self.save_model(mode='last')
    def run_epoch(self):
        """Run a single epoch of training and validation
        """

        print("Training")

        for batch_idx, inputs in enumerate(self.train_loader):

            before_op_time = time.time()

            # position
            self.set_train_0()
            _, losses_0 = self.process_batch_0(inputs)
            self.model_optimizer_0.zero_grad()
            losses_0["loss"].backward()
            self.model_optimizer_0.step()

            # depth, pose, transform
            self.set_train()
            outputs, losses = self.process_batch(inputs)
            self.model_optimizer.zero_grad()
            losses["loss"].backward()
            self.model_optimizer.step()
            
            duration = time.time() - before_op_time

            phase = batch_idx % self.opt.log_frequency == 0

            if phase:

                self.log_time(batch_idx, duration, losses["loss"].cpu().data)
                self.log("train", inputs, outputs, losses)
                self.val()

            self.step += 1
            
        self.model_lr_scheduler.step()
        self.model_lr_scheduler_0.step()

    def run_epoch_eval(self):
        """Run a single epoch of evaluation
        """

        print("Evaluating")
        MIN_DEPTH = 1e-3
        MAX_DEPTH = 150
        
        self.set_eval()
        pred_depths = []
        for batch_idx, inputs in enumerate(self.test_loader):
            input_color = inputs[("color", 0, 0)].cuda()

            if self.opt.post_process:
                # Post-processed results require each image to have two forward passes
                input_color = torch.cat((input_color, torch.flip(input_color, [3])), 0)

            # output = self.models["depth"](self.models["encoder"](input_color))
            output = self.models["depth_model"](input_color)
            _, pred_depth = disp_to_depth(output[("disp", 0)], self.opt.min_depth, self.opt.max_depth)
            pred_depth = pred_depth[:, 0].cpu().detach().numpy()
            pred_depths.append(pred_depth)
            
        pred_depths = np.concatenate(pred_depths)
        
        errors = []
        ratios = []
        
        for i in range(pred_depths.shape[0]):
            gt_depth = self.gt_depths[i]
            gt_height, gt_width = gt_depth.shape[:2]

            pred_depth = pred_depths[i]
            pred_depth = cv2.resize(pred_depth, (gt_width, gt_height))
            
            mask = np.logical_and(gt_depth > MIN_DEPTH, gt_depth < MAX_DEPTH)
            pred_depth = pred_depth[mask]
            gt_depth = gt_depth[mask]

            pred_depth *= self.opt.pred_depth_scale_factor
            # print(pred_depth.max(), pred_depth.min())
            if not self.opt.disable_median_scaling:
                ratio = np.median(gt_depth) / np.median(pred_depth)
                ratios.append(ratio)
                pred_depth *= ratio

            pred_depth[pred_depth < MIN_DEPTH] = MIN_DEPTH
            pred_depth[pred_depth > MAX_DEPTH] = MAX_DEPTH
            
            errors.append(compute_errors(gt_depth, pred_depth))
        if not self.opt.disable_median_scaling:
            ratios = np.array(ratios)
            med = np.median(ratios)
            print(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, np.std(ratios / med)))

        mean_errors = np.array(errors).mean(0)

        writer = self.writers["train"]
        for i in range(len(mean_errors)):
            writer.add_scalar(self.depth_metric_names[i], mean_errors[i], self.epoch)
        print("\n  " + ("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
        print(("&{: 8.3f}  " * 7).format(*mean_errors.tolist()) + "\\\\")
        
        self.set_train()
        
        return mean_errors[2], mean_errors[4]
    def process_batch_0(self, inputs):
        """Pass a minibatch through the network and generate images and losses
        """
        for key, ipt in inputs.items():
            inputs[key] = ipt.to(self.device)

        outputs = {}
        outputs.update(self.predict_poses_0(inputs))
        losses = self.compute_losses_0(inputs, outputs)

        return outputs, losses

    def predict_poses_0(self, inputs):
        """Predict poses between input frames for monocular sequences.
        """
        outputs = {}
        if self.num_pose_frames == 2:
            pose_feats = {f_i: inputs["color_aug", f_i, 0] for f_i in self.opt.frame_ids}

            for f_i in self.opt.frame_ids[1:]:

                if f_i != "s":

                    inputs_all = [pose_feats[f_i], pose_feats[0]]
                    inputs_all_reverse = [pose_feats[0], pose_feats[f_i]]

                    # position
                    position_inputs = self.models["position_encoder"](torch.cat(inputs_all, 1))
                    position_inputs_reverse = self.models["position_encoder"](torch.cat(inputs_all_reverse, 1))
                    outputs_0 = self.models["position"](position_inputs)
                    outputs_1 = self.models["position"](position_inputs_reverse)

                    for scale in self.opt.scales:
                        outputs[("position", scale, f_i)] = outputs_0[("position", scale)]
                        outputs[("position", "high", scale, f_i)] = F.interpolate(
                            outputs[("position", scale, f_i)], [self.opt.height, self.opt.width], mode="bilinear",
                            align_corners=True)
                        outputs[("registration", scale, f_i)] = self.spatial_transform(inputs[("color", f_i, 0)],
                                                                                       outputs[(
                                                                                       "position", "high", scale, f_i)])

                        outputs[("position_reverse", scale, f_i)] = outputs_1[("position", scale)]
                        outputs[("position_reverse", "high", scale, f_i)] = F.interpolate(
                            outputs[("position_reverse", scale, f_i)], [self.opt.height, self.opt.width],
                            mode="bilinear", align_corners=True)
                        outputs[("occu_mask_backward", scale, f_i)], _ = self.get_occu_mask_backward(
                            outputs[("position_reverse", "high", scale, f_i)])
                        outputs[("occu_map_bidirection", scale, f_i)] = self.get_occu_mask_bidirection(
                            outputs[("position", "high", scale, f_i)],
                            outputs[("position_reverse", "high", scale, f_i)])

                    # transform
                    transform_input = [outputs[("registration", 0, f_i)], inputs[("color", 0, 0)]]
                    transform_inputs = self.models["transform_encoder"](torch.cat(transform_input, 1))
                    outputs_2 = self.models["transform"](transform_inputs)

                    for scale in self.opt.scales:
                        outputs[("transform", scale, f_i)] = outputs_2[("transform", scale)]
                        outputs[("transform", "high", scale, f_i)] = F.interpolate(
                            outputs[("transform", scale, f_i)], [self.opt.height, self.opt.width], mode="bilinear",
                            align_corners=True)
                        outputs[("refined", scale, f_i)] = (outputs[("transform", "high", scale, f_i)] * outputs[
                            ("occu_mask_backward", 0, f_i)].detach() + inputs[("color", 0, 0)])
                        outputs[("refined", scale, f_i)] = torch.clamp(outputs[("refined", scale, f_i)], min=0.0,
                                                                       max=1.0)
        return outputs

    def compute_losses_0(self, inputs, outputs):

        losses = {}
        total_loss = 0

        for scale in self.opt.scales:

            loss = 0
            loss_smooth_registration = 0
            loss_registration = 0

            color = inputs[("color", 0, scale)]

            for frame_id in self.opt.frame_ids[1:]:
                occu_mask_backward = outputs[("occu_mask_backward", 0, frame_id)].detach()
                loss_smooth_registration += (get_smooth_loss(outputs[("position", scale, frame_id)], color))
                loss_registration += (
                    self.compute_reprojection_loss(outputs[("registration", scale, frame_id)], outputs[("refined", scale, frame_id)].detach()) * occu_mask_backward).sum() / occu_mask_backward.sum()

            loss += loss_registration / 2.0
            loss += self.opt.position_smoothness * (loss_smooth_registration / 2.0) / (2 ** scale)

            total_loss += loss
            losses["loss/{}".format(scale)] = loss

        total_loss /= self.num_scales
        losses["loss"] = total_loss
        return losses

    def process_batch(self, inputs):
        """Pass a minibatch through the network and generate images and losses
        """
        for key, ipt in inputs.items():
            inputs[key] = ipt.to(self.device)
        outputs = self.models["depth_model"](inputs["color_aug", 0, 0])

        if self.use_pose_net:
            outputs.update(self.predict_poses(inputs, outputs))

        self.generate_images_pred(inputs, outputs)
        losses = self.compute_losses(inputs, outputs)

        return outputs, losses

    def predict_poses(self, inputs, disps):
        """Predict poses between input frames for monocular sequences.
        """
        outputs = {}
        if self.num_pose_frames == 2:
            pose_feats = {f_i: inputs["color_aug", f_i, 0] for f_i in self.opt.frame_ids}
                
            for f_i in self.opt.frame_ids[1:]:

                if f_i != "s":
                    
                    inputs_all = [pose_feats[f_i], pose_feats[0]]
                    inputs_all_reverse = [pose_feats[0], pose_feats[f_i]]

                    # position
                    position_inputs = self.models["position_encoder"](torch.cat(inputs_all, 1))
                    position_inputs_reverse = self.models["position_encoder"](torch.cat(inputs_all_reverse, 1))
                    outputs_0 = self.models["position"](position_inputs)
                    outputs_1 = self.models["position"](position_inputs_reverse)

                    for scale in self.opt.scales:

                        outputs[("position", scale, f_i)] = outputs_0[("position", scale)]
                        outputs[("position", "high", scale, f_i)] = F.interpolate(
                            outputs[("position", scale, f_i)], [self.opt.height, self.opt.width], mode="bilinear", align_corners=True)
                        outputs[("registration", scale, f_i)] = self.spatial_transform(inputs[("color", f_i, 0)], outputs[("position", "high", scale, f_i)])
                    
                        outputs[("position_reverse", scale, f_i)] = outputs_1[("position", scale)]
                        outputs[("position_reverse", "high", scale, f_i)] = F.interpolate(
                            outputs[("position_reverse", scale, f_i)], [self.opt.height, self.opt.width], mode="bilinear", align_corners=True)
                        outputs[("occu_mask_backward", scale, f_i)],  outputs[("occu_map_backward", scale, f_i)]= self.get_occu_mask_backward(outputs[("position_reverse", "high", scale, f_i)])
                        outputs[("occu_map_bidirection", scale, f_i)] = self.get_occu_mask_bidirection(outputs[("position", "high", scale, f_i)],
                                                                                                          outputs[("position_reverse", "high", scale, f_i)])

                    # transform
                    transform_input = [outputs[("registration", 0, f_i)], inputs[("color", 0, 0)]]
                    transform_inputs = self.models["transform_encoder"](torch.cat(transform_input, 1))
                    outputs_2 = self.models["transform"](transform_inputs)

                    for scale in self.opt.scales:

                        outputs[("transform", scale, f_i)] = outputs_2[("transform", scale)]
                        outputs[("transform", "high", scale, f_i)] = F.interpolate(
                            outputs[("transform", scale, f_i)], [self.opt.height, self.opt.width], mode="bilinear", align_corners=True)
                        outputs[("refined", scale, f_i)] = (outputs[("transform", "high", scale, f_i)] * outputs[("occu_mask_backward", 0, f_i)].detach()  + inputs[("color", 0, 0)])
                        outputs[("refined", scale, f_i)] = torch.clamp(outputs[("refined", scale, f_i)], min=0.0, max=1.0)
                        # outputs[("grad_refined", scale, f_i)] = get_gradmap(outputs[("refined", scale, f_i)])
                                                                                            

                    # pose
                    pose_inputs = [self.models["pose_encoder"](torch.cat(inputs_all, 1))]
                    axisangle, translation, intermediate_feature = self.models["pose"](pose_inputs)

                    if self.opt.learn_intrinsics:
                        cam_K = self.models['intrinsics_head'](
                        intermediate_feature, self.opt.width, self.opt.height)
                        inv_K = torch.inverse(cam_K)
                        outputs[('K', 0)] = cam_K
                        outputs[('inv_K', 0)] = inv_K
                    
                    outputs[("axisangle", 0, f_i)] = axisangle
                    outputs[("translation", 0, f_i)] = translation
                    outputs[("cam_T_cam", 0, f_i)] = transformation_from_parameters(
                        axisangle[:, 0], translation[:, 0])
                    
        return outputs

    def generate_images_pred(self, inputs, outputs):
        """Generate the warped (reprojected) color images for a minibatch.
        Generated images are saved into the `outputs` dictionary.
        """
        for scale in self.opt.scales:
            
            disp = outputs[("disp", scale)]
            if self.opt.v1_multiscale:
                source_scale = scale
            else:
                disp = F.interpolate(
                    disp, [self.opt.height, self.opt.width], mode="bilinear", align_corners=True)

            _, depth = disp_to_depth(disp, self.opt.min_depth, self.opt.max_depth)

            outputs[("depth", 0, scale)] = depth

            source_scale = 0
            if not self.opt.learn_intrinsics:
                cam_K = inputs[("K", source_scale)]
                inv_K = inputs[("inv_K", source_scale)]
            else:
                cam_K = outputs[('K', source_scale)]
                inv_K = outputs[('inv_K', source_scale)]
                # if self.step % (self.opt.log_frequency*5) == 0:
                #     print("predicted K:", cam_K[0])
                #     print("true K:", inputs[("K", 0)][0] )
            for i, frame_id in enumerate(self.opt.frame_ids[1:]):

                if frame_id == "s":
                    T = inputs["stereo_T"]
                else:
                    T = outputs[("cam_T_cam", 0, frame_id)]

                # from the authors of https://arxiv.org/abs/1712.00175
                if self.opt.pose_model_type == "posecnn":

                    axisangle = outputs[("axisangle", 0, frame_id)]
                    translation = outputs[("translation", 0, frame_id)]

                    inv_depth = 1 / depth
                    mean_inv_depth = inv_depth.mean(3, True).mean(2, True)

                    T = transformation_from_parameters(
                        axisangle[:, 0], translation[:, 0] * mean_inv_depth[:, 0], frame_id < 0)

                cam_points = self.backproject_depth[source_scale](
                    depth, inv_K)
                pix_coords = self.project_3d[source_scale](
                    cam_points, cam_K, T)

                outputs[("sample", frame_id, scale)] = pix_coords

                outputs[("color", frame_id, scale)] = F.grid_sample(
                    inputs[("color", frame_id, source_scale)],
                    outputs[("sample", frame_id, scale)],
                    padding_mode="border",
                    align_corners=True)

                outputs[("position_depth", scale, frame_id)] = self.position_depth[source_scale](
                        cam_points, cam_K, T)

    def compute_reprojection_loss(self, pred, target):

        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(self, inputs, outputs):

        losses = {}
        total_loss = 0

        for scale in self.opt.scales:
            
            loss = 0
            loss_reprojection = 0
            loss_transform = 0
            loss_cvt = 0

            disp = outputs[("disp", scale)]
            color = inputs[("color", 0, scale)]

            for frame_id in self.opt.frame_ids[1:]:
                
                occu_mask_backward = outputs[("occu_mask_backward", 0, frame_id)].detach()
                
                loss_reprojection += (
                    self.compute_reprojection_loss(outputs[("color", frame_id, scale)], outputs[("refined", scale, frame_id)]) * occu_mask_backward).sum() / occu_mask_backward.sum()  
                loss_transform += (
                    torch.abs(outputs[("refined", scale, frame_id)] - outputs[("registration", 0, frame_id)].detach()).mean(1, True) * occu_mask_backward).sum() / occu_mask_backward.sum()
                loss_cvt += get_smooth_bright(
                    outputs[("transform", "high", scale, frame_id)], inputs[("color", 0, 0)], outputs[("registration", scale, frame_id)].detach(), occu_mask_backward)

            mean_disp = disp.mean(2, True).mean(3, True)
            norm_disp = disp / (mean_disp + 1e-7)
            smooth_loss = get_smooth_loss(norm_disp, color)

            loss += loss_reprojection / 2.0
            loss += self.opt.transform_constraint * (loss_transform / 2.0)
            loss += self.opt.transform_smoothness * (loss_cvt / 2.0) 
            loss += self.opt.disparity_smoothness * smooth_loss / (2 ** scale)

            total_loss += loss
            losses["loss/{}".format(scale)] = loss

        total_loss /= self.num_scales
        losses["loss"] = total_loss
        return losses
    
    def val(self):
        """Validate the model on a single minibatch
        """
        self.set_eval()
        try:
            inputs = next(self.val_iter)
        except StopIteration:
            self.val_iter = iter(self.val_loader)
            inputs = next(self.val_iter)

        with torch.no_grad():
            outputs, losses = self.process_batch_val(inputs)
            self.log("val", inputs, outputs, losses)
            del inputs, outputs, losses

        self.set_train()

    def process_batch_val(self, inputs):
        """Pass a minibatch through the network and generate images and losses
        """
        for key, ipt in inputs.items():
            inputs[key] = ipt.to(self.device)
        outputs = self.models["depth_model"](inputs["color_aug", 0, 0])

        if self.use_pose_net:
            outputs.update(self.predict_poses(inputs, outputs))

        self.generate_images_pred(inputs, outputs)
        losses = self.compute_losses_val(inputs, outputs)

        return outputs, losses

    def compute_losses_val(self, inputs, outputs):
        """Compute the reprojection, perception_loss and smoothness losses for a minibatch
        """
        losses = {}
        total_loss = 0

        for scale in self.opt.scales:

            loss = 0
            registration_losses = []

            target = inputs[("color", 0, 0)]

            for frame_id in self.opt.frame_ids[1:]:
                registration_losses.append(
                    ncc_loss(outputs[("registration", scale, frame_id)].mean(1, True), target.mean(1, True)))

            registration_losses = torch.cat(registration_losses, 1)
            registration_losses, idxs_registration = torch.min(registration_losses, dim=1)

            loss += registration_losses.mean()
            total_loss += loss
            losses["loss/{}".format(scale)] = loss

        total_loss /= self.num_scales
        losses["loss"] = -1 * total_loss

        return losses

    def log_time(self, batch_idx, duration, loss):
        """Print a logging statement to the terminal
        """
        samples_per_sec = self.opt.batch_size / duration
        time_sofar = time.time() - self.start_time
        training_time_left = (
            self.num_total_steps / self.step - 1.0) * time_sofar if self.step > 0 else 0
        print_string = "epoch {:>3} | batch {:>6} | examples/s: {:5.1f}" + \
            " | loss: {:.5f} | time elapsed: {} | time left: {}"
        print(print_string.format(self.epoch, batch_idx, samples_per_sec, loss,
                                  sec_to_hm_str(time_sofar), sec_to_hm_str(training_time_left)))

    def log(self, mode, inputs, outputs, losses):
        """Write an event to the tensorboard events file
        """
        writer = self.writers[mode]
        for l, v in losses.items():
            writer.add_scalar("{}".format(l), v, self.step)

        for j in range(min(4, self.opt.batch_size)):  # write a maxmimum of four images
            for s in self.opt.scales:
                for frame_id in self.opt.frame_ids[1:]:

                    writer.add_image(
                        "brightness_{}_{}/{}".format(frame_id, s, j),
                        outputs[("transform", "high", s, frame_id)][j].data, self.step)
                    writer.add_image(
                        "registration_{}_{}/{}".format(frame_id, s, j),
                        outputs[("registration", s, frame_id)][j].data, self.step)
                    writer.add_image(
                        "refined_{}_{}/{}".format(frame_id, s, j),
                        outputs[("refined", s, frame_id)][j].data, self.step)
                    if s == 0:
                        writer.add_image(
                            "occu_mask_backward_{}_{}/{}".format(frame_id, s, j),
                            outputs[("occu_mask_backward", s, frame_id)][j].data, self.step)
            
                writer.add_image(
                    "disp_{}/{}".format(s, j),
                    normalize_image(outputs[("disp", s)][j]), self.step)

    def save_opts(self):
        """Save options to disk so we know what we ran this experiment with
        """
        models_dir = os.path.join(self.log_path, "models")
        if not os.path.exists(models_dir):
            os.makedirs(models_dir)
        to_save = self.opt.__dict__.copy()

        with open(os.path.join(models_dir, 'opt.json'), 'w') as f:
            json.dump(to_save, f, indent=2)

    def save_model(self, mode='epoch'):
        """Save model weights to disk
        """
        if mode == 'epoch':
            save_folder = os.path.join(self.log_path, "models", "weights_{}".format(self.epoch))
        elif mode == 'last':
            save_folder = os.path.join(self.log_path, "models", "weights_last")
        if not os.path.exists(save_folder):
            os.makedirs(save_folder)

        for model_name, model in self.models.items():
            save_path = os.path.join(save_folder, "{}.pth".format(model_name))
            to_save = model.state_dict()
            if model_name == 'depth_model':
                # save the sizes - these are needed at prediction time
                to_save['height'] = self.opt.height
                to_save['width'] = self.opt.width
                to_save['use_stereo'] = self.opt.use_stereo
            torch.save(to_save, save_path)

        save_path = os.path.join(save_folder, "{}.pth".format("adam"))
        torch.save(self.model_optimizer.state_dict(), save_path)

    def load_model(self):
        """Load model(s) from disk
        """
        self.opt.load_weights_folder = os.path.expanduser(self.opt.load_weights_folder)

        assert os.path.isdir(self.opt.load_weights_folder), \
            "Cannot find folder {}".format(self.opt.load_weights_folder)
        print("loading model from folder {}".format(self.opt.load_weights_folder))

        for n in self.opt.models_to_load:
            print("Loading {} weights...".format(n))
            path = os.path.join(self.opt.load_weights_folder, "{}.pth".format(n))
            model_dict = self.models[n].state_dict()
            pretrained_dict = torch.load(path)
            pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
            model_dict.update(pretrained_dict)
            self.models[n].load_state_dict(model_dict)

        # loading adam state
        # optimizer_load_path = os.path.join(self.opt.load_weights_folder, "adam.pth")
        # if os.path.isfile(optimizer_load_path):
            # print("Loading Adam weights")
            # optimizer_dict = torch.load(optimizer_load_path)
            # self.model_optimizer.load_state_dict(optimizer_dict)
        # else:
        print("Adam is randomly initialized")