import torch
import torch.nn as nn
import torch.nn.functional as F
from .submodule import *

Align_Corners_Range = False

class DepthNet(nn.Module):
    def __init__(self):
        super(DepthNet, self).__init__()
    def forward(self, features, proj_matrices, depth_values, num_depth, cost_regularization,REM, prob_volume_init=None):
        proj_matrices = torch.unbind(proj_matrices, 1)
        assert len(features) == len(proj_matrices), "Different number of images and projection matrices"
        assert depth_values.shape[1] == num_depth, "depth_values.shape[1]:{}  num_depth:{}".format(depth_values.shape[1], num_depth)
        num_views = len(features)
        # step 1. feature extraction
        # in: images; out: 32-channel feature maps
        ref_feature, src_features = features[0], features[1:]
        ref_proj, src_projs = proj_matrices[0], proj_matrices[1:]

        # step 2. differentiable homograph, build cost volume
        ref_volume = ref_feature.unsqueeze(2).repeat(1, 1, num_depth, 1, 1)
        volume_sum = ref_volume
        volume_sq_sum = ref_volume ** 2
        del ref_volume
        for src_fea, src_proj in zip(src_features, src_projs):
            #warpped features
            src_proj_new = src_proj[:, 0].clone()
            src_proj_new[:, :3, :4] = torch.matmul(src_proj[:, 1, :3, :3], src_proj[:, 0, :3, :4])
            ref_proj_new = ref_proj[:, 0].clone()
            ref_proj_new[:, :3, :4] = torch.matmul(ref_proj[:, 1, :3, :3], ref_proj[:, 0, :3, :4])
            warped_volume = homo_warping(src_fea, src_proj_new, ref_proj_new, depth_values)
            # warped_volume = homo_warping(src_fea, src_proj[:, 2], ref_proj[:, 2], depth_values)

            if self.training:
                volume_sum = volume_sum + warped_volume
                volume_sq_sum = volume_sq_sum + warped_volume ** 2
            else:
                # TODO: this is only a temporal solution to save memory, better way?
                volume_sum += warped_volume
                volume_sq_sum += warped_volume.pow_(2)  # the memory of warped_volume has been modified
            del warped_volume
        # aggregate multiple feature volumes by variance
        volume_variance = volume_sq_sum.div_(num_views).sub_(volume_sum.div_(num_views).pow_(2))

        # step 3. cost volume regularization
        cost_reg = cost_regularization(volume_variance)
        # cost_reg = F.upsample(cost_reg, [num_depth * 4, img_height, img_width], mode='trilinear')
        prob_volume_pre = cost_reg.squeeze(1)

        if prob_volume_init is not None:
            prob_volume_pre += prob_volume_init

        prob_volume = F.softmax(prob_volume_pre, dim=1)
        depth = depth_regression(prob_volume, depth_values=depth_values)
        depth_values=depth_values.detach()
        prob_volume=prob_volume.detach()
        # step 4. learning uncertainty map through REM
        uncertaintyMap=REM(prob_volume).squeeze(1)

        with torch.no_grad():
            # photometric confidence
            prob_volume_sum4 = 4 * F.avg_pool3d(F.pad(prob_volume.unsqueeze(1), pad=(0, 0, 0, 0, 1, 2)), (4, 1, 1), stride=1, padding=0).squeeze(1)
            depth_index = depth_regression(prob_volume, depth_values=torch.arange(num_depth, device=prob_volume.device, dtype=torch.float)).long()
            depth_index = depth_index.clamp(min=0, max=num_depth-1)
            photometric_confidence = torch.gather(prob_volume_sum4, 1, depth_index.unsqueeze(1)).squeeze(1)

        return {"depth": depth,  "photometric_confidence": photometric_confidence,"uncertaintyMap":uncertaintyMap,"prob_volume":prob_volume,"depth_values":depth_values}
class DepthNet_Stage3(nn.Module):
    def __init__(self):
        super(DepthNet_Stage3, self).__init__()     
    def forward(self, features, proj_matrices, depth_values, num_depth, cost_regularization, prob_volume_init=None):
        proj_matrices = torch.unbind(proj_matrices, 1)
        assert len(features) == len(proj_matrices), "Different number of images and projection matrices"
        assert depth_values.shape[1] == num_depth, "depth_values.shape[1]:{}  num_depth:{}".format(depth_values.shape[1], num_depth)
        num_views = len(features)

        # step 1. feature extraction
        # in: images; out: 32-channel feature maps
        ref_feature, src_features = features[0], features[1:]
        ref_proj, src_projs = proj_matrices[0], proj_matrices[1:]

        # step 2. differentiable homograph, build cost volume
        ref_volume = ref_feature.unsqueeze(2).repeat(1, 1, num_depth, 1, 1)
        volume_sum = ref_volume
        volume_sq_sum = ref_volume ** 2
        del ref_volume
        for src_fea, src_proj in zip(src_features, src_projs):
            #warpped features
            src_proj_new = src_proj[:, 0].clone()
            src_proj_new[:, :3, :4] = torch.matmul(src_proj[:, 1, :3, :3], src_proj[:, 0, :3, :4])
            ref_proj_new = ref_proj[:, 0].clone()
            ref_proj_new[:, :3, :4] = torch.matmul(ref_proj[:, 1, :3, :3], ref_proj[:, 0, :3, :4])
            warped_volume = homo_warping(src_fea, src_proj_new, ref_proj_new, depth_values)
            # warped_volume = homo_warping(src_fea, src_proj[:, 2], ref_proj[:, 2], depth_values)

            if self.training:
                volume_sum = volume_sum + warped_volume
                volume_sq_sum = volume_sq_sum + warped_volume ** 2
            else:
                # TODO: this is only a temporal solution to save memory, better way?
                volume_sum += warped_volume
                volume_sq_sum += warped_volume.pow_(2)  # the memory of warped_volume has been modified
            del warped_volume
        # aggregate multiple feature volumes by variance
        volume_variance = volume_sq_sum.div_(num_views).sub_(volume_sum.div_(num_views).pow_(2))

        # step 3. cost volume regularization
        cost_reg = cost_regularization(volume_variance)
        # cost_reg = F.upsample(cost_reg, [num_depth * 4, img_height, img_width], mode='trilinear')
        prob_volume_pre = cost_reg.squeeze(1)

        if prob_volume_init is not None:
            prob_volume_pre += prob_volume_init

        prob_volume = F.softmax(prob_volume_pre, dim=1)
        depth = depth_regression(prob_volume, depth_values=depth_values)
        depth_values=depth_values.detach()
        uncertaintyMap=None

        with torch.no_grad():
            # photometric confidence
            prob_volume_sum4 = 4 * F.avg_pool3d(F.pad(prob_volume.unsqueeze(1), pad=(0, 0, 0, 0, 1, 2)), (4, 1, 1), stride=1, padding=0).squeeze(1)
            depth_index = depth_regression(prob_volume, depth_values=torch.arange(num_depth, device=prob_volume.device, dtype=torch.float)).long()
            depth_index = depth_index.clamp(min=0, max=num_depth-1)
            photometric_confidence = torch.gather(prob_volume_sum4, 1, depth_index.unsqueeze(1)).squeeze(1)
        
        return {"depth": depth,  "photometric_confidence": photometric_confidence,"uncertaintyMap":uncertaintyMap,"prob_volume":prob_volume,"depth_values":depth_values}

class DDR_Net(nn.Module):
    def __init__(self, refine=False, ndepths=[48, 32, 8], depth_interals_ratio=[4, 2, 1], share_cr=False,
                 grad_method="detach", arch_mode="fpn", cr_base_chs=[8, 8, 8]):
        super(DDR_Net, self).__init__()
        self.refine = refine
        self.share_cr = share_cr
        self.ndepths = ndepths
        self.depth_interals_ratio = depth_interals_ratio
        self.grad_method = grad_method
        self.arch_mode = arch_mode
        self.cr_base_chs = cr_base_chs
        self.num_stage = len(ndepths)
        print("**********netphs:{}, depth_intervals_ratio:{},  grad:{}, chs:{}************".format(ndepths,
              depth_interals_ratio, self.grad_method, self.cr_base_chs))

        assert len(ndepths) == len(depth_interals_ratio)

        self.stage_infos = {
            "stage1":{
                "scale": 4.0,
            },
            "stage2": {
                "scale": 2.0,
            },
            "stage3": {
                "scale": 1.0,
            }
        }

        self.feature = FeatureNet(base_channels=8, stride=4, num_stage=self.num_stage, arch_mode=self.arch_mode)
        if self.share_cr:
            self.cost_regularization = CostRegNet(in_channels=self.feature.out_channels, base_channels=8)
        else:
            self.cost_regularization = nn.ModuleList([CostRegNet(in_channels=self.feature.out_channels[i],
                                                                 base_channels=self.cr_base_chs[i])
                                                      for i in range(self.num_stage)])
        self.REM=nn.ModuleList([REM(in_channels=self.ndepths[i])for i in range(self.num_stage-1)])
        if self.refine:
            self.refine_network = RefineNet()
        self.DepthNet = DepthNet()
        self.DepthNet_stage_3=DepthNet_Stage3()

    def forward(self, imgs, proj_matrices, depth_values):
        depth_min = float(depth_values[0, 0].cpu().numpy())
        depth_max = float(depth_values[0, -1].cpu().numpy())
        depth_interval = (depth_max - depth_min) / depth_values.size(1)

        # step 1. feature extraction
        features = []
        for nview_idx in range(imgs.size(1)):  #imgs shape (B, N, C, H, W)
            img = imgs[:, nview_idx]
            features.append(self.feature(img))

        outputs = {}
        refine_depth_map=[]
        depth, cur_depth,uncertainty_map = None, None,None
        for stage_idx in range(self.num_stage):
            # print("*********************stage{}*********************".format(stage_idx + 1))
            #stage feature, proj_mats, scales
            features_stage = [feat["stage{}".format(stage_idx + 1)] for feat in features]
            proj_matrices_stage = proj_matrices["stage{}".format(stage_idx + 1)]
            stage_scale = self.stage_infos["stage{}".format(stage_idx + 1)]["scale"]
            if (stage_idx==0):
                min_depth=depth_values[:,0]
                max_depth=depth_values[:,-1]
            else:
                min_depth=depth_min
                max_depth=depth_max
            if depth is not None:
                if self.grad_method == "detach":
                    cur_depth = depth.detach()
                else:
                    cur_depth = depth 
            if (stage_idx == 1):
                depth_range_samples, depth_min, depth_max= get_depth_range_samples(cur_depth=cur_depth,
                                                                                    ndepth=self.ndepths[stage_idx],
                                                                                    depth_inteval_pixel=
                                                                                    self.depth_interals_ratio[
                                                                                        stage_idx] * depth_interval,
                                                                                    dtype=img[0].dtype,
                                                                                    device=img[0].device,
                                                                                    shape=[img.shape[0], cur_h, cur_w],
                                                                                    uncertainty_map=uncertainty_map,
                                                                                    min_depth=min_depth,
                                                                                    max_depth=max_depth)
                depth_map_by_loss_strategy = Loss_strategy(pro_volume=prob_volume, depth_values=depth_values_last,
                                                   depth_min=depth_min.unsqueeze(1),
                                                   depth_max=depth_max.unsqueeze(1)
                                                   )
                refine_depth_map.append(depth_map_by_loss_strategy)
            cur_h = img.shape[2] // int(stage_scale)
            cur_w = img.shape[3] // int(stage_scale)
            if depth is not None:
                cur_depth = F.interpolate(cur_depth.unsqueeze(1),[cur_h, cur_w], mode='bilinear',align_corners=Align_Corners_Range).squeeze(1)
                uncertainty_map=F.interpolate(uncertainty_map.unsqueeze(1),[cur_h, cur_w], mode='bilinear',align_corners=Align_Corners_Range).squeeze(1)
            else:
                cur_depth = depth_values
            depth_range_samples,depth_min,depth_max = get_depth_range_samples(cur_depth=cur_depth,
                                                        ndepth=self.ndepths[stage_idx],
                                                        depth_inteval_pixel=self.depth_interals_ratio[stage_idx] * depth_interval,
                                                        dtype=img[0].dtype,
                                                        device=img[0].device,
                                                        shape=[img.shape[0], cur_h, cur_w],
                                                        uncertainty_map=uncertainty_map,
                                                        min_depth=min_depth,
                                                        max_depth=max_depth)

            if (stage_idx !=2):
                outputs_stage = self.DepthNet(features_stage, proj_matrices_stage,
                                          depth_values=depth_range_samples,
                                          num_depth=self.ndepths[stage_idx],
                                          cost_regularization=self.cost_regularization if self.share_cr else self.cost_regularization[stage_idx],
                                          REM=self.REM[stage_idx]
                                          )
            else:
                outputs_stage = self.DepthNet_stage_3(features_stage, proj_matrices_stage,
                                          depth_values=depth_range_samples,
                                          num_depth=self.ndepths[stage_idx],
                                          cost_regularization=self.cost_regularization if self.share_cr else self.cost_regularization[stage_idx]
                                          )


            depth = outputs_stage['depth']
            prob_volume= outputs_stage['prob_volume']
            uncertainty_map=outputs_stage['uncertaintyMap']
            depth_values_last=outputs_stage['depth_values']
            outputs["stage{}".format(stage_idx + 1)] = outputs_stage
            outputs.update(outputs_stage)

        # depth map refinement
        if self.refine:
            refined_depth = self.refine_network(torch.cat((imgs[:, 0], depth), 1))
            outputs["refined_depth"] = refined_depth

        return outputs,refine_depth_map
import torch
import torch.nn as nn
import torch.nn.functional as F
from .submodule import *

Align_Corners_Range = False

class DepthNet(nn.Module):
    def __init__(self):
        super(DepthNet, self).__init__()
    def forward(self, features, proj_matrices, depth_values, num_depth, cost_regularization,REM, prob_volume_init=None):
        proj_matrices = torch.unbind(proj_matrices, 1)
        assert len(features) == len(proj_matrices), "Different number of images and projection matrices"
        assert depth_values.shape[1] == num_depth, "depth_values.shape[1]:{}  num_depth:{}".format(depth_values.shape[1], num_depth)
        num_views = len(features)
        # step 1. feature extraction
        # in: images; out: 32-channel feature maps
        ref_feature, src_features = features[0], features[1:]
        ref_proj, src_projs = proj_matrices[0], proj_matrices[1:]

        # step 2. differentiable homograph, build cost volume
        ref_volume = ref_feature.unsqueeze(2).repeat(1, 1, num_depth, 1, 1)
        volume_sum = ref_volume
        volume_sq_sum = ref_volume ** 2
        del ref_volume
        for src_fea, src_proj in zip(src_features, src_projs):
            #warpped features
            src_proj_new = src_proj[:, 0].clone()
            src_proj_new[:, :3, :4] = torch.matmul(src_proj[:, 1, :3, :3], src_proj[:, 0, :3, :4])
            ref_proj_new = ref_proj[:, 0].clone()
            ref_proj_new[:, :3, :4] = torch.matmul(ref_proj[:, 1, :3, :3], ref_proj[:, 0, :3, :4])
            warped_volume = homo_warping(src_fea, src_proj_new, ref_proj_new, depth_values)
            # warped_volume = homo_warping(src_fea, src_proj[:, 2], ref_proj[:, 2], depth_values)

            if self.training:
                volume_sum = volume_sum + warped_volume
                volume_sq_sum = volume_sq_sum + warped_volume ** 2
            else:
                # TODO: this is only a temporal solution to save memory, better way?
                volume_sum += warped_volume
                volume_sq_sum += warped_volume.pow_(2)  # the memory of warped_volume has been modified
            del warped_volume
        # aggregate multiple feature volumes by variance
        volume_variance = volume_sq_sum.div_(num_views).sub_(volume_sum.div_(num_views).pow_(2))

        # step 3. cost volume regularization
        cost_reg = cost_regularization(volume_variance)
        # cost_reg = F.upsample(cost_reg, [num_depth * 4, img_height, img_width], mode='trilinear')
        prob_volume_pre = cost_reg.squeeze(1)

        if prob_volume_init is not None:
            prob_volume_pre += prob_volume_init

        prob_volume = F.softmax(prob_volume_pre, dim=1)
        depth = depth_regression(prob_volume, depth_values=depth_values)
        depth_values=depth_values.detach()
        prob_volume=prob_volume.detach()
        # step 4. learning uncertainty map through REM
        uncertaintyMap=REM(prob_volume).squeeze(1)

        with torch.no_grad():
            # photometric confidence
            prob_volume_sum4 = 4 * F.avg_pool3d(F.pad(prob_volume.unsqueeze(1), pad=(0, 0, 0, 0, 1, 2)), (4, 1, 1), stride=1, padding=0).squeeze(1)
            depth_index = depth_regression(prob_volume, depth_values=torch.arange(num_depth, device=prob_volume.device, dtype=torch.float)).long()
            depth_index = depth_index.clamp(min=0, max=num_depth-1)
            photometric_confidence = torch.gather(prob_volume_sum4, 1, depth_index.unsqueeze(1)).squeeze(1)

        return {"depth": depth,  "photometric_confidence": photometric_confidence,"uncertaintyMap":uncertaintyMap,"prob_volume":prob_volume,"depth_values":depth_values}
class DepthNet_Stage3(nn.Module):
    def __init__(self):
        super(DepthNet_Stage3, self).__init__()     
    def forward(self, features, proj_matrices, depth_values, num_depth, cost_regularization, prob_volume_init=None):
        proj_matrices = torch.unbind(proj_matrices, 1)
        assert len(features) == len(proj_matrices), "Different number of images and projection matrices"
        assert depth_values.shape[1] == num_depth, "depth_values.shape[1]:{}  num_depth:{}".format(depth_values.shape[1], num_depth)
        num_views = len(features)

        # step 1. feature extraction
        # in: images; out: 32-channel feature maps
        ref_feature, src_features = features[0], features[1:]
        ref_proj, src_projs = proj_matrices[0], proj_matrices[1:]

        # step 2. differentiable homograph, build cost volume
        ref_volume = ref_feature.unsqueeze(2).repeat(1, 1, num_depth, 1, 1)
        volume_sum = ref_volume
        volume_sq_sum = ref_volume ** 2
        del ref_volume
        for src_fea, src_proj in zip(src_features, src_projs):
            #warpped features
            src_proj_new = src_proj[:, 0].clone()
            src_proj_new[:, :3, :4] = torch.matmul(src_proj[:, 1, :3, :3], src_proj[:, 0, :3, :4])
            ref_proj_new = ref_proj[:, 0].clone()
            ref_proj_new[:, :3, :4] = torch.matmul(ref_proj[:, 1, :3, :3], ref_proj[:, 0, :3, :4])
            warped_volume = homo_warping(src_fea, src_proj_new, ref_proj_new, depth_values)
            # warped_volume = homo_warping(src_fea, src_proj[:, 2], ref_proj[:, 2], depth_values)

            if self.training:
                volume_sum = volume_sum + warped_volume
                volume_sq_sum = volume_sq_sum + warped_volume ** 2
            else:
                # TODO: this is only a temporal solution to save memory, better way?
                volume_sum += warped_volume
                volume_sq_sum += warped_volume.pow_(2)  # the memory of warped_volume has been modified
            del warped_volume
        # aggregate multiple feature volumes by variance
        volume_variance = volume_sq_sum.div_(num_views).sub_(volume_sum.div_(num_views).pow_(2))

        # step 3. cost volume regularization
        cost_reg = cost_regularization(volume_variance)
        # cost_reg = F.upsample(cost_reg, [num_depth * 4, img_height, img_width], mode='trilinear')
        prob_volume_pre = cost_reg.squeeze(1)

        if prob_volume_init is not None:
            prob_volume_pre += prob_volume_init

        prob_volume = F.softmax(prob_volume_pre, dim=1)
        depth = depth_regression(prob_volume, depth_values=depth_values)
        depth_values=depth_values.detach()
        uncertaintyMap=None

        with torch.no_grad():
            # photometric confidence
            prob_volume_sum4 = 4 * F.avg_pool3d(F.pad(prob_volume.unsqueeze(1), pad=(0, 0, 0, 0, 1, 2)), (4, 1, 1), stride=1, padding=0).squeeze(1)
            depth_index = depth_regression(prob_volume, depth_values=torch.arange(num_depth, device=prob_volume.device, dtype=torch.float)).long()
            depth_index = depth_index.clamp(min=0, max=num_depth-1)
            photometric_confidence = torch.gather(prob_volume_sum4, 1, depth_index.unsqueeze(1)).squeeze(1)
        
        return {"depth": depth,  "photometric_confidence": photometric_confidence,"uncertaintyMap":uncertaintyMap,"prob_volume":prob_volume,"depth_values":depth_values}

class DDR_Net(nn.Module):
    def __init__(self, refine=False, ndepths=[48, 32, 8], depth_interals_ratio=[4, 2, 1], share_cr=False,
                 grad_method="detach", arch_mode="fpn", cr_base_chs=[8, 8, 8]):
        super(DDR_Net, self).__init__()
        self.refine = refine
        self.share_cr = share_cr
        self.ndepths = ndepths
        self.depth_interals_ratio = depth_interals_ratio
        self.grad_method = grad_method
        self.arch_mode = arch_mode
        self.cr_base_chs = cr_base_chs
        self.num_stage = len(ndepths)
        print("**********netphs:{}, depth_intervals_ratio:{},  grad:{}, chs:{}************".format(ndepths,
              depth_interals_ratio, self.grad_method, self.cr_base_chs))

        assert len(ndepths) == len(depth_interals_ratio)

        self.stage_infos = {
            "stage1":{
                "scale": 4.0,
            },
            "stage2": {
                "scale": 2.0,
            },
            "stage3": {
                "scale": 1.0,
            }
        }

        self.feature = FeatureNet(base_channels=8, stride=4, num_stage=self.num_stage, arch_mode=self.arch_mode)
        if self.share_cr:
            self.cost_regularization = CostRegNet(in_channels=self.feature.out_channels, base_channels=8)
        else:
            self.cost_regularization = nn.ModuleList([CostRegNet(in_channels=self.feature.out_channels[i],
                                                                 base_channels=self.cr_base_chs[i])
                                                      for i in range(self.num_stage)])
        self.REM=nn.ModuleList([REM(in_channels=self.ndepths[i])for i in range(self.num_stage-1)])
        if self.refine:
            self.refine_network = RefineNet()
        self.DepthNet = DepthNet()
        self.DepthNet_stage_3=DepthNet_Stage3()

    def forward(self, imgs, proj_matrices, depth_values):
        depth_min = float(depth_values[0, 0].cpu().numpy())
        depth_max = float(depth_values[0, -1].cpu().numpy())
        depth_interval = (depth_max - depth_min) / depth_values.size(1)

        # step 1. feature extraction
        features = []
        for nview_idx in range(imgs.size(1)):  #imgs shape (B, N, C, H, W)
            img = imgs[:, nview_idx]
            features.append(self.feature(img))

        outputs = {}
        refine_depth_map=[]
        depth, cur_depth,uncertainty_map = None, None,None
        for stage_idx in range(self.num_stage):
            # print("*********************stage{}*********************".format(stage_idx + 1))
            #stage feature, proj_mats, scales
            features_stage = [feat["stage{}".format(stage_idx + 1)] for feat in features]
            proj_matrices_stage = proj_matrices["stage{}".format(stage_idx + 1)]
            stage_scale = self.stage_infos["stage{}".format(stage_idx + 1)]["scale"]
            if (stage_idx==0):
                min_depth=depth_values[:,0]
                max_depth=depth_values[:,-1]
            else:
                min_depth=depth_min
                max_depth=depth_max
            if depth is not None:
                if self.grad_method == "detach":
                    cur_depth = depth.detach()
                else:
                    cur_depth = depth 
            if (stage_idx == 1):
                depth_range_samples, depth_min, depth_max= get_depth_range_samples(cur_depth=cur_depth,
                                                                                    ndepth=self.ndepths[stage_idx],
                                                                                    depth_inteval_pixel=
                                                                                    self.depth_interals_ratio[
                                                                                        stage_idx] * depth_interval,
                                                                                    dtype=img[0].dtype,
                                                                                    device=img[0].device,
                                                                                    shape=[img.shape[0], cur_h, cur_w],
                                                                                    uncertainty_map=uncertainty_map,
                                                                                    min_depth=min_depth,
                                                                                    max_depth=max_depth)
                depth_map_by_loss_strategy = Loss_strategy(pro_volume=prob_volume, depth_values=depth_values_last,
                                                   depth_min=depth_min.unsqueeze(1),
                                                   depth_max=depth_max.unsqueeze(1)
                                                   )
                refine_depth_map.append(depth_map_by_loss_strategy)
            cur_h = img.shape[2] // int(stage_scale)
            cur_w = img.shape[3] // int(stage_scale)
            if depth is not None:
                cur_depth = F.interpolate(cur_depth.unsqueeze(1),[cur_h, cur_w], mode='bilinear',align_corners=Align_Corners_Range).squeeze(1)
                uncertainty_map=F.interpolate(uncertainty_map.unsqueeze(1),[cur_h, cur_w], mode='bilinear',align_corners=Align_Corners_Range).squeeze(1)
            else:
                cur_depth = depth_values
            depth_range_samples,depth_min,depth_max = get_depth_range_samples(cur_depth=cur_depth,
                                                        ndepth=self.ndepths[stage_idx],
                                                        depth_inteval_pixel=self.depth_interals_ratio[stage_idx] * depth_interval,
                                                        dtype=img[0].dtype,
                                                        device=img[0].device,
                                                        shape=[img.shape[0], cur_h, cur_w],
                                                        uncertainty_map=uncertainty_map,
                                                        min_depth=min_depth,
                                                        max_depth=max_depth)

            if (stage_idx !=2):
                outputs_stage = self.DepthNet(features_stage, proj_matrices_stage,
                                          depth_values=depth_range_samples,
                                          num_depth=self.ndepths[stage_idx],
                                          cost_regularization=self.cost_regularization if self.share_cr else self.cost_regularization[stage_idx],
                                          REM=self.REM[stage_idx]
                                          )
            else:
                outputs_stage = self.DepthNet_stage_3(features_stage, proj_matrices_stage,
                                          depth_values=depth_range_samples,
                                          num_depth=self.ndepths[stage_idx],
                                          cost_regularization=self.cost_regularization if self.share_cr else self.cost_regularization[stage_idx]
                                          )


            depth = outputs_stage['depth']
            prob_volume= outputs_stage['prob_volume']
            uncertainty_map=outputs_stage['uncertaintyMap']
            depth_values_last=outputs_stage['depth_values']
            outputs["stage{}".format(stage_idx + 1)] = outputs_stage
            outputs.update(outputs_stage)

        # depth map refinement
        if self.refine:
            refined_depth = self.refine_network(torch.cat((imgs[:, 0], depth), 1))
            outputs["refined_depth"] = refined_depth

        return outputs,refine_depth_map
# step 1. feature extraction # in: images; out: 32-channel feature maps # step 2. differentiable homograph, build cost volume #warpped features # warped_volume = homo_warping(src_fea, src_proj[:, 2], ref_proj[:, 2], depth_values) # TODO: this is only a temporal solution to save memory, better way? # the memory of warped_volume has been modified # aggregate multiple feature volumes by variance # step 3. cost volume regularization # cost_reg = F.upsample(cost_reg, [num_depth * 4, img_height, img_width], mode='trilinear') # step 4. learning uncertainty map through REM # photometric confidence # step 1. feature extraction # in: images; out: 32-channel feature maps # step 2. differentiable homograph, build cost volume #warpped features # warped_volume = homo_warping(src_fea, src_proj[:, 2], ref_proj[:, 2], depth_values) # TODO: this is only a temporal solution to save memory, better way? # the memory of warped_volume has been modified # aggregate multiple feature volumes by variance # step 3. cost volume regularization # cost_reg = F.upsample(cost_reg, [num_depth * 4, img_height, img_width], mode='trilinear') # photometric confidence # step 1. feature extraction #imgs shape (B, N, C, H, W) # print("*********************stage{}*********************".format(stage_idx + 1)) #stage feature, proj_mats, scales # depth map refinement