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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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class DepthLoss(nn.Module): |
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def __init__(self, type='l1'): |
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super(DepthLoss, self).__init__() |
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self.type = type |
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def forward(self, depth_pred, depth_gt, mask=None): |
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if (depth_gt < 0).sum() > 0: |
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return torch.tensor(0.0).to(depth_pred.device) |
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if mask is not None: |
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mask_d = (depth_gt > 0).float() |
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mask = mask * mask_d |
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mask_sum = mask.sum() + 1e-5 |
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depth_error = (depth_pred - depth_gt) * mask |
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depth_loss = F.l1_loss(depth_error, torch.zeros_like(depth_error).to(depth_error.device), |
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reduction='sum') / mask_sum |
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else: |
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depth_error = depth_pred - depth_gt |
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depth_loss = F.l1_loss(depth_error, torch.zeros_like(depth_error).to(depth_error.device), |
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reduction='mean') |
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return depth_loss |
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def forward(self, depth_pred, depth_gt, mask=None): |
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if mask is not None: |
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mask_d = (depth_gt > 0).float() |
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mask = mask * mask_d |
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mask_sum = mask.sum() + 1e-5 |
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depth_error = (depth_pred - depth_gt) * mask |
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depth_loss = F.l1_loss(depth_error, torch.zeros_like(depth_error).to(depth_error.device), |
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reduction='sum') / mask_sum |
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else: |
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depth_error = depth_pred - depth_gt |
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depth_loss = F.l1_loss(depth_error, torch.zeros_like(depth_error).to(depth_error.device), |
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reduction='mean') |
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return depth_loss |
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class DepthSmoothLoss(nn.Module): |
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def __init__(self): |
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super(DepthSmoothLoss, self).__init__() |
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def forward(self, disp, img, mask): |
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""" |
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Computes the smoothness loss for a disparity image |
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The color image is used for edge-aware smoothness |
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:param disp: [B, 1, H, W] |
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:param img: [B, 1, H, W] |
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:param mask: [B, 1, H, W] |
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:return: |
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""" |
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grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:]) |
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grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :]) |
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grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True) |
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grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True) |
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grad_disp_x *= torch.exp(-grad_img_x) |
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grad_disp_y *= torch.exp(-grad_img_y) |
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grad_disp = (grad_disp_x * mask[:, :, :, :-1]).mean() + (grad_disp_y * mask[:, :, :-1, :]).mean() |
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return grad_disp |
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