54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
import tensorflow as tf
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def l1_loss(target_img, reprojected_img):
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"""
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Calculates the l1 norm between the target and reprojected image
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:param target_img: Tensor (batch, height, width, 3)
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:param reprojected_img: Tensor, same shape as target_img
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:return: The per-pixel l1 norm -> Tensor (batch, height, width, 1)
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"""
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return tf.reduce_mean(tf.abs(target_img - reprojected_img), axis=3)
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def l2_loss(target_img, reprojected_img):
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"""
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Calculates the l2 norm between the target and reprojected image
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:param target_img: Tensor (batch, height, width, 3)
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:param reprojected_img: Tensor, same shape as target_img
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:return: The per-pixel l2 norm -> Tensor (batch, height, width, 1)
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"""
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return tf.reduce_mean((target_img - reprojected_img) ** 2 ** (1 / 2), axis=3)
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def make_combined_ssim_l1_loss(ssim_weight: int = 0.85, other_loss_fn=l1_loss):
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"""
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Create a loss function that will calculate ssim for the two images, and use the other_loss_fn to calculate the
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per pixel loss
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:param ssim_weight: Weighting that should be applied to SSIM weight vs l1 difference between target and
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reprojected image
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:param other_loss_fn: Function to combine with the ssim
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:return: Function to calculate the per-pixel combined ssim with other loss function
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"""
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def combined_ssim_loss(target_img, reprojected_img):
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"""
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Calculates the per-pixel photometric reconstruction loss for each source image,
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combined this with the SSIM between the reconstructed image and the actual image.
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Calculates the following:
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ssim_weight * SSIM(target_img, reprojected_img) + (1 - ssim_weight) * other_loss_fn(target_img - reprojected_img)
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:param target_img: Tensor with shape (batch, height, width, 3) - current image we're training on
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:param reprojected_img: Tensor with same shape as target_img, Reprojected from some source image that
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should be as close as possible to the target image
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:return: Per-pixel loss -> Tensor with shape (batch, height, width, 1), where height and width match target_img
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height and width
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"""
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ssim = tf.image.ssim(target_img, reprojected_img, axis=3, keepdim=True)
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return ssim_weight * ssim + (1 - ssim_weight) * other_loss_fn(target_img, reprojected_img)
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return combined_ssim_loss
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