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9eeb456e82
Summary: Update all FB license strings to the new format. Reviewed By: patricklabatut Differential Revision: D33403538 fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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def calc_mse(x: torch.Tensor, y: torch.Tensor):
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"""
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Calculates the mean square error between tensors `x` and `y`.
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"""
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return torch.mean((x - y) ** 2)
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def calc_psnr(x: torch.Tensor, y: torch.Tensor):
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"""
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Calculates the Peak-signal-to-noise ratio between tensors `x` and `y`.
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"""
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mse = calc_mse(x, y)
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psnr = -10.0 * torch.log10(mse)
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return psnr
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def sample_images_at_mc_locs(
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target_images: torch.Tensor,
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sampled_rays_xy: torch.Tensor,
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):
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"""
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Given a set of pixel locations `sampled_rays_xy` this method samples the tensor
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`target_images` at the respective 2D locations.
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This function is used in order to extract the colors from ground truth images
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that correspond to the colors rendered using a Monte Carlo rendering.
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Args:
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target_images: A tensor of shape `(batch_size, ..., 3)`.
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sampled_rays_xy: A tensor of shape `(batch_size, S_1, ..., S_N, 2)`.
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Returns:
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images_sampled: A tensor of shape `(batch_size, S_1, ..., S_N, 3)`
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containing `target_images` sampled at `sampled_rays_xy`.
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"""
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ba = target_images.shape[0]
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dim = target_images.shape[-1]
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spatial_size = sampled_rays_xy.shape[1:-1]
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# The coordinate grid convention for grid_sample has both x and y
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# directions inverted.
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xy_sample = -sampled_rays_xy.view(ba, -1, 1, 2).clone()
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images_sampled = torch.nn.functional.grid_sample(
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target_images.permute(0, 3, 1, 2),
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xy_sample,
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align_corners=True,
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mode="bilinear",
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)
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return images_sampled.permute(0, 2, 3, 1).view(ba, *spatial_size, dim)
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