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"""
Copyright (c) Meta Platforms, Inc. and affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
from typing import List
import torch as th
import torch.nn as nn
from torchvision.transforms.functional import gaussian_blur
class LearnableBlur(nn.Module):
# TODO: should we make this conditional?
def __init__(self, cameras: List[str]) -> None:
super().__init__()
self.cameras = cameras
self.register_parameter(
"weights_raw", nn.Parameter(th.ones(len(cameras), 3, dtype=th.float32))
)
def name_to_idx(self, cameras: List[str]) -> th.Tensor:
return th.tensor(
[self.cameras.index(c) for c in cameras],
device=self.weights_raw.device,
dtype=th.long,
)
# pyre-ignore
def reg(self, cameras: List[str]):
# pyre-ignore
idxs = self.name_to_idx(cameras)
# pyre-ignore
return self.weights_raw[idxs]
# pyre-ignore
def forward(self, img: th.Tensor, cameras: List[str]):
B = img.shape[0]
# B, C, H, W
idxs = self.name_to_idx(cameras)
# TODO: mask?
# pyre-ignore
weights = th.softmax(self.weights_raw[idxs], dim=-1)
weights = weights.reshape(B, 3, 1, 1, 1)
return (
weights[:, 0] * img
+ weights[:, 1] * gaussian_blur(img, [3, 3])
+ weights[:, 2] * gaussian_blur(img, [7, 7])
)
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