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94dc344 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# pyre-unsafe
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
import torch
from pytorch3d.implicitron.models.renderer.base import EvaluationMode
from pytorch3d.implicitron.tools.config import ReplaceableBase
from pytorch3d.renderer.cameras import CamerasBase
@dataclass
class ImplicitronRender:
"""
Holds the tensors that describe a result of rendering.
"""
depth_render: Optional[torch.Tensor] = None
image_render: Optional[torch.Tensor] = None
mask_render: Optional[torch.Tensor] = None
camera_distance: Optional[torch.Tensor] = None
def clone(self) -> "ImplicitronRender":
def safe_clone(t: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
return t.detach().clone() if t is not None else None
return ImplicitronRender(
depth_render=safe_clone(self.depth_render),
image_render=safe_clone(self.image_render),
mask_render=safe_clone(self.mask_render),
camera_distance=safe_clone(self.camera_distance),
)
class ImplicitronModelBase(ReplaceableBase, torch.nn.Module):
"""
Replaceable abstract base for all image generation / rendering models.
`forward()` method produces a render with a depth map. Derives from Module
so we can rely on basic functionality provided to torch for model
optimization.
"""
# The keys from `preds` (output of ImplicitronModelBase.forward) to be logged in
# the training loop.
log_vars: List[str] = field(default_factory=lambda: ["objective"])
def forward(
self,
*, # force keyword-only arguments
image_rgb: Optional[torch.Tensor],
camera: CamerasBase,
fg_probability: Optional[torch.Tensor],
mask_crop: Optional[torch.Tensor],
depth_map: Optional[torch.Tensor],
sequence_name: Optional[List[str]],
evaluation_mode: EvaluationMode = EvaluationMode.EVALUATION,
**kwargs,
) -> Dict[str, Any]:
"""
Args:
image_rgb: A tensor of shape `(B, 3, H, W)` containing a batch of rgb images;
the first `min(B, n_train_target_views)` images are considered targets and
are used to supervise the renders; the rest corresponding to the source
viewpoints from which features will be extracted.
camera: An instance of CamerasBase containing a batch of `B` cameras corresponding
to the viewpoints of target images, from which the rays will be sampled,
and source images, which will be used for intersecting with target rays.
fg_probability: A tensor of shape `(B, 1, H, W)` containing a batch of
foreground masks.
mask_crop: A binary tensor of shape `(B, 1, H, W)` deonting valid
regions in the input images (i.e. regions that do not correspond
to, e.g., zero-padding). When the `RaySampler`'s sampling mode is set to
"mask_sample", rays will be sampled in the non zero regions.
depth_map: A tensor of shape `(B, 1, H, W)` containing a batch of depth maps.
sequence_name: A list of `B` strings corresponding to the sequence names
from which images `image_rgb` were extracted. They are used to match
target frames with relevant source frames.
evaluation_mode: one of EvaluationMode.TRAINING or
EvaluationMode.EVALUATION which determines the settings used for
rendering.
Returns:
preds: A dictionary containing all outputs of the forward pass. All models should
output an instance of `ImplicitronRender` in `preds["implicitron_render"]`.
"""
raise NotImplementedError()
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