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Summary: Collection of spelling things, mostly in docs / tutorials.

Reviewed By: gkioxari

Differential Revision: D26101323

fbshipit-source-id: 652f62bc9d71a4ff872efa21141225e43191353a
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Jeremy Reizenstein
2021-04-09 09:57:55 -07:00
committed by Facebook GitHub Bot
parent c2e62a5087
commit 124bb5e391
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docs/notes/batching.md
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@@ -5,7 +5,7 @@ sidebar_label: Batching
# Batching
In deep learning, every optimization step operates on multiple input examples for robust training. Thus, efficient batching is crucial. For image inputs, batching is straighforward; N images are resized to the same height and width and stacked as a 4 dimensional tensor of shape `N x 3 x H x W`. For meshes, batching is less straighforward.
In deep learning, every optimization step operates on multiple input examples for robust training. Thus, efficient batching is crucial. For image inputs, batching is straightforward; N images are resized to the same height and width and stacked as a 4 dimensional tensor of shape `N x 3 x H x W`. For meshes, batching is less straightforward.
<img src="assets/batch_intro.png" alt="batch_intro" align="middle"/>
@@ -21,7 +21,7 @@ Assume you want to construct a batch containing two meshes, with `mesh1 = (v1: V
## Use cases for batch modes
The need for different mesh batch modes is inherent to the way pytorch operators are implemented. To fully utilize the optimized pytorch ops, the [Meshes][meshes] data structure allows for efficient conversion between the different batch modes. This is crucial when aiming for a fast and efficient training cycle. An example of this is [Mesh R-CNN][meshrcnn]. Here, in the same forward pass different parts of the network assume different inputs, which are computed by converting between the different batch modes. In particular, [vert_align][vert_align] assumes a *padded* input tensor while immediately after [graph_conv][graphconv] assumes a *packed* input tensor.
The need for different mesh batch modes is inherent to the way PyTorch operators are implemented. To fully utilize the optimized PyTorch ops, the [Meshes][meshes] data structure allows for efficient conversion between the different batch modes. This is crucial when aiming for a fast and efficient training cycle. An example of this is [Mesh R-CNN][meshrcnn]. Here, in the same forward pass different parts of the network assume different inputs, which are computed by converting between the different batch modes. In particular, [vert_align][vert_align] assumes a *padded* input tensor while immediately after [graph_conv][graphconv] assumes a *packed* input tensor.
<img src="assets/meshrcnn.png" alt="meshrcnn" width="700" align="middle" />