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Segment Anything - process images (#766)

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* Start processing images.

* Add LayerNorm2d.

* Properly use LayerNorm2d.

* Tweak eps.

* Use LayerNorm on inputs with a rank different from 3.

* Window partitioning.

* Fix a couple todos.

* More todos.

* Hard-code the einsums.

* More padding support.

* Some sizes tweaks.

* Use the hub to get the weights.

* Use a batch matmul.

* Tweaks.

* More fixes.

* Get some predictions to be generated.
This commit is contained in:
Laurent Mazare
2023-09-07 19:22:45 +01:00
committed by GitHub
parent 7b50f3e106
commit 7396b8ed1a
10 changed files with 303 additions and 105 deletions
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8
candle-nn/src/layer_norm.rs
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@ -28,7 +28,7 @@
//! ```
//!
//! [`Layer Normalization`]: https://arxiv.org/abs/1607.06450
use candle::{DType, Result, Tensor};
use candle::{DType, Result, Tensor, D};
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct LayerNormConfig {
@ -104,15 +104,15 @@ impl crate::Module for LayerNorm {
DType::F16 | DType::BF16 => DType::F32,
d => d,
};
let (_bsize, _seq_len, hidden_size) = x.dims3()?;
let hidden_size = x.dim(D::Minus1)?;
let x = x.to_dtype(internal_dtype)?;
let x = if self.remove_mean {
let mean_x = (x.sum_keepdim(2)? / hidden_size as f64)?;
let mean_x = (x.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
x.broadcast_sub(&mean_x)?
} else {
x
};
let norm_x = (x.sqr()?.sum_keepdim(2)? / hidden_size as f64)?;
let norm_x = (x.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
let x = x_normed.to_dtype(x_dtype)?.broadcast_mul(&self.weight)?;
match &self.bias {