mirror of
https://github.com/huggingface/candle.git
synced 2025-06-16 18:48:51 +00:00
430 lines
12 KiB
Rust
430 lines
12 KiB
Rust
//! EfficientNet implementation.
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//!
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//! https://arxiv.org/abs/1905.11946
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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#[cfg(feature = "accelerate")]
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extern crate accelerate_src;
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use clap::{Parser, ValueEnum};
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use candle::{DType, IndexOp, Result, Tensor, D};
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use candle_nn as nn;
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use nn::{Module, VarBuilder};
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// Based on the Python version from torchvision.
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// https://github.com/pytorch/vision/blob/0d75d9e5516f446c9c0ef93bd4ed9fea13992d06/torchvision/models/efficientnet.py#L47
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#[derive(Debug, Clone, Copy)]
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pub struct MBConvConfig {
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expand_ratio: f64,
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kernel: usize,
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stride: usize,
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input_channels: usize,
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out_channels: usize,
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num_layers: usize,
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}
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fn make_divisible(v: f64, divisor: usize) -> usize {
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let min_value = divisor;
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let new_v = usize::max(
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min_value,
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(v + divisor as f64 * 0.5) as usize / divisor * divisor,
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);
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if (new_v as f64) < 0.9 * v {
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new_v + divisor
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} else {
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new_v
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}
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}
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fn bneck_confs(width_mult: f64, depth_mult: f64) -> Vec<MBConvConfig> {
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let bneck_conf = |e, k, s, i, o, n| {
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let input_channels = make_divisible(i as f64 * width_mult, 8);
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let out_channels = make_divisible(o as f64 * width_mult, 8);
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let num_layers = (n as f64 * depth_mult).ceil() as usize;
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MBConvConfig {
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expand_ratio: e,
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kernel: k,
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stride: s,
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input_channels,
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out_channels,
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num_layers,
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}
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};
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vec![
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bneck_conf(1., 3, 1, 32, 16, 1),
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bneck_conf(6., 3, 2, 16, 24, 2),
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bneck_conf(6., 5, 2, 24, 40, 2),
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bneck_conf(6., 3, 2, 40, 80, 3),
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bneck_conf(6., 5, 1, 80, 112, 3),
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bneck_conf(6., 5, 2, 112, 192, 4),
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bneck_conf(6., 3, 1, 192, 320, 1),
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]
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}
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impl MBConvConfig {
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fn b0() -> Vec<Self> {
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bneck_confs(1.0, 1.0)
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}
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fn b1() -> Vec<Self> {
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bneck_confs(1.0, 1.1)
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}
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fn b2() -> Vec<Self> {
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bneck_confs(1.1, 1.2)
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}
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fn b3() -> Vec<Self> {
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bneck_confs(1.2, 1.4)
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}
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fn b4() -> Vec<Self> {
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bneck_confs(1.4, 1.8)
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}
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fn b5() -> Vec<Self> {
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bneck_confs(1.6, 2.2)
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}
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fn b6() -> Vec<Self> {
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bneck_confs(1.8, 2.6)
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}
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fn b7() -> Vec<Self> {
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bneck_confs(2.0, 3.1)
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}
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}
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/// Conv2D with same padding.
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#[derive(Debug)]
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struct Conv2DSame {
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conv2d: nn::Conv2d,
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s: usize,
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k: usize,
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}
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impl Conv2DSame {
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fn new(
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vb: VarBuilder,
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i: usize,
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o: usize,
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k: usize,
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stride: usize,
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groups: usize,
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bias: bool,
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) -> Result<Self> {
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let conv_config = nn::Conv2dConfig {
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stride,
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groups,
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..Default::default()
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};
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let conv2d = if bias {
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nn::conv2d(i, o, k, conv_config, vb)?
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} else {
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nn::conv2d_no_bias(i, o, k, conv_config, vb)?
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};
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Ok(Self {
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conv2d,
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s: stride,
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k,
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})
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}
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}
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impl Module for Conv2DSame {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let s = self.s;
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let k = self.k;
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let (_, _, ih, iw) = xs.dims4()?;
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let oh = (ih + s - 1) / s;
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let ow = (iw + s - 1) / s;
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let pad_h = usize::max((oh - 1) * s + k - ih, 0);
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let pad_w = usize::max((ow - 1) * s + k - iw, 0);
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if pad_h > 0 || pad_w > 0 {
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let xs = xs.pad_with_zeros(2, pad_h / 2, pad_h - pad_h / 2)?;
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let xs = xs.pad_with_zeros(3, pad_w / 2, pad_w - pad_w / 2)?;
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self.conv2d.forward(&xs)
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} else {
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self.conv2d.forward(xs)
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}
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}
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}
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#[derive(Debug)]
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struct ConvNormActivation {
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conv2d: Conv2DSame,
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bn2d: nn::BatchNorm,
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activation: bool,
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}
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impl ConvNormActivation {
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fn new(
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vb: VarBuilder,
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i: usize,
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o: usize,
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k: usize,
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stride: usize,
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groups: usize,
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) -> Result<Self> {
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let conv2d = Conv2DSame::new(vb.pp("0"), i, o, k, stride, groups, false)?;
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let bn2d = nn::batch_norm(o, 1e-3, vb.pp("1"))?;
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Ok(Self {
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conv2d,
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bn2d,
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activation: true,
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})
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}
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fn no_activation(self) -> Self {
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Self {
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activation: false,
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..self
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}
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}
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}
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impl Module for ConvNormActivation {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let xs = self.conv2d.forward(xs)?;
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let xs = self.bn2d.forward(&xs)?;
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if self.activation {
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swish(&xs)
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} else {
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Ok(xs)
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}
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}
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}
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#[derive(Debug)]
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struct SqueezeExcitation {
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fc1: Conv2DSame,
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fc2: Conv2DSame,
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}
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impl SqueezeExcitation {
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fn new(vb: VarBuilder, in_channels: usize, squeeze_channels: usize) -> Result<Self> {
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let fc1 = Conv2DSame::new(vb.pp("fc1"), in_channels, squeeze_channels, 1, 1, 1, true)?;
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let fc2 = Conv2DSame::new(vb.pp("fc2"), squeeze_channels, in_channels, 1, 1, 1, true)?;
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Ok(Self { fc1, fc2 })
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}
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}
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impl Module for SqueezeExcitation {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let residual = xs;
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// equivalent to adaptive_avg_pool2d([1, 1])
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let xs = xs.mean_keepdim(D::Minus2)?.mean_keepdim(D::Minus1)?;
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let xs = self.fc1.forward(&xs)?;
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let xs = swish(&xs)?;
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let xs = self.fc2.forward(&xs)?;
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let xs = nn::ops::sigmoid(&xs)?;
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residual.broadcast_mul(&xs)
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}
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}
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#[derive(Debug)]
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struct MBConv {
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expand_cna: Option<ConvNormActivation>,
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depthwise_cna: ConvNormActivation,
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squeeze_excitation: SqueezeExcitation,
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project_cna: ConvNormActivation,
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config: MBConvConfig,
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}
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impl MBConv {
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fn new(vb: VarBuilder, c: MBConvConfig) -> Result<Self> {
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let vb = vb.pp("block");
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let exp = make_divisible(c.input_channels as f64 * c.expand_ratio, 8);
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let expand_cna = if exp != c.input_channels {
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Some(ConvNormActivation::new(
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vb.pp("0"),
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c.input_channels,
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exp,
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1,
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1,
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1,
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)?)
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} else {
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None
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};
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let start_index = if expand_cna.is_some() { 1 } else { 0 };
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let depthwise_cna =
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ConvNormActivation::new(vb.pp(start_index), exp, exp, c.kernel, c.stride, exp)?;
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let squeeze_channels = usize::max(1, c.input_channels / 4);
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let squeeze_excitation =
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SqueezeExcitation::new(vb.pp(start_index + 1), exp, squeeze_channels)?;
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let project_cna =
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ConvNormActivation::new(vb.pp(start_index + 2), exp, c.out_channels, 1, 1, 1)?
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.no_activation();
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Ok(Self {
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expand_cna,
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depthwise_cna,
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squeeze_excitation,
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project_cna,
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config: c,
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})
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}
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}
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impl Module for MBConv {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let use_res_connect =
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self.config.stride == 1 && self.config.input_channels == self.config.out_channels;
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let ys = match &self.expand_cna {
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Some(expand_cna) => expand_cna.forward(xs)?,
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None => xs.clone(),
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};
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let ys = self.depthwise_cna.forward(&ys)?;
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let ys = self.squeeze_excitation.forward(&ys)?;
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let ys = self.project_cna.forward(&ys)?;
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if use_res_connect {
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ys + xs
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} else {
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Ok(ys)
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}
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}
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}
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fn swish(s: &Tensor) -> Result<Tensor> {
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s * nn::ops::sigmoid(s)?
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}
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#[derive(Debug)]
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struct EfficientNet {
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init_cna: ConvNormActivation,
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blocks: Vec<MBConv>,
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final_cna: ConvNormActivation,
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classifier: nn::Linear,
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}
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impl EfficientNet {
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fn new(p: VarBuilder, configs: Vec<MBConvConfig>, nclasses: usize) -> Result<Self> {
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let f_p = p.pp("features");
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let first_in_c = configs[0].input_channels;
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let last_out_c = configs.last().unwrap().out_channels;
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let final_out_c = 4 * last_out_c;
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let init_cna = ConvNormActivation::new(f_p.pp(0), 3, first_in_c, 3, 2, 1)?;
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let nconfigs = configs.len();
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let mut blocks = vec![];
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for (index, cnf) in configs.into_iter().enumerate() {
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let f_p = f_p.pp(index + 1);
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for r_index in 0..cnf.num_layers {
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let cnf = if r_index == 0 {
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cnf
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} else {
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MBConvConfig {
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input_channels: cnf.out_channels,
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stride: 1,
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..cnf
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}
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};
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blocks.push(MBConv::new(f_p.pp(r_index), cnf)?)
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}
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}
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let final_cna =
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ConvNormActivation::new(f_p.pp(nconfigs + 1), last_out_c, final_out_c, 1, 1, 1)?;
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let classifier = nn::linear(final_out_c, nclasses, p.pp("classifier.1"))?;
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Ok(Self {
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init_cna,
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blocks,
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final_cna,
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classifier,
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})
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}
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}
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impl Module for EfficientNet {
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fn forward(&self, xs: &Tensor) -> Result<Tensor> {
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let mut xs = self.init_cna.forward(xs)?;
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for block in self.blocks.iter() {
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xs = block.forward(&xs)?
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}
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let xs = self.final_cna.forward(&xs)?;
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// Equivalent to adaptive_avg_pool2d([1, 1]) -> squeeze(-1) -> squeeze(-1)
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let xs = xs.mean(D::Minus1)?.mean(D::Minus1)?;
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self.classifier.forward(&xs)
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}
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}
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#[derive(Clone, Copy, Debug, ValueEnum)]
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enum Which {
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B0,
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B1,
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B2,
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B3,
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B4,
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B5,
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B6,
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B7,
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}
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#[derive(Parser)]
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struct Args {
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#[arg(long)]
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model: Option<String>,
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#[arg(long)]
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image: String,
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/// Run on CPU rather than on GPU.
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#[arg(long)]
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cpu: bool,
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/// Variant of the model to use.
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#[arg(value_enum, long, default_value_t = Which::B2)]
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which: Which,
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}
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pub fn main() -> anyhow::Result<()> {
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let args = Args::parse();
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let device = candle_examples::device(args.cpu)?;
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let image = candle_examples::imagenet::load_image224(args.image)?;
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println!("loaded image {image:?}");
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let model_file = match args.model {
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None => {
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let api = hf_hub::api::sync::Api::new()?;
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let api = api.model("lmz/candle-efficientnet".into());
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let filename = match args.which {
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Which::B0 => "efficientnet-b0.safetensors",
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Which::B1 => "efficientnet-b1.safetensors",
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Which::B2 => "efficientnet-b2.safetensors",
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Which::B3 => "efficientnet-b3.safetensors",
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Which::B4 => "efficientnet-b4.safetensors",
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Which::B5 => "efficientnet-b5.safetensors",
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Which::B6 => "efficientnet-b6.safetensors",
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Which::B7 => "efficientnet-b7.safetensors",
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};
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api.get(filename)?
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}
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Some(model) => model.into(),
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};
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let weights = unsafe { candle::safetensors::MmapedFile::new(model_file)? };
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let weights = weights.deserialize()?;
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let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
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let cfg = match args.which {
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Which::B0 => MBConvConfig::b0(),
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Which::B1 => MBConvConfig::b1(),
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Which::B2 => MBConvConfig::b2(),
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Which::B3 => MBConvConfig::b3(),
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Which::B4 => MBConvConfig::b4(),
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Which::B5 => MBConvConfig::b5(),
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Which::B6 => MBConvConfig::b6(),
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Which::B7 => MBConvConfig::b7(),
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};
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let model = EfficientNet::new(vb, cfg, candle_examples::imagenet::CLASS_COUNT as usize)?;
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println!("model built");
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let logits = model.forward(&image.unsqueeze(0)?)?;
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let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
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.i(0)?
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.to_vec1::<f32>()?;
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let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
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prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
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for &(category_idx, pr) in prs.iter().take(5) {
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println!(
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"{:24}: {:.2}%",
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candle_examples::imagenet::CLASSES[category_idx],
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100. * pr
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);
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}
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Ok(())
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}
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