Add Efficientnet (#572)

* EfficientNet.

* Complete the efficientnet implementation.

* Improve group handling.

* Get the efficientnet to work.

candle-core/src/conv.rs

@@ -93,8 +93,8 @@ impl Tensor {
         let params = ParamsConv1D {
             b_size,
             l_in,
-            c_out,
+            c_out: c_out / groups,
-            c_in,
+            c_in: c_in / groups,
             k_size,
             padding,
             stride,
@@ -103,9 +103,11 @@ impl Tensor {
             self.conv1d_single_group(kernel, &params)
         } else {
             let blocks = self.chunk(groups, 1)?;
+            let kernel = kernel.chunk(groups, 0)?;
             let blocks = blocks
                 .iter()
-                .map(|block| block.conv1d_single_group(kernel, &params))
+                .zip(&kernel)
+                .map(|(block, kernel)| block.conv1d_single_group(kernel, &params))
                 .collect::<Result<Vec<_>>>()?;
             Tensor::cat(&blocks, 1)
         }
@@ -146,8 +148,8 @@ impl Tensor {
             i_w,
             k_h,
             k_w,
-            c_out,
+            c_out: c_out / groups,
-            c_in,
+            c_in: c_in / groups,
             padding,
             stride,
         };
@@ -155,9 +157,11 @@ impl Tensor {
             self.conv2d_single_group(kernel, &params)
         } else {
             let blocks = self.chunk(groups, 1)?;
+            let kernel = kernel.chunk(groups, 0)?;
             let blocks = blocks
                 .iter()
-                .map(|block| block.conv2d_single_group(kernel, &params))
+                .zip(&kernel)
+                .map(|(block, kernel)| block.conv2d_single_group(kernel, &params))
                 .collect::<Result<Vec<_>>>()?;
             Tensor::cat(&blocks, 1)
         }

candle-examples/examples/efficientnet/main.rs

@@ -0,0 +1,419 @@
+//! EfficientNet implementation.
+//!
+//! https://arxiv.org/abs/1905.11946
+
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn as nn;
+use nn::{Module, VarBuilder};
+
+// Based on the Python version from torchvision.
+// https://github.com/pytorch/vision/blob/0d75d9e5516f446c9c0ef93bd4ed9fea13992d06/torchvision/models/efficientnet.py#L47
+#[derive(Debug, Clone, Copy)]
+pub struct MBConvConfig {
+    expand_ratio: f64,
+    kernel: usize,
+    stride: usize,
+    input_channels: usize,
+    out_channels: usize,
+    num_layers: usize,
+}
+
+fn make_divisible(v: f64, divisor: usize) -> usize {
+    let min_value = divisor;
+    let new_v = usize::max(
+        min_value,
+        (v + divisor as f64 * 0.5) as usize / divisor * divisor,
+    );
+    if (new_v as f64) < 0.9 * v {
+        new_v + divisor
+    } else {
+        new_v
+    }
+}
+
+fn bneck_confs(width_mult: f64, depth_mult: f64) -> Vec<MBConvConfig> {
+    let bneck_conf = |e, k, s, i, o, n| {
+        let input_channels = make_divisible(i as f64 * width_mult, 8);
+        let out_channels = make_divisible(o as f64 * width_mult, 8);
+        let num_layers = (n as f64 * depth_mult).ceil() as usize;
+        MBConvConfig {
+            expand_ratio: e,
+            kernel: k,
+            stride: s,
+            input_channels,
+            out_channels,
+            num_layers,
+        }
+    };
+    vec![
+        bneck_conf(1., 3, 1, 32, 16, 1),
+        bneck_conf(6., 3, 2, 16, 24, 2),
+        bneck_conf(6., 5, 2, 24, 40, 2),
+        bneck_conf(6., 3, 2, 40, 80, 3),
+        bneck_conf(6., 5, 1, 80, 112, 3),
+        bneck_conf(6., 5, 2, 112, 192, 4),
+        bneck_conf(6., 3, 1, 192, 320, 1),
+    ]
+}
+
+impl MBConvConfig {
+    fn b0() -> Vec<Self> {
+        bneck_confs(1.0, 1.0)
+    }
+    fn b1() -> Vec<Self> {
+        bneck_confs(1.0, 1.1)
+    }
+    fn b2() -> Vec<Self> {
+        bneck_confs(1.1, 1.2)
+    }
+    fn b3() -> Vec<Self> {
+        bneck_confs(1.2, 1.4)
+    }
+    fn b4() -> Vec<Self> {
+        bneck_confs(1.4, 1.8)
+    }
+    fn b5() -> Vec<Self> {
+        bneck_confs(1.6, 2.2)
+    }
+    fn b6() -> Vec<Self> {
+        bneck_confs(1.8, 2.6)
+    }
+    fn b7() -> Vec<Self> {
+        bneck_confs(2.0, 3.1)
+    }
+}
+
+/// Conv2D with same padding.
+#[derive(Debug)]
+struct Conv2DSame {
+    conv2d: nn::Conv2d,
+    s: usize,
+    k: usize,
+}
+
+impl Conv2DSame {
+    fn new(
+        vb: VarBuilder,
+        i: usize,
+        o: usize,
+        k: usize,
+        stride: usize,
+        groups: usize,
+        bias: bool,
+    ) -> Result<Self> {
+        let conv_config = nn::Conv2dConfig {
+            stride,
+            groups,
+            ..Default::default()
+        };
+        let conv2d = if bias {
+            nn::conv2d(i, o, k, conv_config, vb)?
+        } else {
+            nn::conv2d_no_bias(i, o, k, conv_config, vb)?
+        };
+        Ok(Self {
+            conv2d,
+            s: stride,
+            k,
+        })
+    }
+}
+
+impl Module for Conv2DSame {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let s = self.s;
+        let k = self.k;
+        let (_, _, ih, iw) = xs.dims4()?;
+        let oh = (ih + s - 1) / s;
+        let ow = (iw + s - 1) / s;
+        let pad_h = usize::max((oh - 1) * s + k - ih, 0);
+        let pad_w = usize::max((ow - 1) * s + k - iw, 0);
+        if pad_h > 0 || pad_w > 0 {
+            let xs = xs.pad_with_zeros(2, pad_h / 2, pad_h - pad_h / 2)?;
+            let xs = xs.pad_with_zeros(3, pad_w / 2, pad_w - pad_w / 2)?;
+            self.conv2d.forward(&xs)
+        } else {
+            self.conv2d.forward(xs)
+        }
+    }
+}
+
+#[derive(Debug)]
+struct ConvNormActivation {
+    conv2d: Conv2DSame,
+    bn2d: nn::BatchNorm,
+    activation: bool,
+}
+
+impl ConvNormActivation {
+    fn new(
+        vb: VarBuilder,
+        i: usize,
+        o: usize,
+        k: usize,
+        stride: usize,
+        groups: usize,
+    ) -> Result<Self> {
+        let conv2d = Conv2DSame::new(vb.pp("0"), i, o, k, stride, groups, false)?;
+        let bn2d = nn::batch_norm(o, 1e-3, vb.pp("1"))?;
+        Ok(Self {
+            conv2d,
+            bn2d,
+            activation: true,
+        })
+    }
+
+    fn no_activation(self) -> Self {
+        Self {
+            activation: false,
+            ..self
+        }
+    }
+}
+
+impl Module for ConvNormActivation {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.conv2d.forward(xs)?;
+        let xs = self.bn2d.forward(&xs)?;
+        if self.activation {
+            swish(&xs)
+        } else {
+            Ok(xs)
+        }
+    }
+}
+
+#[derive(Debug)]
+struct SqueezeExcitation {
+    fc1: Conv2DSame,
+    fc2: Conv2DSame,
+}
+
+impl SqueezeExcitation {
+    fn new(vb: VarBuilder, in_channels: usize, squeeze_channels: usize) -> Result<Self> {
+        let fc1 = Conv2DSame::new(vb.pp("fc1"), in_channels, squeeze_channels, 1, 1, 1, true)?;
+        let fc2 = Conv2DSame::new(vb.pp("fc2"), squeeze_channels, in_channels, 1, 1, 1, true)?;
+        Ok(Self { fc1, fc2 })
+    }
+}
+
+impl Module for SqueezeExcitation {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let residual = xs;
+        // equivalent to adaptive_avg_pool2d([1, 1])
+        let xs = xs.mean_keepdim(D::Minus2)?.mean_keepdim(D::Minus1)?;
+        let xs = self.fc1.forward(&xs)?;
+        let xs = swish(&xs)?;
+        let xs = self.fc2.forward(&xs)?;
+        let xs = nn::ops::sigmoid(&xs)?;
+        residual.broadcast_mul(&xs)
+    }
+}
+
+#[derive(Debug)]
+struct MBConv {
+    expand_cna: Option<ConvNormActivation>,
+    depthwise_cna: ConvNormActivation,
+    squeeze_excitation: SqueezeExcitation,
+    project_cna: ConvNormActivation,
+    config: MBConvConfig,
+}
+
+impl MBConv {
+    fn new(vb: VarBuilder, c: MBConvConfig) -> Result<Self> {
+        let vb = vb.pp("block");
+        let exp = make_divisible(c.input_channels as f64 * c.expand_ratio, 8);
+        let expand_cna = if exp != c.input_channels {
+            Some(ConvNormActivation::new(
+                vb.pp("0"),
+                c.input_channels,
+                exp,
+                1,
+                1,
+                1,
+            )?)
+        } else {
+            None
+        };
+        let start_index = if expand_cna.is_some() { 1 } else { 0 };
+        let depthwise_cna =
+            ConvNormActivation::new(vb.pp(start_index), exp, exp, c.kernel, c.stride, exp)?;
+        let squeeze_channels = usize::max(1, c.input_channels / 4);
+        let squeeze_excitation =
+            SqueezeExcitation::new(vb.pp(start_index + 1), exp, squeeze_channels)?;
+        let project_cna =
+            ConvNormActivation::new(vb.pp(start_index + 2), exp, c.out_channels, 1, 1, 1)?
+                .no_activation();
+        Ok(Self {
+            expand_cna,
+            depthwise_cna,
+            squeeze_excitation,
+            project_cna,
+            config: c,
+        })
+    }
+}
+
+impl Module for MBConv {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let use_res_connect =
+            self.config.stride == 1 && self.config.input_channels == self.config.out_channels;
+        let ys = match &self.expand_cna {
+            Some(expand_cna) => expand_cna.forward(xs)?,
+            None => xs.clone(),
+        };
+        let ys = self.depthwise_cna.forward(&ys)?;
+        let ys = self.squeeze_excitation.forward(&ys)?;
+        let ys = self.project_cna.forward(&ys)?;
+        if use_res_connect {
+            ys + xs
+        } else {
+            Ok(ys)
+        }
+    }
+}
+
+fn swish(s: &Tensor) -> Result<Tensor> {
+    s * nn::ops::sigmoid(s)?
+}
+
+#[derive(Debug)]
+struct EfficientNet {
+    init_cna: ConvNormActivation,
+    blocks: Vec<MBConv>,
+    final_cna: ConvNormActivation,
+    classifier: nn::Linear,
+}
+
+impl EfficientNet {
+    fn new(p: VarBuilder, configs: Vec<MBConvConfig>, nclasses: usize) -> Result<Self> {
+        let f_p = p.pp("features");
+        let first_in_c = configs[0].input_channels;
+        let last_out_c = configs.last().unwrap().out_channels;
+        let final_out_c = 4 * last_out_c;
+        let init_cna = ConvNormActivation::new(f_p.pp(0), 3, first_in_c, 3, 2, 1)?;
+        let nconfigs = configs.len();
+        let mut blocks = vec![];
+        for (index, cnf) in configs.into_iter().enumerate() {
+            let f_p = f_p.pp(index + 1);
+            for r_index in 0..cnf.num_layers {
+                let cnf = if r_index == 0 {
+                    cnf
+                } else {
+                    MBConvConfig {
+                        input_channels: cnf.out_channels,
+                        stride: 1,
+                        ..cnf
+                    }
+                };
+                blocks.push(MBConv::new(f_p.pp(r_index), cnf)?)
+            }
+        }
+        let final_cna =
+            ConvNormActivation::new(f_p.pp(nconfigs + 1), last_out_c, final_out_c, 1, 1, 1)?;
+        let classifier = nn::linear(final_out_c, nclasses, p.pp("classifier.1"))?;
+        Ok(Self {
+            init_cna,
+            blocks,
+            final_cna,
+            classifier,
+        })
+    }
+}
+
+impl Module for EfficientNet {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = self.init_cna.forward(xs)?;
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        let xs = self.final_cna.forward(&xs)?;
+        // Equivalent to adaptive_avg_pool2d([1, 1]) -> squeeze(-1) -> squeeze(-1)
+        let xs = xs.mean(D::Minus1)?.mean(D::Minus1)?;
+        self.classifier.forward(&xs)
+    }
+}
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    B0,
+    B1,
+    B2,
+    B3,
+    B4,
+    B5,
+    B6,
+    B7,
+}
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    #[arg(long)]
+    image: String,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Variant of the model to use.
+    #[arg(value_enum, long, default_value_t = Which::B2)]
+    which: Which,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+
+    let device = candle_examples::device(args.cpu)?;
+
+    let image = candle_examples::imagenet::load_image224(args.image)?;
+    println!("loaded image {image:?}");
+
+    let model_file = match args.model {
+        None => {
+            let api = hf_hub::api::sync::Api::new()?;
+            let api = api.model("lmz/candle-dino-v2".into());
+            api.get("dinov2_vits14.safetensors")?
+        }
+        Some(model) => model.into(),
+    };
+    let weights = unsafe { candle::safetensors::MmapedFile::new(model_file)? };
+    let weights = weights.deserialize()?;
+    let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+    let cfg = match args.which {
+        Which::B0 => MBConvConfig::b0(),
+        Which::B1 => MBConvConfig::b1(),
+        Which::B2 => MBConvConfig::b2(),
+        Which::B3 => MBConvConfig::b3(),
+        Which::B4 => MBConvConfig::b4(),
+        Which::B5 => MBConvConfig::b5(),
+        Which::B6 => MBConvConfig::b6(),
+        Which::B7 => MBConvConfig::b7(),
+    };
+    let model = EfficientNet::new(vb, cfg, candle_examples::imagenet::CLASS_COUNT as usize)?;
+    println!("model built");
+    let logits = model.forward(&image.unsqueeze(0)?)?;
+    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
+        .i(0)?
+        .to_vec1::<f32>()?;
+    let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
+    prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
+    for &(category_idx, pr) in prs.iter().take(5) {
+        println!(
+            "{:24}: {:.2}%",
+            candle_examples::imagenet::CLASSES[category_idx],
+            100. * pr
+        );
+    }
+    Ok(())
+}

candle-nn/src/conv.rs

@@ -124,7 +124,11 @@ pub fn conv1d(
     vs: crate::VarBuilder,
 ) -> Result<Conv1d> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vs.get_or_init((out_channels, in_channels, kernel_size), "weight", init_ws)?;
+    let ws = vs.get_or_init(
+        (out_channels, in_channels / cfg.groups, kernel_size),
+        "weight",
+        init_ws,
+    )?;
     let bound = 1. / (in_channels as f64).sqrt();
     let init_bs = crate::Init::Uniform {
         lo: -bound,
@@ -143,7 +147,12 @@ pub fn conv2d(
 ) -> Result<Conv2d> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
     let ws = vs.get_or_init(
-        (out_channels, in_channels, kernel_size, kernel_size),
+        (
+            out_channels,
+            in_channels / cfg.groups,
+            kernel_size,
+            kernel_size,
+        ),
         "weight",
         init_ws,
     )?;
@@ -165,7 +174,12 @@ pub fn conv2d_no_bias(
 ) -> Result<Conv2d> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
     let ws = vs.get_or_init(
-        (out_channels, in_channels, kernel_size, kernel_size),
+        (
+            out_channels,
+            in_channels / cfg.groups,
+            kernel_size,
+            kernel_size,
+        ),
         "weight",
         init_ws,
     )?;

candle-nn/src/var_builder.rs

@@ -129,7 +129,7 @@ impl<'a> VarBuilder<'a> {
         })
     }
 
-    pub fn push_prefix(&self, s: &str) -> Self {
+    pub fn push_prefix<S: ToString>(&self, s: S) -> Self {
         let mut path = self.path.clone();
         path.push(s.to_string());
         Self {
@@ -139,7 +139,7 @@ impl<'a> VarBuilder<'a> {
     }
 
     /// Short alias for `push_prefix`.
-    pub fn pp(&self, s: &str) -> Self {
+    pub fn pp<S: ToString>(&self, s: S) -> Self {
         self.push_prefix(s)
     }