Support ResNet 50/101/152. (#1130)

This commit is contained in:
Laurent Mazare
2023-10-19 10:48:31 +01:00
committed by GitHub
parent 6f76383f38
commit cd53c472df

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@ -129,3 +129,121 @@ pub fn resnet34(num_classes: usize, vb: VarBuilder) -> Result<Func> {
pub fn resnet34_no_final_layer(vb: VarBuilder) -> Result<Func> {
resnet(None, 3, 4, 6, 3, vb)
}
// Bottleneck versions for ResNet 50, 101, and 152.
fn bottleneck_block(
c_in: usize,
c_out: usize,
stride: usize,
e: usize,
vb: VarBuilder,
) -> Result<Func> {
let e_dim = e * c_out;
let conv1 = conv2d(c_in, c_out, 1, 0, 1, vb.pp("conv1"))?;
let bn1 = batch_norm(c_out, 1e-5, vb.pp("bn1"))?;
let conv2 = conv2d(c_out, c_out, 3, 1, stride, vb.pp("conv2"))?;
let bn2 = batch_norm(c_out, 1e-5, vb.pp("bn2"))?;
let conv3 = conv2d(c_out, e_dim, 1, 0, 1, vb.pp("conv3"))?;
let bn3 = batch_norm(e_dim, 1e-5, vb.pp("bn3"))?;
let downsample = downsample(c_in, e_dim, stride, vb.pp("downsample"))?;
Ok(Func::new(move |xs| {
let ys = xs
.apply(&conv1)?
.apply(&bn1)?
.relu()?
.apply(&conv2)?
.apply(&bn2)?
.relu()?
.apply(&conv3)?
.apply(&bn3)?;
(xs.apply(&downsample)? + ys)?.relu()
}))
}
fn bottleneck_layer(
c_in: usize,
c_out: usize,
stride: usize,
cnt: usize,
vb: VarBuilder,
) -> Result<Func> {
let mut layers = Vec::with_capacity(cnt);
for index in 0..cnt {
let l_in = if index == 0 { c_in } else { 4 * c_out };
let stride = if index == 0 { stride } else { 1 };
layers.push(bottleneck_block(l_in, c_out, stride, 4, vb.pp(index))?)
}
Ok(Func::new(move |xs| {
let mut xs = xs.clone();
for layer in layers.iter() {
xs = xs.apply(layer)?
}
Ok(xs)
}))
}
fn bottleneck_resnet(
nclasses: Option<usize>,
c1: usize,
c2: usize,
c3: usize,
c4: usize,
vb: VarBuilder,
) -> Result<Func> {
let conv1 = conv2d(3, 64, 7, 3, 2, vb.pp("conv1"))?;
let bn1 = batch_norm(64, 1e-5, vb.pp("bn1"))?;
let layer1 = bottleneck_layer(64, 64, 1, c1, vb.pp("layer1"))?;
let layer2 = bottleneck_layer(4 * 64, 128, 2, c2, vb.pp("layer2"))?;
let layer3 = bottleneck_layer(4 * 128, 256, 2, c3, vb.pp("layer3"))?;
let layer4 = bottleneck_layer(4 * 256, 512, 2, c4, vb.pp("layer4"))?;
let fc = match nclasses {
None => None,
Some(nclasses) => {
let linear = candle_nn::linear(4 * 512, nclasses, vb.pp("fc"))?;
Some(linear)
}
};
Ok(Func::new(move |xs| {
let xs = xs
.apply(&conv1)?
.apply(&bn1)?
.relu()?
.pad_with_same(D::Minus1, 1, 1)?
.pad_with_same(D::Minus2, 1, 1)?
.max_pool2d_with_stride(3, 2)?
.apply(&layer1)?
.apply(&layer2)?
.apply(&layer3)?
.apply(&layer4)?
.mean(D::Minus1)?
.mean(D::Minus1)?;
match &fc {
None => Ok(xs),
Some(fc) => xs.apply(fc),
}
}))
}
pub fn resnet50(num_classes: usize, vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(Some(num_classes), 3, 4, 6, 3, vb)
}
pub fn resnet50_no_final_layer(vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(None, 3, 4, 6, 3, vb)
}
pub fn resnet101(num_classes: usize, vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(Some(num_classes), 3, 4, 23, 3, vb)
}
pub fn resnet101_no_final_layer(vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(None, 3, 4, 23, 3, vb)
}
pub fn resnet152(num_classes: usize, vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(Some(num_classes), 3, 8, 36, 3, vb)
}
pub fn resnet152_no_final_layer(vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(None, 3, 8, 36, 3, vb)
}