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113 lines
3.5 KiB
Rust
113 lines
3.5 KiB
Rust
use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
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// https://github.com/huggingface/candle/issues/364
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fn avg_pool2d(dev: &Device) -> Result<()> {
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let data: Vec<f32> = vec![
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1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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];
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let t = Tensor::from_vec(data, (1, 1, 4, 4), dev)?;
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let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
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assert_eq!(pool.to_vec2::<f32>()?, [[0.5f32, 1.], [1., 1.]]);
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let data: Vec<f32> = vec![
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1., 2., 1., 3., 0., 0., 1., 1., 1., 1., 1., 1., 5., 1., 1., 1.,
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];
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let t = Tensor::from_vec(data, (1, 1, 2, 8), dev)?;
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let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
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assert_eq!(pool.to_vec2::<f32>()?, [[5. / 4., 6. / 4., 6. / 4., 1.]]);
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Ok(())
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}
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fn max_pool2d(dev: &Device) -> Result<()> {
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let data: Vec<f32> = vec![
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1., 2., 1., 3., 0., 0., 1., 1., 1., 1., 1., 1., 5., 1., 1., 1.,
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];
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let t = Tensor::from_vec(data, (1, 1, 4, 4), dev)?;
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let pool = t.max_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
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assert_eq!(pool.to_vec2::<f32>()?, [[2f32, 3.], [5., 1.]]);
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let t = t.reshape((1, 1, 2, 8))?;
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let pool = t.max_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
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assert_eq!(pool.to_vec2::<f32>()?, [[2.0, 3.0, 5.0, 1.0]]);
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Ok(())
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}
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/* This test corresponds to the following PyTorch script.
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import torch
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torch.manual_seed(4242)
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t = torch.randn((1, 2, 4, 4))
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print(t.flatten())
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res = torch.nn.functional.avg_pool2d(t, 2)
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print(res)
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*/
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fn avg_pool2d_pytorch(dev: &Device) -> Result<()> {
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let t = Tensor::new(
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&[
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0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,
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1.6541, 0.0964, -0.8338, -1.6523, -0.8323, -0.1699, 0.0823, 0.3526, 0.6843, 0.2395,
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1.2279, -0.9287, -1.7030, 0.1370, 0.6047, 0.3770, -0.6266, 0.3529, 2.2013, -0.6836,
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0.2477, 1.3127,
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],
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dev,
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)?
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.reshape((1, 2, 4, 4))?;
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let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?;
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assert_eq!(
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test_utils::to_vec3_round(&pool, 4)?,
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[
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[[-1.1926, -0.0395], [0.2688, 0.1871]],
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[[0.1835, -0.1606], [0.6249, 0.3217]]
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]
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);
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let pool = t.avg_pool2d((3, 3), (3, 3))?.squeeze(0)?;
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assert_eq!(
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test_utils::to_vec3_round(&pool, 4)?,
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[[[0.085]], [[0.0078]]]
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);
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let t = t.reshape((1, 1, 4, 8))?;
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let pool = t.avg_pool2d((2, 2), (2, 2))?.squeeze(0)?.squeeze(0)?;
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assert_eq!(
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test_utils::to_vec2_round(&pool, 4)?,
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[
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[0.7745, 0.0276, -1.6983, 0.12],
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[0.3542, 0.1625, 0.4542, -0.0014]
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]
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);
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Ok(())
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}
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fn upsample_nearest2d(dev: &Device) -> Result<()> {
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let t = Tensor::arange(0f32, 6f32, dev)?.reshape((1, 1, 2, 3))?;
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let upsampled = t.upsample_nearest2d(4, 6)?.i(0)?.i(0)?;
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assert_eq!(
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t.i(0)?.i(0)?.to_vec2::<f32>()?,
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[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]
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);
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assert_eq!(
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upsampled.to_vec2::<f32>()?,
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[
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[0.0, 0.0, 1.0, 1.0, 2.0, 2.0],
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[0.0, 0.0, 1.0, 1.0, 2.0, 2.0],
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[3.0, 3.0, 4.0, 4.0, 5.0, 5.0],
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[3.0, 3.0, 4.0, 4.0, 5.0, 5.0]
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]
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);
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Ok(())
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}
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test_device!(avg_pool2d, avg_pool2d_cpu, avg_pool2d_gpu);
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test_device!(
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avg_pool2d_pytorch,
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avg_pool2d_pytorch_cpu,
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avg_pool2d_pytorch_gpu
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);
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test_device!(max_pool2d, max_pool2d_cpu, max_pool2d_gpu);
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test_device!(
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upsample_nearest2d,
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upsample_nearest2d_cpu,
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upsample_nearest2d_gpu
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);
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