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| author | Laurent Mazare <laurent.mazare@gmail.com> | 2023-08-29 16:12:11 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-08-29 16:12:11 +0100 |
| commit | a044907ffce553a0394db3a1204f21e3691e54af (patch) | |
| tree | 8ce11fae8ee11e4eb181f7240344994356625791 /candle-core/examples | |
| parent | ee8bb1bde1a44738c314dfaacba743f4eabf917c (diff) | |
| download | candle-a044907ffce553a0394db3a1204f21e3691e54af.tar.gz candle-a044907ffce553a0394db3a1204f21e3691e54af.tar.bz2 candle-a044907ffce553a0394db3a1204f21e3691e54af.zip | |
Dilated convolutions (#657)
* Add the dilation parameter.
* Restore the basic optimizer example.
* Dilation support in cudnn.
* Use the dilation parameter in the cpu backend.
* More dilation support.
* No support for dilation in transposed convolutions.
* Add dilation to a test.
* Remove a print.
* Helper function.
Diffstat (limited to 'candle-core/examples')
| -rw-r--r-- | candle-core/examples/basics.rs | 2 | ||||
| -rw-r--r-- | candle-core/examples/cpu_benchmarks.rs | 4 | ||||
| -rw-r--r-- | candle-core/examples/cuda_basics.rs | 6 |
3 files changed, 6 insertions, 6 deletions
diff --git a/candle-core/examples/basics.rs b/candle-core/examples/basics.rs index 9d4734de..ad008177 100644 --- a/candle-core/examples/basics.rs +++ b/candle-core/examples/basics.rs @@ -11,7 +11,7 @@ fn main() -> Result<()> { let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?; let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?; let start = std::time::Instant::now(); - let res = inp.conv2d(&w, 0, 1, 1)?; + let res = inp.conv2d(&w, 0, 1, 1, 1)?; println!("{:?}", start.elapsed()); println!("{res:?}"); Ok(()) diff --git a/candle-core/examples/cpu_benchmarks.rs b/candle-core/examples/cpu_benchmarks.rs index 1ebd9b75..13175ac1 100644 --- a/candle-core/examples/cpu_benchmarks.rs +++ b/candle-core/examples/cpu_benchmarks.rs @@ -40,7 +40,7 @@ impl Benchmark for Conv1d { } fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> { - d.0.conv1d(&d.1, 0, 1, 1) + d.0.conv1d(&d.1, 0, 1, 1, 1) } const ITERS: usize = 5; @@ -59,7 +59,7 @@ impl Benchmark for Conv2d { } fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> { - d.0.conv2d(&d.1, 0, 1, 1) + d.0.conv2d(&d.1, 0, 1, 1, 1) } const ITERS: usize = 1; diff --git a/candle-core/examples/cuda_basics.rs b/candle-core/examples/cuda_basics.rs index cbdafd64..ad207461 100644 --- a/candle-core/examples/cuda_basics.rs +++ b/candle-core/examples/cuda_basics.rs @@ -11,11 +11,11 @@ fn main() -> Result<()> { let device = Device::new_cuda(0)?; let in_t = Tensor::rand(-1f32, 1f32, (1, 3, 12, 7), &device)?; let k_t = Tensor::rand(-1f32, 1f32, (6, 3, 1, 1), &device)?; - let out_t = in_t.conv2d(&k_t, 0, 1, 1)?; + let out_t = in_t.conv2d(&k_t, 0, 1, 1, 1)?; println!("{out_t}"); let in_t = in_t.to_device(&Device::Cpu)?; let k_t = k_t.to_device(&Device::Cpu)?; - let out_t2 = in_t.conv2d(&k_t, 0, 1, 1)?; + let out_t2 = in_t.conv2d(&k_t, 0, 1, 1, 1)?; let diff = (out_t.to_device(&Device::Cpu)? - out_t2)? .sqr()? .sum_all()?; @@ -23,7 +23,7 @@ fn main() -> Result<()> { let t = Tensor::randn(0f32, 1f32, (2, 4, 96, 96), &device)?; let w = Tensor::randn(0f32, 1f32, (320, 4, 3, 3), &device)?; - let res = t.conv2d(&w, 1, 1, 1)?; + let res = t.conv2d(&w, 1, 1, 1, 1)?; println!("{res:?}"); Ok(()) } |