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| author | Laurent Mazare <laurent.mazare@gmail.com> | 2023-08-20 23:19:15 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-08-20 23:19:15 +0100 |
| commit | 11c7e7bd672bea0da05207d8fdea0dfe8bb14e46 (patch) | |
| tree | 5090e6f687fb8e3819867d00224cb13556036bfd /candle-nn/tests | |
| parent | a1812f934f4e0830ed3c2f147d13837ccf67f2bd (diff) | |
| download | candle-11c7e7bd672bea0da05207d8fdea0dfe8bb14e46.tar.gz candle-11c7e7bd672bea0da05207d8fdea0dfe8bb14e46.tar.bz2 candle-11c7e7bd672bea0da05207d8fdea0dfe8bb14e46.zip | |
Some fixes for yolo-v3. (#529)
* Some fixes for yolo-v3.
* Use the running stats for inference in the batch-norm layer.
* Get some proper predictions for yolo.
* Avoid the quadratic insertion.
Diffstat (limited to 'candle-nn/tests')
| -rw-r--r-- | candle-nn/tests/batch_norm.rs | 14 |
1 files changed, 9 insertions, 5 deletions
diff --git a/candle-nn/tests/batch_norm.rs b/candle-nn/tests/batch_norm.rs index d5c72dfc..7a3cfc18 100644 --- a/candle-nn/tests/batch_norm.rs +++ b/candle-nn/tests/batch_norm.rs @@ -7,8 +7,8 @@ extern crate accelerate_src; mod test_utils; use anyhow::Result; -use candle::{Device, Tensor}; -use candle_nn::{BatchNorm, Module}; +use candle::{DType, Device, Tensor}; +use candle_nn::BatchNorm; /* The test below has been generated using the following PyTorch code: import torch @@ -21,7 +21,9 @@ print(output.flatten()) */ #[test] fn batch_norm() -> Result<()> { - let bn = BatchNorm::new_no_bias(5, 1e-8)?; + let running_mean = Tensor::zeros(5, DType::F32, &Device::Cpu)?; + let running_var = Tensor::ones(5, DType::F32, &Device::Cpu)?; + let bn = BatchNorm::new_no_bias(5, running_mean.clone(), running_var.clone(), 1e-8)?; let input: [f32; 120] = [ -0.7493, -1.0410, 1.6977, -0.6579, 1.7982, -0.0087, 0.2812, -0.1190, 0.2908, -0.5975, -0.0278, -0.2138, -1.3130, -1.6048, -2.2028, 0.9452, 0.4002, 0.0831, 1.0004, 0.1860, @@ -37,7 +39,7 @@ fn batch_norm() -> Result<()> { 1.4252, -0.9115, -0.1093, -0.3100, -0.6734, -1.4357, 0.9205, ]; let input = Tensor::new(&input, &Device::Cpu)?.reshape((2, 5, 3, 4))?; - let output = bn.forward(&input)?; + let output = bn.forward_learning(&input)?; assert_eq!(output.dims(), &[2, 5, 3, 4]); let output = output.flatten_all()?; assert_eq!( @@ -59,11 +61,13 @@ fn batch_norm() -> Result<()> { ); let bn2 = BatchNorm::new( 5, + running_mean.clone(), + running_var.clone(), Tensor::new(&[0.5f32], &Device::Cpu)?.broadcast_as(5)?, Tensor::new(&[-1.5f32], &Device::Cpu)?.broadcast_as(5)?, 1e-8, )?; - let output2 = bn2.forward(&input)?; + let output2 = bn2.forward_learning(&input)?; assert_eq!(output2.dims(), &[2, 5, 3, 4]); let output2 = output2.flatten_all()?; let diff2 = ((output2 - (output * 0.5)?)? + 1.5)?.sqr()?; |