feat/pooling: stride before padding argument

The original issue is autumnai/collenchyma-nn#9

Author: Bernhard Schuster

src/frameworks/cuda/mod.rs

@@ -911,8 +911,8 @@ impl<T> Pooling<T> for Backend<Cuda>
 {
     fn new_pooling_config(&self,
                           window: &[i32],
-                          padding: &[i32],
-                          stride: &[i32])
+                          stride: &[i32],
+                          padding: &[i32])
                           -> Result<Self::CPOOL, ::co::error::Error> {
         let pooling_avg = ::cudnn::PoolingDescriptor::new(::cudnn::cudnnPoolingMode_t::CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING, window, padding, stride).unwrap();
         let pooling_max =

src/frameworks/native/mod.rs

@@ -412,13 +412,13 @@ impl<T> ::plugin::Pooling<T> for Backend<Native>
 {
     fn new_pooling_config(&self,
                           window: &[i32],
-                          padding: &[i32],
-                          stride: &[i32])
+                          stride: &[i32],
+                          padding: &[i32])
                           -> Result<Self::CPOOL, ::co::error::Error> {
         Ok(helper::PoolingConfig {
                window: window.to_vec(),
-               padding: padding.to_vec(),
                stride: stride.to_vec(),
+               padding: padding.to_vec(),
            })
     }
 
@@ -513,8 +513,8 @@ impl<T> ::plugin::Pooling<T> for Backend<Native>
                       input_idx_base: &mut [usize],
                       window: &[i32],
                       depth: usize,
-                      padding: &[i32],
                       stride: &[i32],
+                      padding: &[i32],
                       output: &mut [T],
                       output_stride: &[usize],
                       output_dim: &[usize],
@@ -541,8 +541,8 @@ impl<T> ::plugin::Pooling<T> for Backend<Native>
                             input_idx_base,
                             window,
                             depth + 1,
-                            padding,
                             &stride[1..],
+                            padding,
                             output,
                             &output_stride[1..],
                             &output_dim[1..],
@@ -570,8 +570,8 @@ impl<T> ::plugin::Pooling<T> for Backend<Native>
         output_idx.resize(output_dim.len(), 0);
 
         let window = &config.window[..];
-        let padding = &config.padding[..];
         let stride = &config.stride[..];
+        let padding = &config.padding[..];
         // do everything for each batch
         for batch in 0..input_dim[0] {
             // iterate over the batches!
@@ -591,8 +591,8 @@ impl<T> ::plugin::Pooling<T> for Backend<Native>
                         &mut input_idx,
                         &window,
                         0,
-                        &padding,
                         &stride,
+                        &padding,
                         output,
                         &output_stride[2..],
                         &output_dim[2..],

src/plugin.rs

@@ -409,7 +409,7 @@ pub trait LRN<F> : NN<F> {
 /// Provides the functionality for a Backend to support Pooling operations.
 pub trait Pooling<F> : NN<F> {
     /// Creates a new PoolingConfig, which needs to be passed to further pooling Operations.
-    fn new_pooling_config(&self, window: &[i32], padding: &[i32], stride: &[i32])
+    fn new_pooling_config(&self, window: &[i32], stride: &[i32], padding: &[i32])
                           -> Result<Self::CPOOL, ::co::error::Error>;
 
     /// Computes non-linear down-sampling ([max Pooling][pooling]) over the input Tensor `x`.

src/tests/pooling.rs

@@ -49,7 +49,7 @@ pub fn test_pooling_max<T, F: IFramework>(backend: Backend<F>)
 
     let x  = filled_tensor(&backend,&[4, 4, 4, 4], &inp);
     let mut r = SharedTensor::<T>::new(&[4, 4, 2, 4]);
-    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[0, 0], &[2, 1])
+    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[2, 1], &[0, 0])
         .unwrap();
 
     backend.pooling_max(&x, &mut r, &conf).unwrap();
@@ -69,7 +69,7 @@ pub fn test_pooling_max_grad<T, F: IFramework>(backend: Backend<F>)
     let dx = filled_tensor(&backend,&[4, 4, 4, 4], &inp);
     let r  = filled_tensor(&backend,&[4, 4, 2, 2], &inp[0..64]);
     let mut dr = SharedTensor::<T>::new(&[4, 4, 2, 2]);
-    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[0, 0], &[2, 2])
+    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[2, 2], &[0, 0])
         .unwrap();
 
     backend.pooling_max_grad(&x, &dx, &r, &mut dr, &conf).unwrap();
@@ -90,7 +90,7 @@ pub fn test_pooling_avg<T, F: IFramework>(backend: Backend<F>)
 
     let x  = filled_tensor(&backend, &[4, 4, 4, 4], &inp);
     let mut r = SharedTensor::<T>::new(&[4, 4, 2, 2]);
-    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[0, 0], &[2, 2])
+    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[2, 2], &[0, 0])
         .unwrap();
 
     backend.pooling_avg(&x, &mut r, &conf).unwrap();
@@ -111,7 +111,7 @@ pub fn test_pooling_avg_grad<T, F: IFramework>(backend: Backend<F>)
     let dx = filled_tensor(&backend, &[8, 4, 4, 4], &inp);
     let r  = filled_tensor(&backend, &[8, 4, 2, 2], &inp[0..128]);
     let mut dr = SharedTensor::<T>::new(&[8, 4, 2, 2]);
-    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[0, 0], &[2, 2])
+    let conf = Pooling::<T>::new_pooling_config(&backend, &[2, 2], &[2, 2], &[0, 0])
         .unwrap();
 
     backend.pooling_avg_grad(&x, &dx, &r, &mut dr, &conf).unwrap();
@@ -124,6 +124,36 @@ pub fn test_pooling_avg_grad<T, F: IFramework>(backend: Backend<F>)
     tensor_assert_eq(&dr, &dr_test, 1.0);
 }
 
+pub fn cross_test_pooling_max<F: IFramework, G: IFramework>(backend_a: Backend<F>, backend_b: Backend<G>)
+        where
+          Backend<F>: Pooling<f32> + IBackend,
+          Backend<G>: Pooling<f32> + IBackend {
+
+    let mut inp = vec![1.0; 256];
+    inp[0] = 2.0;
+
+    let lower : f32 = -128.;
+    let upper : f32 = 127.;
+    let x = uniformly_random_tensor(&backend_a, &[4, 4, 4, 4], lower, upper);
+
+    let mut r_a = SharedTensor::<f32>::new(&[4, 4, 2, 4]);
+    let mut r_b = SharedTensor::<f32>::new(&[4, 4, 2, 4]);
+
+    let conf_a = Pooling::<f32>::new_pooling_config(&backend_a, &[2, 2], &[2, 1], &[0, 0])
+        .unwrap();
+    let conf_b = Pooling::<f32>::new_pooling_config(&backend_b, &[2, 2], &[2, 1], &[0, 0])
+        .unwrap();
+
+    backend_a.pooling_max(&x, &mut r_a, &conf_a).unwrap();
+    backend_b.pooling_max(&x, &mut r_b, &conf_b).unwrap();
+    tensor_assert_eq_tensor(&r_a, &r_b, 3.0);
+}
+
+mod cross {
+    use super::*;
+    test_cross!(cross_test_pooling_max, cross_test_pooling_max_f32);
+}
+
 mod cuda {
     use super::*;
     test_cuda!(test_pooling_avg, pooling_avg_f32, pooling_avg_f64);