Skeleton for the avg-pool2d and upsample-nearest2d ops. (#337)

* Skeleton for the avg-pool2d and upsample-nearest2d ops.

* Preliminary conv2d support.

candle-core/src/tensor.rs

@@ -817,6 +817,35 @@ impl Tensor {
         Ok(from_storage(storage, out_dims, op, false))
     }
 
+    pub fn conv2d(&self, _kernel: &Self, _padding: usize, _stride: usize) -> Result<Self> {
+        todo!()
+    }
+
+    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+        let (n, c, _h, _w) = self.dims4()?;
+        let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
+        let storage = self
+            .storage()
+            .upsample_nearest2d(self.layout(), target_h, target_w)?;
+        Ok(from_storage(storage, (n, c, target_h, target_w), op, false))
+    }
+
+    pub fn avg_pool2d(&self, kernel_size: (usize, usize), stride: (usize, usize)) -> Result<Self> {
+        let (n, c, h, w) = self.dims4()?;
+        // https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d
+        let h_out = (h - kernel_size.0) / stride.0 + 1;
+        let w_out = (w - kernel_size.1) / stride.1 + 1;
+        let op = BackpropOp::new1(self, |arg| Op::AvgPool2D {
+            arg,
+            kernel_size,
+            stride,
+        });
+        let storage = self
+            .storage()
+            .avg_pool2d(self.layout(), kernel_size, stride)?;
+        Ok(from_storage(storage, (n, c, h_out, w_out), op, false))
+    }
+
     /// Returns the matrix-multiplication of the input tensor with the other provided tensor.
     ///
     /// # Arguments