fix math formula for conv1d and conv2d (pytorch#12740)

Summary:
- fix math formula
- test plan: build html and view on a browser
Pull Request resolved: pytorch#12740

Differential Revision: D10419430

Pulled By: weiyangfb

fbshipit-source-id: b8eee9e75c3ce6e37535e3de597431ef5030e9ac

Author: weiyangfb

torch/nn/modules/conv.py

@@ -71,9 +71,9 @@ class Conv1d(_ConvNd):
     precisely described as:
 
     .. math::
-        \op{out}(N_i, C_{\text{out}_j}) = \op{bias}(C_{\text{out}_j}) +
-        \sum_{k = 0}^{C_{in} - 1} \op{weight}(C_{\text{out}_j}, k)
-        \star \op{input}(N_i, k)
+        \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
+        \sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k)
+        \star \text{input}(N_i, k)
 
     where :math:`\star` is the valid `cross-correlation`_ operator,
     :math:`N` is a batch size, :math:`C` denotes a number of channels,
@@ -190,8 +190,8 @@ class Conv2d(_ConvNd):
     can be precisely described as:
 
     .. math::
-        \op{out}(N_i, C_{\text{out}_j}) = \op{bias}(C_{\text{out}_j}) +
-        \sum_{k = 0}^{C_{\text{in}} - 1} \op{weight}(C_{\text{out}_j}, k) \star \op{input}(N_i, k)
+        \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
+        \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)
 
 
     where :math:`\star` is the valid 2D `cross-correlation`_ operator,