Pick commits from 2.x branch (#2284)

* [Fix] Fix the calculation error of out_w in MaskedConv2dFunction (#2264)

* [Fix] Fix two intergers can not use / operator in torch1.6

* should not use kernel_h when calculates out_w

* [Docs] Fix some mistakes in data_process.md (#2283)

* correction

* correction en docs
Co-authored-by: yxzhao <33384732+yxzhao2022@users.noreply.github.com>

docs/en/understand_mmcv/data_process.md

@@ -135,7 +135,7 @@ bboxes = np.array([[10, 10, 100, 120], [0, 0, 50, 50]])
 patches = mmcv.imcrop(img, bboxes)
 
 # crop two regions, and rescale the patches by 1.2x
-patches = mmcv.imcrop(img, bboxes, scale_ratio=1.2)
+patches = mmcv.imcrop(img, bboxes, scale=1.2)
 ```
 
 #### Padding
@@ -150,14 +150,14 @@ img = mmcv.imread('tests/data/color.jpg')
 img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=0)
 
 # pad the image to (1000, 1200) with different values for three channels.
-img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=[100, 50, 200])
+img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=(100, 50, 200))
 
 # pad the image on left, right, top, bottom borders with all zeros
 img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=0)
 
 # pad the image on left, right, top, bottom borders with different values
 # for three channels.
-img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=[100, 50, 200])
+img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=(100, 50, 200))
 
 # pad an image so that each edge is a multiple of some value.
 img_ = mmcv.impad_to_multiple(img, 32)

docs/zh_cn/understand_mmcv/data_process.md

@@ -130,7 +130,7 @@ bboxes = np.array([[10, 10, 100, 120], [0, 0, 50, 50]])
 patches = mmcv.imcrop(img, bboxes)
 
 # 裁剪两个区域并且缩放区域1.2倍
-patches = mmcv.imcrop(img, bboxes, scale_ratio=1.2)
+patches = mmcv.imcrop(img, bboxes, scale=1.2)
 ```
 
 #### 填充
@@ -144,13 +144,13 @@ img = mmcv.imread('tests/data/color.jpg')
 img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=0)
 
 # 用给定值分别填充图像的3个通道至 (1000, 1200)
-img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=[100, 50, 200])
+img_ = mmcv.impad(img, shape=(1000, 1200), pad_val=(100, 50, 200))
 
 # 用给定值填充图像的左、右、上、下四条边
 img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=0)
 
 # 用3个值分别填充图像的左、右、上、下四条边的3个通道
-img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=[100, 50, 200])
+img_ = mmcv.impad(img, padding=(10, 20, 30, 40), pad_val=(100, 50, 200))
 
 # 将图像的四条边填充至能够被给定值整除
 img_ = mmcv.impad_to_multiple(img, 32)

mmcv/ops/masked_conv.py

@@ -47,11 +47,13 @@ class MaskedConv2dFunction(Function):
 
         batch_size = features.size(0)
         out_h = int(
-            math.floor((features.size(2) + 2 * pad_h -
-                        (kernel_h - 1) - 1) / stride_h + 1))
+            math.floor(
+                torch.true_divide((features.size(2) + 2 * pad_h -
+                                   (kernel_h - 1) - 1), stride_h) + 1))
         out_w = int(
-            math.floor((features.size(3) + 2 * pad_w -
-                        (kernel_h - 1) - 1) / stride_w + 1))
+            math.floor(
+                torch.true_divide((features.size(3) + 2 * pad_w -
+                                   (kernel_w - 1) - 1), stride_w) + 1))
         mask_inds = torch.nonzero(mask[0] > 0, as_tuple=False)
         output = features.new_zeros(batch_size, out_channel, out_h, out_w)
         if mask_inds.numel() > 0: