Revert "changed narrow to standardize negative length and negative offset beh…"revert-1113-narrow_edit

3 files changed, 59 insertions, 68 deletions

diff --git a/Narrow.lua b/Narrow.lua
index 01be934..a6ebaa3 100644
--- a/Narrow.lua
+++ b/Narrow.lua
@@ -13,16 +13,14 @@ end
 function Narrow:updateOutput(input)
    local dim = self.dimension < 0 and input:dim() + self.dimension + 1 or self.dimension
    local length = self.length
-   local index = self.index
-
-   if index < 0 then
-      index = input:size(dim) + self.index + 1
-   end
-
    if length < 0 then
-      length = input:size(dim) - index + 1 - torch.abs(length)
+      length = input:size(dim) - self.index + self.length + 2
+   end
+   local index = self.index
+   if self.index < 0 then
+      index = 1
+      length = input:size(dim) - length
    end
-
    local output=input:narrow(dim, index, length)
    self.output = self.output:typeAs(output)
    self.output:resizeAs(output):copy(output)
@@ -32,16 +30,14 @@ end
 function Narrow:updateGradInput(input, gradOutput)
    local dim = self.dimension < 0 and input:dim() + self.dimension + 1 or self.dimension
    local length = self.length
-   local index = self.index
-
-   if index < 0 then
-      index = input:size(dim) + self.index + 1
-   end
-
    if length < 0 then
-      length = input:size(dim) - index + 1 - torch.abs(length)
+      length = input:size(dim) - self.index + self.length + 2
+   end
+   local index = self.index
+   if self.index < 0 then
+      index = 1
+      length = input:size(dim) - length
    end
-
    self.gradInput = self.gradInput:typeAs(input)
    self.gradInput:resizeAs(input):zero()
    self.gradInput:narrow(dim,index,length):copy(gradOutput)
diff --git a/doc/simple.md b/doc/simple.md
index 6b0e4d5..09c60ca 100644
--- a/doc/simple.md
+++ b/doc/simple.md
@@ -685,45 +685,47 @@ Narrow is application of [narrow](https://github.com/torch/torch7/blob/master/do
 > x = torch.rand(4, 5)
 
 > x
- 0.2746  0.8704  0.6839  0.9137  0.5994
- 0.6099  0.6365  0.0923  0.0795  0.4404
- 0.3270  0.9202  0.6142  0.8548  0.8239
- 0.7058  0.6300  0.8553  0.7736  0.3567
+ 0.3695  0.2017  0.4485  0.4638  0.0513
+ 0.9222  0.1877  0.3388  0.6265  0.5659
+ 0.8785  0.7394  0.8265  0.9212  0.0129
+ 0.2290  0.7971  0.2113  0.1097  0.3166
 [torch.DoubleTensor of size 4x5]
 
 > nn.Narrow(1, 2, 3):forward(x)
- 0.6099  0.6365  0.0923  0.0795  0.4404
- 0.3270  0.9202  0.6142  0.8548  0.8239
- 0.7058  0.6300  0.8553  0.7736  0.3567
+ 0.9222  0.1877  0.3388  0.6265  0.5659
+ 0.8785  0.7394  0.8265  0.9212  0.0129
+ 0.2290  0.7971  0.2113  0.1097  0.3166
 [torch.DoubleTensor of size 3x5]
 
 > nn.Narrow(1, 2, -1):forward(x)
- 0.6099  0.6365  0.0923  0.0795  0.4404
- 0.3270  0.9202  0.6142  0.8548  0.8239
-[torch.DoubleTensor of size 2x5]
+ 0.9222  0.1877  0.3388  0.6265  0.5659
+ 0.8785  0.7394  0.8265  0.9212  0.0129
+ 0.2290  0.7971  0.2113  0.1097  0.3166
+[torch.DoubleTensor of size 3x5]
 
 > nn.Narrow(1, 2, 2):forward(x)
- 0.6099  0.6365  0.0923  0.0795  0.4404
- 0.3270  0.9202  0.6142  0.8548  0.8239
+ 0.9222  0.1877  0.3388  0.6265  0.5659
+ 0.8785  0.7394  0.8265  0.9212  0.0129
 [torch.DoubleTensor of size 2x5]
 
 > nn.Narrow(1, 2, -2):forward(x)
- 0.6099  0.6365  0.0923  0.0795  0.4404
-[torch.DoubleTensor of size 1x5]
-
-> nn.Narrow(2,2,3):forward(x)
- 0.8704  0.6839  0.9137
- 0.6365  0.0923  0.0795
- 0.9202  0.6142  0.8548
- 0.6300  0.8553  0.7736
+ 0.9222  0.1877  0.3388  0.6265  0.5659
+ 0.8785  0.7394  0.8265  0.9212  0.0129
+[torch.DoubleTensor of size 2x5]
+
+> nn.Narrow(2, 2, 3):forward(x)
+ 0.2017  0.4485  0.4638
+ 0.1877  0.3388  0.6265
+ 0.7394  0.8265  0.9212
+ 0.7971  0.2113  0.1097
 [torch.DoubleTensor of size 4x3]
 
 > nn.Narrow(2, 2, -2):forward(x)
- 0.8704  0.6839
- 0.6365  0.0923
- 0.9202  0.6142
- 0.6300  0.8553
-[torch.DoubleTensor of size 4x2]
+ 0.2017  0.4485  0.4638
+ 0.1877  0.3388  0.6265
+ 0.7394  0.8265  0.9212
+ 0.7971  0.2113  0.1097
+[torch.DoubleTensor of size 4x3]
 ```
 
 <a name="nn.Replicate"></a>
diff --git a/test.lua b/test.lua
index f985f2d..b19d6b3 100644
--- a/test.lua
+++ b/test.lua
@@ -5854,10 +5854,9 @@ function nntest.Narrow()
    local module1 = nn.Narrow(1, 3, 5)
    local output1 = module1:forward(input)
    local gradInput1 = module1:backward(input, gradOutput)
-   local module2 = nn.Narrow(1, 3, -2)
+   local module2 = nn.Narrow(1, 3, -3)
    local output2 = module2:forward(input)
    local gradInput2 = module2:backward(input, gradOutput)
-
    mytester:assertTensorEq(output, output1, 0.0000001, "Narrow #1 output err")
    mytester:assertTensorEq(gradInput, gradInput1, 0.00001, "Narrow #1 gradInput err")
    mytester:assertTensorEq(output, output2, 0.0000001, "Narrow #1 negative output err")
@@ -5872,7 +5871,7 @@ function nntest.Narrow()
    local module1 = nn.Narrow(2, 5, 3)
    local output1 = module1:forward(input)
    local gradInput1 = module1:backward(input, gradOutput)
-   local module2 = nn.Narrow(2, 5, -3)
+   local module2 = nn.Narrow(2, 5, -4)
    local output2 = module2:forward(input)
    local gradInput2 = module2:backward(input, gradOutput)
    mytester:assertTensorEq(output, output1, 0.0000001, "Narrow #2 output err")
@@ -5889,7 +5888,7 @@ function nntest.Narrow()
    local module1 = nn.Narrow(3, 1, 1)
    local output1 = module1:forward(input)
    local gradInput1 = module1:backward(input, gradOutput)
-   local module2 = nn.Narrow(3, 1, -6)
+   local module2 = nn.Narrow(3, 1, -7)
    local output2 = module2:forward(input)
    local gradInput2 = module2:backward(input, gradOutput)
    mytester:assertTensorEq(output, output1, 0.0000001, "Narrow #3 output err")
@@ -5897,28 +5896,22 @@ function nntest.Narrow()
    mytester:assertTensorEq(output, output2, 0.0000001, "Narrow #3 negative output err")
    mytester:assertTensorEq(gradInput, gradInput2, 0.00001, "Narrow #3 negative gradInput err")
 
-   -- check narrow negative offset and negative length
-   local input = torch.Tensor({1, 2, 3, 4, 5})
-
-   local output = torch.Tensor({2})
-   local modelOutput = nn.Narrow(1, 2, 1):forward(input)
-   mytester:assertTensorEq(output, modelOutput, 0.0000001, "Narrow #4.1 output err")
-
-   local output = torch.Tensor({4})
-   local modelOutput = nn.Narrow(1, -2, 1):forward(input)
-   mytester:assertTensorEq(output, modelOutput, 0.0000001, "Narrow #4.2 output err")
-
-   local output = torch.Tensor({5})
-   local modelOutput = nn.Narrow(1, -1, 1):forward(input)
-   mytester:assertTensorEq(output, modelOutput, 0.0000001, "Narrow #4.3 output err")
-
-   local output = torch.Tensor({4})
-   local modelOutput = nn.Narrow(1, -2, -1):forward(input)
-   mytester:assertTensorEq(output, modelOutput, 0.0000001, "Narrow #4.4 output err")
-
-   local output = torch.Tensor({2, 3, 4})
-   local modelOutput = nn.Narrow(1, 2, -1):forward(input)
-   mytester:assertTensorEq(output, modelOutput, 0.0000001, "Narrow #4.5 output err")
+   -- check basic narrow functionality #4
+   local input = torch.rand(3, 10, 4)
+   local output = input:narrow(2, 5, 3)
+   local gradOutput = torch.rand(3, 3, 4)
+   local gradInput = torch.zeros(3, 10, 4)
+   gradInput:narrow(2, 5, 3):copy(gradOutput)
+   local module1 = nn.Narrow(-2, 5, 3)
+   local output1 = module1:forward(input)
+   local gradInput1 = module1:backward(input, gradOutput)
+   local module2 = nn.Narrow(-2, 5, -4)
+   local output2 = module2:forward(input)
+   local gradInput2 = module2:backward(input, gradOutput)
+   mytester:assertTensorEq(output, output1, 0.0000001, "Narrow #4 output err")
+   mytester:assertTensorEq(gradInput, gradInput1, 0.00001, "Narrow #4 gradInput err")
+   mytester:assertTensorEq(output, output2, 0.0000001, "Narrow #4 negative output err")
+   mytester:assertTensorEq(gradInput, gradInput2, 0.00001, "Narrow #4 negative gradInput err")
 
    -- check narrow negative offset
    local input = torch.rand(3, 10, 4)
@@ -5926,7 +5919,7 @@ function nntest.Narrow()
    local gradOutput = torch.rand(3, 3, 4)
    local gradInput = torch.zeros(3, 10, 4)
    gradInput:narrow(2, 1, 3):copy(gradOutput)
-   local module1 = nn.Narrow(2, -10, 3)
+   local module1 = nn.Narrow(2, -1, 7)
    local output1 = module1:forward(input)
    local gradInput1 = module1:backward(input, gradOutput)
    local module2 = nn.Narrow(2, 1, 3)