Merge pull request SeanNaren#52 from miguelvr/torch-update

Update to pytorch 0.4

README.md

@@ -6,7 +6,7 @@ This is an extension onto the original repo found [here](https://github.com/baid
 
 ## Installation
 
-Install [PyTorch](https://github.com/pytorch/pytorch#installation).
+Install [PyTorch](https://github.com/pytorch/pytorch#installation) v0.4.
 
 `WARP_CTC_PATH` should be set to the location of a built WarpCTC
 (i.e. `libwarpctc.so`).  This defaults to `../build`, so from within a
@@ -21,13 +21,13 @@ make
 ```
 
 Now install the bindings:
-```
+```bash
 cd pytorch_binding
 python setup.py install
 ```
 
 If you try the above and get a dlopen error on OSX with anaconda3 (as recommended by pytorch):
-```
+```bash
 cd ../pytorch_binding
 python setup.py install
 cd ../build
@@ -38,18 +38,17 @@ This will resolve the library not loaded error. This can be easily modified to w
 Example to use the bindings below.
 
 ```python
-    import torch
-    from torch.autograd import Variable
-    from warpctc_pytorch import CTCLoss
-    ctc_loss = CTCLoss()
-    # expected shape of seqLength x batchSize x alphabet_size
-    probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous()
-    labels = Variable(torch.IntTensor([1, 2]))
-    label_sizes = Variable(torch.IntTensor([2]))
-    probs_sizes = Variable(torch.IntTensor([2]))
-    probs = Variable(probs, requires_grad=True) # tells autograd to compute gradients for probs
-    cost = ctc_loss(probs, labels, probs_sizes, label_sizes)
-    cost.backward()
+import torch
+from warpctc_pytorch import CTCLoss
+ctc_loss = CTCLoss()
+# expected shape of seqLength x batchSize x alphabet_size
+probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous()
+labels = torch.IntTensor([1, 2])
+label_sizes = torch.IntTensor([2])
+probs_sizes = torch.IntTensor([2])
+probs.requires_grad_(True)  # tells autograd to compute gradients for probs
+cost = ctc_loss(probs, labels, probs_sizes, label_sizes)
+cost.backward()
 ```
 
 ## Documentation

pytorch_binding/warpctc_pytorch/__init__.py

@@ -1,7 +1,6 @@
 import torch
 import warpctc_pytorch as warp_ctc
 from torch.autograd import Function
-from torch.autograd import Variable
 from torch.nn import Module
 from torch.nn.modules.loss import _assert_no_grad
 
@@ -38,7 +37,7 @@ def forward(ctx, acts, labels, act_lens, label_lens, size_average=False,
             grads = grads / minibatch_size
             costs = costs / minibatch_size
 
-        ctx.grads = Variable(grads, volatile=True)
+        ctx.grads = grads
         return costs
 
     @staticmethod