Add documenation to some dnn functions and make the file importable without a GPU.

theano/sandbox/cuda/dnn.py

@@ -7,7 +7,8 @@ from theano.gof.type import CDataType
 from theano.compat import PY3
 from theano.compat.six import StringIO
 from theano.sandbox.cuda.type import CudaNdarrayType
-from theano.sandbox.cuda import GpuOp, active_device_number, device_properties
+from theano.sandbox.cuda import (GpuOp, cuda_available, active_device_number,
+                                 device_properties)
 from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
                                            gpu_contiguous)
 from theano.sandbox.cuda.blas import GpuConv
@@ -376,12 +377,21 @@ class GpuDnnConvGradI(GpuDnnConvBase):
     conv_op = 'cudnnConvolutionBackwardData'
 
 
-from theano.sandbox.cuda.opt import (local_optimizer, gpu_contiguous,
-                                     gpu_optimizer)
-
-
 def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
              conv_mode='conv'):
+    """
+    GPU convolution using cuDNN from NVIDIA.
+
+    :param img: images to do the convolution over
+    :param kerns: convolution filters
+    :param border_mode: one of 'valid', 'full' (default: 'valid')
+    :param subsample: perform subsampling of the output (default: (1, 1))
+    :param conv_mode: perform convolution (kernels flipped) or cross-correlation.  One of 'conv', 'cross'. (default: 'conv')
+
+    :warning: The cuDNN library only works with GPU that have a compute
+      capability of 3.0 or higer.  This means that older GPU will not
+      work with this Op.
+    """
     img = gpu_contiguous(img)
     kerns = gpu_contiguous(kerns)
     desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
@@ -389,20 +399,6 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
     return GpuDnnConv()(img, kerns, desc)
 
 
-@local_optimizer([GpuConv])
-def local_conv_dnn(node):
-    if isinstance(node.op, GpuConv):
-        if node.op.border_mode not in ['full', 'valid']:
-            return
-        img, kern = node.inputs
-        border_mode = node.op.border_mode
-        subsample = node.op.subsample
-        return [dnn_conv(gpu_contiguous(img), gpu_contiguous(kern),
-                         border_mode=border_mode, subsample=subsample)]
-
-gpu_optimizer.register("conv_cudnn", local_conv_dnn, 'cudnn')
-
-
 class GpuDnnSoftmax(DnnBase):
     """
     Op for the cuDNN Softmax.
@@ -555,12 +551,32 @@ err%(name)s = cudnnSoftmaxForward(
         return (0, 3)
 
 
-@local_optimizer([GpuSoftmax])
-def local_softmax_dnn(node):
+# We need this since other stuff from opt is not importable.
+if cuda_available:
+
+    from theano.sandbox.cuda.opt import (local_optimizer, gpu_contiguous,
+                                         gpu_optimizer)
+
+    @local_optimizer([GpuConv])
+    def local_conv_dnn(node):
+        if isinstance(node.op, GpuConv):
+            if node.op.border_mode not in ['full', 'valid']:
+                return
+            img, kern = node.inputs
+            border_mode = node.op.border_mode
+            subsample = node.op.subsample
+            return [dnn_conv(gpu_contiguous(img), gpu_contiguous(kern),
+                             border_mode=border_mode, subsample=subsample)]
+
+    gpu_optimizer.register("conv_cudnn", local_conv_dnn, 'cudnn')
+
+
+    @local_optimizer([GpuSoftmax])
+    def local_softmax_dnn(node):
         if isinstance(node.op, GpuSoftmax):
             ins = node.inputs[0].dimshuffle(0, 1, 'x', 'x')
             out = GpuDnnSoftmax('bc01', 'accurate', 'channel')(gpu_contiguous(ins))
             out = as_cuda_ndarray_variable(out.dimshuffle(0, 1))
             return [out]
 
-gpu_optimizer.register("softmax_cudnn", local_softmax_dnn, 'cudnn')
+    gpu_optimizer.register("softmax_cudnn", local_softmax_dnn, 'cudnn')