Update opt to replace Conv3D, ConvGrad3D and ConvTransp3D instead of GpuConv3D,…

theano/sandbox/cuda/opt.py

@@ -1256,8 +1256,8 @@ def local_conv_fft_full(node):
 gpu_optimizer.register("conv_fft_valid", local_conv_fft_valid)
 gpu_optimizer.register("conv_fft_full", local_conv_fft_full)
 
-from theano.sandbox.cuda.GpuConv3D import GpuConv3D
-@local_optimizer([GpuConv3D])
+from theano.tensor.nnet.Conv3D import Conv3D
+@local_optimizer([Conv3D])
 def local_conv3d_fft(node):
     try:
         stride_x = tensor.get_scalar_constant_value(node.inputs[3][0])
@@ -1265,7 +1265,7 @@ def local_conv3d_fft(node):
         stride_z = tensor.get_scalar_constant_value(node.inputs[3][2])
     except tensor.NotScalarConstantError:
         return False
-    if (isinstance(node.op, GpuConv3D) and
+    if (isinstance(node.op, Conv3D) and
         (stride_x, stride_y, stride_z) == (1, 1, 1)):
         # we import conv3d_fft locally to avoid pycuda warnings
         from theano.sandbox.cuda.fftconv import conv3d_fft
@@ -1277,13 +1277,13 @@ def local_conv3d_fft(node):
         f = gpu_from_host(f.dimshuffle(0, 4, 1, 2, 3))
         rval = conv3d_fft(x, f, border_mode='valid', pad_last_dim=True)
         # Shuffle from (oc, c, 0, 1, t) to (oc, 0, 1, t, c)
-        return [gpu_from_host(rval.dimshuffle(0, 2, 3, 4, 1) + node.inputs[2])]
+        return [rval.dimshuffle(0, 2, 3, 4, 1) + node.inputs[2]]
 
 
 gpu_optimizer.register("conv3d_fft", local_conv3d_fft)
 
-from theano.sandbox.cuda.GpuConvGrad3D import GpuConvGrad3D
-@local_optimizer([GpuConvGrad3D])
+from theano.tensor.nnet.ConvGrad3D import ConvGrad3D
+@local_optimizer([ConvGrad3D])
 def local_convgrad3d_fft(node):
     try:
         stride_x = tensor.get_scalar_constant_value(node.inputs[1][0])
@@ -1291,7 +1291,7 @@ def local_convgrad3d_fft(node):
         stride_z = tensor.get_scalar_constant_value(node.inputs[1][2])
     except tensor.NotScalarConstantError:
         return False
-    if (isinstance(node.op, GpuConvGrad3D) and
+    if (isinstance(node.op, ConvGrad3D) and
         (stride_x, stride_y, stride_z) == (1, 1, 1)):
         # we import conv3d_fft locally to avoid pycuda warnings
         from theano.sandbox.cuda.fftconv import conv3d_fft
@@ -1303,13 +1303,13 @@ def local_convgrad3d_fft(node):
         f = f.dimshuffle(4, 0, 1, 2, 3)
         rval = conv3d_fft(x, f, border_mode='valid', pad_last_dim=True)
         # Shuffle from (ic, oc, 0, 1, t) to (oc, 0, 1, t, ic)
-        return [gpu_from_host(rval.dimshuffle(1, 2, 3, 4, 0))]
+        return [rval.dimshuffle(1, 2, 3, 4, 0)]
 
 
 gpu_optimizer.register("convgrad3d_fft", local_convgrad3d_fft)
 
-from theano.sandbox.cuda.GpuConvTransp3D import GpuConvTransp3D
-@local_optimizer([GpuConvTransp3D])
+from theano.tensor.nnet.ConvTransp3D import ConvTransp3D
+@local_optimizer([ConvTransp3D])
 def local_convtransp3d_fft(node):
     try:
         stride_x = tensor.get_scalar_constant_value(node.inputs[2][0])
@@ -1317,7 +1317,7 @@ def local_convtransp3d_fft(node):
         stride_z = tensor.get_scalar_constant_value(node.inputs[2][2])
     except tensor.NotScalarConstantError:
         return False
-    if (isinstance(node.op, GpuConvTransp3D) and
+    if (isinstance(node.op, ConvTransp3D) and
         (stride_x, stride_y, stride_z) == (1, 1, 1)):
         # we import conv3d_fft locally to avoid pycuda warnings
         from theano.sandbox.cuda.fftconv import conv3d_fft
@@ -1329,12 +1329,11 @@ def local_convtransp3d_fft(node):
         f = f.dimshuffle(0, 4, 1, 2, 3)
         rval = conv3d_fft(f, x, border_mode='full', pad_last_dim=True)
         # Shuffle from (ic, b, 0, 1, t) to (b, 0, 1, t, ic)
-        return [gpu_from_host(rval.dimshuffle(0, 2, 3, 4, 1))]
+        return [rval.dimshuffle(0, 2, 3, 4, 1)]
 
 gpu_optimizer.register("convtransp3d_fft", local_convtransp3d_fft)
 
 
-
 import theano.tensor.signal.downsample as downsample