pep8

theano/misc/tests/test_pycuda_example.py

@@ -5,7 +5,8 @@ import theano.misc.pycuda_init
 
 if not theano.misc.pycuda_init.pycuda_available:
     from nose.plugins.skip import SkipTest
-    raise SkipTest("Pycuda not installed. Skip test of theano op with pycuda code.")
+    raise SkipTest("Pycuda not installed. Skip test of theano op"
+                   " with pycuda code.")
 
 import theano.sandbox.cuda as cuda_ndarray
 if cuda_ndarray.cuda_available == False:
@@ -14,71 +15,89 @@ if cuda_ndarray.cuda_available == False:
 
 import theano
 import theano.tensor as T
-from theano.misc.pycuda_example import PycudaElemwiseSourceModuleOp, PycudaElemwiseKernelOp, PycudaElemwiseSourceModuleMakeThunkOp
+from theano.misc.pycuda_example import (PycudaElemwiseSourceModuleOp,
+                                        PycudaElemwiseKernelOp,
+                                        PycudaElemwiseSourceModuleMakeThunkOp)
 
-if theano.config.mode=='FAST_COMPILE':
+if theano.config.mode == 'FAST_COMPILE':
     mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
-    mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
+    mode_without_gpu = theano.compile.mode.get_mode(
+        'FAST_RUN').excluding('gpu')
 else:
     mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
     mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
 
+
 def test_pycuda_elemwise_source_module():
-    for shape in [(5,5), (10,49), (50,49),(500,501),(5000,5001)]:
+    for shape in [(5, 5), (10, 49), (50, 49), (500, 501), (5000, 5001)]:
         for op in [theano.scalar.basic.mul, theano.scalar.basic.add]:
-            x=T.fmatrix('x')
-            y=T.fmatrix('y')
+            x = T.fmatrix('x')
+            y = T.fmatrix('y')
             elemwise_op = theano.tensor.Elemwise(op)
             pycuda_op = PycudaElemwiseSourceModuleOp(op)
             pycuda_op_thunk = PycudaElemwiseSourceModuleMakeThunkOp(op)
-            f=theano.function([x,y], elemwise_op(x,y), mode=mode_with_gpu)
-            f2 = theano.function([x,y], theano.sandbox.cuda.host_from_gpu(pycuda_op(x,y)))
-            f3 = theano.function([x,y], elemwise_op(x,y),
-                                 mode=mode_with_gpu.including("local_pycuda_gpu_elemwise"))
-            f4 = theano.function([x,y], theano.sandbox.cuda.host_from_gpu(pycuda_op_thunk(x,y)))
+            f = theano.function([x, y], elemwise_op(x, y), mode=mode_with_gpu)
+            f2 = theano.function([x, y],
+                                 theano.sandbox.cuda.host_from_gpu(
+                                     pycuda_op(x, y)))
+            mode_pycuda = mode_with_gpu.including("local_pycuda_gpu_elemwise")
+            f3 = theano.function([x, y], elemwise_op(x, y),
+                                 mode=mode_pycuda)
+            f4 = theano.function([x, y],
+                                 theano.sandbox.cuda.host_from_gpu(
+                                     pycuda_op_thunk(x, y)))
 
-            assert any([ isinstance(node.op, theano.sandbox.cuda.GpuElemwise) for node in f.maker.env.toposort()])
-            assert any([ isinstance(node.op, PycudaElemwiseSourceModuleOp) for node in f2.maker.env.toposort()])
-            assert any([ isinstance(node.op, PycudaElemwiseSourceModuleOp) for node in f3.maker.env.toposort()])
-            assert any([ isinstance(node.op, PycudaElemwiseSourceModuleMakeThunkOp) for node in f4.maker.env.toposort()])
+            assert any([isinstance(node.op, theano.sandbox.cuda.GpuElemwise)
+                        for node in f.maker.env.toposort()])
+            assert any([isinstance(node.op, PycudaElemwiseSourceModuleOp)
+                        for node in f2.maker.env.toposort()])
+            assert any([isinstance(node.op, PycudaElemwiseSourceModuleOp)
+                        for node in f3.maker.env.toposort()])
+            assert any([isinstance(node.op,
+                                   PycudaElemwiseSourceModuleMakeThunkOp)
+                        for node in f4.maker.env.toposort()])
 
             val1 = numpy.asarray(numpy.random.rand(*shape), dtype='float32')
             val2 = numpy.asarray(numpy.random.rand(*shape), dtype='float32')
-            assert (f(val1,val2) == f2(val1,val2)).all()
-            assert (f(val1,val2) == f3(val1,val2)).all()
-            assert (f(val1,val2) == f4(val1,val2)).all()
+            assert (f(val1, val2) == f2(val1, val2)).all()
+            assert (f(val1, val2) == f3(val1, val2)).all()
+            assert (f(val1, val2) == f4(val1, val2)).all()
             #print f(val1,val2)
             #print f2(val1,val2)
 
+
 def test_pycuda_elemwise_kernel():
-    x=T.fmatrix('x')
-    y=T.fmatrix('y')
-    f=theano.function([x,y],x+y, mode=mode_with_gpu)
+    x = T.fmatrix('x')
+    y = T.fmatrix('y')
+    f = theano.function([x, y], x + y, mode=mode_with_gpu)
     print f.maker.env.toposort()
-    f2 = theano.function([x,y],x+y, mode=mode_with_gpu.including("local_pycuda_gpu_elemwise_kernel"))
+    mode_pycuda = mode_with_gpu.including("local_pycuda_gpu_elemwise_kernel")
+    f2 = theano.function([x, y], x + y, mode=mode_pycuda)
     print f2.maker.env.toposort()
 
-    assert any([ isinstance(node.op, theano.sandbox.cuda.GpuElemwise) for node in f.maker.env.toposort()])
-    assert any([ isinstance(node.op, PycudaElemwiseKernelOp) for node in f2.maker.env.toposort()])
+    assert any([isinstance(node.op, theano.sandbox.cuda.GpuElemwise)
+                for node in f.maker.env.toposort()])
+    assert any([isinstance(node.op, PycudaElemwiseKernelOp)
+                for node in f2.maker.env.toposort()])
 
-    val1 = numpy.asarray(numpy.random.rand(5,5), dtype='float32')
-    val2 = numpy.asarray(numpy.random.rand(5,5), dtype='float32')
+    val1 = numpy.asarray(numpy.random.rand(5, 5), dtype='float32')
+    val2 = numpy.asarray(numpy.random.rand(5, 5), dtype='float32')
     #val1 = numpy.ones((5,5))
     #val2 = numpy.arange(25).reshape(5,5)
-    assert (f(val1,val2) == f2(val1,val2)).all()
-    print f(val1,val2)
-    print f2(val1,val2)
-
+    assert (f(val1, val2) == f2(val1, val2)).all()
+    print f(val1, val2)
+    print f2(val1, val2)
 
-    x3=T.ftensor3('x')
-    y3=T.ftensor3('y')
-    z3=T.ftensor3('y')
+    x3 = T.ftensor3('x')
+    y3 = T.ftensor3('y')
+    z3 = T.ftensor3('y')
 
-    f4 = theano.function([x3,y3,z3],x3*y3+z3, mode=mode_with_gpu.including("local_pycuda_gpu_elemwise_kernel"))
+    f4 = theano.function([x3, y3, z3], x3 * y3 + z3, mode=mode_pycuda)
     print f4.maker.env.toposort()
-    assert any([ isinstance(node.op, PycudaElemwiseKernelOp) for node in f4.maker.env.toposort()])
+    assert any([isinstance(node.op, PycudaElemwiseKernelOp)
+                for node in f4.maker.env.toposort()])
 
-    val1 = numpy.random.rand(2,2,2)
+    val1 = numpy.random.rand(2, 2, 2)
     print val1
-    print f4(val1,val1,val1)
-    assert numpy.allclose(f4(val1,val1,val1),val1*val1+val1)
+    print f4(val1, val1, val1)
+    assert numpy.allclose(f4(val1, val1, val1), val1 * val1 + val1)