Skip to content

P
pytensor
提交 4bf3c385 authored 作者: Frederic Bastien's avatar Frederic Bastien
浏览文件
操作

white space fix.

上级 ef1fa2fd
隐藏空白字符变更
内嵌 并排
正在显示 包含 31 行增加 和 34 行删除
theano/sandbox/cuda/tests/test_basic_ops.py
浏览文件 @ 4bf3c385
......@@ -43,7 +43,7 @@ def test_sum():
others implemented by reshape that are not tested
0011,0101,0110,1001,1010,1100
1110,1101,1011
TODO: test with broadcast
"""
for shape, pattern in [((100,3,1300),[1]),
......@@ -98,7 +98,7 @@ def test_sum():
assert f2(val)==f(val), ('shape', shape, 'pattern', pattern)
else:
assert _allclose(f2(val),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
#test with dimshuffle
#we shuffle the 2 outer dims.
......@@ -121,7 +121,7 @@ def test_sum():
assert tcn.GpuSum in [x.op.__class__ for x in f.maker.env.toposort()]
assert T.Sum in [x.op.__class__ for x in f2.maker.env.toposort()]
assert _allclose(f2(val),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
#test with broadcast
for shape, pattern in [((5,),[0]),
......@@ -155,7 +155,7 @@ def test_sum():
assert tcn.GpuSum in [x.op.__class__ for x in f2.maker.env.toposort()]
assert T.Sum in [x.op.__class__ for x in f.maker.env.toposort()]
assert _allclose(f2(val2),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
def test_flatten():
x = cuda.fmatrix('x')
f = theano.function([x], x.flatten())
......@@ -289,7 +289,7 @@ def test_elemwise2():
#let debugmode catch errors
print >> sys.stdout, 'pattern', pattern
f(theano._asarray(rng.rand(*shape),dtype='float32')*.3)
shape = (3,4,5,6)
a = tcn.shared_constructor(theano._asarray(rng.rand(*shape),dtype='float32'), 'a')
b = tensor.Tensor(dtype='float32', broadcastable=[0]*len(shape))()
......@@ -305,7 +305,7 @@ def test_elemwise2():
def test_elemwise3():
""" Several kinds of elemwise expressions with dimension permutations and broadcasting"""
shape = (3,4,5,6)
a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape), dtype='float32'), 'a')
b = tensor.fvector()
......@@ -326,7 +326,7 @@ def test_elemwise3():
def test_elemwise4():
""" Test that two vectors can be broadcast to form an outer product (by performing rank-1 matrix update"""
shape = (3,4)
a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape), dtype='float32'), 'a')
b = tensor.fvector()
......@@ -343,7 +343,7 @@ def test_elemwise4():
def speed_elemwise_collapse():
""" used to time if the collapse of ccontiguous dims are usefull """
shape = (30,40,50,600)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -361,13 +361,13 @@ def speed_elemwise_collapse():
print id, n
t1=time.time()
for i in range(100):
#let debugmode catch errors
f(v)
#let debugmode catch errors
f(v)
t2=time.time()
def speed_elemwise_collapse2():
""" used to test the speed up of the generalised collapse of ccontiguous dims"""
shape = (30,40,50,600)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -385,13 +385,13 @@ def speed_elemwise_collapse2():
print id, n
t1=time.time()
for i in range(100):
#let debugmode catch errors
f(v)
#let debugmode catch errors
f(v)
t2=time.time()
def test_elemwise_collapse():
""" Test when all inputs have one(and the same) broadcastable dimension """
shape = (4,5,60)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -414,7 +414,7 @@ def test_elemwise_collapse():
def test_elemwise_collapse2():
""" Test when only one inputs have one broadcastable dimension """
shape = (4,5,60)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -437,7 +437,7 @@ def test_elemwise_collapse2():
def test_elemwise_collapse3():
""" Test when only one inputs have two broadcastable dimension at each ends """
shape = (4,5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -460,7 +460,7 @@ def test_elemwise_collapse3():
def test_elemwise_collapse4():
""" Test when only one inputs have two broadcastable dimension at each ends and we add a scalar"""
shape = (4,5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -483,7 +483,7 @@ def test_elemwise_collapse4():
def test_elemwise_collapse5():
""" Test when only one inputs have two broadcastable dimension at the beginning and we add a scalar"""
shape = (4,5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -506,7 +506,7 @@ def test_elemwise_collapse5():
def test_elemwise_collapse6():
""" Test when all inputs have two broadcastable dimension at the beginning"""
shape = (4,5)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -528,7 +528,7 @@ def test_elemwise_collapse6():
def test_elemwise_collapse7(atol=1e-6):
""" Test when one input have one broadcastable dimension and the other is a scalar"""
shape = (5,4,1)
a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
......@@ -639,7 +639,7 @@ def test_gpujoin_preserves_broadcasting():
assert c.type.broadcastable == (False,True,False)
f = theano.function([], c, mode=mode_with_gpu)
res = f()
a_reshaped = numpy.asarray([[[1,3]],[[2,4]]], dtype='float32')
......@@ -657,14 +657,14 @@ def test_gpujoin_assert_cndas():
try:
c = gpu_join(1,a)
# can't "assert False" here, as we want the assertion
# can't "assert False" here, as we want the assertion
# error from gpu_join
except AssertionError:
assert True
return
assert False
def test_gpujoin_no_rebroadcast():
_a = numpy.asarray([[1,2],[3,4]],dtype='float32')
a = tcn.shared_constructor(_a)
......@@ -696,7 +696,7 @@ def test_gpujoin_gpualloc():
f = theano.function([a,b], T.join(0,T.zeros_like(a),T.ones_like(b))+4, mode=mode_without_gpu)
f_gpu = theano.function([a,b], T.join(0,T.zeros_like(a),T.ones_like(b)), mode=mode_with_gpu)
f_gpu2 = theano.function([a,b], T.join(0,T.zeros_like(a),T.ones_like(b))+4, mode=mode_with_gpu)
assert sum([node.op == T.alloc for node in f.maker.env.toposort()])==2
assert sum([node.op == T.join for node in f.maker.env.toposort()])==1
assert sum([node.op == B.gpu_alloc for node in f_gpu.maker.env.toposort()])==2
......@@ -767,10 +767,10 @@ def test_many_arg_elemwise():
for nb_dim in [2,3,4,5]:
shapes = [rng.randint(1,5) for i in range(nb_dim)]
args = [ numpy.cast['float32'](rng.randn(*shapes)) for arg in xrange(0,num_args) ]
symb_args = [ theano.tensor.TensorType('float32', (False,)*nb_dim)() for arg in xrange(0,num_args) ]
symb_args = [ theano.tensor.TensorType('float32', (False,)*nb_dim)() for arg in xrange(0,num_args) ]
outputs = []
for mode in [ mode_with_gpu, mode_without_gpu ]:
#test the optijmization local_gpu_elemwise_0
......@@ -779,17 +779,17 @@ def test_many_arg_elemwise():
#assert that the test was done on the gpu.
if mode is mode_with_gpu:
assert any([isinstance(node.op, cuda.GpuElemwise) for node in f.maker.env.nodes])
#test the optijmization local_gpu_elemwise_1
f = theano.function( symb_args,
cuda.gpu_from_host(op_to_test(*symb_args)),
f = theano.function( symb_args,
cuda.gpu_from_host(op_to_test(*symb_args)),
mode = mode.excluding("local_gpu_elemwise_0") )
out = f( * args)
#assert that the test was done on the gpu.
if mode is mode_with_gpu:
assert any([isinstance(node.op, cuda.GpuElemwise) for node in f.maker.env.nodes])
assert numpy.allclose(out, outputs[-1])
results_gpu, results_cpu = outputs
assert numpy.allclose(results_gpu, results_cpu)
......@@ -831,6 +831,3 @@ if __name__ == '__main__':
test_gpujoin_assert_cndas()
test_gpujoin_preserves_broadcasting()
test_gpujoin_twomatrices_badshapes()
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论