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提交 1331a0f5 authored 作者: Frederic's avatar Frederic
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remove print in tests.

上级 5cd26de6
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正在显示 包含 15 行增加 和 64 行删除
theano/sandbox/cuda/tests/test_basic_ops.py
浏览文件 @ 1331a0f5
...@@ -265,11 +265,7 @@ def test_elemwise0(): ...@@ -265,11 +265,7 @@ def test_elemwise0():
assert f.maker.env.toposort()[1].op.destroy_map.items() == [(0, [0])] assert f.maker.env.toposort()[1].op.destroy_map.items() == [(0, [0])]
a0 = a.get_value() * 1.0 a0 = a.get_value() * 1.0
print 'BEFORE ADD', a.get_value()
for i, node in enumerate(f.maker.env.toposort()):
print i, node
f(numpy.ones((4, 4), dtype='float32')) f(numpy.ones((4, 4), dtype='float32'))
print 'AFTER ADD', a.get_value()
assert numpy.all(a0 + 1.0 == a.get_value()) assert numpy.all(a0 + 1.0 == a.get_value())
...@@ -279,7 +275,6 @@ def test_elemwise_bad_broadcast(): ...@@ -279,7 +275,6 @@ def test_elemwise_bad_broadcast():
y = cuda.fmatrix('y') y = cuda.fmatrix('y')
f = theano.function([x, y], x * y, mode=mode_with_gpu) f = theano.function([x, y], x * y, mode=mode_with_gpu)
print f.maker.env.toposort()
assert len(f.maker.env.toposort()) == 2 assert len(f.maker.env.toposort()) == 2
assert isinstance(f.maker.env.toposort()[0].op, cuda.GpuElemwise) assert isinstance(f.maker.env.toposort()[0].op, cuda.GpuElemwise)
assert f.maker.env.toposort()[1].op == cuda.host_from_gpu assert f.maker.env.toposort()[1].op == cuda.host_from_gpu
...@@ -302,20 +297,13 @@ def test_elemwise1(): ...@@ -302,20 +297,13 @@ def test_elemwise1():
b = tensor.fmatrix() b = tensor.fmatrix()
#let debugmode catch any mistakes #let debugmode catch any mistakes
print >> sys.stdout, "STARTING FUNCTION 1"
f = pfunc([b], [], updates=[(a, b ** a)], mode=mode_with_gpu) f = pfunc([b], [], updates=[(a, b ** a)], mode=mode_with_gpu)
for i, node in enumerate(f.maker.env.toposort()):
print i, node
f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3) f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3)
print >> sys.stdout, "STARTING FUNCTION 2"
#let debugmode catch any mistakes #let debugmode catch any mistakes
f = pfunc([b], [], updates=[(a, tensor.exp(b ** a))], mode=mode_with_gpu) f = pfunc([b], [], updates=[(a, tensor.exp(b ** a))], mode=mode_with_gpu)
for i, node in enumerate(f.maker.env.toposort()):
print i, node
f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3) f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3)
print >> sys.stdout, "STARTING FUNCTION 3"
#let debugmode catch any mistakes #let debugmode catch any mistakes
f = pfunc([b], [], updates=[(a, a + b * tensor.exp(b ** a))], f = pfunc([b], [], updates=[(a, a + b * tensor.exp(b ** a))],
mode=mode_with_gpu) mode=mode_with_gpu)
...@@ -325,7 +313,6 @@ def test_elemwise1(): ...@@ -325,7 +313,6 @@ def test_elemwise1():
def test_elemwise2(): def test_elemwise2():
""" Several kinds of elemwise expressions with dimension permutations """ """ Several kinds of elemwise expressions with dimension permutations """
rng = numpy.random.RandomState(int(time.time())) rng = numpy.random.RandomState(int(time.time()))
print 'random?', rng.rand(3)
shape = (3, 5) shape = (3, 5)
for pattern in [(0, 1), (1, 0)]: for pattern in [(0, 1), (1, 0)]:
a = tcn.shared_constructor(theano._asarray(rng.rand(*shape), a = tcn.shared_constructor(theano._asarray(rng.rand(*shape),
...@@ -335,11 +322,9 @@ def test_elemwise2(): ...@@ -335,11 +322,9 @@ def test_elemwise2():
mode=mode_with_gpu) mode=mode_with_gpu)
has_elemwise = False has_elemwise = False
for i, node in enumerate(f.maker.env.toposort()): for i, node in enumerate(f.maker.env.toposort()):
print >> sys.stdout, i, node
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise) has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
assert not has_elemwise assert not has_elemwise
#let debugmode catch errors #let debugmode catch errors
print >> sys.stdout, 'pattern', pattern
f(theano._asarray(rng.rand(*shape), dtype='float32') * .3) f(theano._asarray(rng.rand(*shape), dtype='float32') * .3)
shape = (3, 4, 5, 6) shape = (3, 4, 5, 6)
...@@ -350,7 +335,6 @@ def test_elemwise2(): ...@@ -350,7 +335,6 @@ def test_elemwise2():
tensor.exp(b ** a).dimshuffle([2, 0, 3, 1]))], mode=mode_with_gpu) tensor.exp(b ** a).dimshuffle([2, 0, 3, 1]))], mode=mode_with_gpu)
has_elemwise = False has_elemwise = False
for i, node in enumerate(f.maker.env.toposort()): for i, node in enumerate(f.maker.env.toposort()):
print i, node
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise) has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
assert not has_elemwise assert not has_elemwise
#let debugmode catch errors #let debugmode catch errors
...@@ -365,17 +349,11 @@ def test_elemwise3(): ...@@ -365,17 +349,11 @@ def test_elemwise3():
a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape), a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape),
dtype='float32'), 'a') dtype='float32'), 'a')
b = tensor.fvector() b = tensor.fvector()
print b.type
print tensor.constant(1).type
print (1 + b).type
print (1 + b ** a).type
print tensor.exp((1 + b ** a)).type
new_val = (a + b).dimshuffle([2, 0, 3, 1]) new_val = (a + b).dimshuffle([2, 0, 3, 1])
new_val *= tensor.exp(1 + b ** a).dimshuffle([2, 0, 3, 1]) new_val *= tensor.exp(1 + b ** a).dimshuffle([2, 0, 3, 1])
f = pfunc([b], [], updates=[(a, new_val)], mode=mode_with_gpu) f = pfunc([b], [], updates=[(a, new_val)], mode=mode_with_gpu)
has_elemwise = False has_elemwise = False
for i, node in enumerate(f.maker.env.toposort()): for i, node in enumerate(f.maker.env.toposort()):
print >> sys.stdout, i, node
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise) has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
assert not has_elemwise assert not has_elemwise
#let debugmode catch errors #let debugmode catch errors
...@@ -396,7 +374,6 @@ def test_elemwise4(): ...@@ -396,7 +374,6 @@ def test_elemwise4():
mode=mode_with_gpu) mode=mode_with_gpu)
has_elemwise = False has_elemwise = False
for i, node in enumerate(f.maker.env.toposort()): for i, node in enumerate(f.maker.env.toposort()):
print >> sys.stdout, i, node
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise) has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
assert not has_elemwise assert not has_elemwise
#let debugmode catch errors #let debugmode catch errors
...@@ -420,7 +397,6 @@ def test_elemwise_comparaison_cast(): ...@@ -420,7 +397,6 @@ def test_elemwise_comparaison_cast():
f = pfunc([a, b], tensor.cast(g(a, b), 'float32'), mode=mode_with_gpu) f = pfunc([a, b], tensor.cast(g(a, b), 'float32'), mode=mode_with_gpu)
#theano.printing.debugprint(f)
out = f(av, bv) out = f(av, bv)
assert numpy.all(out == ans) assert numpy.all(out == ans)
assert any([isinstance(node.op, cuda.GpuElemwise) assert any([isinstance(node.op, cuda.GpuElemwise)
...@@ -451,7 +427,6 @@ def test_elemwise_composite_float64(): ...@@ -451,7 +427,6 @@ def test_elemwise_composite_float64():
b), b),
'float32'), mode=mode) 'float32'), mode=mode)
#theano.printing.debugprint(f, print_type=True)
out = f(av, bv) out = f(av, bv)
assert numpy.all(out == ((av ** 2) < bv)) assert numpy.all(out == ((av ** 2) < bv))
for node in f.maker.env.toposort(): for node in f.maker.env.toposort():
...@@ -509,8 +484,6 @@ def speed_elemwise_collapse(): ...@@ -509,8 +484,6 @@ def speed_elemwise_collapse():
v = theano._asarray(numpy.random.rand(*shape), dtype='float32') v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, ::2, :, :] v = v[:, ::2, :, :]
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
for id, n in enumerate(f.maker.env.toposort()):
print id, n
t1 = time.time() t1 = time.time()
for i in range(100): for i in range(100):
#let debugmode catch errors #let debugmode catch errors
...@@ -535,8 +508,6 @@ def speed_elemwise_collapse2(): ...@@ -535,8 +508,6 @@ def speed_elemwise_collapse2():
v = theano._asarray(numpy.random.rand(*shape), dtype='float32') v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, :, :, ::2] v = v[:, :, :, ::2]
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
for id, n in enumerate(f.maker.env.toposort()):
print id, n
t1 = time.time() t1 = time.time()
for i in range(100): for i in range(100):
#let debugmode catch errors #let debugmode catch errors
...@@ -560,13 +531,11 @@ def test_elemwise_collapse(): ...@@ -560,13 +531,11 @@ def test_elemwise_collapse():
v = theano._asarray(numpy.random.rand(shape[0], 1, *shape[1:]), v = theano._asarray(numpy.random.rand(shape[0], 1, *shape[1:]),
dtype='float32') dtype='float32')
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f(v)[0] out = f(v)[0]
assert numpy.allclose(out, a.reshape(shape[0], 1, *shape[1:]) + v) assert numpy.allclose(out, a.reshape(shape[0], 1, *shape[1:]) + v)
print "Expected collapse of all dimensions" #print "Expected collapse of all dimensions"
def test_elemwise_collapse2(): def test_elemwise_collapse2():
...@@ -585,13 +554,10 @@ def test_elemwise_collapse2(): ...@@ -585,13 +554,10 @@ def test_elemwise_collapse2():
v = theano._asarray(numpy.random.rand(shape[0], 5, *shape[1:]), v = theano._asarray(numpy.random.rand(shape[0], 5, *shape[1:]),
dtype='float32') dtype='float32')
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f(v)[0] out = f(v)[0]
assert numpy.allclose(out, a.reshape(shape[0], 1, *shape[1:]) + v) assert numpy.allclose(out, a.reshape(shape[0], 1, *shape[1:]) + v)
print "Expected collapse to 3 dimensions" #print "Expected collapse to 3 dimensions"
def test_elemwise_collapse3(): def test_elemwise_collapse3():
...@@ -611,13 +577,11 @@ def test_elemwise_collapse3(): ...@@ -611,13 +577,11 @@ def test_elemwise_collapse3():
v = theano._asarray(numpy.random.rand(5, shape[0], shape[1], 4), v = theano._asarray(numpy.random.rand(5, shape[0], shape[1], 4),
dtype='float32') dtype='float32')
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f(v)[0] out = f(v)[0]
assert numpy.allclose(out, a.reshape(1, shape[0], shape[1], 1) + v) assert numpy.allclose(out, a.reshape(1, shape[0], shape[1], 1) + v)
print "Expected collapse to 3 dimensions" #print "Expected collapse to 3 dimensions"
def test_elemwise_collapse4(): def test_elemwise_collapse4():
...@@ -637,13 +601,10 @@ def test_elemwise_collapse4(): ...@@ -637,13 +601,10 @@ def test_elemwise_collapse4():
v = theano._asarray(numpy.random.rand(5, shape[0], shape[1], 4), v = theano._asarray(numpy.random.rand(5, shape[0], shape[1], 4),
dtype='float32') dtype='float32')
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f(v)[0] out = f(v)[0]
assert numpy.allclose(out, a.reshape(1, shape[0], shape[1], 1) + v + 2) assert numpy.allclose(out, a.reshape(1, shape[0], shape[1], 1) + v + 2)
print "Expected collapse to 3 dimensions" #print "Expected collapse to 3 dimensions"
def test_elemwise_collapse5(): def test_elemwise_collapse5():
...@@ -663,13 +624,11 @@ def test_elemwise_collapse5(): ...@@ -663,13 +624,11 @@ def test_elemwise_collapse5():
v = theano._asarray(numpy.random.rand(5, 4, shape[0], shape[1]), v = theano._asarray(numpy.random.rand(5, 4, shape[0], shape[1]),
dtype='float32') dtype='float32')
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f(v)[0] out = f(v)[0]
assert numpy.allclose(out, a.reshape(1, 1, shape[0], shape[1]) + v + 2) assert numpy.allclose(out, a.reshape(1, 1, shape[0], shape[1]) + v + 2)
print "Expected collapse to 2 dimensions" #print "Expected collapse to 2 dimensions"
def test_elemwise_collapse6(): def test_elemwise_collapse6():
...@@ -688,13 +647,10 @@ def test_elemwise_collapse6(): ...@@ -688,13 +647,10 @@ def test_elemwise_collapse6():
v = theano._asarray(numpy.random.rand(1, 1, shape[0], shape[1]), v = theano._asarray(numpy.random.rand(1, 1, shape[0], shape[1]),
dtype='float32') dtype='float32')
v = cuda_ndarray.CudaNdarray(v) v = cuda_ndarray.CudaNdarray(v)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f(v)[0] out = f(v)[0]
assert numpy.allclose(out, a.reshape(1, 1, shape[0], shape[1]) + v) assert numpy.allclose(out, a.reshape(1, 1, shape[0], shape[1]) + v)
print "Expected collapse to c contiguous" #print "Expected collapse to c contiguous"
def test_elemwise_collapse7(atol=1e-6): def test_elemwise_collapse7(atol=1e-6):
...@@ -709,14 +665,11 @@ def test_elemwise_collapse7(atol=1e-6): ...@@ -709,14 +665,11 @@ def test_elemwise_collapse7(atol=1e-6):
a3 = a2.dimshuffle(0, 'x', 1, 2) a3 = a2.dimshuffle(0, 'x', 1, 2)
f = pfunc([], [a3 + 2], mode=mode_with_gpu) f = pfunc([], [a3 + 2], mode=mode_with_gpu)
if False:
for id, n in enumerate(f.maker.env.toposort()):
print id, n
#let debugmode catch errors #let debugmode catch errors
out = f()[0] out = f()[0]
ans = (a + 2).reshape(shape[0], 1, shape[1], shape[2]) ans = (a + 2).reshape(shape[0], 1, shape[1], shape[2])
assert numpy.allclose(out, ans, atol=atol) assert numpy.allclose(out, ans, atol=atol)
print "Expected collapse to c contiguous" #print "Expected collapse to c contiguous"
def test_hostfromgpu_shape_i(): def test_hostfromgpu_shape_i():
...@@ -838,10 +791,8 @@ def test_gpualloc_output_to_gpu(): ...@@ -838,10 +791,8 @@ def test_gpualloc_output_to_gpu():
f_gpu = theano.function([b], B.gpu_from_host(T.ones_like(a)) + b, f_gpu = theano.function([b], B.gpu_from_host(T.ones_like(a)) + b,
mode=mode_with_gpu) mode=mode_with_gpu)
print f.maker.env.toposort() f(2)
print f_gpu.maker.env.toposort() f_gpu(2)
print f(2)
print f_gpu(2)
assert sum([node.op == T.alloc for node in f.maker.env.toposort()]) == 1 assert sum([node.op == T.alloc for node in f.maker.env.toposort()]) == 1
assert sum([node.op == B.gpu_alloc assert sum([node.op == B.gpu_alloc
...@@ -924,7 +875,7 @@ def test_inc_subtensor(): ...@@ -924,7 +875,7 @@ def test_inc_subtensor():
dtype='float32') dtype='float32')
expr = T.inc_subtensor(x[:, 1:3], y[:, 1:3]) expr = T.inc_subtensor(x[:, 1:3], y[:, 1:3])
f = theano.function([x, y], expr, mode=mode_with_gpu) f = theano.function([x, y], expr, mode=mode_with_gpu)
print f.maker.env.toposort()
assert sum([isinstance(node.op, cuda.GpuSubtensor) assert sum([isinstance(node.op, cuda.GpuSubtensor)
for node in f.maker.env.toposort()]) == 1 for node in f.maker.env.toposort()]) == 1
assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
...@@ -949,7 +900,7 @@ def test_set_subtensor(): ...@@ -949,7 +900,7 @@ def test_set_subtensor():
assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
node.op.set_instead_of_inc == True node.op.set_instead_of_inc == True
for node in f.maker.env.toposort()]) == 1 for node in f.maker.env.toposort()]) == 1
print f(xval, yval) f(xval, yval)
def test_many_arg_elemwise(): def test_many_arg_elemwise():
......
theano/tensor/tests/test_sharedvar.py
浏览文件 @ 1331a0f5
...@@ -432,7 +432,7 @@ def makeSharedTester(shared_constructor_, ...@@ -432,7 +432,7 @@ def makeSharedTester(shared_constructor_,
else: else:
shape_grad = tensor.grad(x1_specify_shape.sum(), x1_shared) shape_grad = tensor.grad(x1_specify_shape.sum(), x1_shared)
shape_constant_fct_grad = theano.function([], shape_grad) shape_constant_fct_grad = theano.function([], shape_grad)
theano.printing.debugprint(shape_constant_fct_grad) #theano.printing.debugprint(shape_constant_fct_grad)
shape_constant_fct_grad() shape_constant_fct_grad()
#Test that we can replace with values of the different shape #Test that we can replace with values of the different shape
......
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