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提交 34a45383 authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Merge pull request #2164 from nouiz/tests

Fix buildbot errors.
上级 a14c3e9b e46d75d2
隐藏空白字符变更
内嵌 并排
正在显示 包含 33 行增加 和 29 行删除
theano/gof/tests/test_types.py
浏览文件 @ 34a45383
......@@ -64,9 +64,12 @@ def test_cdata():
i = TensorType('float32', (False,))()
c = ProdOp()(i)
i2 = GetOp()(c)
mode = None
if theano.config.mode == "FAST_COMPILE":
mode = "FAST_RUN"
# This should be a passthrough function for vectors
f = theano.function([i], i2)
f = theano.function([i], i2, mode=mode)
v = numpy.random.randn(9).astype('float32')
......
theano/sandbox/cuda/tests/test_gemmcorr3d.py
浏览文件 @ 34a45383
......@@ -10,7 +10,8 @@ import theano.sandbox.cuda as cuda_ndarray
if not cuda_ndarray.cuda_available:
raise SkipTest('Optional package cuda not available')
from theano.sandbox.cuda import float32_shared_constructor as shared
from theano.sandbox.cuda.blas import GpuCorr3dMM, GpuCorr3dMM_gradWeights, GpuCorr3dMM_gradInputs
from theano.sandbox.cuda.blas import (
GpuCorr3dMM, GpuCorr3dMM_gradWeights, GpuCorr3dMM_gradInputs)
from theano.sandbox.cuda.basic_ops import gpu_contiguous
if theano.config.mode == 'FAST_COMPILE':
......@@ -31,9 +32,10 @@ class TestCorr3DMM(unittest.TestCase):
bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))
conv_ref = theano.tensor.nnet.conv3D(V=inputs, W=filters,
b=bias, d=subsample)
conv = GpuCorr3dMM(border_mode = "valid",
subsample=subsample)(inputs.dimshuffle(0, 4, 1, 2, 3),
filters.dimshuffle(0, 4, 1, 2, 3))
conv = GpuCorr3dMM(border_mode="valid",
subsample=subsample)(
inputs.dimshuffle(0, 4, 1, 2, 3),
filters.dimshuffle(0, 4, 1, 2, 3))
conv = conv.dimshuffle(0, 2, 3, 4, 1)
f_ref = theano.function([], conv_ref)
......@@ -66,27 +68,26 @@ class TestCorr3DMM(unittest.TestCase):
subsample=(1, 2, 3))
def run_gradweight(self, inputs_shape, filters_shape, dCdH_shape,
subsample=(1, 1, 1)):
subsample=(1, 1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
dCdH_val = numpy.random.random(dCdH_shape).astype('float32')
inputs = shared(inputs_val)
dCdH = shared(dCdH_val)
conv = theano.tensor.nnet.convGrad3D(V=inputs, dCdH=dCdH,
WShape=filters_shape,
d=subsample)
WShape=filters_shape,
d=subsample)
img = gpu_contiguous(inputs.dimshuffle(0, 4, 1, 2, 3))
topgrad = gpu_contiguous(dCdH.dimshuffle(0, 4, 1, 2, 3))
if (subsample == (1, 1, 1)):
conv_gemm = GpuCorr3dMM_gradWeights(subsample=subsample)(img,
topgrad)
else:
conv_gemm = GpuCorr3dMM_gradWeights(subsample=subsample)(img,
topgrad,
shape=filters_shape[1:4])
conv_gemm = GpuCorr3dMM_gradWeights(subsample=subsample)(
img, topgrad, shape=filters_shape[1:4])
conv_gemm = conv_gemm.dimshuffle(0, 2, 3, 4, 1)
f_ref = theano.function([], conv)
f = theano.function([], conv_gemm)
f = theano.function([], conv_gemm, mode=mode_with_gpu)
res_ref = f_ref()
res = f()
......@@ -124,7 +125,7 @@ class TestCorr3DMM(unittest.TestCase):
f_ref = theano.function([], conv)
res_ref = f_ref()
### Get bottom shape using convTransp3D
# Get bottom shape using convTransp3D
bottom_shape = res_ref.shape
bottom_val = numpy.random.random(bottom_shape).astype('float32')
bottom = shared(bottom_val)
......@@ -132,12 +133,14 @@ class TestCorr3DMM(unittest.TestCase):
weight = gpu_contiguous(filters.dimshuffle(0, 4, 1, 2, 3))
top = gpu_contiguous(inputs.dimshuffle(0, 4, 1, 2, 3))
if (subsample == (1, 1, 1)):
conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(kern=weight, topgrad=top)
conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(
kern=weight, topgrad=top)
else:
conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(kern=weight, topgrad=top,
shape=bottom.shape[1:4])
conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(
kern=weight, topgrad=top,
shape=bottom.shape[1:4])
conv_gemm = conv_gemm.dimshuffle(0, 2, 3, 4, 1)
f = theano.function([], conv_gemm)
f = theano.function([], conv_gemm, mode=mode_with_gpu)
res = f()
utt.assert_allclose(res_ref, res)
......@@ -147,14 +150,13 @@ class TestCorr3DMM(unittest.TestCase):
filters_shape=(10, 6, 12, 4, 1))
self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
filters_shape=(10, 6, 12, 4, 1),
subsample=(2,2,2))
subsample=(2, 2, 2))
self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
filters_shape=(10, 6, 12, 4, 1),
subsample=(3,3,3))
subsample=(3, 3, 3))
self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
filters_shape=(10, 6, 12, 4, 1),
subsample=(3,1,2))
subsample=(3, 1, 2))
def test_opt_conv3d_gemm(self):
inputs_shape = (16, 20, 32, 16, 1)
......@@ -168,7 +170,7 @@ class TestCorr3DMM(unittest.TestCase):
bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))
conv = theano.tensor.nnet.conv3D(V=inputs, W=filters,
b=bias, d=(1,1,1))
b=bias, d=(1, 1, 1))
mode = mode_with_gpu.including('conv3d_gemm')
f_ref = theano.function([], conv)
......@@ -195,7 +197,7 @@ class TestCorr3DMM(unittest.TestCase):
conv = theano.tensor.nnet.convGrad3D(V=inputs, dCdH=dCdH,
WShape=filters_shape,
d=(1,1,1))
d=(1, 1, 1))
mode = mode_with_gpu.including('convgrad3d_gemm')
f_ref = theano.function([], conv)
......@@ -209,7 +211,6 @@ class TestCorr3DMM(unittest.TestCase):
res_gemm = f_gemm()
utt.assert_allclose(res_ref, res_gemm)
def test_opt_convtransp3d_gemm(self):
inputs_shape = (16, 15, 12, 12, 10)
filters_shape = (10, 6, 12, 4, 1)
......@@ -221,7 +222,7 @@ class TestCorr3DMM(unittest.TestCase):
inputs = shared(inputs_val)
filters = shared(filters_val)
conv = theano.tensor.nnet.convTransp3D(W=filters, b=bias, d=(1,1,1),
conv = theano.tensor.nnet.convTransp3D(W=filters, b=bias, d=(1, 1, 1),
H=inputs)
mode = mode_with_gpu.including('convtransp3d_gemm')
......@@ -235,4 +236,3 @@ class TestCorr3DMM(unittest.TestCase):
res_ref = f_ref()
res_gemm = f_gemm()
utt.assert_allclose(res_ref, res_gemm)
theano/tensor/tests/test_opt.py
浏览文件 @ 34a45383
......@@ -1576,12 +1576,13 @@ def test_log_add():
def test_local_useless_inc_subtensor():
x = tensor.matrix('x')
y = tensor.matrix('y')
mode = compile.get_default_mode().including("local_useless_inc_subtensor")
for sub in [slice(None), slice(None, None, -1)]:
o = tensor.set_subtensor(x[::, sub], y)
f = theano.function([x, y], o)
f = theano.function([x, y], o, mode=mode)
o_shape = tensor.set_subtensor(x[::, sub],
tensor.specify_shape(y, x.shape))
f_shape = theano.function([x, y], o_shape)
f_shape = theano.function([x, y], o_shape, mode=mode)
# Test with shape info
topo = f_shape.maker.fgraph.toposort()
......@@ -1614,7 +1615,7 @@ def test_local_useless_inc_subtensor():
tensor.specify_shape(y, sub.shape))
f_shape = theano.function([x, y], o_shape)
topo = f_shape.maker.fgraph.toposort()
theano.printing.debugprint(f_shape)
# theano.printing.debugprint(f_shape)
assert any(isinstance(n.op, tensor.IncSubtensor) for n in topo)
out = f_shape([[2, 3, 6, 7]], [[8, 9]])
assert (out == numpy.asarray([[8, 3, 9, 7]])).all()
......
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