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pytensor
提交 6c8f1a15 authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Refactor how the parameters are iterated over to reduce the total time of the…

Refactor how the parameters are iterated over to reduce the total time of the test. (This covers less cases, but should be almost equivalent.)
上级 6f4a125d
隐藏空白字符变更
内嵌 并排
正在显示 包含 171 行增加 和 182 行删除
theano/sandbox/cuda/tests/test_abstractconv.py
浏览文件 @ 6c8f1a15
...@@ -24,70 +24,59 @@ else: ...@@ -24,70 +24,59 @@ else:
class TestDnnConv2d(test_abstract_conv.TestConv2d): class TestDnnConv2d(test_abstract_conv.TestConv2d):
def setUp(self): def setUp(self):
super(TestDnnConv2d, self).setUp() super(TestDnnConv2d, self).setUp()
# provide_shape is not used by the CuDNN impementation
self.provide_shape = [False]
self.shared = gpu_shared self.shared = gpu_shared
def test_dnn_conv(self): def tcase(self, i, f, s, b, flip, provide_shape):
if not dnn_available(): if not dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg) raise SkipTest(cuda.dnn.dnn_available.msg)
mode = mode_with_gpu mode = mode_with_gpu
# provide_shape is not used by the CuDNN impementation o = self.get_output_shape(i, f, s, b)
provide_shape = False self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
for (i, f), s, b, flip in itertools.product( verify_grad=True, mode=mode,
zip(self.inputs_shapes, self.filters_shapes), provide_shape=provide_shape, border_mode=b,
self.subsamples, filter_flip=flip, target_op=GpuDnnConv)
self.border_modes, self.run_gradweight(inputs_shape=i, filters_shape=f,
self.filter_flip): output_shape=o, subsample=s,
o = self.get_output_shape(i, f, s, b) verify_grad=True, mode=mode,
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, provide_shape=provide_shape, border_mode=b,
verify_grad=True, mode=mode, filter_flip=flip, target_op=GpuDnnConvGradW)
provide_shape=provide_shape, border_mode=b, self.run_gradinput(inputs_shape=i, filters_shape=f,
filter_flip=flip, target_op=GpuDnnConv) output_shape=o, subsample=s,
self.run_gradweight(inputs_shape=i, filters_shape=f, verify_grad=True, mode=mode,
output_shape=o, subsample=s, provide_shape=provide_shape, border_mode=b,
verify_grad=True, mode=mode, filter_flip=flip, target_op=GpuDnnConvGradI)
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=GpuDnnConvGradW)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=GpuDnnConvGradI)
class TestCorrMMConv2d(test_abstract_conv.TestConv2d): class TestCorrMMConv2d(test_abstract_conv.TestConv2d):
def setUp(self): def setUp(self):
super(TestCorrMMConv2d, self).setUp() super(TestCorrMMConv2d, self).setUp()
self.shared = gpu_shared self.shared = gpu_shared
self.mode = mode_with_gpu.excluding('cudnn')
def test_gpucorrmm_conv(self): def test_gpucorrmm_conv(self, i, f, s, b, flip, provide_shape):
mode = mode_with_gpu.excluding('cudnn') mode = self.mode
for (i, f), s, b, flip, provide_shape in itertools.product( o = self.get_output_shape(i, f, s, b)
zip(self.inputs_shapes, self.filters_shapes), self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
self.subsamples, verify_grad=True, mode=mode,
self.border_modes, provide_shape=provide_shape, border_mode=b,
self.filter_flip, filter_flip=flip,
[False, True]): target_op=(GpuCorrMM,
GpuCorrMM_gradWeights,
o = self.get_output_shape(i, f, s, b) GpuCorrMM_gradInputs))
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, self.run_gradweight(inputs_shape=i, filters_shape=f,
verify_grad=True, mode=mode, output_shape=o, subsample=s,
provide_shape=provide_shape, border_mode=b, verify_grad=True, mode=mode,
filter_flip=flip, provide_shape=provide_shape, border_mode=b,
target_op=(GpuCorrMM, filter_flip=flip,
GpuCorrMM_gradWeights, target_op=GpuCorrMM_gradWeights)
GpuCorrMM_gradInputs)) self.run_gradinput(inputs_shape=i, filters_shape=f,
self.run_gradweight(inputs_shape=i, filters_shape=f, output_shape=o, subsample=s,
output_shape=o, subsample=s, verify_grad=True, mode=mode,
verify_grad=True, mode=mode, provide_shape=provide_shape, border_mode=b,
provide_shape=provide_shape, border_mode=b, filter_flip=flip,
filter_flip=flip, target_op=GpuCorrMM_gradInputs)
target_op=GpuCorrMM_gradWeights)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=GpuCorrMM_gradInputs)
class TestDnnConvTypes(test_abstract_conv.TestConvTypes): class TestDnnConvTypes(test_abstract_conv.TestConvTypes):
......
theano/sandbox/gpuarray/tests/test_abstractconv.py
浏览文件 @ 6c8f1a15
...@@ -15,34 +15,29 @@ class TestDnnConv2d(test_abstract_conv.TestConv2d): ...@@ -15,34 +15,29 @@ class TestDnnConv2d(test_abstract_conv.TestConv2d):
def setUp(self): def setUp(self):
super(TestDnnConv2d, self).setUp() super(TestDnnConv2d, self).setUp()
self.shared = gpuarray_shared_constructor self.shared = gpuarray_shared_constructor
# provide_shape is not used by the CuDNN impementation
self.provide_shape = [False]
def test_dnn_conv(self): def tcase(self, i, f, s, b, flip, provide_shape):
if not dnn_available(test_ctx_name): if not dnn_available(test_ctx_name):
raise SkipTest(dnn_available.msg) raise SkipTest(dnn_available.msg)
mode = mode_with_gpu mode = mode_with_gpu
# provide_shape is not used by the CuDNN impementation
provide_shape = False o = self.get_output_shape(i, f, s, b)
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
for (i, f), s, b, flip in itertools.product( verify_grad=True, mode=mode,
zip(self.inputs_shapes, self.filters_shapes), provide_shape=provide_shape, border_mode=b,
self.subsamples, filter_flip=flip, target_op=GpuDnnConv)
self.border_modes, self.run_gradweight(inputs_shape=i, filters_shape=f,
self.filter_flip): output_shape=o, subsample=s,
o = self.get_output_shape(i, f, s, b) verify_grad=True, mode=mode,
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, provide_shape=provide_shape, border_mode=b,
verify_grad=True, mode=mode, filter_flip=flip, target_op=GpuDnnConvGradW)
provide_shape=provide_shape, border_mode=b, self.run_gradinput(inputs_shape=i, filters_shape=f,
filter_flip=flip, target_op=GpuDnnConv) output_shape=o, subsample=s,
self.run_gradweight(inputs_shape=i, filters_shape=f, verify_grad=True, mode=mode,
output_shape=o, subsample=s, provide_shape=provide_shape, border_mode=b,
verify_grad=True, mode=mode, filter_flip=flip, target_op=GpuDnnConvGradI)
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=GpuDnnConvGradW)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=True, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=GpuDnnConvGradI)
class TestDnnConvTypes(test_abstract_conv.TestConvTypes): class TestDnnConvTypes(test_abstract_conv.TestConvTypes):
......
theano/tensor/nnet/tests/test_abstract_conv.py
浏览文件 @ 6c8f1a15
...@@ -67,6 +67,7 @@ class TestConv2d(unittest.TestCase): ...@@ -67,6 +67,7 @@ class TestConv2d(unittest.TestCase):
self.subsamples = [(1, 1), (2, 2), (2, 4)] self.subsamples = [(1, 1), (2, 2), (2, 4)]
self.border_modes = ["valid", "full", (0, 0), (1, 1), (5, 5), (5, 2)] self.border_modes = ["valid", "full", (0, 0), (1, 1), (5, 5), (5, 2)]
self.filter_flip = [True, False] self.filter_flip = [True, False]
self.provide_shape = [True, False]
self.shared = theano.compile.shared self.shared = theano.compile.shared
def get_output_shape(self, inputs_shape, filters_shape, subsample, def get_output_shape(self, inputs_shape, filters_shape, subsample,
...@@ -237,119 +238,123 @@ class TestConv2d(unittest.TestCase): ...@@ -237,119 +238,123 @@ class TestConv2d(unittest.TestCase):
[filters_val, output_val], [filters_val, output_val],
mode=mode, eps=1) mode=mode, eps=1)
def test_all(self):
ds = [0, 0]
db = (0, 0)
dflip = True in self.filter_flip
dprovide_shape = True in self.provide_shapes
for (i, f) in zip(self.inputs_shapes, self.filters_shapes):
for provide_shape in self.provide_shape:
self.tcase(i, f, ds, db, dflip, provide_shape)
for s in self.subsamples:
self.tcase(i, f, s, db, dflip, dprovide_shape)
for b in self.border_modes:
self.tcase(i, f, ds, b, dflip, dprovide_shape)
for flip in self.filter_flip:
self.tcase(i, f, ds, db, flip, dprovide_shape)
class TestCorrConv2d(TestConv2d): class TestCorrConv2d(TestConv2d):
def test_corrmm_conv(self): def tcase(self, i, f, s, b, flip, provide_shape):
for (i, f), s, b, flip, provide_shape in itertools.product( o = self.get_output_shape(i, f, s, b)
zip(self.inputs_shapes, self.filters_shapes), self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
self.subsamples, verify_grad=True, provide_shape=provide_shape,
self.border_modes, border_mode=b, filter_flip=flip, target_op=CorrMM)
self.filter_flip, self.run_gradweight(inputs_shape=i, filters_shape=f,
[False, True]): output_shape=o, subsample=s, verify_grad=True,
provide_shape=provide_shape, border_mode=b,
o = self.get_output_shape(i, f, s, b) filter_flip=flip, target_op=CorrMM_gradWeights)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, verify_grad=True,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=CorrMM_gradInputs)
class TestCpuConv2d(TestConv2d):
def setUp(self):
super(TestCpuConv2d, self).setUp()
self.mode = theano.compile.mode.get_default_mode().excluding('conv_gemm')
def tcase(self, i, f, s, b, flip, provide_shape):
mode = self.mode
o = self.get_output_shape(i, f, s, b)
fwd_OK = True
gradweight_OK = True
gradinput_OK = True
if not flip:
fwd_OK = False
gradweight_OK = False
gradinput_OK = False
if b not in ((0, 0), 'valid', 'full'):
fwd_OK = False
gradweight_OK = False
gradinput_OK = False
if (not provide_shape) and (s != (1, 1)) and (b == 'full'):
gradweight_OK = False
gradinput_OK = False
if ((s[0] not in (1, 2)) or (s[1] not in (1, 2))) and (b == 'full'):
gradweight_OK = False
gradinput_OK = False
if fwd_OK:
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s, self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, provide_shape=provide_shape, verify_grad=(gradweights_ok and gradinput_ok),
border_mode=b, filter_flip=flip, target_op=CorrMM) mode=mode, provide_shape=provide_shape,
border_mode=b, filter_flip=flip, target_op=ConvOp)
else:
self.assertRaises(NotImplementedError,
self.run_fwd,
inputs_shape=i,
filters_shape=f,
subsample=s,
verify_grad=False,
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
if gradweight_OK:
self.run_gradweight(inputs_shape=i, filters_shape=f, self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, verify_grad=True, output_shape=o, subsample=s,
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b, provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=CorrMM_gradWeights) filter_flip=flip,
target_op=(ConvOp, ConvGrad3D))
else:
self.assertRaises(NotImplementedError,
self.run_gradweight,
inputs_shape=i,
filters_shape=f,
output_shape=o,
subsample=s,
verify_grad=False,
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
if gradinput_OK:
self.run_gradinput(inputs_shape=i, filters_shape=f, self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, verify_grad=True, output_shape=o, subsample=s,
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b, provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=CorrMM_gradInputs) filter_flip=flip,
target_op=(ConvOp, ConvTransp3D))
else:
class TestCpuConv2d(TestConv2d): self.assertRaises(NotImplementedError,
def test_cpu_conv(self): self.run_gradinput,
mode = theano.compile.mode.get_default_mode().excluding('conv_gemm') inputs_shape=i,
for (i, f), s, b, flip, provide_shape in itertools.product( filters_shape=f,
zip(self.inputs_shapes, self.filters_shapes), output_shape=o,
self.subsamples, subsample=s,
self.border_modes, verify_grad=False,
self.filter_flip, mode=mode,
[False, True]): provide_shape=provide_shape,
border_mode=b,
o = self.get_output_shape(i, f, s, b) filter_flip=flip)
fwd_OK = True
gradweight_OK = True
gradinput_OK = True
if not flip:
fwd_OK = False
gradweight_OK = False
gradinput_OK = False
if b not in ((0, 0), 'valid', 'full'):
fwd_OK = False
gradweight_OK = False
gradinput_OK = False
if (not provide_shape) and (s != (1, 1)) and (b == 'full'):
gradweight_OK = False
gradinput_OK = False
if ((s[0] not in (1, 2)) or (s[1] not in (1, 2))) and (b == 'full'):
gradweight_OK = False
gradinput_OK = False
if fwd_OK:
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=ConvOp)
else:
self.assertRaises(NotImplementedError,
self.run_fwd,
inputs_shape=i,
filters_shape=f,
subsample=s,
verify_grad=False,
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
if gradweight_OK:
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=(ConvOp, ConvGrad3D))
else:
self.assertRaises(NotImplementedError,
self.run_gradweight,
inputs_shape=i,
filters_shape=f,
output_shape=o,
subsample=s,
verify_grad=False,
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
if gradinput_OK:
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s,
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=(ConvOp, ConvTransp3D))
else:
self.assertRaises(NotImplementedError,
self.run_gradinput,
inputs_shape=i,
filters_shape=f,
output_shape=o,
subsample=s,
verify_grad=False,
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
class TestConvTypes(unittest.TestCase): class TestConvTypes(unittest.TestCase):
......
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