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提交 45f9b15a authored 作者: affanv14's avatar affanv14
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add more test cases

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正在显示 包含 206 行增加 和 12 行删除
theano/gpuarray/tests/test_opt.py
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...@@ -709,7 +709,8 @@ def test_crossentropycategorical1hot_lifter(): ...@@ -709,7 +709,8 @@ def test_crossentropycategorical1hot_lifter():
class Conv_opt_test(unittest.TestCase): class Conv_opt_test(unittest.TestCase):
def optimizer_2d(self, input_shapes, direction, include_tags, exclude_tags, def optimizer_2d(self, input_shapes, direction, include_tags, exclude_tags,
op, border_mode='valid', subsample=(1, 1), filter_dilation=(1, 1)): op, border_mode='valid', subsample=(1, 1),
filter_dilation=(1, 1), num_groups=1):
inp1 = theano.shared(np.random.random(input_shapes[0]).astype(theano.config.floatX)) inp1 = theano.shared(np.random.random(input_shapes[0]).astype(theano.config.floatX))
inp2 = theano.shared(np.random.random(input_shapes[1]).astype(theano.config.floatX)) inp2 = theano.shared(np.random.random(input_shapes[1]).astype(theano.config.floatX))
...@@ -720,7 +721,8 @@ class Conv_opt_test(unittest.TestCase): ...@@ -720,7 +721,8 @@ class Conv_opt_test(unittest.TestCase):
input_shapes[1], input_shapes[1],
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
filter_dilation=filter_dilation) filter_dilation=filter_dilation,
num_groups=num_groups)
if(direction == 1): if(direction == 1):
conv_op = abstract_conv.conv2d_grad_wrt_weights(inp1, conv_op = abstract_conv.conv2d_grad_wrt_weights(inp1,
...@@ -729,7 +731,8 @@ class Conv_opt_test(unittest.TestCase): ...@@ -729,7 +731,8 @@ class Conv_opt_test(unittest.TestCase):
input_shapes[0], input_shapes[0],
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
filter_dilation=filter_dilation) filter_dilation=filter_dilation,
num_groups=num_groups)
if(direction == 2): if(direction == 2):
conv_op = abstract_conv.conv2d_grad_wrt_inputs(inp1, conv_op = abstract_conv.conv2d_grad_wrt_inputs(inp1,
...@@ -738,16 +741,24 @@ class Conv_opt_test(unittest.TestCase): ...@@ -738,16 +741,24 @@ class Conv_opt_test(unittest.TestCase):
input_shapes[1], input_shapes[1],
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
filter_dilation=filter_dilation) filter_dilation=filter_dilation,
num_groups=num_groups)
theano.config.metaopt.optimizer_including = include_tags theano.config.metaopt.optimizer_including = include_tags
theano.config.metaopt.optimizer_excluding = exclude_tags theano.config.metaopt.optimizer_excluding = exclude_tags
mode = mode_with_gpu.including('conv_meta') mode = mode_with_gpu.including('conv_meta').excluding('conv_dnn').excluding('conv_gemm')
ref_func = theano.function([], conv_op, mode=mode_with_gpu) ref_func = theano.function([], conv_op, mode=mode_with_gpu)
# All meta optimizer compile a new function. This need to know # All meta optimizer compile a new function. This need to know
# the current linker, but this information is not available, # the current linker, but this information is not available,
# so it use the default mode. # so it use the default mode.
if op is None:
# No convolutions optimization takes place
with theano.change_flags(mode=mode):
with self.assertRaises(AssertionError):
theano.function([], conv_op, mode=mode)
return
else:
with theano.change_flags(mode=mode): with theano.change_flags(mode=mode):
conv_func = theano.function([], conv_op, mode=mode) conv_func = theano.function([], conv_op, mode=mode)
assert any([isinstance(node.op, op) assert any([isinstance(node.op, op)
...@@ -756,7 +767,7 @@ class Conv_opt_test(unittest.TestCase): ...@@ -756,7 +767,7 @@ class Conv_opt_test(unittest.TestCase):
def optimizer_3d(self, input_shapes, direction, include_tags, exclude_tags, def optimizer_3d(self, input_shapes, direction, include_tags, exclude_tags,
op, border_mode='valid', subsample=(1, 1, 1), op, border_mode='valid', subsample=(1, 1, 1),
filter_dilation=(1, 1, 1)): filter_dilation=(1, 1, 1), num_groups=1):
inp1 = theano.shared(np.random.random(input_shapes[0]).astype(theano.config.floatX)) inp1 = theano.shared(np.random.random(input_shapes[0]).astype(theano.config.floatX))
inp2 = theano.shared(np.random.random(input_shapes[1]).astype(theano.config.floatX)) inp2 = theano.shared(np.random.random(input_shapes[1]).astype(theano.config.floatX))
if(direction == 0): if(direction == 0):
...@@ -766,7 +777,8 @@ class Conv_opt_test(unittest.TestCase): ...@@ -766,7 +777,8 @@ class Conv_opt_test(unittest.TestCase):
input_shapes[1], input_shapes[1],
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
filter_dilation=filter_dilation) filter_dilation=filter_dilation,
num_groups=num_groups)
if(direction == 1): if(direction == 1):
conv_op = abstract_conv.conv3d_grad_wrt_weights(inp1, conv_op = abstract_conv.conv3d_grad_wrt_weights(inp1,
...@@ -775,7 +787,8 @@ class Conv_opt_test(unittest.TestCase): ...@@ -775,7 +787,8 @@ class Conv_opt_test(unittest.TestCase):
input_shapes[0], input_shapes[0],
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
filter_dilation=filter_dilation) filter_dilation=filter_dilation,
num_groups=num_groups)
if(direction == 2): if(direction == 2):
conv_op = abstract_conv.conv3d_grad_wrt_inputs(inp1, conv_op = abstract_conv.conv3d_grad_wrt_inputs(inp1,
...@@ -784,21 +797,34 @@ class Conv_opt_test(unittest.TestCase): ...@@ -784,21 +797,34 @@ class Conv_opt_test(unittest.TestCase):
input_shapes[1], input_shapes[1],
border_mode=border_mode, border_mode=border_mode,
subsample=subsample, subsample=subsample,
filter_dilation=filter_dilation) filter_dilation=filter_dilation,
num_groups=num_groups)
theano.config.metaopt.optimizer_including = include_tags theano.config.metaopt.optimizer_including = include_tags
theano.config.metaopt.optimizer_excluding = exclude_tags theano.config.metaopt.optimizer_excluding = exclude_tags
mode = mode_with_gpu.including('conv_meta') mode = mode_with_gpu.including('conv_meta').excluding('conv_dnn').excluding('conv_gemm')
ref_func = theano.function([], conv_op, mode=mode_with_gpu) ref_func = theano.function([], conv_op, mode=mode_with_gpu)
# All meta optimizer compile a new function. This need to know # All meta optimizer compile a new function. This need to know
# the current linker, but this information is not available, # the current linker, but this information is not available,
# so it use the default mode. # so it use the default mode.
if op is None:
# No convolutions optimization takes place
with theano.change_flags(mode=mode):
with self.assertRaises(AssertionError):
theano.function([], conv_op, mode=mode)
return
elif op != 'conv3d2d':
with theano.change_flags(mode=mode): with theano.change_flags(mode=mode):
conv_func = theano.function([], conv_op, mode=mode) conv_func = theano.function([], conv_op, mode=mode)
if op is not None:
assert any([isinstance(node.op, op) assert any([isinstance(node.op, op)
for node in conv_func.maker.fgraph.toposort()]) for node in conv_func.maker.fgraph.toposort()])
else:
with theano.change_flags(mode=mode):
conv_func = theano.function(
[], conv_op,
mode=mode_with_gpu.including('conv_meta'))
utt.assert_allclose(conv_func(), ref_func()) utt.assert_allclose(conv_func(), ref_func())
def test_optimizers_2d(self): def test_optimizers_2d(self):
...@@ -883,7 +909,7 @@ class Conv_opt_test(unittest.TestCase): ...@@ -883,7 +909,7 @@ class Conv_opt_test(unittest.TestCase):
self.optimizer_3d([imshp, kshp, tshp], 0, self.optimizer_3d([imshp, kshp, tshp], 0,
'conv3d2d', 'conv3d2d',
'default', 'default',
None) 'conv3d2d')
self.optimizer_3d([imshp, kshp, tshp], 0, self.optimizer_3d([imshp, kshp, tshp], 0,
'alternative', 'alternative',
'conv_gemm:default:conv3d2d', 'conv_gemm:default:conv3d2d',
...@@ -989,3 +1015,171 @@ class Conv_opt_test(unittest.TestCase): ...@@ -989,3 +1015,171 @@ class Conv_opt_test(unittest.TestCase):
dnn.GpuDnnConvGradI, dnn.GpuDnnConvGradI,
border_mode='full', border_mode='full',
filter_dilation=fdil) filter_dilation=fdil)
# test non default num_groups for default optimizers
imshp2d = [(2, 6, 5, 5), (2, 4, 5, 5)]
kshp2d = [(3, 2, 3, 3), (2, 2, 3, 3)]
tshp2d = [(2, 3, 3, 3), (2, 2, 3, 3)]
num_groups = [3, 2]
for imshp, kshp, tshp, groups in zip(imshp2d, kshp2d, tshp2d, num_groups):
# forward pass
self.optimizer_2d([imshp, kshp, tshp], 0,
'',
'conv_dnn:alternative',
blas.GpuCorrMM,
num_groups=groups)
self.optimizer_2d([imshp, kshp, tshp], 0,
'',
'conv_gemm:alternative',
dnn.GpuDnnConv,
num_groups=groups)
# grad with respect to weights
self.optimizer_2d([imshp, tshp, kshp], 1,
'',
'conv_dnn:alternative',
blas.GpuCorrMM_gradWeights,
num_groups=groups)
self.optimizer_2d([imshp, tshp, kshp], 1,
'',
'conv_gemm:alternative',
dnn.GpuDnnConvGradW,
num_groups=groups)
# grad with respect to inputs
self.optimizer_2d([tshp, kshp, imshp], 2,
'',
'conv_dnn:alternative',
blas.GpuCorrMM_gradInputs,
num_groups=groups)
self.optimizer_2d([tshp, kshp, imshp], 2,
'',
'conv_gemm:alternative',
dnn.GpuDnnConvGradI,
num_groups=groups)
imshp3d = [(2, 6, 5, 5, 5), (2, 4, 5, 5, 5)]
kshp3d = [(3, 2, 3, 3, 3), (2, 2, 3, 3, 3)]
tshp3d = [(2, 3, 3, 3, 3), (2, 2, 3, 3, 3)]
num_groups = [3, 2]
for imshp, kshp, tshp, groups in zip(imshp3d, kshp3d, tshp3d, num_groups):
# forward pass
self.optimizer_3d([imshp, kshp, tshp], 0,
'',
'conv_dnn:alternative:conv3d2d',
blas.GpuCorr3dMM,
num_groups=groups)
self.optimizer_3d([imshp, kshp, tshp], 0,
'',
'conv_gemm:alternative:conv3d2d',
dnn.GpuDnnConv,
num_groups=groups)
# grad with respect to weights
self.optimizer_3d([imshp, tshp, kshp], 1,
'',
'conv_dnn:alternative:conv3d2d',
blas.GpuCorr3dMM_gradWeights,
num_groups=groups)
self.optimizer_3d([imshp, tshp, kshp], 1,
'',
'conv_gemm:alternative:conv3d2d',
dnn.GpuDnnConvGradW,
num_groups=groups)
# grad with respect to inputs
self.optimizer_3d([tshp, kshp, imshp], 2,
'',
'conv_dnn:alternative:conv3d2d',
blas.GpuCorr3dMM_gradInputs,
num_groups=groups)
self.optimizer_3d([tshp, kshp, imshp], 2,
'',
'conv_gemm:alternative:conv3d2d',
dnn.GpuDnnConvGradI,
num_groups=groups)
def test_returns_none(self):
if theano.config.cxx == "":
raise SkipTest("Need a c compiler.")
# values given dont matter since it returns None
imshp = (2, 3, 5, 5)
kshp = (4, 3, 3, 3)
tshp = (2, 4, 3, 3)
exclude_string = ['conv_dnn:default', 'conv_gemm:default']
conv_direction = [0, 1, 2]
# test that non default subsample returns None
for string in exclude_string:
for direction in conv_direction:
self.optimizer_2d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
subsample=(2, 2))
# test that non default num_groups returns None
for string in exclude_string:
for direction in conv_direction:
self.optimizer_2d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
num_groups=3)
# test that border_mode=half returns None
for string in exclude_string:
for direction in conv_direction:
self.optimizer_2d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
border_mode='half')
# test that Non-default filter dilation return None for
# direction 1
for string in exclude_string:
direction = 1
self.optimizer_2d([imshp, kshp, tshp],
direction,
'alternative',
'conv_dnn:default',
None,
filter_dilation=(2, 2))
imshp = (2, 3, 5, 5, 5)
kshp = (4, 3, 3, 3, 3)
tshp = (2, 4, 3, 3, 3)
exclude_string = ['conv_dnn:default', 'conv_gemm:default']
# test that non default subsample returns None
for string in exclude_string:
for direction in conv_direction:
self.optimizer_3d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
subsample=(2, 2, 2))
# test that non default num_groups returns None
for string in exclude_string:
for direction in conv_direction:
self.optimizer_3d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
num_groups=3)
# test that border_mode=half returns None
for string in exclude_string:
for direction in conv_direction:
self.optimizer_3d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
border_mode='half')
# test that Non-default filter dilation return None for
# direction 1
for string in exclude_string:
direction = 1
self.optimizer_3d([imshp, kshp, tshp],
direction,
'alternative',
string,
None,
filter_dilation=(2, 2, 2))
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