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提交 de38fdfc authored 作者: affanv14's avatar affanv14
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add tests for grouped conv3d

上级 28f858c6
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正在显示 包含 112 行增加 和 51 行删除
theano/tensor/nnet/tests/test_abstract_conv.py
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...@@ -1710,12 +1710,12 @@ class TestConv2dGrads(unittest.TestCase): ...@@ -1710,12 +1710,12 @@ class TestConv2dGrads(unittest.TestCase):
class Grouped_conv_noOptim(unittest.TestCase): class Grouped_conv_noOptim(unittest.TestCase):
conv2d = theano.tensor.nnet.abstract_conv.AbstractConv2d conv = theano.tensor.nnet.abstract_conv.AbstractConv2d
conv2d_gradw = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights conv_gradw = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
conv2d_gradi = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs conv_gradi = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
conv2d_op = theano.tensor.nnet.abstract_conv.AbstractConv2d conv_op = theano.tensor.nnet.abstract_conv.AbstractConv2d
conv2d_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights conv_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
conv2d_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs conv_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
mode = theano.Mode(optimizer=None) mode = theano.Mode(optimizer=None)
flip_filter = False flip_filter = False
is_dnn = False is_dnn = False
...@@ -1729,36 +1729,49 @@ class Grouped_conv_noOptim(unittest.TestCase): ...@@ -1729,36 +1729,49 @@ class Grouped_conv_noOptim(unittest.TestCase):
self.top_shape = [(5, 6, 3, 3), (4, 6, 3, 3), (3, 4, 3, 1), (2, 4, 5, 3)] self.top_shape = [(5, 6, 3, 3), (4, 6, 3, 3), (3, 4, 3, 1), (2, 4, 5, 3)]
self.filter_dilation = (1, 1) self.filter_dilation = (1, 1)
self.ref_mode = 'FAST_RUN' self.ref_mode = 'FAST_RUN'
self.convdim = 2
self.corr_fwd = conv2d_corr
self.corr_gradw = conv2d_corr_gw
self.corr_gradi = conv2d_corr_gi
if theano.config.cxx == "": if theano.config.cxx == "":
raise SkipTest("CorrMM needs cxx") raise SkipTest("CorrMM needs cxx")
def test_fwd(self): def test_fwd(self):
img_sym = theano.tensor.tensor4('img') if self.convdim == 2:
kern_sym = theano.tensor.tensor4('kern') img_sym = theano.tensor.tensor4('img')
kern_sym = theano.tensor.tensor4('kern')
else:
img_sym = theano.tensor.tensor5('img')
kern_sym = theano.tensor.tensor5('kern')
for imshp, kshp, groups in zip(self.img_shape, self.kern_shape, self.num_groups): for imshp, kshp, groups in zip(self.img_shape, self.kern_shape, self.num_groups):
img = np.random.random(imshp).astype(theano.config.floatX) img = np.random.random(imshp).astype(theano.config.floatX)
kern = np.random.random(kshp).astype(theano.config.floatX) kern = np.random.random(kshp).astype(theano.config.floatX)
split_imgs = np.split(img, groups, axis=1) split_imgs = np.split(img, groups, axis=1)
split_kern = np.split(kern, groups, axis=0) split_kern = np.split(kern, groups, axis=0)
grouped_conv_op = self.conv2d(border_mode=self.border_mode, grouped_conv_op = self.conv(border_mode=self.border_mode,
subsample=self.subsample, subsample=self.subsample,
filter_dilation=self.filter_dilation, filter_dilation=self.filter_dilation,
num_groups=groups) num_groups=groups)
if self.flip_filter: if self.flip_filter:
grouped_conv_output = grouped_conv_op(img_sym, kern_sym[:, :, ::-1, ::-1]) if self.convdim == 2:
grouped_conv_output = grouped_conv_op(img_sym, kern_sym[:, :, ::-1, ::-1])
else:
grouped_conv_output = grouped_conv_op(img_sym, kern_sym[:, :, ::-1, ::-1, ::-1])
else: else:
grouped_conv_output = grouped_conv_op(img_sym, kern_sym) grouped_conv_output = grouped_conv_op(img_sym, kern_sym)
grouped_func = theano.function([img_sym, kern_sym], grouped_conv_output, mode=self.mode) grouped_func = theano.function([img_sym, kern_sym], grouped_conv_output, mode=self.mode)
assert any([isinstance(node.op, self.conv2d_op) assert any([isinstance(node.op, self.conv_op)
for node in grouped_func.maker.fgraph.toposort()]) for node in grouped_func.maker.fgraph.toposort()])
grouped_output = grouped_func(img, kern) grouped_output = grouped_func(img, kern)
ref_conv_op = conv2d_corr(img_sym, ref_conv_op = self.corr_fwd(img_sym,
kern_sym, kern_sym,
border_mode=self.border_mode, border_mode=self.border_mode,
subsample=self.subsample, subsample=self.subsample,
filter_dilation=self.filter_dilation) filter_dilation=self.filter_dilation)
ref_func = theano.function([img_sym, kern_sym], ref_conv_op, ref_func = theano.function([img_sym, kern_sym], ref_conv_op,
mode=self.ref_mode) mode=self.ref_mode)
ref_concat_output = [ref_func(img_arr, kern_arr) ref_concat_output = [ref_func(img_arr, kern_arr)
...@@ -1773,34 +1786,43 @@ class Grouped_conv_noOptim(unittest.TestCase): ...@@ -1773,34 +1786,43 @@ class Grouped_conv_noOptim(unittest.TestCase):
eps=1) eps=1)
def test_gradweights(self): def test_gradweights(self):
img_sym = theano.tensor.tensor4('img') if self.convdim == 2:
top_sym = theano.tensor.tensor4('top') img_sym = theano.tensor.tensor4('img')
top_sym = theano.tensor.tensor4('kern')
else:
img_sym = theano.tensor.tensor5('img')
top_sym = theano.tensor.tensor5('kern')
for imshp, kshp, tshp, groups in zip(self.img_shape, self.kern_shape, self.top_shape, self.num_groups): for imshp, kshp, tshp, groups in zip(self.img_shape, self.kern_shape, self.top_shape, self.num_groups):
img = np.random.random(imshp).astype(theano.config.floatX) img = np.random.random(imshp).astype(theano.config.floatX)
top = np.random.random(tshp).astype(theano.config.floatX) top = np.random.random(tshp).astype(theano.config.floatX)
split_imgs = np.split(img, groups, axis=1) split_imgs = np.split(img, groups, axis=1)
split_top = np.split(top, groups, axis=1) split_top = np.split(top, groups, axis=1)
grouped_convgrad_op = self.conv2d_gradw(border_mode=self.border_mode, grouped_convgrad_op = self.conv_gradw(border_mode=self.border_mode,
subsample=self.subsample, subsample=self.subsample,
filter_dilation=self.filter_dilation, filter_dilation=self.filter_dilation,
num_groups=groups) num_groups=groups)
grouped_conv_output = grouped_convgrad_op(img_sym, grouped_conv_output = grouped_convgrad_op(img_sym,
top_sym, top_sym,
tensor.as_tensor_variable(kshp if self.is_dnn else kshp[-2:])) tensor.as_tensor_variable(
kshp if self.is_dnn
else kshp[-self.convdim:]))
if self.flip_filter: if self.flip_filter:
grouped_conv_output = grouped_conv_output[:, :, ::-1, ::-1] if self.convdim == 2:
grouped_conv_output = grouped_conv_output[:, :, ::-1, ::-1]
else:
grouped_conv_output = grouped_conv_output[:, :, ::-1, ::-1, ::-1]
grouped_func = theano.function([img_sym, top_sym], grouped_conv_output, mode=self.mode) grouped_func = theano.function([img_sym, top_sym], grouped_conv_output, mode=self.mode)
assert any([isinstance(node.op, self.conv2d_gradw_op) assert any([isinstance(node.op, self.conv_gradw_op)
for node in grouped_func.maker.fgraph.toposort()]) for node in grouped_func.maker.fgraph.toposort()])
grouped_output = grouped_func(img, top) grouped_output = grouped_func(img, top)
ref_conv_op = conv2d_corr_gw(img_sym, ref_conv_op = self.corr_gradw(img_sym,
top_sym, top_sym,
kshp, kshp,
border_mode=self.border_mode, border_mode=self.border_mode,
subsample=self.subsample, subsample=self.subsample,
filter_dilation=self.filter_dilation) filter_dilation=self.filter_dilation)
ref_func = theano.function([img_sym, top_sym], ref_conv_op, ref_func = theano.function([img_sym, top_sym], ref_conv_op,
mode=self.ref_mode) mode=self.ref_mode)
ref_concat_output = [ref_func(img_arr, top_arr) ref_concat_output = [ref_func(img_arr, top_arr)
...@@ -1811,42 +1833,51 @@ class Grouped_conv_noOptim(unittest.TestCase): ...@@ -1811,42 +1833,51 @@ class Grouped_conv_noOptim(unittest.TestCase):
def conv_gradweight(inputs_val, output_val): def conv_gradweight(inputs_val, output_val):
return grouped_convgrad_op(inputs_val, output_val, return grouped_convgrad_op(inputs_val, output_val,
tensor.as_tensor_variable(kshp if self.is_dnn else kshp[-2:])) tensor.as_tensor_variable(
kshp if self.is_dnn
else kshp[-self.convdim:]))
utt.verify_grad(conv_gradweight, utt.verify_grad(conv_gradweight,
[img, top], [img, top],
mode=self.mode, eps=1) mode=self.mode, eps=1)
def test_gradinputs(self): def test_gradinputs(self):
kern_sym = theano.tensor.tensor4('kern') if self.convdim == 2:
top_sym = theano.tensor.tensor4('top') kern_sym = theano.tensor.tensor4('kern')
top_sym = theano.tensor.tensor4('top')
else:
kern_sym = theano.tensor.tensor5('kern')
top_sym = theano.tensor.tensor5('top')
for imshp, kshp, tshp, groups in zip(self.img_shape, self.kern_shape, self.top_shape, self.num_groups): for imshp, kshp, tshp, groups in zip(self.img_shape, self.kern_shape, self.top_shape, self.num_groups):
kern = np.random.random(kshp).astype(theano.config.floatX) kern = np.random.random(kshp).astype(theano.config.floatX)
top = np.random.random(tshp).astype(theano.config.floatX) top = np.random.random(tshp).astype(theano.config.floatX)
split_kerns = np.split(kern, groups, axis=0) split_kerns = np.split(kern, groups, axis=0)
split_top = np.split(top, groups, axis=1) split_top = np.split(top, groups, axis=1)
grouped_convgrad_op = self.conv2d_gradi(border_mode=self.border_mode, grouped_convgrad_op = self.conv_gradi(border_mode=self.border_mode,
subsample=self.subsample, subsample=self.subsample,
filter_dilation=self.filter_dilation, filter_dilation=self.filter_dilation,
num_groups=groups) num_groups=groups)
if self.flip_filter: if self.flip_filter:
grouped_conv_output = grouped_convgrad_op(kern_sym[:, :, ::-1, ::-1], top_sym, tensor.as_tensor_variable(imshp[-2:])) grouped_conv_output = grouped_convgrad_op(kern_sym[:, :, ::-1, ::-1], top_sym,
tensor.as_tensor_variable(imshp[-self.convdim:]))
else: else:
grouped_conv_output = grouped_convgrad_op(kern_sym, grouped_conv_output = grouped_convgrad_op(kern_sym,
top_sym, top_sym,
tensor.as_tensor_variable(imshp if self.is_dnn else imshp[-2:])) tensor.as_tensor_variable(
imshp if self.is_dnn
else imshp[-self.convdim:]))
grouped_func = theano.function([kern_sym, top_sym], grouped_conv_output, mode=self.mode) grouped_func = theano.function([kern_sym, top_sym], grouped_conv_output, mode=self.mode)
assert any([isinstance(node.op, self.conv2d_gradi_op) assert any([isinstance(node.op, self.conv_gradi_op)
for node in grouped_func.maker.fgraph.toposort()]) for node in grouped_func.maker.fgraph.toposort()])
grouped_output = grouped_func(kern, top) grouped_output = grouped_func(kern, top)
ref_conv_op = conv2d_corr_gi(kern_sym, ref_conv_op = self.corr_gradi(kern_sym,
top_sym, top_sym,
imshp, imshp,
border_mode=self.border_mode, border_mode=self.border_mode,
subsample=self.subsample, subsample=self.subsample,
filter_dilation=self.filter_dilation) filter_dilation=self.filter_dilation)
ref_func = theano.function([kern_sym, top_sym], ref_conv_op, ref_func = theano.function([kern_sym, top_sym], ref_conv_op,
mode=self.ref_mode) mode=self.ref_mode)
ref_concat_output = [ref_func(kern_arr, top_arr) ref_concat_output = [ref_func(kern_arr, top_arr)
...@@ -1857,13 +1888,43 @@ class Grouped_conv_noOptim(unittest.TestCase): ...@@ -1857,13 +1888,43 @@ class Grouped_conv_noOptim(unittest.TestCase):
def conv_gradinputs(filters_val, output_val): def conv_gradinputs(filters_val, output_val):
return grouped_convgrad_op(filters_val, output_val, return grouped_convgrad_op(filters_val, output_val,
tensor.as_tensor_variable(imshp if self.is_dnn else imshp[-2:])) tensor.as_tensor_variable(
imshp if self.is_dnn
else imshp[-self.convdim:]))
utt.verify_grad(conv_gradinputs, utt.verify_grad(conv_gradinputs,
[kern, top], [kern, top],
mode=self.mode, eps=1) mode=self.mode, eps=1)
class Grouped_conv3d_noOptim(Grouped_conv_noOptim):
conv = theano.tensor.nnet.abstract_conv.AbstractConv3d
conv_gradw = theano.tensor.nnet.abstract_conv.AbstractConv3d_gradWeights
conv_gradi = theano.tensor.nnet.abstract_conv.AbstractConv3d_gradInputs
conv_op = theano.tensor.nnet.abstract_conv.AbstractConv3d
conv_gradw_op = theano.tensor.nnet.abstract_conv.AbstractConv3d_gradWeights
conv_gradi_op = theano.tensor.nnet.abstract_conv.AbstractConv3d_gradInputs
mode = theano.Mode(optimizer=None)
flip_filter = False
is_dnn = False
def setUp(self):
self.num_groups = [3, 2, 4, 4]
self.border_mode = 'valid'
self.subsample = (1, 1, 1)
self.img_shape = [(2, 6, 5, 5, 5), (1, 4, 7, 5, 7), (1, 8, 5, 3, 5), (2, 4, 7, 7, 7)]
self.kern_shape = [(3, 2, 3, 3, 3), (6, 2, 5, 3, 5), (4, 2, 3, 3, 3), (4, 1, 3, 5, 3)]
self.top_shape = [(2, 3, 3, 3, 3), (1, 6, 3, 3, 3), (1, 4, 3, 1, 3), (2, 4, 5, 3, 5)]
self.filter_dilation = (1, 1, 1)
self.ref_mode = 'FAST_RUN'
self.convdim = 3
self.corr_fwd = conv3d_corr
self.corr_gradw = conv3d_corr_gw
self.corr_gradi = conv3d_corr_gi
if theano.config.cxx == "":
raise SkipTest("CorrMM needs cxx")
class Separable_conv(unittest.TestCase): class Separable_conv(unittest.TestCase):
def test_interface(self): def test_interface(self):
......
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