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提交 106eb11f authored 作者: affanv14's avatar affanv14 提交者： Arnaud Bergeron
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add tests for separable conv3d

上级 e11121cd
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正在显示 包含 84 行增加 和 32 行删除
theano/tensor/nnet/tests/test_abstract_conv.py
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...@@ -23,7 +23,7 @@ from theano.tensor.nnet.abstract_conv import AbstractConv2d_gradWeights ...@@ -23,7 +23,7 @@ from theano.tensor.nnet.abstract_conv import AbstractConv2d_gradWeights
from theano.tensor.nnet.abstract_conv import bilinear_kernel_1D from theano.tensor.nnet.abstract_conv import bilinear_kernel_1D
from theano.tensor.nnet.abstract_conv import bilinear_kernel_2D from theano.tensor.nnet.abstract_conv import bilinear_kernel_2D
from theano.tensor.nnet.abstract_conv import bilinear_upsampling from theano.tensor.nnet.abstract_conv import bilinear_upsampling
from theano.tensor.nnet.abstract_conv import separable_conv2d from theano.tensor.nnet.abstract_conv import separable_conv2d, separable_conv3d
from theano.tensor.nnet.corr import (CorrMM, CorrMM_gradWeights, from theano.tensor.nnet.corr import (CorrMM, CorrMM_gradWeights,
CorrMM_gradInputs) CorrMM_gradInputs)
from theano.tensor.nnet.corr3d import (Corr3dMM, Corr3dMM_gradWeights, from theano.tensor.nnet.corr3d import (Corr3dMM, Corr3dMM_gradWeights,
...@@ -1652,35 +1652,96 @@ class Grouped_conv3d_noOptim(Grouped_conv_noOptim): ...@@ -1652,35 +1652,96 @@ class Grouped_conv3d_noOptim(Grouped_conv_noOptim):
class Separable_conv(unittest.TestCase): class Separable_conv(unittest.TestCase):
def setUp(self):
self.x = np.array([[[[1, 2, 3, 4, 5], [3, 2, 1, 4, 5], [3, 3, 1, 3, 6], [5, 3, 2, 1, 1], [4, 7, 1, 2, 1]],
[[3, 3, 1, 2, 6], [6, 5, 4, 3, 1], [3, 4, 5, 2, 3], [6, 4, 1, 3, 4], [2, 3, 4, 2, 5]]]]).astype(theano.config.floatX)
def test_interface(self): self.depthwise_filter = np.array([[[[3, 2, 1], [5, 3, 2], [6, 4, 2]]], [[[5, 5, 2], [3, 7, 4], [3, 5, 4]]],
x = np.array([[[[1, 2, 3, 4, 5], [3, 2, 1, 4, 5], [3, 3, 1, 3, 6], [5, 3, 2, 1, 1], [4, 7, 1, 2, 1]], [[[7, 4, 7], [5, 3, 3], [1, 3, 1]]], [[[4, 4, 4], [2, 4, 6], [0, 0, 7]]]]).astype(theano.config.floatX)
[[3, 3, 1, 2, 6], [6, 5, 4, 3, 1], [3, 4, 5, 2, 3], [6, 4, 1, 3, 4], [2, 3, 4, 2, 5]]]]).astype(theano.config.floatX)
self.pointwise_filter = np.array([[[[4]], [[1]], [[3]], [[5]]], [[[2]], [[1]], [[2]], [[8]]]]).astype(theano.config.floatX)
depthwise_filter = np.array([[[[3, 2, 1], [5, 3, 2], [6, 4, 2]]], [[[5, 5, 2], [3, 7, 4], [3, 5, 4]]], self.precomp_output_valid = np.array([[[[1385, 1333, 1339], [1382, 1243, 1291], [1303, 1120, 1228]],
[[[7, 4, 7], [5, 3, 3], [1, 3, 1]]], [[[4, 4, 4], [2, 4, 6], [0, 0, 7]]]]).astype(theano.config.floatX) [[1532, 1410, 1259], [1522, 1346, 1314], [1379, 1192, 1286]]]]).astype(theano.config.floatX)
pointwise_filter = np.array([[[[4]], [[1]], [[3]], [[5]]], [[[2]], [[1]], [[2]], [[8]]]]).astype(theano.config.floatX) self.precomp_output_full = np.array([[[[140, 266, 343, 206, 59],
precomp_output = np.array([[[[1385, 1333, 1339], [1382, 1243, 1291], [1303, 1120, 1228]], [395, 697, 979, 585, 245],
[[1532, 1410, 1259], [1522, 1346, 1314], [1379, 1192, 1286]]]]).astype(theano.config.floatX) [429, 863, 1385, 919, 453],
[243, 499, 864, 627, 371],
[90, 183, 291, 254, 202]],
[[149, 289, 359, 213, 58],
[400, 750, 1076, 662, 266],
[387, 854, 1532, 1091, 540],
[174, 411, 971, 786, 518],
[51, 110, 286, 299, 298]]]]).astype(theano.config.floatX)
def test_interface2d(self):
x_sym = theano.tensor.tensor4('x') x_sym = theano.tensor.tensor4('x')
dfilter_sym = theano.tensor.tensor4('d') dfilter_sym = theano.tensor.tensor4('d')
pfilter_sym = theano.tensor.tensor4('p') pfilter_sym = theano.tensor.tensor4('p')
sep_op = separable_conv2d(x_sym, dfilter_sym, pfilter_sym, x.shape[1]) sep_op = separable_conv2d(x_sym, dfilter_sym, pfilter_sym, self.x.shape[1])
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN') fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
# test for square matrix # test for square matrix
top = fun(x, depthwise_filter, pointwise_filter) top = fun(self.x, self.depthwise_filter, self.pointwise_filter)
utt.assert_allclose(top, precomp_output) utt.assert_allclose(top, self.precomp_output_valid)
# test for non-square matrix # test for non-square matrix
top = fun(x[:, :, :3, :], depthwise_filter, pointwise_filter) top = fun(self.x[:, :, :3, :], self.depthwise_filter, self.pointwise_filter)
utt.assert_allclose(top, precomp_output[:, :, :1, :]) utt.assert_allclose(top, self.precomp_output_valid[:, :, :1, :])
# test if it infers shape # test if it infers shape
sep_op = separable_conv2d(x_sym, sep_op = separable_conv2d(x_sym,
dfilter_sym,
pfilter_sym,
self.x.shape[1],
input_shape=self.x.shape,
depthwise_filter_shape=self.depthwise_filter.shape,
pointwise_filter_shape=self.pointwise_filter.shape)
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
top = fun(self.x, self.depthwise_filter, self.pointwise_filter)
utt.assert_allclose(top, self.precomp_output_valid)
# test non-default subsample
sep_op = separable_conv2d(x_sym,
dfilter_sym,
pfilter_sym,
self.x.shape[1],
subsample=(2, 2))
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
top = fun(self.x, self.depthwise_filter, self.pointwise_filter)
utt.assert_allclose(top, np.delete(np.delete(self.precomp_output_valid, 1, axis=3), 1, axis=2))
# test non-default border_mode
sep_op = separable_conv2d(x_sym, dfilter_sym, pfilter_sym, self.x.shape[1], border_mode='full')
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
top = fun(self.x[:, :, :3, :3], self.depthwise_filter, self.pointwise_filter)
utt.assert_allclose(top, self.precomp_output_full)
def test_interface3d(self):
# Expand the filter along the depth
x = np.tile(np.expand_dims(self.x, axis=2), (1, 1, 5, 1, 1))
depthwise_filter = np.tile(np.expand_dims(self.depthwise_filter, axis=2), (1, 1, 3, 1, 1))
pointwise_filter = np.expand_dims(self.pointwise_filter, axis=2)
precomp_output = np.tile(np.expand_dims(self.precomp_output_valid, axis=2), (1, 1, 3, 1, 1)) * 3
x_sym = theano.tensor.tensor5('x')
dfilter_sym = theano.tensor.tensor5('d')
pfilter_sym = theano.tensor.tensor5('p')
sep_op = separable_conv3d(x_sym, dfilter_sym, pfilter_sym, x.shape[1])
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
# test for square matrix
top = fun(x, depthwise_filter, pointwise_filter)
utt.assert_allclose(top, precomp_output)
# test for non-square matrix
top = fun(x[:, :, :3, :, :3], depthwise_filter, pointwise_filter)
utt.assert_allclose(top, precomp_output[:, :, :1, :, :1])
# test if it infers shape
sep_op = separable_conv3d(x_sym,
dfilter_sym, dfilter_sym,
pfilter_sym, pfilter_sym,
x.shape[1], x.shape[1],
...@@ -1692,29 +1753,20 @@ class Separable_conv(unittest.TestCase): ...@@ -1692,29 +1753,20 @@ class Separable_conv(unittest.TestCase):
utt.assert_allclose(top, precomp_output) utt.assert_allclose(top, precomp_output)
# test non-default subsample # test non-default subsample
sep_op = separable_conv2d(x_sym, sep_op = separable_conv3d(x_sym,
dfilter_sym, dfilter_sym,
pfilter_sym, pfilter_sym,
x.shape[1], x.shape[1],
subsample=(2, 2)) subsample=(2, 2, 2))
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN') fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
top = fun(x, depthwise_filter, pointwise_filter) top = fun(x, depthwise_filter, pointwise_filter)
utt.assert_allclose(top, np.delete(np.delete(precomp_output, 1, axis=3), 1, axis=2)) utt.assert_allclose(top, np.delete(np.delete(
np.delete(precomp_output, 1, axis=4), 1, axis=3), 1, axis=2))
# test non-default border_mode # test non-default border_mode
precomp_output = np.array([[[[140, 266, 343, 206, 59], precomp_output = np.tile(np.expand_dims(self.precomp_output_full, axis=2),
[395, 697, 979, 585, 245], (1, 1, 5, 1, 1)) * np.array([[[[[1]], [[2]], [[3]], [[2]], [[1]]]]])
[429, 863, 1385, 919, 453],
[243, 499, 864, 627, 371], sep_op = separable_conv3d(x_sym, dfilter_sym, pfilter_sym, x.shape[1], border_mode='full')
[90, 183, 291, 254, 202]],
[[149, 289, 359, 213, 58],
[400, 750, 1076, 662, 266],
[387, 854, 1532, 1091, 540],
[174, 411, 971, 786, 518],
[51, 110, 286, 299, 298]]]]).astype(theano.config.floatX)
sep_op = separable_conv2d(x_sym, dfilter_sym, pfilter_sym, x.shape[1], border_mode='full')
fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN') fun = theano.function([x_sym, dfilter_sym, pfilter_sym], sep_op, mode='FAST_RUN')
top = fun(x[:, :, :3, :3], depthwise_filter, pointwise_filter) top = fun(x[:, :, :3, :3, :3], depthwise_filter, pointwise_filter)
utt.assert_allclose(top, precomp_output) utt.assert_allclose(top, precomp_output)
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