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提交 ebcc6a41 authored 作者: Frederic's avatar Frederic
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Make ConvOp detect more frequently when the hardcoded shape isn't the run time one.

上级 0838ae4d
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正在显示 包含 134 行增加 和 82 行删除
theano/tensor/nnet/conv.py
浏览文件 @ ebcc6a41
......@@ -671,15 +671,23 @@ class ConvOp(OpenMPOp):
imshp = self.imshp
if any(x is None for x in imshp):
imshp = tuple(img2d.shape[1:])
if imshp != img2d.shape[1:]:
raise ValueError("bad shape", imshp, img2d.shape[1:])
kshp = self.kshp
if any(x is None for x in kshp):
kshp = tuple(filtersflipped.shape[2:])
if kshp != filtersflipped.shape[2:]:
raise ValueError("bad shape", kshp, filtersflipped.shape[2:])
bsize = self.bsize
if bsize is None:
bsize = img2d.shape[0]
elif bsize != img2d.shape[0]:
raise ValueError("bad shape", bsize, img2d.shape[0])
nkern = self.nkern
if nkern is None:
nkern = filtersflipped.shape[0]
elif nkern != filtersflipped.shape[0]:
raise ValueError("bad shape", nkern, filtersflipped.shape[0])
imshp_logical = self.imshp_logical
if imshp_logical[0] is None:
......@@ -974,7 +982,7 @@ class ConvOp(OpenMPOp):
return ['<numpy/noprefix.h>', '<iostream>', '<sstream>']
def c_code_cache_version(self):
return (13, self.openmp, blas.blas_header_version())
return (14, self.openmp, blas.blas_header_version())
def c_support_code(self):
return """
......@@ -1069,25 +1077,126 @@ using namespace std;
d["self_imshp2"] = "PyArray_DIMS(%(img2d)s)[3]" % d
d["self_kshp0"] = "PyArray_DIMS(%(filtersflipped)s)[2]" % d
d["self_kshp1"] = "PyArray_DIMS(%(filtersflipped)s)[3]" % d
d["assert_size"] = ""
# Override the default value if we have it
if self.kshp[0] is not None:
expected = d["self_kshp0"]
value = self.kshp[0]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_kshp0"] = self.kshp[0]
if self.kshp[1] is not None:
expected = d["self_kshp1"]
value = self.kshp[1]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_kshp1"] = self.kshp[1]
if self.outshp[0] is not None:
expected = "dim_zz[0]"
value = self.outshp[0]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_outshp0"] = self.outshp[0]
if self.outshp[1] is not None:
expected = "dim_zz[1]"
value = self.outshp[1]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_outshp1"] = self.outshp[1]
if self.imshp[0] is not None:
expected = d["self_imshp0"]
value = self.imshp[0]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
expected = "kerns_dim[1]"
value = self.imshp[0]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_imshp0"] = self.imshp[0]
if self.imshp[1] is not None:
expected = d["self_imshp1"]
value = self.imshp[1]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_imshp1"] = self.imshp[1]
if self.imshp[2] is not None:
expected = d["self_imshp2"]
value = self.imshp[2]
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_imshp2"] = self.imshp[2]
if self.bsize is not None:
expected = d["self_bsize"]
value = self.bsize
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_bsize"] = self.bsize
if self.nkern is not None:
expected = d["self_nkern"]
value = self.nkern
d["assert_size"] += """
if(%(value)s != %(expected)s){
PyErr_Format(PyExc_ValueError,
"the hard coded shape (%%ld) isn't the run time shape (%%ld).",
(long)%(value)s, (long)%(expected)s);
%(fail)s;
}
""" % dict(expected=expected, value=value, **sub)
d["self_nkern"] = self.nkern
# Other hard coded stuff only if we have all shapes
......@@ -1111,86 +1220,6 @@ using namespace std;
d["all_shape"] = "1"
d["dim_zz_const"] = "const"
d["dim_zz_affect"] = ""
d["assert_size"] = """
// Check the batch size and the number of kernels (sometimes constant in the graph)
if(img2d_dim[0] != %(self_bsize)s!=0){
PyErr_Format(PyExc_ValueError,
"the batch size in the image (%%ld) at run time is different"
" than at build time (%%ld) for the ConvOp.",
(long)img2d_dim[0], (long)%(self_bsize)s);
%(fail)s;
}
if(kerns_dim[0] != %(self_nkern)s!=0){
PyErr_Format(PyExc_ValueError,
"the number of kernels in the filter (%%ld) at run time is"
" different than at build time (%%ld) for the ConvOp.",
(long)kerns_dim[0], (long)%(self_nkern)s);
%(fail)s;
}
// Check the size of the image (sometimes constant in the graph)
if(img2d_dim[1] != %(self_imshp0)s){
PyErr_Format(PyExc_ValueError,
"the image stack size (%%ld) at run time is different than"
" at build time (%%ld) for the ConvOp.",
(long)img2d_dim[1], (long)%(self_imshp0)s);
%(fail)s;
}
if(img2d_dim[2] != %(self_imshp1)s){
PyErr_Format(PyExc_ValueError,
"the number of rows in the image (%%ld) at run time is different"
" than at build time (%%ld) for the ConvOp.",
(long)img2d_dim[2], (long)%(self_imshp1)s);
%(fail)s;
}
if(img2d_dim[3] != %(self_imshp2)s){
PyErr_Format(PyExc_ValueError,
"the number of columns in the image (%%ld) at run time is"
" different than at build time (%%ld) for the ConvOp.",
(long)img2d_dim[3], (long)%(self_imshp2)s);
%(fail)s;
}
// Check the size of the output (sometimes constant in the graph)
if(dim_zz[0] != %(self_outshp0)s!=0){
PyErr_Format(PyExc_ValueError,
"the precomputed number of rows in the output (%%ld) at run time"
" is different than at build time (%%ld) for the ConvOp.",
(long)dim_zz[0], (long)%(self_outshp0)s);
%(fail)s;
}
if(dim_zz[1] != %(self_outshp1)s!=0){
PyErr_Format(PyExc_ValueError,
"the precomputed number of columns in the output (%%ld) at run"
" time is different than at build time (%%ld) for the ConvOp.",
(long)dim_zz[1], (long)%(self_outshp1)s);
%(fail)s;
}
// Check the size of the filter (sometimes constant in the graph)
if(kerns_dim[1] %% %(self_imshp0)s!=0){
PyErr_Format(PyExc_ValueError,
"the filter stack size (%%ld) at run time is different than at"
" build time (%%ld) for the ConvOp.",
(long)kerns_dim[1], (long)%(self_imshp0)s);
%(fail)s;
}
if(kerns_dim[2] %% %(self_kshp0)s!=0){
PyErr_Format(PyExc_ValueError,
"the number of rows in the filter (%%ld) at run time is different"
" than at build time (%%ld) for the ConvOp.",
(long)kerns_dim[2], (long)%(self_kshp0)s);
%(fail)s;
}
if(kerns_dim[3] %% %(self_kshp1)s!=0){
PyErr_Format(PyExc_ValueError,
"the number of columns in the filter (%%ld) at run time is"
" different than at build time (%%ld) for the ConvOp.",
(long)kerns_dim[3], (long)%(self_kshp1)s);
%(fail)s;
}
""" % (locals())
else:
d["affectation"] = "+="
d["all_shape"] = "0"
......@@ -1204,7 +1233,7 @@ if(kerns_dim[3] %% %(self_kshp1)s!=0){
dim_zz[1] = (int)ceil((dim_im[1]-dim_ker1+1)/float(%(self_dy)s));
}
""" % d
d["assert_size"] = """
d["assert_size"] += """
// Check the stack size of the filter and images are equals
if(kerns_dim[1] != img2d_dim[1]){
PyErr_Format(PyExc_ValueError,
......
theano/tensor/nnet/tests/test_conv.py
浏览文件 @ ebcc6a41
......@@ -449,6 +449,29 @@ class TestConv2D(utt.InferShapeTester):
print t2 - t1,
print
def test_fail(self):
k = theano.shared(numpy.ones((1, 1, 3, 3), dtype='float32'))
im = T.ftensor4()
out = theano.function([im],
T.nnet.conv2d(im, k, image_shape=(1, 1, 10, 10)))
self.assertRaises(ValueError, out, numpy.ones((1, 1, 20, 10),
dtype='float32'))
out = theano.function([im],
T.nnet.conv2d(im, k, filter_shape=(1, 1, 3, 2)))
self.assertRaises(ValueError, out, numpy.ones((1, 1, 10, 10),
dtype='float32'))
out = theano.function([im],
T.nnet.conv2d(im, k, filter_shape=(2, None,
None, None)))
self.assertRaises(ValueError, out, numpy.ones((1, 1, 10, 10),
dtype='float32'))
out = theano.function([im],
T.nnet.conv2d(im, k, image_shape=(1, None,
None, None)))
self.assertRaises(ValueError, out, numpy.ones((2, 1, 10, 10),
dtype='float32'))
def test_infer_shape(self):
# Note: infer_shape is incomplete and thus input and filter shapes
# must be provided explicitly
......
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