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提交 c81f8c36 authored 作者: Samira Shabanian's avatar Samira Shabanian
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theano/tensor/signal/conv.py
浏览文件 @ c81f8c36
...@@ -3,8 +3,6 @@ Contains a wrapper function for tensor.nnet.ConvOp, which can be used to perform ...@@ -3,8 +3,6 @@ Contains a wrapper function for tensor.nnet.ConvOp, which can be used to perform
generic 2D convolution. generic 2D convolution.
""" """
__docformat__ = "restructuredtext en"
import warnings import warnings
import theano import theano
...@@ -12,6 +10,10 @@ import theano.tensor as tensor ...@@ -12,6 +10,10 @@ import theano.tensor as tensor
from theano.tensor.nnet import conv from theano.tensor.nnet import conv
import logging import logging
__docformat__ = "restructuredtext en"
_logger = logging.getLogger("theano.tensor.signal.conv") _logger = logging.getLogger("theano.tensor.signal.conv")
...@@ -52,7 +54,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None, ...@@ -52,7 +54,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
assert input.ndim in (2, 3) assert input.ndim in (2, 3)
assert filters.ndim in (2, 3) assert filters.ndim in (2, 3)
### use shape information if it is given to us ### # use shape information if it is given to us ###
if filter_shape and image_shape: if filter_shape and image_shape:
if input.ndim == 3: if input.ndim == 3:
bsize = image_shape[0] bsize = image_shape[0]
...@@ -69,7 +71,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None, ...@@ -69,7 +71,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
nkern, kshp = None, None nkern, kshp = None, None
bsize, imshp = None, None bsize, imshp = None, None
### reshape tensors to 4D, for compatibility with ConvOp ### # reshape tensors to 4D, for compatibility with ConvOp ###
if input.ndim == 3: if input.ndim == 3:
sym_bsize = input.shape[0] sym_bsize = input.shape[0]
else: else:
...@@ -86,7 +88,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None, ...@@ -86,7 +88,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
new_filter_shape = tensor.join(0, tensor.stack([sym_nkern, 1]), filters.shape[-2:]) new_filter_shape = tensor.join(0, tensor.stack([sym_nkern, 1]), filters.shape[-2:])
filters4D = tensor.reshape(filters, new_filter_shape, ndim=4) filters4D = tensor.reshape(filters, new_filter_shape, ndim=4)
### perform actual convolution ### # perform actual convolution ###
op = conv.ConvOp(output_mode=border_mode, op = conv.ConvOp(output_mode=border_mode,
dx=subsample[0], dy=subsample[1], dx=subsample[0], dy=subsample[1],
imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize, **kargs) imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize, **kargs)
......
theano/tensor/signal/downsample.py
浏览文件 @ c81f8c36
...@@ -18,6 +18,7 @@ from theano import gof, Op, tensor, Variable, Apply ...@@ -18,6 +18,7 @@ from theano import gof, Op, tensor, Variable, Apply
from theano.tensor.opt import register_canonicalize from theano.tensor.opt import register_canonicalize
def max_pool2D(*args, **kwargs): def max_pool2D(*args, **kwargs):
import sys import sys
print("DEPRECATION: max_pool2D renamed to max_pool_2d", file=sys.stderr) print("DEPRECATION: max_pool2D renamed to max_pool_2d", file=sys.stderr)
...@@ -206,8 +207,8 @@ class DownsampleFactorMax(Op): ...@@ -206,8 +207,8 @@ class DownsampleFactorMax(Op):
if isinstance(r, theano.Variable): if isinstance(r, theano.Variable):
nr = tensor.switch(tensor.ge(st[0], ds[0]), nr = tensor.switch(tensor.ge(st[0], ds[0]),
(r - 1) // st[0] + 1, (r - 1) // st[0] + 1,
tensor.maximum(0, (r - 1 - ds[0]) tensor.maximum(0, (r - 1 - ds[0]) //
// st[0] + 1) + 1) st[0] + 1) + 1)
elif st[0] >= ds[0]: elif st[0] >= ds[0]:
nr = (r - 1) // st[0] + 1 nr = (r - 1) // st[0] + 1
else: else:
...@@ -216,8 +217,8 @@ class DownsampleFactorMax(Op): ...@@ -216,8 +217,8 @@ class DownsampleFactorMax(Op):
if isinstance(c, theano.Variable): if isinstance(c, theano.Variable):
nc = tensor.switch(tensor.ge(st[1], ds[1]), nc = tensor.switch(tensor.ge(st[1], ds[1]),
(c - 1) // st[1] + 1, (c - 1) // st[1] + 1,
tensor.maximum(0, (c - 1 - ds[1]) tensor.maximum(0, (c - 1 - ds[1]) //
// st[1] + 1) + 1) st[1] + 1) + 1)
elif st[1] >= ds[1]: elif st[1] >= ds[1]:
nc = (c - 1) // st[1] + 1 nc = (c - 1) // st[1] + 1
else: else:
...@@ -289,7 +290,7 @@ class DownsampleFactorMax(Op): ...@@ -289,7 +290,7 @@ class DownsampleFactorMax(Op):
y = numpy.zeros( y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols), (x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype) dtype=x.dtype)
y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else: else:
y = x y = x
func = numpy.max func = numpy.max
...@@ -336,6 +337,7 @@ class DownsampleFactorMax(Op): ...@@ -336,6 +337,7 @@ class DownsampleFactorMax(Op):
st=self.st, padding=self.padding, st=self.st, padding=self.padding,
mode=self.mode)( mode=self.mode)(
x, gz)] x, gz)]
def c_headers(self): def c_headers(self):
return ['<algorithm>'] return ['<algorithm>']
...@@ -522,6 +524,7 @@ class DownsampleFactorMax(Op): ...@@ -522,6 +524,7 @@ class DownsampleFactorMax(Op):
def c_code_cache_version(self): def c_code_cache_version(self):
return (0, 6, 8, 3) return (0, 6, 8, 3)
class PoolGrad(Op): class PoolGrad(Op):
__props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode') __props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
...@@ -582,8 +585,8 @@ class PoolGrad(Op): ...@@ -582,8 +585,8 @@ class PoolGrad(Op):
if isinstance(r, theano.Variable): if isinstance(r, theano.Variable):
nr = tensor.switch(tensor.ge(st[0], ds[0]), nr = tensor.switch(tensor.ge(st[0], ds[0]),
(r - 1) // st[0] + 1, (r - 1) // st[0] + 1,
tensor.maximum(0, (r - 1 - ds[0]) tensor.maximum(0, (r - 1 - ds[0]) //
// st[0] + 1) + 1) st[0] + 1) + 1)
elif st[0] >= ds[0]: elif st[0] >= ds[0]:
nr = (r - 1) // st[0] + 1 nr = (r - 1) // st[0] + 1
else: else:
...@@ -592,8 +595,8 @@ class PoolGrad(Op): ...@@ -592,8 +595,8 @@ class PoolGrad(Op):
if isinstance(c, theano.Variable): if isinstance(c, theano.Variable):
nc = tensor.switch(tensor.ge(st[1], ds[1]), nc = tensor.switch(tensor.ge(st[1], ds[1]),
(c - 1) // st[1] + 1, (c - 1) // st[1] + 1,
tensor.maximum(0, (c - 1 - ds[1]) tensor.maximum(0, (c - 1 - ds[1]) //
// st[1] + 1) + 1) st[1] + 1) + 1)
elif st[1] >= ds[1]: elif st[1] >= ds[1]:
nc = (c - 1) // st[1] + 1 nc = (c - 1) // st[1] + 1
else: else:
...@@ -656,7 +659,7 @@ class MaxPoolGrad(PoolGrad): ...@@ -656,7 +659,7 @@ class MaxPoolGrad(PoolGrad):
y = numpy.zeros( y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols), (x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype) dtype=x.dtype)
y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else: else:
y = x y = x
gx = numpy.zeros_like(y) gx = numpy.zeros_like(y)
...@@ -673,7 +676,7 @@ class MaxPoolGrad(PoolGrad): ...@@ -673,7 +676,7 @@ class MaxPoolGrad(PoolGrad):
if (maxout[n, k, r, c] == y[n, k, row_ind, col_ind]): if (maxout[n, k, r, c] == y[n, k, row_ind, col_ind]):
gx[n, k, row_ind, col_ind] += gz[n, k, r, c] gx[n, k, row_ind, col_ind] += gz[n, k, r, c]
# unpad the image # unpad the image
gx = gx[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx gx_stg[0] = gx
def grad(self, inp, grads): def grad(self, inp, grads):
...@@ -804,6 +807,7 @@ class MaxPoolGrad(PoolGrad): ...@@ -804,6 +807,7 @@ class MaxPoolGrad(PoolGrad):
DownsampleFactorMaxGrad = MaxPoolGrad DownsampleFactorMaxGrad = MaxPoolGrad
class AveragePoolGrad(PoolGrad): class AveragePoolGrad(PoolGrad):
def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='average_inc_pad'): def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='average_inc_pad'):
...@@ -848,7 +852,7 @@ class AveragePoolGrad(PoolGrad): ...@@ -848,7 +852,7 @@ class AveragePoolGrad(PoolGrad):
y = numpy.zeros( y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols), (x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype) dtype=x.dtype)
y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x y[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)] = x
else: else:
y = x y = x
gx = numpy.zeros_like(y) gx = numpy.zeros_like(y)
...@@ -874,7 +878,7 @@ class AveragePoolGrad(PoolGrad): ...@@ -874,7 +878,7 @@ class AveragePoolGrad(PoolGrad):
(col_end - col_st)) (col_end - col_st))
gx[n, k, row_st:row_end, col_st:col_end] += val gx[n, k, row_st:row_end, col_st:col_end] += val
# unpad the image # unpad the image
gx = gx[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] gx = gx[:, :, pad_h:(img_rows - pad_h), pad_w:(img_cols - pad_w)]
gx_stg[0] = gx gx_stg[0] = gx
def grad(self, inp, grads): def grad(self, inp, grads):
...@@ -885,6 +889,7 @@ class AveragePoolGrad(PoolGrad): ...@@ -885,6 +889,7 @@ class AveragePoolGrad(PoolGrad):
self.ds, ignore_border=self.ignore_border, self.ds, ignore_border=self.ignore_border,
st=self.st, padding=self.padding, mode=self.mode)(ggx)] st=self.st, padding=self.padding, mode=self.mode)(ggx)]
class DownsampleFactorMaxGradGrad(Op): class DownsampleFactorMaxGradGrad(Op):
__props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode') __props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
...@@ -946,8 +951,8 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -946,8 +951,8 @@ class DownsampleFactorMaxGradGrad(Op):
if isinstance(r, theano.Variable): if isinstance(r, theano.Variable):
nr = tensor.switch(tensor.ge(st[0], ds[0]), nr = tensor.switch(tensor.ge(st[0], ds[0]),
(r - 1) // st[0] + 1, (r - 1) // st[0] + 1,
tensor.maximum(0, (r - 1 - ds[0]) tensor.maximum(0, (r - 1 - ds[0]) //
// st[0] + 1) + 1) st[0] + 1) + 1)
elif st[0] >= ds[0]: elif st[0] >= ds[0]:
nr = (r - 1) // st[0] + 1 nr = (r - 1) // st[0] + 1
else: else:
...@@ -956,8 +961,8 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -956,8 +961,8 @@ class DownsampleFactorMaxGradGrad(Op):
if isinstance(c, theano.Variable): if isinstance(c, theano.Variable):
nc = tensor.switch(tensor.ge(st[1], ds[1]), nc = tensor.switch(tensor.ge(st[1], ds[1]),
(c - 1) // st[1] + 1, (c - 1) // st[1] + 1,
tensor.maximum(0, (c - 1 - ds[1]) tensor.maximum(0, (c - 1 - ds[1]) //
// st[1] + 1) + 1) st[1] + 1) + 1)
elif st[1] >= ds[1]: elif st[1] >= ds[1]:
nc = (c - 1) // st[1] + 1 nc = (c - 1) // st[1] + 1
else: else:
...@@ -966,7 +971,7 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -966,7 +971,7 @@ class DownsampleFactorMaxGradGrad(Op):
rval = list(imgshape[:-2]) + [nr, nc] rval = list(imgshape[:-2]) + [nr, nc]
return rval return rval
def __init__(self, ds, ignore_border, st=None, padding=(0,0), mode='max'): def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
self.ds = tuple(ds) self.ds = tuple(ds)
if not all([isinstance(d, int) for d in ds]): if not all([isinstance(d, int) for d in ds]):
raise ValueError( raise ValueError(
...@@ -1025,11 +1030,11 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -1025,11 +1030,11 @@ class DownsampleFactorMaxGradGrad(Op):
y_padded = numpy.zeros( y_padded = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols), (x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype) + x.min() - 1 dtype=x.dtype) + x.min() - 1
y_padded[:, :, pd0:(img_rows-pd0), pd1:(img_cols-pd1)] = x y_padded[:, :, pd0:(img_rows - pd0), pd1:(img_cols - pd1)] = x
ggx_padded = numpy.zeros( ggx_padded = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols), (x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype) dtype=x.dtype)
ggx_padded[:, :, pd0:(img_rows-pd0), pd1:(img_cols-pd1)] = ggx ggx_padded[:, :, pd0:(img_rows - pd0), pd1:(img_cols - pd1)] = ggx
else: else:
y_padded = x y_padded = x
...@@ -1130,10 +1135,11 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -1130,10 +1135,11 @@ class DownsampleFactorMaxGradGrad(Op):
} }
} }
} }
"""%locals() """ % locals()
def c_code_cache_version(self): def c_code_cache_version(self):
return (0,1) return (0, 1)
@register_canonicalize('fast_compile') @register_canonicalize('fast_compile')
@gof.local_optimizer([MaxPoolGrad]) @gof.local_optimizer([MaxPoolGrad])
...@@ -1141,7 +1147,7 @@ def local_average_pool_grad(node): ...@@ -1141,7 +1147,7 @@ def local_average_pool_grad(node):
# To assure backward compatibility with # To assure backward compatibility with
# DownsampleFactorMaxGrad # DownsampleFactorMaxGrad
if (not isinstance(node.op, MaxPoolGrad) or node.op.mode not in if (not isinstance(node.op, MaxPoolGrad) or node.op.mode not in
['sum','average_exc_pad', 'average_inc_pad']): ['sum', 'average_exc_pad', 'average_inc_pad']):
return False return False
return [AveragePoolGrad(ds=node.op.ds, return [AveragePoolGrad(ds=node.op.ds,
ignore_border=node.op.ignore_border, ignore_border=node.op.ignore_border,
......
theano/tensor/signal/tests/test_conv.py
浏览文件 @ c81f8c36
...@@ -31,7 +31,7 @@ class TestSignalConv2D(unittest.TestCase): ...@@ -31,7 +31,7 @@ class TestSignalConv2D(unittest.TestCase):
if filter_dim != 3: if filter_dim != 3:
nkern = 1 nkern = 1
############# THEANO IMPLEMENTATION ############ # THEANO IMPLEMENTATION ############
# we create a symbolic function so that verify_grad can work # we create a symbolic function so that verify_grad can work
def sym_conv2d(input, filters): def sym_conv2d(input, filters):
return conv.conv2d(input, filters) return conv.conv2d(input, filters)
...@@ -44,9 +44,8 @@ class TestSignalConv2D(unittest.TestCase): ...@@ -44,9 +44,8 @@ class TestSignalConv2D(unittest.TestCase):
filter_data = numpy.random.random(filter_shape) filter_data = numpy.random.random(filter_shape)
theano_output = theano_conv(image_data, filter_data) theano_output = theano_conv(image_data, filter_data)
############# REFERENCE IMPLEMENTATION ############ # REFERENCE IMPLEMENTATION ############
out_shape2d = numpy.array(image_shape[-2:]) -\ out_shape2d = numpy.array(image_shape[-2:]) - numpy.array(filter_shape[-2:]) + 1
numpy.array(filter_shape[-2:]) + 1
ref_output = numpy.zeros(tuple(out_shape2d)) ref_output = numpy.zeros(tuple(out_shape2d))
# reshape as 3D input tensors to make life easier # reshape as 3D input tensors to make life easier
...@@ -76,7 +75,7 @@ class TestSignalConv2D(unittest.TestCase): ...@@ -76,7 +75,7 @@ class TestSignalConv2D(unittest.TestCase):
self.assertTrue(_allclose(theano_output4d[b, k, :, :], self.assertTrue(_allclose(theano_output4d[b, k, :, :],
output2d)) output2d))
############# TEST GRADIENT ############ # TEST GRADIENT ############
if verify_grad: if verify_grad:
utt.verify_grad(sym_conv2d, [image_data, filter_data]) utt.verify_grad(sym_conv2d, [image_data, filter_data])
...@@ -87,7 +86,7 @@ class TestSignalConv2D(unittest.TestCase): ...@@ -87,7 +86,7 @@ class TestSignalConv2D(unittest.TestCase):
signal.conv.conv2d can support inputs and filters of type signal.conv.conv2d can support inputs and filters of type
matrix or tensor3. matrix or tensor3.
""" """
if (not theano.tensor.nnet.conv.imported_scipy_signal and if(not theano.tensor.nnet.conv.imported_scipy_signal and
theano.config.cxx == ""): theano.config.cxx == ""):
raise SkipTest("conv2d tests need SciPy or a c++ compiler") raise SkipTest("conv2d tests need SciPy or a c++ compiler")
......
theano/tensor/signal/tests/test_downsample.py
浏览文件 @ c81f8c36
...@@ -63,9 +63,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -63,9 +63,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def pad_img(x): def pad_img(x):
y = numpy.zeros( y = numpy.zeros(
(x.shape[0], x.shape[1], (x.shape[0], x.shape[1],
x.shape[2]+pad_h*2, x.shape[3]+pad_w*2), x.shape[2] + pad_h * 2, x.shape[3] + pad_w * 2),
dtype=x.dtype) dtype=x.dtype)
y[:, :, pad_h:(x.shape[2]+pad_h), pad_w:(x.shape[3]+pad_w)] = x y[:, :, pad_h:(x.shape[2] + pad_h), pad_w:(x.shape[3] + pad_w)] = x
return y return y
img_rows = h + 2 * pad_h img_rows = h + 2 * pad_h
...@@ -78,7 +78,6 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -78,7 +78,6 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
ds0, ds1 = ds ds0, ds1 = ds
st0, st1 = st st0, st1 = st
output_val = numpy.zeros(out_shp) output_val = numpy.zeros(out_shp)
tt = []
y = pad_img(x) y = pad_img(x)
func = numpy.max func = numpy.max
if mode == 'sum': if mode == 'sum':
...@@ -117,8 +116,6 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -117,8 +116,6 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
if st is None: if st is None:
st = ds st = ds
xi = 0
yi = 0
img_rows = input.shape[-2] img_rows = input.shape[-2]
img_cols = input.shape[-1] img_cols = input.shape[-1]
...@@ -581,10 +578,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -581,10 +578,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
stridesize = stridesizes[i] stridesize = stridesizes[i]
paddingsize = paddingsizes[i] paddingsize = paddingsizes[i]
grad_shape = DownsampleFactorMaxGradGrad.out_shape( grad_shape = DownsampleFactorMaxGradGrad.out_shape(imval.shape,
imval.shape, maxpoolsize, st=stridesize, maxpoolsize, st=stridesize,
ignore_border=True, padding=paddingsize) ignore_border=True,
padding=paddingsize)
grad_val = rng.rand(*grad_shape) * 10.0 grad_val = rng.rand(*grad_shape) * 10.0
def mp(input, grad): def mp(input, grad):
out = DownsampleFactorMax( out = DownsampleFactorMax(
maxpoolsize, ignore_border=True, maxpoolsize, ignore_border=True,
...@@ -610,12 +609,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -610,12 +609,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
stridesize = stridesizes[i] stridesize = stridesizes[i]
paddingsize = paddingsizes[i] paddingsize = paddingsizes[i]
#'average_exc_pad' with non-zero padding is not implemented # 'average_exc_pad' with non-zero padding is not implemented
for mode in ['sum', 'average_inc_pad']: for mode in ['sum', 'average_inc_pad']:
grad_shape = DownsampleFactorMax.out_shape( grad_shape = DownsampleFactorMax.out_shape(imval.shape,
imval.shape, avgpoolsize, st=stridesize, avgpoolsize, st=stridesize,
ignore_border=True, padding=paddingsize) ignore_border=True, padding=paddingsize)
grad_val = rng.rand(*grad_shape) * 10.0 grad_val = rng.rand(*grad_shape) * 10.0
def mp(input, grad): def mp(input, grad):
grad_op = AveragePoolGrad(avgpoolsize, ignore_border=True, grad_op = AveragePoolGrad(avgpoolsize, ignore_border=True,
st=stridesize, padding=paddingsize, st=stridesize, padding=paddingsize,
...@@ -637,7 +637,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -637,7 +637,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
# The value has been manually computed from the theoretical gradient, # The value has been manually computed from the theoretical gradient,
# and confirmed by the implementation. # and confirmed by the implementation.
assert numpy.allclose(fn_hess([1, 2]), [[0., 0.], [0., 982.7667]])
assert numpy.allclose(fn_hess( [1, 2]), [[0., 0.], [0., 982.7667]])
def test_max_pool_2d_2D(self): def test_max_pool_2d_2D(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = numpy.random.RandomState(utt.fetch_seed())
...@@ -683,9 +684,11 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -683,9 +684,11 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
op_output = function([input], op)(test_input_array) op_output = function([input], op)(test_input_array)
assert numpy.all(op_output == test_answer_array), ( assert numpy.all(op_output == test_answer_array), (
"op_output is %s, test_answer_array is %s" % ( "op_output is %s, test_answer_array is %s" % (
op_output, numpy_output_val
op_output, test_answer_array
) )
) )
def mp(input): def mp(input):
return max_pool_2d_same_size(input, patch_size) return max_pool_2d_same_size(input, patch_size)
utt.verify_grad(mp, [test_input_array], rng=rng) utt.verify_grad(mp, [test_input_array], rng=rng)
...@@ -712,14 +715,6 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -712,14 +715,6 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
assert numpy.all(output_val == numpy_output_val), ( assert numpy.all(output_val == numpy_output_val), (
"output_val is %s, numpy_output_val is %s" "output_val is %s, numpy_output_val is %s"
% (output_val, numpy_output_val)) % (output_val, numpy_output_val))
c = tensor.sum(output)
c_val = function([images], c)(imval)
g = tensor.grad(c, images)
g_val = function([images],
[g.shape,
tensor.min(g, axis=(0, 1, 2)),
tensor.max(g, axis=(0, 1, 2))]
)(imval)
# removed as already tested in test_max_pool_2d_2D # removed as already tested in test_max_pool_2d_2D
# This make test in debug mode too slow. # This make test in debug mode too slow.
...@@ -780,7 +775,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -780,7 +775,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for i, maxpoolshp in enumerate(maxpoolshps): for i, maxpoolshp in enumerate(maxpoolshps):
for j, ignore_border in enumerate([True, False]): for j, ignore_border in enumerate([True, False]):
for k, padding in enumerate([(0,0), (1,1), (1,2)]): for k, padding in enumerate([(0, 0), (1, 1), (1, 2)]):
if out_shapes[k][i][j] is None: if out_shapes[k][i][j] is None:
continue continue
# checking shapes generated by DownsampleFactorMax # checking shapes generated by DownsampleFactorMax
...@@ -817,12 +812,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -817,12 +812,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
maxout = theano.tensor.tensor4() maxout = theano.tensor.tensor4()
grad = theano.tensor.tensor4() grad = theano.tensor.tensor4()
compilation_mode=theano.compile.get_default_mode().including( compilation_mode = theano.compile.get_default_mode().including(
'local_average_pool_grad') 'local_average_pool_grad')
for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']: for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']:
f = theano.function([im, maxout, grad], f = theano.function([im, maxout, grad],
DownsampleFactorMaxGrad(ds=(3,3), DownsampleFactorMaxGrad(ds=(3, 3),
ignore_border=False, ignore_border=False,
mode=mode)(im, maxout, grad), mode=mode)(im, maxout, grad),
mode=compilation_mode) mode=compilation_mode)
......
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