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提交 6e7a904e authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Merge pull request #3139 from sebastien-j/average_pooling

Average pooling
上级 079181cf 8298b5b1
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正在显示 包含 408 行增加 和 68 行删除
theano/sandbox/cuda/dnn.py
浏览文件 @ 6e7a904e
...@@ -13,7 +13,7 @@ from theano.compile.ops import shape_i ...@@ -13,7 +13,7 @@ from theano.compile.ops import shape_i
from theano.configparser import AddConfigVar, EnumStr from theano.configparser import AddConfigVar, EnumStr
from theano.tensor.nnet import SoftmaxGrad from theano.tensor.nnet import SoftmaxGrad
from theano.tensor.signal.downsample import ( from theano.tensor.signal.downsample import (
DownsampleFactorMax, DownsampleFactorMaxGrad) DownsampleFactorMax, MaxPoolGrad, AveragePoolGrad)
from theano.sandbox.cuda import GpuOp from theano.sandbox.cuda import GpuOp
from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable, from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
host_from_gpu, host_from_gpu,
...@@ -2204,11 +2204,11 @@ if True: ...@@ -2204,11 +2204,11 @@ if True:
desc)] desc)]
@register_opt('cudnn') @register_opt('cudnn')
@local_optimizer([DownsampleFactorMaxGrad]) @local_optimizer([MaxPoolGrad])
def local_pool_dnn_grad_stride(node): def local_pool_dnn_grad_stride(node):
if not dnn_available(): if not dnn_available():
return return
if isinstance(node.op, DownsampleFactorMaxGrad): if isinstance(node.op, MaxPoolGrad):
if not node.op.ignore_border: if not node.op.ignore_border:
return return
inp, out, inp_grad = node.inputs inp, out, inp_grad = node.inputs
...@@ -2228,6 +2228,31 @@ if True: ...@@ -2228,6 +2228,31 @@ if True:
desc) desc)
return [host_from_gpu(ret)] return [host_from_gpu(ret)]
@register_opt('cudnn')
@local_optimizer([AveragePoolGrad])
def local_avgpool_dnn_grad_stride(node):
if not dnn_available():
return
if isinstance(node.op, AveragePoolGrad):
if not node.op.ignore_border:
return
inp, inp_grad = node.inputs
ds = node.op.ds
st = node.op.st
pad = node.op.padding
mode = node.op.mode
if ((inp.owner and isinstance(inp.owner.op, HostFromGpu)) or
(inp_grad.owner and isinstance(inp_grad.owner.op,
HostFromGpu))):
desc = GpuDnnPoolDesc(ws=ds, stride=st, mode=mode, pad=pad)()
ret = GpuDnnPoolGrad()(gpu_contiguous(inp),
gpu_contiguous(numpy.empty((1,1,1,1),
dtype=numpy.float32)),
gpu_contiguous(inp_grad),
desc)
return [host_from_gpu(ret)]
@register_opt('cudnn') @register_opt('cudnn')
@local_optimizer([GpuSoftmax]) @local_optimizer([GpuSoftmax])
def local_softmax_dnn(node): def local_softmax_dnn(node):
......
theano/sandbox/cuda/opt.py
浏览文件 @ 6e7a904e
...@@ -120,7 +120,8 @@ cpu_ops_moved_to_gpu = [ ...@@ -120,7 +120,8 @@ cpu_ops_moved_to_gpu = [
tensor.blas.Dot22, tensor.blas.Dot22Scalar, tensor.blas.Gemm, tensor.blas.Dot22, tensor.blas.Dot22Scalar, tensor.blas.Gemm,
tensor.blas.Gemv, tensor.blas.Ger, tensor.nnet.conv.ConvOp, tensor.blas.Gemv, tensor.blas.Ger, tensor.nnet.conv.ConvOp,
tensor.signal.downsample.DownsampleFactorMax, tensor.signal.downsample.DownsampleFactorMax,
tensor.signal.downsample.DownsampleFactorMaxGrad, tensor.signal.downsample.MaxPoolGrad,
tensor.signal.downsample.AveragePoolGrad,
theano.tensor.nnet.neighbours.Images2Neibs, theano.tensor.nnet.neighbours.Images2Neibs,
tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias, tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias,
tensor.nnet.CrossentropySoftmax1HotWithBiasDx, tensor.nnet.CrossentropySoftmax1HotWithBiasDx,
...@@ -1765,9 +1766,9 @@ def local_gpu_downsample_factor_max(node): ...@@ -1765,9 +1766,9 @@ def local_gpu_downsample_factor_max(node):
@register_opt() @register_opt()
@local_optimizer([downsample.DownsampleFactorMaxGrad]) @local_optimizer([downsample.MaxPoolGrad])
def local_gpu_downsample_factor_max_grad(node): def local_gpu_downsample_factor_max_grad(node):
if (isinstance(node.op, downsample.DownsampleFactorMaxGrad) and if (isinstance(node.op, downsample.MaxPoolGrad) and
node.op.ds == node.op.st): node.op.ds == node.op.st):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding', assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding',
'mode') 'mode')
......
theano/sandbox/cuda/tests/test_dnn.py
浏览文件 @ 6e7a904e
...@@ -10,7 +10,7 @@ import theano.tensor as T ...@@ -10,7 +10,7 @@ import theano.tensor as T
import theano.tests.unittest_tools as utt import theano.tests.unittest_tools as utt
from theano.sandbox.neighbours import images2neibs from theano.sandbox.neighbours import images2neibs
from theano.tensor.signal.downsample import max_pool_2d from theano.tensor.signal.downsample import max_pool_2d
from theano.tensor.signal.downsample import DownsampleFactorMaxGrad from theano.tensor.signal.downsample import MaxPoolGrad, AveragePoolGrad
import theano.sandbox.cuda.dnn as dnn import theano.sandbox.cuda.dnn as dnn
from theano.sandbox.cuda.basic_ops import GpuAllocEmpty, gpu_alloc_empty from theano.sandbox.cuda.basic_ops import GpuAllocEmpty, gpu_alloc_empty
from theano.sandbox.cuda import float32_shared_constructor as shared from theano.sandbox.cuda import float32_shared_constructor as shared
...@@ -316,7 +316,11 @@ def test_pooling(): ...@@ -316,7 +316,11 @@ def test_pooling():
ignore_border=True, mode=mode) ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x), fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu) mode=mode_without_gpu)
assert any([isinstance(node.op, DownsampleFactorMaxGrad) if mode == 'max':
assert any([isinstance(node.op, MaxPoolGrad)
for node in fc.maker.fgraph.toposort()])
else:
assert any([isinstance(node.op, AveragePoolGrad)
for node in fc.maker.fgraph.toposort()]) for node in fc.maker.fgraph.toposort()])
c_out = fc(data) c_out = fc(data)
assert numpy.allclose(c_out, g_out) assert numpy.allclose(c_out, g_out)
......
theano/sandbox/gpuarray/dnn.py
浏览文件 @ 6e7a904e
...@@ -13,7 +13,7 @@ from theano.compile.ops import shape_i ...@@ -13,7 +13,7 @@ from theano.compile.ops import shape_i
from theano.configparser import AddConfigVar, EnumStr, StrParam from theano.configparser import AddConfigVar, EnumStr, StrParam
from theano.tensor.nnet import SoftmaxGrad from theano.tensor.nnet import SoftmaxGrad
from theano.tensor.signal.downsample import ( from theano.tensor.signal.downsample import (
DownsampleFactorMax, DownsampleFactorMaxGrad) DownsampleFactorMax, MaxPoolGrad, AveragePoolGrad)
from . import pygpu, init_dev from . import pygpu, init_dev
from .basic_ops import (as_gpuarray_variable, from .basic_ops import (as_gpuarray_variable,
...@@ -1659,7 +1659,7 @@ def local_pool_dnn_alternative(node): ...@@ -1659,7 +1659,7 @@ def local_pool_dnn_alternative(node):
@register_opt('cudnn') @register_opt('cudnn')
@op_lifter([DownsampleFactorMaxGrad]) @op_lifter([MaxPoolGrad])
def local_pool_dnn_grad_stride(node): def local_pool_dnn_grad_stride(node):
if not dnn_available(): if not dnn_available():
return return
...@@ -1678,6 +1678,26 @@ def local_pool_dnn_grad_stride(node): ...@@ -1678,6 +1678,26 @@ def local_pool_dnn_grad_stride(node):
desc) desc)
@register_opt('cudnn')
@op_lifter([AveragePoolGrad])
def local_avg_pool_dnn_grad_stride(node):
if not dnn_available():
return
if not node.op.ignore_border:
return
inp, inp_grad = node.inputs
ds = node.op.ds
st = node.op.st
pad = node.op.padding
mode = node.op.mode
desc = GpuDnnPoolDesc(ws=ds, stride=st, mode=mode, pad=pad)()
return GpuDnnPoolGrad()(gpu_contiguous(inp),
gpu_contiguous(numpy.empty((1, 1, 1, 1), dtype=numpy.float32)),
gpu_contiguous(inp_grad),
desc)
@register_opt('cudnn') @register_opt('cudnn')
@local_optimizer([GpuSoftmax]) @local_optimizer([GpuSoftmax])
def local_softmax_dnn(node): def local_softmax_dnn(node):
......
theano/sandbox/gpuarray/tests/test_dnn.py
浏览文件 @ 6e7a904e
...@@ -10,7 +10,7 @@ import theano.tensor as T ...@@ -10,7 +10,7 @@ import theano.tensor as T
import theano.tests.unittest_tools as utt import theano.tests.unittest_tools as utt
from theano.sandbox.neighbours import images2neibs from theano.sandbox.neighbours import images2neibs
from theano.tensor.signal.downsample import max_pool_2d from theano.tensor.signal.downsample import max_pool_2d
from theano.tensor.signal.downsample import DownsampleFactorMaxGrad from theano.tensor.signal.downsample import MaxPoolGrad, AveragePoolGrad
from .. import dnn from .. import dnn
from ..basic_ops import GpuAllocEmpty from ..basic_ops import GpuAllocEmpty
...@@ -264,7 +264,11 @@ def test_pooling(): ...@@ -264,7 +264,11 @@ def test_pooling():
ignore_border=True, mode=mode) ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x), fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu) mode=mode_without_gpu)
assert any([isinstance(node.op, DownsampleFactorMaxGrad) if mode == 'max':
assert any([isinstance(node.op, MaxPoolGrad)
for node in fc.maker.fgraph.toposort()])
else:
assert any([isinstance(node.op, AveragePoolGrad)
for node in fc.maker.fgraph.toposort()]) for node in fc.maker.fgraph.toposort()])
c_out = fc(data) c_out = fc(data)
assert numpy.allclose(c_out, g_out) assert numpy.allclose(c_out, g_out)
......
theano/tensor/signal/downsample.py
浏览文件 @ 6e7a904e
...@@ -14,6 +14,7 @@ import numpy ...@@ -14,6 +14,7 @@ import numpy
import theano import theano
from theano import gof, Op, tensor, Variable, Apply from theano import gof, Op, tensor, Variable, Apply
from theano.tensor.opt import register_canonicalize
def max_pool2D(*args, **kwargs): def max_pool2D(*args, **kwargs):
import sys import sys
...@@ -36,7 +37,7 @@ def max_pool_2d_same_size(input, patch_size): ...@@ -36,7 +37,7 @@ def max_pool_2d_same_size(input, patch_size):
(2,2) will retain only one non-zero value per patch of 4 values. (2,2) will retain only one non-zero value per patch of 4 values.
""" """
output = DownsampleFactorMax(patch_size, True)(input) output = DownsampleFactorMax(patch_size, True)(input)
outs = DownsampleFactorMaxGrad(patch_size, True)(input, output, output) outs = MaxPoolGrad(patch_size, True)(input, output, output)
return outs return outs
...@@ -309,13 +310,18 @@ class DownsampleFactorMax(Op): ...@@ -309,13 +310,18 @@ class DownsampleFactorMax(Op):
def grad(self, inp, grads): def grad(self, inp, grads):
x, = inp x, = inp
gz, = grads gz, = grads
if self.mode == 'max':
maxout = self(x) maxout = self(x)
return [DownsampleFactorMaxGrad(self.ds, return [MaxPoolGrad(self.ds,
ignore_border=self.ignore_border,
st=self.st, padding=self.padding)(
x, maxout, gz)]
else:
return [AveragePoolGrad(self.ds,
ignore_border=self.ignore_border, ignore_border=self.ignore_border,
st=self.st, padding=self.padding, st=self.st, padding=self.padding,
mode=self.mode)( mode=self.mode)(
x, maxout, gz)] x, gz)]
def c_headers(self): def c_headers(self):
return ['<algorithm>'] return ['<algorithm>']
...@@ -502,9 +508,86 @@ class DownsampleFactorMax(Op): ...@@ -502,9 +508,86 @@ 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 DownsampleFactorMaxGrad(Op): class PoolGrad(Op):
__props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode') __props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
@staticmethod
def out_shape(imgshape, ds, ignore_border=False, st=None, padding=(0, 0)):
"""Return the shape of the output from this op, for input of given
shape and flags.
:param imgshape: the shape of a tensor of images. The last two elements
are interpreted as the number of rows, and the number of cols.
:type imgshape: tuple, list, or similar of integer or
scalar Theano variable.
:param ds: downsample factor over rows and columns
this parameter indicates the size of the pooling region
:type ds: list or tuple of two ints
:param st: the stride size. This is the distance between the pooling
regions. If it's set to None, in which case it equlas ds.
:type st: list or tuple of two ints
:param ignore_border: if ds doesn't divide imgshape, do we include an
extra row/col of partial downsampling (False) or ignore it (True).
:type ignore_border: bool
:param padding: (pad_h, pad_w), pad zeros to extend beyond four borders
of the images, pad_h is the size of the top and bottom margins,
and pad_w is the size of the left and right margins.
:type padding: tuple of two ints
:rtype: list
:returns: the shape of the output from this op, for input of given
shape. This will have the same length as imgshape, but with last
two elements reduced as per the downsampling & ignore_border flags.
"""
if len(imgshape) < 2:
raise TypeError('imgshape must have at least two elements '
'(rows, cols)')
if st is None:
st = ds
r, c = imgshape[-2:]
r += padding[0] * 2
c += padding[1] * 2
if ignore_border:
out_r = (r - ds[0]) // st[0] + 1
out_c = (c - ds[1]) // st[1] + 1
if isinstance(r, theano.Variable):
nr = tensor.maximum(out_r, 0)
else:
nr = numpy.maximum(out_r, 0)
if isinstance(c, theano.Variable):
nc = tensor.maximum(out_c, 0)
else:
nc = numpy.maximum(out_c, 0)
else:
if isinstance(r, theano.Variable):
nr = tensor.switch(tensor.ge(st[0], ds[0]),
(r - 1) // st[0] + 1,
tensor.maximum(0, (r - 1 - ds[0])
// st[0] + 1) + 1)
elif st[0] >= ds[0]:
nr = (r - 1) // st[0] + 1
else:
nr = max(0, (r - 1 - ds[0]) // st[0] + 1) + 1
if isinstance(c, theano.Variable):
nc = tensor.switch(tensor.ge(st[1], ds[1]),
(c - 1) // st[1] + 1,
tensor.maximum(0, (c - 1 - ds[1])
// st[1] + 1) + 1)
elif st[1] >= ds[1]:
nc = (c - 1) // st[1] + 1
else:
nc = max(0, (c - 1 - ds[1]) // st[1] + 1) + 1
rval = list(imgshape[:-2]) + [nr, nc]
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)
self.ignore_border = ignore_border self.ignore_border = ignore_border
...@@ -518,6 +601,15 @@ class DownsampleFactorMaxGrad(Op): ...@@ -518,6 +601,15 @@ class DownsampleFactorMaxGrad(Op):
" 'average_inc_pad' and 'average_exc_pad'. Got %s" % mode) " 'average_inc_pad' and 'average_exc_pad'. Got %s" % mode)
self.mode = mode self.mode = mode
def infer_shape(self, node, in_shapes):
return [in_shapes[0]]
class MaxPoolGrad(PoolGrad):
def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
PoolGrad.__init__(self, ds, ignore_border, st, padding, mode)
def make_node(self, x, maxout, gz): def make_node(self, x, maxout, gz):
# make_node should only be called by the grad function of # make_node should only be called by the grad function of
# DownsampleFactorMax, so these asserts should not fail. # DownsampleFactorMax, so these asserts should not fail.
...@@ -531,8 +623,7 @@ class DownsampleFactorMaxGrad(Op): ...@@ -531,8 +623,7 @@ class DownsampleFactorMaxGrad(Op):
return Apply(self, [x, maxout, gz], [x.type()]) return Apply(self, [x, maxout, gz], [x.type()])
def perform(self, node, inp, out): def perform(self, node, inp, out):
if self.mode not in ('max', 'sum') and self.padding != (0, 0): assert self.mode == 'max'
raise NotImplementedError()
x, maxout, gz = inp x, maxout, gz = inp
gx_stg, = out gx_stg, = out
# number of pooling output rows # number of pooling output rows
...@@ -545,8 +636,6 @@ class DownsampleFactorMaxGrad(Op): ...@@ -545,8 +636,6 @@ class DownsampleFactorMaxGrad(Op):
pad_w = self.padding[1] pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
sum_mode = self.mode == 'sum'
# pad the image # pad the image
if self.padding != (0, 0): if self.padding != (0, 0):
...@@ -557,7 +646,6 @@ class DownsampleFactorMaxGrad(Op): ...@@ -557,7 +646,6 @@ class DownsampleFactorMaxGrad(Op):
else: else:
y = x y = x
gx = numpy.zeros_like(y) gx = numpy.zeros_like(y)
if self.mode == 'max':
for n in xrange(x.shape[0]): for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]): for k in xrange(x.shape[1]):
for r in xrange(pr): for r in xrange(pr):
...@@ -570,53 +658,21 @@ class DownsampleFactorMaxGrad(Op): ...@@ -570,53 +658,21 @@ class DownsampleFactorMaxGrad(Op):
for col_ind in xrange(col_st, col_end): for col_ind in xrange(col_st, col_end):
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]
else:
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
if sum_mode or inc_pad:
row_st = r * st0
else:
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
if sum_mode or inc_pad:
col_st = c * st1
else:
col_st = builtins.max(c * st1,
self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
if sum_mode:
val = gz[n, k, r, c]
else:
val = gz[n, k, r, c] / ((row_end - row_st) *
(col_end - col_st))
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 infer_shape(self, node, in_shapes):
return [in_shapes[0]]
def grad(self, inp, grads): def grad(self, inp, grads):
x, maxout, gz = inp x, maxout, gz = inp
ggx, = grads ggx, = grads
if self.mode == 'max':
return [theano.tensor.zeros_like(x), return [theano.tensor.zeros_like(x),
theano.tensor.zeros_like(maxout), theano.tensor.zeros_like(maxout),
DownsampleFactorMaxGradGrad( DownsampleFactorMaxGradGrad(
self.ds, ignore_border=self.ignore_border, self.ds, ignore_border=self.ignore_border,
st=self.st, padding=self.padding)(x, maxout, ggx)] st=self.st, padding=self.padding)(x, maxout, ggx)]
else:
return [theano.tensor.zeros_like(x),
theano.tensor.zeros_like(maxout),
theano.gradient.grad_not_implemented(
self, 2, gz, 'Hessian not implemented with padding')]
def c_code(self, node, name, inp, out, sub): def c_code(self, node, name, inp, out, sub):
if self.mode != 'max': assert self.mode == 'max'
raise theano.gof.utils.MethodNotDefined()
x, z, gz = inp x, z, gz = inp
gx, = out gx, = out
fail = sub['fail'] fail = sub['fail']
...@@ -732,6 +788,89 @@ class DownsampleFactorMaxGrad(Op): ...@@ -732,6 +788,89 @@ class DownsampleFactorMaxGrad(Op):
def c_code_cache_version(self): def c_code_cache_version(self):
return (0, 7) return (0, 7)
DownsampleFactorMaxGrad = MaxPoolGrad
class AveragePoolGrad(PoolGrad):
def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='average_inc_pad'):
assert mode in ['sum', 'average_inc_pad', 'average_exc_pad']
PoolGrad.__init__(self, ds, ignore_border, st, padding, mode)
def make_node(self, x, gz):
# make_node should only be called by the grad function of
# DownsampleFactorMax, so these asserts should not fail.
assert isinstance(x, Variable) and x.ndim == 4
assert isinstance(gz, Variable) and gz.ndim == 4
x = tensor.as_tensor_variable(x)
gz = tensor.as_tensor_variable(gz)
return Apply(self, [x, gz], [x.type()])
def perform(self, node, inp, out):
if self.mode == 'average_exc_pad' and self.padding != (0, 0):
raise NotImplementedError()
x, gz = inp
gx_stg, = out
z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
self.padding)
if (gx_stg[0] is None) or (gx_stg[0].shape != z_shape):
gx_stg[0] = numpy.empty(z_shape, dtype=x.dtype)
zz = gx_stg[0]
# number of pooling output rows
pr = zz.shape[-2]
# number of pooling output cols
pc = zz.shape[-1]
ds0, ds1 = self.ds
st0, st1 = self.st
pad_h = self.padding[0]
pad_w = self.padding[1]
img_rows = x.shape[-2] + 2 * pad_h
img_cols = x.shape[-1] + 2 * pad_w
inc_pad = self.mode == 'average_inc_pad'
sum_mode = self.mode == 'sum'
# pad the image
if self.padding != (0, 0):
y = numpy.zeros(
(x.shape[0], x.shape[1], img_rows, img_cols),
dtype=x.dtype)
y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
else:
y = x
gx = numpy.zeros_like(y)
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
if sum_mode or inc_pad:
row_st = r * st0
else:
row_st = builtins.max(r * st0, self.padding[0])
row_end = builtins.min(row_st + ds0, img_rows)
for c in xrange(pc):
if sum_mode or inc_pad:
col_st = c * st1
else:
col_st = builtins.max(c * st1,
self.padding[1])
col_end = builtins.min(col_st + ds1, img_cols)
if sum_mode:
val = gz[n, k, r, c]
else:
val = gz[n, k, r, c] / ((row_end - row_st) *
(col_end - col_st))
gx[n, k, row_st:row_end, col_st:col_end] += val
# unpad the image
gx = gx[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)]
gx_stg[0] = gx
def grad(self, inp, grads):
x, gz = inp
ggx, = grads
return [theano.tensor.zeros_like(x),
DownsampleFactorMax(
self.ds, ignore_border=self.ignore_border,
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')
...@@ -831,22 +970,21 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -831,22 +970,21 @@ class DownsampleFactorMaxGradGrad(Op):
raise NotImplementedError( raise NotImplementedError(
'padding_h and padding_w must be smaller than strides') 'padding_h and padding_w must be smaller than strides')
self.mode = mode self.mode = mode
assert self.mode == 'max'
def make_node(self, x, maxout, gz): def make_node(self, x, maxout, gz):
# make_node should only be called by the grad function of # make_node should only be called by the grad function of
# DownsampleFactorMaxGrad, so these asserts should not fail. # MaxPoolGrad, so these asserts should not fail.
assert isinstance(x, Variable) and x.ndim == 4
assert isinstance(maxout, Variable) and maxout.ndim == 4
assert isinstance(gz, Variable) and gz.ndim == 4
x = tensor.as_tensor_variable(x) x = tensor.as_tensor_variable(x)
maxout = tensor.as_tensor_variable(maxout) maxout = tensor.as_tensor_variable(maxout)
gz = tensor.as_tensor_variable(gz) gz = tensor.as_tensor_variable(gz)
assert x.ndim == 4
assert maxout.ndim == 4
assert gz.ndim == 4
return Apply(self, [x, maxout, gz], [x.type()]) return Apply(self, [x, maxout, gz], [x.type()])
def perform(self, node, inp, out): def perform(self, node, inp, out):
if self.mode != 'max':
raise theano.gof.utils.MethodNotDefined()
x, maxout, ggx = inp x, maxout, ggx = inp
z, = out z, = out
if len(x.shape) != 4: if len(x.shape) != 4:
...@@ -982,3 +1120,17 @@ class DownsampleFactorMaxGradGrad(Op): ...@@ -982,3 +1120,17 @@ class DownsampleFactorMaxGradGrad(Op):
def c_code_cache_version(self): def c_code_cache_version(self):
return (0,1) return (0,1)
@register_canonicalize('fast_compile')
@gof.local_optimizer([MaxPoolGrad])
def local_average_pool_grad(node):
# To assure backward compatibility with
# DownsampleFactorMaxGrad
if (not isinstance(node.op, MaxPoolGrad) or node.op.mode not in
['sum','average_exc_pad', 'average_inc_pad']):
return False
return [AveragePoolGrad(ds=node.op.ds,
ignore_border=node.op.ignore_border,
st=node.op.st,
padding=node.op.padding,
mode=node.op.mode)(node.inputs[0],
node.inputs[2])]
theano/tensor/signal/tests/test_downsample.py
浏览文件 @ 6e7a904e
...@@ -8,6 +8,7 @@ import theano ...@@ -8,6 +8,7 @@ import theano
import theano.tensor as tensor import theano.tensor as tensor
from theano.tests import unittest_tools as utt from theano.tests import unittest_tools as utt
from theano.tensor.signal.downsample import (DownsampleFactorMax, max_pool_2d, from theano.tensor.signal.downsample import (DownsampleFactorMax, max_pool_2d,
MaxPoolGrad, AveragePoolGrad,
DownsampleFactorMaxGrad, DownsampleFactorMaxGrad,
DownsampleFactorMaxGradGrad, DownsampleFactorMaxGradGrad,
max_pool_2d_same_size) max_pool_2d_same_size)
...@@ -417,12 +418,35 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -417,12 +418,35 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def mp(input, grad): def mp(input, grad):
out = DownsampleFactorMax( out = DownsampleFactorMax(
maxpoolshp, ignore_border=ignore_border)(input) maxpoolshp, ignore_border=ignore_border)(input)
grad_op = DownsampleFactorMaxGrad( grad_op = MaxPoolGrad(
maxpoolshp, ignore_border=ignore_border) maxpoolshp, ignore_border=ignore_border)
return grad_op(input, out, grad) return grad_op(input, out, grad)
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
avgpoolshps = ((1, 1), (3, 2), (2, 3))
imval = rng.rand(2, 3, 3, 4) * 10.0
# more variance means numeric gradient will be more accurate
for avgpoolshp in avgpoolshps:
for ignore_border in [True, False]:
for mode in ['sum', 'average_inc_pad', 'average_exc_pad']:
# print 'maxpoolshp =', maxpoolshp
# print 'ignore_border =', ignore_border
# The shape of the gradient will be the shape of the output
grad_shape = DownsampleFactorMax.out_shape(
imval.shape, avgpoolshp, ignore_border=ignore_border)
grad_val = rng.rand(*grad_shape) * 10.0
def mp(input, grad):
grad_op = AveragePoolGrad(
avgpoolshp, ignore_border=ignore_border, mode=mode)
return grad_op(input, grad)
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxGrad_grad_st(self): def test_DownsampleFactorMaxGrad_grad_st(self):
"""checks the gradient of the gradient for """checks the gradient of the gradient for
the case that stride is used""" the case that stride is used"""
...@@ -443,13 +467,38 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -443,13 +467,38 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out = DownsampleFactorMax( out = DownsampleFactorMax(
maxpoolshp, ignore_border=ignore_border, maxpoolshp, ignore_border=ignore_border,
st=stride)(input) st=stride)(input)
grad_op = DownsampleFactorMaxGrad( grad_op = MaxPoolGrad(
maxpoolshp, ignore_border=ignore_border, maxpoolshp, ignore_border=ignore_border,
st=stride) st=stride)
return grad_op(input, out, grad) return grad_op(input, out, grad)
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad_st(self):
"""checks the gradient of the gradient for
the case that stride is used"""
rng = numpy.random.RandomState(utt.fetch_seed())
avgpoolshps = ((1, 1), (3, 3), (5, 3))
stridesizes = ((1, 1), (3, 3), (5, 7))
imval = rng.rand(1, 2, 16, 16)
for avgpoolshp in avgpoolshps:
for ignore_border in [True, False]:
for mode in ['sum', 'average_inc_pad', 'average_exc_pad']:
for stride in stridesizes:
grad_shape = DownsampleFactorMax.out_shape(
imval.shape, avgpoolshp,
ignore_border=ignore_border, st=stride)
grad_val = rng.rand(*grad_shape)
def mp(input, grad):
grad_op = AveragePoolGrad(
avgpoolshp, ignore_border=ignore_border,
st=stride, mode=mode)
return grad_op(input, grad)
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxGrad_grad_st_extra(self): def test_DownsampleFactorMaxGrad_grad_st_extra(self):
"""checks the gradient of the gradient for the case that """checks the gradient of the gradient for the case that
stride is used for extra examples""" stride is used for extra examples"""
...@@ -475,7 +524,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -475,7 +524,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out = DownsampleFactorMax( out = DownsampleFactorMax(
maxpoolshp, ignore_border=ignore_border, maxpoolshp, ignore_border=ignore_border,
st=stride)(input) st=stride)(input)
grad_op = DownsampleFactorMaxGrad( grad_op = MaxPoolGrad(
maxpoolshp, ignore_border=ignore_border, maxpoolshp, ignore_border=ignore_border,
st=stride) st=stride)
return grad_op(input, out, grad) return grad_op(input, out, grad)
...@@ -485,6 +534,39 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -485,6 +534,39 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
continue continue
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad_st_extra(self):
"""checks the gradient of the gradient for the case that
stride is used for extra examples"""
rng = numpy.random.RandomState(utt.fetch_seed())
avgpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9))
stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1),
(2, 3), (10, 10), (1, 1))
imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5),
(8, 5), (8, 5), (8, 5))
for indx in numpy.arange(len(avgpoolshps)):
imvsize = imvsizs[indx]
imval = rng.rand(1, 2, imvsize[0], imvsize[1])
stride = stridesizes[indx]
avgpoolshp = avgpoolshps[indx]
for ignore_border in [True, False]:
for mode in ['sum', 'average_inc_pad', 'average_exc_pad']:
grad_shape = DownsampleFactorMax.out_shape(
imval.shape, avgpoolshp,
ignore_border=ignore_border, st=stride)
grad_val = rng.rand(*grad_shape)
def mp(input, grad):
grad_op = AveragePoolGrad(
avgpoolshp, ignore_border=ignore_border,
st=stride, mode=mode)
return grad_op(input, grad)
# skip the grad verification when the output is empty
if numpy.prod(grad_shape) == 0:
continue
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxPaddingStride_grad_grad(self): def test_DownsampleFactorMaxPaddingStride_grad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = numpy.random.RandomState(utt.fetch_seed())
imgsizes = ((10, 10), (10, 5), (5, 5)) imgsizes = ((10, 10), (10, 5), (5, 5))
...@@ -509,11 +591,38 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -509,11 +591,38 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
st=stridesize, st=stridesize,
padding=paddingsize, padding=paddingsize,
)(input) )(input)
grad_op = DownsampleFactorMaxGrad(maxpoolsize, ignore_border=True, grad_op = MaxPoolGrad(maxpoolsize, ignore_border=True,
st=stridesize, padding=paddingsize) st=stridesize, padding=paddingsize)
return grad_op(input, out, grad) return grad_op(input, out, grad)
utt.verify_grad(mp, [imval, grad_val], rng=rng) utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolPaddingStride_grad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
imgsizes = ((10, 10), (10, 5), (5, 5))
avgpoolsizes = ((5, 3), (3, 5), (3, 3))
stridesizes = ((3, 2), (2, 3), (3, 3))
paddingsizes = ((2, 2), (2, 1), (2, 2))
for i in range(len(imgsizes)):
imgsize = imgsizes[i]
imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0
avgpoolsize = avgpoolsizes[i]
stridesize = stridesizes[i]
paddingsize = paddingsizes[i]
#'average_exc_pad' with non-zero padding is not implemented
for mode in ['sum', 'average_inc_pad']:
grad_shape = DownsampleFactorMax.out_shape(
imval.shape, avgpoolsize, st=stridesize,
ignore_border=True, padding=paddingsize)
grad_val = rng.rand(*grad_shape) * 10.0
def mp(input, grad):
grad_op = AveragePoolGrad(avgpoolsize, ignore_border=True,
st=stridesize, padding=paddingsize,
mode=mode)
return grad_op(input, grad)
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMax_hessian(self): def test_DownsampleFactorMax_hessian(self):
# Example provided by Frans Cronje, see # Example provided by Frans Cronje, see
# https://groups.google.com/d/msg/theano-users/qpqUy_3glhw/JMwIvlN5wX4J # https://groups.google.com/d/msg/theano-users/qpqUy_3glhw/JMwIvlN5wX4J
...@@ -681,18 +790,43 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -681,18 +790,43 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
padding=padding)(image)], padding=padding)(image)],
[image_val], DownsampleFactorMax) [image_val], DownsampleFactorMax)
# checking shapes generated by DownsampleFactorMaxGrad # checking shapes generated by MaxPoolGrad
maxout_val = rng.rand(*out_shapes[k][i][j]) maxout_val = rng.rand(*out_shapes[k][i][j])
gz_val = rng.rand(*out_shapes[k][i][j]) gz_val = rng.rand(*out_shapes[k][i][j])
self._compile_and_check([image, maxout, gz], self._compile_and_check([image, maxout, gz],
[DownsampleFactorMaxGrad(maxpoolshp, [MaxPoolGrad(maxpoolshp,
ignore_border=ignore_border, ignore_border=ignore_border,
padding=padding) padding=padding)
(image, maxout, gz)], (image, maxout, gz)],
[image_val, maxout_val, gz_val], [image_val, maxout_val, gz_val],
DownsampleFactorMaxGrad, MaxPoolGrad,
warn=False) warn=False)
def test_opt_max_to_average(self):
im = theano.tensor.tensor4()
maxout = theano.tensor.tensor4()
grad = theano.tensor.tensor4()
compilation_mode=theano.compile.get_default_mode().including(
'local_average_pool_grad')
for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']:
f = theano.function([im, maxout, grad],
DownsampleFactorMaxGrad(ds=(3,3),
ignore_border=False,
mode=mode)(im, maxout, grad),
mode=compilation_mode)
if mode == 'max':
assert any(isinstance(n.op, MaxPoolGrad)
for n in f.maker.fgraph.toposort())
assert not any(isinstance(n.op, AveragePoolGrad)
for n in f.maker.fgraph.toposort())
else:
assert not any(isinstance(n.op, MaxPoolGrad)
for n in f.maker.fgraph.toposort())
assert any(isinstance(n.op, AveragePoolGrad)
for n in f.maker.fgraph.toposort())
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
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