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提交 512c2c16 authored 作者: Frederic Bastien's avatar Frederic Bastien 提交者： Frederic
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average pool python code

上级 72faef33
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正在显示 包含 170 行增加 和 96 行删除
theano/sandbox/cuda/dnn.py
浏览文件 @ 512c2c16
......@@ -1606,7 +1606,7 @@ if True:
@register_opt('cudnn')
@local_optimizer([DownsampleFactorMax])
def local_pool_dnn_stride(node):
def local_pool_dnn_alternative(node):
if not dnn_available():
return
if isinstance(node.op, DownsampleFactorMax):
......@@ -1616,9 +1616,10 @@ if True:
ds = node.op.ds
stride = node.op.st
pad = node.op.padding
mode = node.op.mode
if (img.owner and isinstance(img.owner.op, HostFromGpu)):
ret = dnn_pool(gpu_contiguous(img.owner.inputs[0]),
ds, stride=stride, pad=pad)
ds, stride=stride, pad=pad, mode=mode)
return [host_from_gpu(ret)]
@register_opt('cudnn')
......@@ -1648,12 +1649,13 @@ if True:
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
(out.owner and isinstance(out.owner.op, HostFromGpu)) or
(inp_grad.owner and isinstance(inp_grad.owner.op,
HostFromGpu))):
desc = GpuDnnPoolDesc(ws=ds, stride=st, mode="max", pad=pad)()
desc = GpuDnnPoolDesc(ws=ds, stride=st, mode=mode, pad=pad)()
if not node.op.ignore_border:
return
ret = GpuDnnPoolGrad()(gpu_contiguous(inp),
......
theano/sandbox/cuda/opt.py
浏览文件 @ 512c2c16
......@@ -1643,8 +1643,9 @@ import theano.tensor.signal.downsample as downsample
def local_gpu_downsample_factor_max(node):
if (isinstance(node.op, downsample.DownsampleFactorMax)
and node.op.ds == node.op.st):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding')
if node.op.padding != (0, 0):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding',
'mode')
if node.op.padding != (0, 0) or node.op.mode != 'max':
return
x, = node.inputs
if (x.owner and isinstance(x.owner.op, HostFromGpu)):
......@@ -1657,8 +1658,9 @@ def local_gpu_downsample_factor_max(node):
def local_gpu_downsample_factor_max_grad(node):
if (isinstance(node.op, downsample.DownsampleFactorMaxGrad) and
node.op.ds == node.op.st):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding')
if node.op.padding != (0, 0):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding',
'mode')
if node.op.padding != (0, 0) or node.op.mode != 'max':
return
x, z, gz = node.inputs
if (x.owner and isinstance(x.owner.op, HostFromGpu)):
......@@ -1673,6 +1675,8 @@ def local_gpu_downsample_factor_max_grad(node):
@local_optimizer([downsample.DownsampleFactorMaxGradGrad])
def local_gpu_downsample_factor_max_grad_grad(node):
if isinstance(node.op, downsample.DownsampleFactorMaxGradGrad):
assert node.op.__props__ == ('ds', 'ignore_border', 'st')
x, z, gx = node.inputs
if (x.owner and isinstance(x.owner.op, HostFromGpu)):
op = GpuDownsampleFactorMaxGradGrad(node.op.ds,
......
theano/sandbox/cuda/tests/test_dnn.py
浏览文件 @ 512c2c16
......@@ -90,6 +90,10 @@ def test_pooling():
x = T.ftensor4()
for func, pad in product((T.max, T.mean),
((0, 0), (1, 0), (1, 0), (2, 3), (3, 2))):
if func is T.max:
mode = 'max'
else:
mode = 'average'
if pad != (0, 0) and cuda.dnn.version() == -1:
continue
......@@ -100,29 +104,23 @@ def test_pooling():
for stride in (2, 3):
if stride > ws:
continue
if func is T.max:
if pad[0] > stride or pad[1] > stride:
# Not implemented
continue
# We will check that the opt introduced it.
out1 = max_pool_2d(x, (ws, ws),
st=(stride, stride),
ignore_border=True,
padding=pad)
else:
out1 = cuda.dnn.dnn_pool(
x, ws=(ws, ws),
stride=(stride, stride),
pad=pad,
mode='max' if func is T.max else "average")
if pad[0] > stride or pad[1] > stride:
# Not implemented
continue
# We will check that the opt introduced it.
out1 = max_pool_2d(x, (ws, ws),
st=(stride, stride),
ignore_border=True,
padding=pad, mode=mode)
out2 = pool_2d_i2n(x, ds=(ws, ws), strides=(stride, stride),
pad=pad,
pool_function=func)
mode_without_gpu2 = mode_without_gpu.including()
mode_without_gpu2.check_isfinite = False
f1 = theano.function([x], out1, mode=mode_with_gpu)
assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f1.maker.fgraph.apply_nodes])
f2 = theano.function([x], out2, mode=mode_without_gpu)
f2 = theano.function([x], out2, mode=mode_without_gpu2)
assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
for node in f2.maker.fgraph.apply_nodes])
for shp in [(1, 10, 100, 100),
......@@ -150,7 +148,7 @@ def test_pooling():
# This test the CPU grad + opt + GPU implemtentation
def fn(x):
return max_pool_2d(x, (ws, ws), ignore_border=True,
padding=pad)
padding=pad, mode=mode)
theano.tests.unittest_tools.verify_grad(fn, [data],
cast_to_output_type=False,
mode=mode_with_gpu)
......@@ -179,11 +177,11 @@ def test_pooling():
for node in fg.maker.fgraph.toposort()])
g_out = fg(data)
if func is T.max:
if True:
# Compare again the CPU result
out = max_pool_2d(x, (ws, ws),
padding=pad,
ignore_border=True)
ignore_border=True, mode=mode)
fc = theano.function([x], theano.grad(out.sum(), x),
mode=mode_without_gpu)
assert any([isinstance(node.op, DownsampleFactorMaxGrad)
......
theano/tensor/signal/downsample.py
浏览文件 @ 512c2c16
......@@ -38,7 +38,8 @@ def max_pool_2d_same_size(input, patch_size):
return outs
def max_pool_2d(input, ds, ignore_border=False, st=None, padding=(0, 0)):
def max_pool_2d(input, ds, ignore_border=False, st=None, padding=(0, 0),
mode='max'):
"""
Takes as input a N-D tensor, where N >= 2. It downscales the input image by
the specified factor, by keeping only the maximum value of non-overlapping
......@@ -62,11 +63,14 @@ def max_pool_2d(input, ds, ignore_border=False, st=None, padding=(0, 0)):
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
:param mode: 'max' or 'average', the operation executed on each window.
:type mode: string
"""
if input.ndim < 2:
raise NotImplementedError('max_pool_2d requires a dimension >= 2')
if input.ndim == 4:
op = DownsampleFactorMax(ds, ignore_border, st=st, padding=padding)
op = DownsampleFactorMax(ds, ignore_border, st=st, padding=padding,
mode=mode)
output = op(input)
return output
......@@ -84,7 +88,8 @@ def max_pool_2d(input, ds, ignore_border=False, st=None, padding=(0, 0)):
input_4D = tensor.reshape(input, new_shape, ndim=4)
# downsample mini-batch of images
op = DownsampleFactorMax(ds, ignore_border, st=st, padding=padding)
op = DownsampleFactorMax(ds, ignore_border, st=st, padding=padding,
mode=mode)
output = op(input_4D)
# restore to original shape
......@@ -99,7 +104,7 @@ class DownsampleFactorMax(Op):
regions.
"""
__props__ = ('ds', 'ignore_border', 'st', 'padding')
__props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
@staticmethod
def out_shape(imgshape, ds, ignore_border=False, st=None, padding=(0, 0)):
......@@ -178,7 +183,8 @@ class DownsampleFactorMax(Op):
rval = list(imgshape[:-2]) + [nr, nc]
return rval
def __init__(self, ds, ignore_border=False, st=None, padding=(0, 0)):
def __init__(self, ds, ignore_border=False, st=None, padding=(0, 0),
mode='max'):
"""
:param ds: downsample factor over rows and column.
ds indicates the pool region size.
......@@ -193,13 +199,15 @@ class DownsampleFactorMax(Op):
over rows/cols to get the the next pool region.
if st is None, it is considered equal to ds
(no overlap on pooling regions)
: type st: list or tuple of two ints
: type st: list or tuple of two ints or None
: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
:param mode: 'max' or 'average'
"""
self.ds = tuple(ds)
if not all([isinstance(d, int) for d in ds]):
......@@ -208,6 +216,7 @@ class DownsampleFactorMax(Op):
" Got %s" % str(ds))
if st is None:
st = ds
assert isinstance(st, (tuple, list))
self.st = tuple(st)
self.ignore_border = ignore_border
self.padding = tuple(padding)
......@@ -217,11 +226,16 @@ class DownsampleFactorMax(Op):
if self.padding[0] >= self.ds[0] or self.padding[1] >= self.ds[1]:
raise NotImplementedError(
'padding_h and padding_w must be smaller than strides')
if mode not in ['max', 'average']:
raise ValueError(
"DownsampleFactorMax mode parameter only support 'max' and"
" 'average'. Got %s" % mode)
self.mode = mode
def __str__(self):
return '%s{%s, %s, %s, %s}' % (
return '%s{%s, %s, %s, %s, %s}' % (
self.__class__.__name__,
self.ds, self.st, self.ignore_border, self.padding)
self.ds, self.st, self.ignore_border, self.padding, self.mode)
def make_node(self, x):
if x.type.ndim != 4:
......@@ -231,6 +245,8 @@ class DownsampleFactorMax(Op):
return gof.Apply(self, [x], [x.type()])
def perform(self, node, inp, out):
if self.mode != 'max' and self.padding != (0, 0):
raise NotImplementedError()
x, = inp
z, = out
if len(x.shape) != 4:
......@@ -261,6 +277,9 @@ class DownsampleFactorMax(Op):
y[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)] = x
else:
y = x
func = numpy.max
if self.mode == 'average':
func = numpy.average
# max pooling
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
......@@ -270,8 +289,8 @@ class DownsampleFactorMax(Op):
for c in xrange(pc):
col_st = c * st1
col_end = __builtin__.min(col_st + ds1, img_cols)
zz[n, k, r, c] = y[
n, k, row_st:row_end, col_st:col_end].max()
zz[n, k, r, c] = func(y[
n, k, row_st:row_end, col_st:col_end])
def infer_shape(self, node, in_shapes):
shp = self.out_shape(in_shapes[0], self.ds,
......@@ -284,13 +303,16 @@ class DownsampleFactorMax(Op):
maxout = self(x)
return [DownsampleFactorMaxGrad(self.ds,
ignore_border=self.ignore_border,
st=self.st, padding=self.padding)(
st=self.st, padding=self.padding,
mode=self.mode)(
x, maxout, gz)]
def c_headers(self):
return ['<algorithm>']
def c_code(self, node, name, inp, out, sub):
if self.mode != 'max':
raise theano.gof.utils.MethodNotDefined()
x, = inp
z, = out
fail = sub['fail']
......@@ -441,20 +463,25 @@ class DownsampleFactorMax(Op):
class DownsampleFactorMaxGrad(Op):
__props__ = ('ds', 'ignore_border', 'st', 'padding')
__props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
def __init__(self, ds, ignore_border, st=None, padding=(0, 0)):
def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
self.ds = tuple(ds)
self.ignore_border = ignore_border
if st is None:
st = ds
self.st = tuple(st)
self.padding = tuple(padding)
if mode not in ['max', 'average']:
raise ValueError(
"DownsampleFactorMax mode parameter only support 'max' and"
" 'average'. Got %s" % mode)
self.mode = mode
def __str__(self):
return '%s{%s, %s, %s, %s}' % (
return '%s{%s, %s, %s, %s, %s}' % (
self.__class__.__name__,
self.ds, self.st, self.ignore_border, self.padding)
self.ds, self.st, self.ignore_border, self.padding, self.mode)
def make_node(self, x, maxout, gz):
# make_node should only be called by the grad function of
......@@ -469,6 +496,8 @@ class DownsampleFactorMaxGrad(Op):
return Apply(self, [x, maxout, gz], [x.type()])
def perform(self, node, inp, out):
if self.mode != 'max' and self.padding != (0, 0):
raise NotImplementedError()
x, maxout, gz = inp
gx_stg, = out
# number of pooling output rows
......@@ -491,18 +520,33 @@ class DownsampleFactorMaxGrad(Op):
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):
row_st = r * st0
row_end = __builtin__.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = c * st1
col_end = __builtin__.min(col_st + ds1, img_cols)
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
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]
if self.mode == 'max':
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = r * st0
row_end = __builtin__.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = c * st1
col_end = __builtin__.min(col_st + ds1, img_cols)
for row_ind in xrange(row_st, row_end):
for col_ind in xrange(col_st, col_end):
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]
elif self.mode == 'average':
for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
row_st = r * st0
row_end = __builtin__.min(row_st + ds0, img_rows)
for c in xrange(pc):
col_st = c * st1
col_end = __builtin__.min(col_st + ds1, img_cols)
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
else:
raise ValueError('mode %s not know' % self.mode)
# unpad the image
gx = gx[:, :, pad_h:(img_rows-pad_h), pad_w:(img_cols-pad_w)]
gx_stg[0] = gx
......@@ -513,7 +557,7 @@ class DownsampleFactorMaxGrad(Op):
def grad(self, inp, grads):
x, maxout, gz = inp
ggx, = grads
if self.padding == (0, 0):
if self.padding == (0, 0) and self.mode == 'max':
return [theano.tensor.zeros_like(x),
theano.tensor.zeros_like(maxout),
DownsampleFactorMaxGradGrad(
......@@ -528,6 +572,8 @@ class DownsampleFactorMaxGrad(Op):
def c_code(self, node, name, inp, out, sub):
if self.ds != self.st or self.padding != (0, 0):
raise theano.gof.utils.MethodNotDefined()
if self.mode != 'max':
raise theano.gof.utils.MethodNotDefined()
x, z, gz = inp
gx, = out
fail = sub['fail']
......@@ -624,6 +670,7 @@ class DownsampleFactorMaxGrad(Op):
class DownsampleFactorMaxGradGrad(Op):
__props__ = ('ds', 'ignore_border', 'st')
@staticmethod
def out_shape(imgshape, ds, ignore_border=False, st=None):
......@@ -702,16 +749,6 @@ class DownsampleFactorMaxGradGrad(Op):
st = ds
self.st = tuple(st)
def __eq__(self, other):
return (type(self) == type(other)
and self.ds == other.ds
and self.st == other.st
and self.ignore_border == other.ignore_border)
def __hash__(self):
return hash(type(self)) ^ hash(self.ds) ^ \
hash(self.st) ^ hash(self.ignore_border)
def __str__(self):
return '%s{%s,%s,%s}' % (self.__class__.__name__,
self.ds, self.st, self.ignore_border)
......
theano/tensor/signal/tests/test_downsample.py
浏览文件 @ 512c2c16
from itertools import product
import unittest
import __builtin__
import numpy
import theano
import theano.tensor as tensor
from theano.tests import unittest_tools as utt
......@@ -12,7 +15,7 @@ from theano import function
class TestDownsampleFactorMax(utt.InferShapeTester):
@staticmethod
def numpy_max_pool_2d(input, ds, ignore_border=False):
def numpy_max_pool_2d(input, ds, ignore_border=False, mode='max'):
'''Helper function, implementing max_pool_2d in pure numpy'''
if len(input.shape) < 2:
raise NotImplementedError('input should have at least 2 dim,'
......@@ -29,6 +32,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp.append(input.shape[-2] / ds[0] + xi)
out_shp.append(input.shape[-1] / ds[1] + yi)
output_val = numpy.zeros(out_shp)
func = numpy.max
if mode == 'average':
func = numpy.average
for k in numpy.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
......@@ -36,7 +42,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for j in range(output_val.shape[-1]):
jj = j * ds[1]
patch = input[k][ii:ii + ds[0], jj:jj + ds[1]]
output_val[k][i, j] = numpy.max(patch)
output_val[k][i, j] = func(patch)
return output_val
@staticmethod
......@@ -83,7 +89,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
return output_val
@staticmethod
def numpy_max_pool_2d_stride(input, ds, ignore_border=False, st=None):
def numpy_max_pool_2d_stride(input, ds, ignore_border=False, st=None,
mode='max'):
'''Helper function, implementing max_pool_2d in pure numpy
this function provides st input to indicate the stide size
for the pooling regions. if not indicated, st == sd.'''
......@@ -128,6 +135,10 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp.append(out_r)
out_shp.append(out_c)
func = numpy.max
if mode == 'average':
func = numpy.average
output_val = numpy.zeros(out_shp)
for k in numpy.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
......@@ -137,32 +148,35 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
jj_st = j * st[1]
jj_end = __builtin__.min(jj_st + ds[1], img_cols)
patch = input[k][ii_st:ii_end, jj_st:jj_end]
output_val[k][i, j] = numpy.max(patch)
output_val[k][i, j] = func(patch)
return output_val
def test_DownsampleFactorMax(self):
rng = numpy.random.RandomState(utt.fetch_seed())
# generate random images
maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3))
imval = rng.rand(4, 10, 64, 64)
imval = rng.rand(4, 2, 16, 16)
images = tensor.dtensor4()
for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]:
for maxpoolshp, ignore_border, mode in product(maxpoolshps,
[True, False],
['max', 'average']):
# print 'maxpoolshp =', maxpoolshp
# print 'ignore_border =', ignore_border
# Pure Numpy computation
numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp,
ignore_border)
output = max_pool_2d(images, maxpoolshp, ignore_border)
ignore_border,
mode=mode)
output = max_pool_2d(images, maxpoolshp, ignore_border,
mode=mode)
f = function([images, ], [output, ])
output_val = f(imval)
assert numpy.all(output_val == numpy_output_val)
# DownsampleFactorMax op
maxpool_op = DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border)(images)
ignore_border=ignore_border,
mode=mode)(images)
f = function([images], maxpool_op)
output_val = f(imval)
utt.assert_allclose(output_val, numpy_output_val)
......@@ -179,24 +193,32 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
(4, 10, 14, 14), (4, 10, 6, 6), (4, 10, 4, 3),
(4, 10, 12, 14), (4, 10, 4, 5), (4, 10, 3, 2),
(4, 10, 12, 14), (4, 10, 5, 6), (4, 10, 4, 3))
outputshps += ((4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3),
(4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3),
(4, 10, 14, 14), (4, 10, 5, 5), (4, 10, 3, 2),
(4, 10, 14, 14), (4, 10, 6, 6), (4, 10, 4, 3),
(4, 10, 12, 14), (4, 10, 4, 5), (4, 10, 3, 2),
(4, 10, 12, 14), (4, 10, 5, 6), (4, 10, 4, 3))
images = tensor.dtensor4()
indx = 0
for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]:
for mode, maxpoolshp, ignore_border in product(['max', 'average'],
maxpoolshps,
[True, False]):
for stride in stridesizes:
outputshp = outputshps[indx]
indx += 1
# DownsampleFactorMax op
numpy_output_val = \
self.numpy_max_pool_2d_stride(imval, maxpoolshp,
ignore_border, stride)
ignore_border, stride,
mode)
assert numpy_output_val.shape == outputshp, (
"outshape is %s, calculated shape is %s"
% (outputshp, numpy_output_val.shape))
maxpool_op = \
DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border,
st=stride)(images)
st=stride, mode=mode)(images)
f = function([images], maxpool_op)
output_val = f(imval)
utt.assert_allclose(output_val, numpy_output_val)
......@@ -219,7 +241,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
imval = rng.rand(4, 10, imvsize[0], imvsize[1])
stride = stridesizes[indx]
maxpoolshp = maxpoolshps[indx]
for ignore_border in [True, False]:
for ignore_border, mode in product([True, False],
['max', 'average']):
indx_out = indx * 2
if not ignore_border:
indx_out += 1
......@@ -227,14 +250,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
# DownsampleFactorMax op
numpy_output_val = \
self.numpy_max_pool_2d_stride(imval, maxpoolshp,
ignore_border, stride)
ignore_border, stride, mode)
assert numpy_output_val.shape == outputshp, (
"outshape is %s, calculated shape is %s"
% (outputshp, numpy_output_val.shape))
maxpool_op = \
DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border,
st=stride)(images)
st=stride, mode=mode)(images)
f = function([images], maxpool_op)
output_val = f(imval)
utt.assert_allclose(output_val, numpy_output_val)
......@@ -447,20 +470,24 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
imval = rng.rand(4, 5)
images = tensor.dmatrix()
for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]:
for maxpoolshp, ignore_border, mode in product(maxpoolshps,
[True, False],
['max', 'average']):
# print 'maxpoolshp =', maxpoolshp
# print 'ignore_border =', ignore_border
numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp,
ignore_border)
output = max_pool_2d(images, maxpoolshp, ignore_border)
ignore_border,
mode=mode)
output = max_pool_2d(images, maxpoolshp, ignore_border,
mode=mode)
output_val = function([images], output)(imval)
assert numpy.all(output_val == numpy_output_val), (
"output_val is %s, numpy_output_val is %s"
% (output_val, numpy_output_val))
def mp(input):
return max_pool_2d(input, maxpoolshp, ignore_border)
return max_pool_2d(input, maxpoolshp, ignore_border,
mode=mode)
utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_2d_2D_same_size(self):
......@@ -492,13 +519,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
imval = rng.rand(2, 3, 4)
images = tensor.dtensor3()
for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]:
for maxpoolshp, ignore_border, mode in product(maxpoolshps,
[True, False],
['max', 'average']):
# print 'maxpoolshp =', maxpoolshp
# print 'ignore_border =', ignore_border
numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp,
ignore_border)
output = max_pool_2d(images, maxpoolshp, ignore_border)
ignore_border,
mode)
output = max_pool_2d(images, maxpoolshp, ignore_border,
mode=mode)
output_val = function([images], output)(imval)
assert numpy.all(output_val == numpy_output_val), (
"output_val is %s, numpy_output_val is %s"
......@@ -524,13 +554,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
imval = rng.rand(2, 1, 1, 1, 3, 4)
images = tensor.TensorType('float64', [False] * 6)()
for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]:
for maxpoolshp, ignore_border, mode in product(maxpoolshps,
[True, False],
['max', 'average']):
# print 'maxpoolshp =', maxpoolshp
# print 'ignore_border =', ignore_border
numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp,
ignore_border)
output = max_pool_2d(images, maxpoolshp, ignore_border)
ignore_border,
mode=mode)
output = max_pool_2d(images, maxpoolshp, ignore_border,
mode=mode)
output_val = function([images], output)(imval)
assert numpy.all(output_val == numpy_output_val)
......
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