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提交 57cd51cb authored 作者: Chiheb Trabelsi's avatar Chiheb Trabelsi
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Functions parameters have been modified in test_pool.py for coherence purposes.…

Functions parameters have been modified in test_pool.py for coherence purposes. The test also contains some examples where it should print a deprecation warning. For that, you should use the following command: nosetests -s test_pool.py.
上级 c49136eb
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正在显示 包含 88 行增加 和 78 行删除
theano/tensor/signal/tests/test_pool.py
浏览文件 @ 57cd51cb
......@@ -30,7 +30,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
assert Pool.out_shape((8, 6), (2, 2)) == [4, 3]
@staticmethod
def numpy_max_pool_2d(input, ds, ignore_border=False, mode='max'):
def numpy_max_pool_2d(input, ws, ignore_border=False, mode='max'):
'''Helper function, implementing pool_2d in pure numpy'''
if len(input.shape) < 2:
raise NotImplementedError('input should have at least 2 dim,'
......@@ -39,13 +39,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
xi = 0
yi = 0
if not ignore_border:
if input.shape[-2] % ds[0]:
if input.shape[-2] % ws[0]:
xi += 1
if input.shape[-1] % ds[1]:
if input.shape[-1] % ws[1]:
yi += 1
out_shp = list(input.shape[:-2])
out_shp.append(input.shape[-2] // ds[0] + xi)
out_shp.append(input.shape[-1] // ds[1] + yi)
out_shp.append(input.shape[-2] // ws[0] + xi)
out_shp.append(input.shape[-1] // ws[1] + yi)
output_val = numpy.zeros(out_shp)
func = numpy.max
if mode == 'sum':
......@@ -55,29 +55,29 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for k in numpy.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
ii = i * ds[0]
ii = i * ws[0]
for j in range(output_val.shape[-1]):
jj = j * ds[1]
patch = input[k][ii:ii + ds[0], jj:jj + ds[1]]
jj = j * ws[1]
patch = input[k][ii:ii + ws[0], jj:jj + ws[1]]
output_val[k][i, j] = func(patch)
return output_val
@staticmethod
def numpy_max_pool_nd(input, ds, ignore_border=False, mode='max'):
def numpy_max_pool_nd(input, ws, ignore_border=False, mode='max'):
'''Helper function, implementing pool_nd in pure numpy'''
if len(input.shape) < len(ds):
if len(input.shape) < len(ws):
raise NotImplementedError('input should have at least %s dim,'
' shape is %s'
% (str(ds), str(input.shape)))
nd = len(ds)
% (str(ws), str(input.shape)))
nd = len(ws)
si = [0] * nd
if not ignore_border:
for i in range(nd):
if input.shape[-nd + i] % ds[i]:
if input.shape[-nd + i] % ws[i]:
si[i] += 1
out_shp = list(input.shape[:-nd])
for i in range(nd):
out_shp.append(input.shape[-nd + i] // ds[i] + si[i])
out_shp.append(input.shape[-nd + i] // ws[i] + si[i])
output_val = numpy.zeros(out_shp)
func = numpy.max
if mode == 'sum':
......@@ -87,21 +87,21 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for l in numpy.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]):
patch = input[l][tuple(slice(r[i] * ds[i], (r[i] + 1) * ds[i])
patch = input[l][tuple(slice(r[i] * ws[i], (r[i] + 1) * ws[i])
for i in range(nd))]
output_val[l][r] = func(patch)
return output_val
@staticmethod
def numpy_max_pool_2d_stride_padding(
x, ds, ignore_border=True, st=None, padding=(0, 0), mode='max'):
x, ws, ignore_border=True, stride=None, pad=(0, 0), mode='max'):
assert ignore_border
pad_h = padding[0]
pad_w = padding[1]
pad_h = pad[0]
pad_w = pad[1]
h = x.shape[-2]
w = x.shape[-1]
assert ds[0] > pad_h
assert ds[1] > pad_w
assert ws[0] > pad_h
assert ws[1] > pad_w
def pad_img(x):
y = numpy.zeros(
......@@ -113,13 +113,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
return y
img_rows = h + 2 * pad_h
img_cols = w + 2 * pad_w
out_r = (img_rows - ds[0]) // st[0] + 1
out_c = (img_cols - ds[1]) // st[1] + 1
out_r = (img_rows - ws[0]) // stride[0] + 1
out_c = (img_cols - ws[1]) // stride[1] + 1
out_shp = list(x.shape[:-2])
out_shp.append(out_r)
out_shp.append(out_c)
ds0, ds1 = ds
st0, st1 = st
ws0, ws1 = ws
st0, st1 = stride
output_val = numpy.zeros(out_shp)
y = pad_img(x)
func = numpy.max
......@@ -131,14 +131,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for k in numpy.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_st = i * st[0]
ii_end = builtins.min(ii_st + ds[0], img_rows)
ii_st = i * stride[0]
ii_end = builtins.min(ii_st + ws[0], img_rows)
if not inc_pad:
ii_st = builtins.max(ii_st, pad_h)
ii_end = builtins.min(ii_end, h + pad_h)
for j in range(output_val.shape[-1]):
jj_st = j * st[1]
jj_end = builtins.min(jj_st + ds[1], img_cols)
jj_st = j * stride[1]
jj_end = builtins.min(jj_st + ws[1], img_cols)
if not inc_pad:
jj_st = builtins.max(jj_st, pad_w)
jj_end = builtins.min(jj_end, w + pad_w)
......@@ -148,25 +148,25 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
@staticmethod
def numpy_max_pool_nd_stride_padding(
input, ds, ignore_border=True, st=None, padding=None, mode='max'):
input, ws, ignore_border=True, stride=None, pad=None, mode='max'):
assert ignore_border
nd = len(ds)
if padding is None:
padding = (0,) * nd
if st is None:
st = (0,) * nd
assert len(padding) == len(ds) == len(st)
assert all(ds[i] > padding[i] for i in range(nd))
nd = len(ws)
if pad is None:
pad = (0,) * nd
if stride is None:
stride = (0,) * nd
assert len(pad) == len(ws) == len(stride)
assert all(ws[i] > pad[i] for i in range(nd))
def pad_img(x):
# initialize padded input
y = numpy.zeros(
x.shape[0:-nd] +
tuple(x.shape[-nd + i] + padding[i] * 2 for i in range(nd)),
tuple(x.shape[-nd + i] + pad[i] * 2 for i in range(nd)),
dtype=x.dtype)
# place the unpadded input in the center
block = ((slice(None),) * (len(x.shape) - nd) +
tuple(slice(padding[i], x.shape[-nd + i] + padding[i])
tuple(slice(pad[i], x.shape[-nd + i] + pad[i])
for i in range(nd)))
y[block] = x
return y
......@@ -174,9 +174,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
pad_img_shp = list(input.shape[:-nd])
out_shp = list(input.shape[:-nd])
for i in range(nd):
padded_size = input.shape[-nd + i] + 2 * padding[i]
padded_size = input.shape[-nd + i] + 2 * pad[i]
pad_img_shp.append(padded_size)
out_shp.append((padded_size - ds[i]) // st[i] + 1)
out_shp.append((padded_size - ws[i]) // stride[i] + 1)
output_val = numpy.zeros(out_shp)
padded_input = pad_img(input)
func = numpy.max
......@@ -190,51 +190,51 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for r in numpy.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_st = r[i] * st[i]
r_end = builtins.min(r_st + ds[i], pad_img_shp[-nd + i])
r_st = r[i] * stride[i]
r_end = builtins.min(r_st + ws[i], pad_img_shp[-nd + i])
if not inc_pad:
r_st = builtins.max(r_st, padding[i])
r_end = builtins.min(r_end, input.shape[-nd + i] + padding[i])
r_st = builtins.max(r_st, pad[i])
r_end = builtins.min(r_end, input.shape[-nd + i] + pad[i])
region.append(slice(r_st, r_end))
patch = padded_input[l][region]
output_val[l][r] = func(patch)
return output_val
@staticmethod
def numpy_max_pool_2d_stride(input, ds, ignore_border=False, st=None,
def numpy_max_pool_2d_stride(input, ws, ignore_border=False, stride=None,
mode='max'):
'''Helper function, implementing pool_2d in pure numpy
this function provides st input to indicate the stide size
for the pooling regions. if not indicated, st == sd.'''
this function provides stride input to indicate the stride size
for the pooling regions. if not indicated, stride == sd.'''
if len(input.shape) < 2:
raise NotImplementedError('input should have at least 2 dim,'
' shape is %s'
% str(input.shape))
if st is None:
st = ds
if stride is None:
stride = ws
img_rows = input.shape[-2]
img_cols = input.shape[-1]
out_r = 0
out_c = 0
if img_rows - ds[0] >= 0:
out_r = (img_rows - ds[0]) // st[0] + 1
if img_cols - ds[1] >= 0:
out_c = (img_cols - ds[1]) // st[1] + 1
if img_rows - ws[0] >= 0:
out_r = (img_rows - ws[0]) // stride[0] + 1
if img_cols - ws[1] >= 0:
out_c = (img_cols - ws[1]) // stride[1] + 1
if not ignore_border:
if out_r > 0:
if img_rows - ((out_r - 1) * st[0] + ds[0]) > 0:
rr = img_rows - out_r * st[0]
if img_rows - ((out_r - 1) * stride[0] + ws[0]) > 0:
rr = img_rows - out_r * stride[0]
if rr > 0:
out_r += 1
else:
if img_rows > 0:
out_r += 1
if out_c > 0:
if img_cols - ((out_c - 1) * st[1] + ds[1]) > 0:
cr = img_cols - out_c * st[1]
if img_cols - ((out_c - 1) * stride[1] + ws[1]) > 0:
cr = img_cols - out_c * stride[1]
if cr > 0:
out_c += 1
else:
......@@ -254,35 +254,35 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
output_val = numpy.zeros(out_shp)
for k in numpy.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_st = i * st[0]
ii_end = builtins.min(ii_st + ds[0], img_rows)
ii_st = i * stride[0]
ii_end = builtins.min(ii_st + ws[0], img_rows)
for j in range(output_val.shape[-1]):
jj_st = j * st[1]
jj_end = builtins.min(jj_st + ds[1], img_cols)
jj_st = j * stride[1]
jj_end = builtins.min(jj_st + ws[1], img_cols)
patch = input[k][ii_st:ii_end, jj_st:jj_end]
output_val[k][i, j] = func(patch)
return output_val
@staticmethod
def numpy_max_pool_nd_stride(input, ds, ignore_border=False, st=None,
def numpy_max_pool_nd_stride(input, ws, ignore_border=False, stride=None,
mode='max'):
'''Helper function, implementing pooling in pure numpy
this function provides st input to indicate the stide size
for the pooling regions. if not indicated, st == sd.'''
nd = len(ds)
if st is None:
st = ds
assert len(st) == len(ds)
this function provides stride input to indicate the stide size
for the pooling regions. if not indicated, stride == sd.'''
nd = len(ws)
if stride is None:
stride = ws
assert len(stride) == len(ws)
out_shp = list(input.shape[:-nd])
for i in range(nd):
out = 0
if input.shape[-nd + i] - ds[i] >= 0:
out = (input.shape[-nd + i] - ds[i]) // st[i] + 1
if input.shape[-nd + i] - ws[i] >= 0:
out = (input.shape[-nd + i] - ws[i]) // stride[i] + 1
if not ignore_border:
if out > 0:
if input.shape[-nd + i] - ((out - 1) * st[i] + ds[i]) > 0:
if input.shape[-nd + i] - out * st[i] > 0:
if input.shape[-nd + i] - ((out - 1) * stride[i] + ws[i]) > 0:
if input.shape[-nd + i] - out * stride[i] > 0:
out += 1
else:
if input.shape[-nd + i] > 0:
......@@ -300,8 +300,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
for r in numpy.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_st = r[i] * st[i]
r_end = builtins.min(r_st + ds[i], input.shape[-nd + i])
r_st = r[i] * stride[i]
r_end = builtins.min(r_st + ws[i], input.shape[-nd + i])
region.append(slice(r_st, r_end))
patch = input[l][region]
output_val[l][r] = func(patch)
......@@ -697,6 +697,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
grad_shape = Pool.out_shape(
imval.shape, maxpoolshp, ndim=len(maxpoolshp),
ignore_border=ignore_border, st=stride)
grad_shape = Pool.out_shape(
imval.shape, maxpoolshp, ndim=len(maxpoolshp),
ignore_border=ignore_border, stride=stride)
# skip the grad verification when the output is empty
if numpy.prod(grad_shape) != 0:
......@@ -728,7 +731,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
grad_shape = Pool.out_shape(
imval.shape, avgpoolshp,
ndim=len(avgpoolshp),
ignore_border=ignore_border, st=stride)
ignore_border=ignore_border, stride=stride)
# skip the grad verification when the output is empty
if numpy.prod(grad_shape) != 0:
......@@ -765,9 +768,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
grad_shape = Pool.out_shape(imval.shape,
maxpoolshp,
ndim=len(maxpoolshp),
st=stridesize,
stride=stridesize,
ignore_border=True,
padding=paddingsize)
grad_shape = Pool.out_shape(imval.shape,
maxpoolshp,
ndim=len(maxpoolshp),
stride=stridesize,
ignore_border=True,
pad=paddingsize)
grad_val = rng.rand(*grad_shape) * 10.0
def mp(input, grad):
......@@ -804,9 +813,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
grad_shape = Pool.out_shape(imval.shape,
avgpoolshp,
ndim=len(avgpoolshp),
st=stridesize,
stride=stridesize,
ignore_border=True,
padding=paddingsize)
pad=paddingsize)
grad_val = rng.rand(*grad_shape) * 10.0
def mp(input, grad):
......@@ -823,6 +832,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
z = tensor.dot(x_vec.dimshuffle(0, 'x'),
x_vec.dimshuffle('x', 0))
y = pool_2d(input=z, ds=(2, 2), ignore_border=True)
y = pool_2d(input=z, ws=(2, 2), ignore_border=True)
C = tensor.exp(tensor.sum(y))
grad_hess = tensor.hessian(cost=C, wrt=x_vec)
......
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