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提交 c60f369f authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 fba196d7
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正在显示 包含 68 行增加 和 68 行删除
theano/tensor/signal/tests/test_pool.py
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......@@ -10,7 +10,7 @@ from six.moves import cPickle
import six.moves.builtins as builtins
import sys
import numpy
import numpy as np
import theano
import theano.tensor as tensor
......@@ -46,14 +46,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp = list(input.shape[:-2])
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
output_val = np.zeros(out_shp)
func = np.max
if mode == 'sum':
func = numpy.sum
func = np.sum
elif mode != 'max':
func = numpy.average
func = np.average
for k in numpy.ndindex(*input.shape[:-2]):
for k in np.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
ii = i * ws[0]
for j in range(output_val.shape[-1]):
......@@ -78,15 +78,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp = list(input.shape[:-nd])
for i in range(nd):
out_shp.append(input.shape[-nd + i] // ws[i] + si[i])
output_val = numpy.zeros(out_shp)
func = numpy.max
output_val = np.zeros(out_shp)
func = np.max
if mode == 'sum':
func = numpy.sum
func = np.sum
elif mode != 'max':
func = numpy.average
func = np.average
for l in numpy.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]):
for l in np.ndindex(*input.shape[:-nd]):
for r in np.ndindex(*output_val.shape[-nd:]):
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)
......@@ -104,7 +104,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
assert ws[1] > pad_w
def pad_img(x):
y = numpy.zeros(
y = np.zeros(
(x.shape[0], x.shape[1],
x.shape[2] + pad_h * 2, x.shape[3] + pad_w * 2),
dtype=x.dtype)
......@@ -120,16 +120,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp.append(out_c)
ws0, ws1 = ws
stride0, stride1 = stride
output_val = numpy.zeros(out_shp)
output_val = np.zeros(out_shp)
y = pad_img(x)
func = numpy.max
func = np.max
if mode == 'sum':
func = numpy.sum
func = np.sum
elif mode != 'max':
func = numpy.average
func = np.average
inc_pad = mode == 'average_inc_pad'
for k in numpy.ndindex(*x.shape[:-2]):
for k in np.ndindex(*x.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_stride = i * stride[0]
ii_end = builtins.min(ii_stride + ws[0], img_rows)
......@@ -160,7 +160,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def pad_img(x):
# initialize padded input
y = numpy.zeros(
y = np.zeros(
x.shape[0:-nd] +
tuple(x.shape[-nd + i] + pad[i] * 2 for i in range(nd)),
dtype=x.dtype)
......@@ -177,17 +177,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
padded_size = input.shape[-nd + i] + 2 * pad[i]
pad_img_shp.append(padded_size)
out_shp.append((padded_size - ws[i]) // stride[i] + 1)
output_val = numpy.zeros(out_shp)
output_val = np.zeros(out_shp)
padded_input = pad_img(input)
func = numpy.max
func = np.max
if mode == 'sum':
func = numpy.sum
func = np.sum
elif mode != 'max':
func = numpy.average
func = np.average
inc_pad = mode == 'average_inc_pad'
for l in numpy.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]):
for l in np.ndindex(*input.shape[:-nd]):
for r in np.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_stride = r[i] * stride[i]
......@@ -245,14 +245,14 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out_shp.append(out_r)
out_shp.append(out_c)
func = numpy.max
func = np.max
if mode == 'sum':
func = numpy.sum
func = np.sum
elif mode != 'max':
func = numpy.average
func = np.average
output_val = numpy.zeros(out_shp)
for k in numpy.ndindex(*input.shape[:-2]):
output_val = np.zeros(out_shp)
for k in np.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_stride = i * stride[0]
ii_end = builtins.min(ii_stride + ws[0], img_rows)
......@@ -289,15 +289,15 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
out += 1
out_shp.append(out)
func = numpy.max
func = np.max
if mode == 'sum':
func = numpy.sum
func = np.sum
elif mode != 'max':
func = numpy.average
func = np.average
output_val = numpy.zeros(out_shp)
for l in numpy.ndindex(*input.shape[:-nd]):
for r in numpy.ndindex(*output_val.shape[-nd:]):
output_val = np.zeros(out_shp)
for l in np.ndindex(*input.shape[:-nd]):
for r in np.ndindex(*output_val.shape[-nd:]):
region = []
for i in range(nd):
r_stride = r[i] * stride[i]
......@@ -308,7 +308,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
return output_val
def test_DownsampleFactorMax(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input size
examples = (
((2,), (16,)),
......@@ -361,13 +361,13 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
output_shape = Pool.out_shape(imval.shape, maxpoolshp,
ndim=len(maxpoolshp),
ignore_border=ignore_border)
utt.assert_allclose(numpy.asarray(output_shape), numpy_output_val.shape)
utt.assert_allclose(np.asarray(output_shape), numpy_output_val.shape)
f = function([], maxpool_op)
output_val = f()
utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxStride(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, ignore_border, input, output sizes
examples = (
((1, 1), (1, 1), True, (4, 10, 16, 16), (4, 10, 16, 16)),
......@@ -426,7 +426,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxStrideExtra(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((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))
......@@ -438,7 +438,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
(4, 10, 4, 2), (4, 10, 1, 0), (4, 10, 1, 1),
(4, 10, 0, 0), (4, 10, 1, 1))
images = tensor.dtensor4()
for indx in numpy.arange(len(maxpoolshps)):
for indx in np.arange(len(maxpoolshps)):
imvsize = imvsizs[indx]
imval = rng.rand(4, 10, imvsize[0], imvsize[1])
stride = stridesizes[indx]
......@@ -468,7 +468,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_DownsampleFactorMaxPaddingStride(self):
ignore_border = True # padding does not support ignore_border=False
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes
examples = (
((3,), (2,), (2,), (5,)),
......@@ -503,7 +503,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.assert_allclose(output_val, numpy_output_val)
def test_DownsampleFactorMaxPaddingStride_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes
examples = (
((10,), (5,), (3,), (2,)),
......@@ -530,7 +530,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng)
def test_DownsampleFactorMax_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input sizes
examples = (
((2,), (3,)),
......@@ -599,7 +599,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
testcase_func_name=utt.custom_name_func)
def test_DownsampleFactorMax_grad_stride(self, example, ignore_border, mode):
# checks the gradient for the case that stride is used
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
(maxpoolshp, stridesize, inputsize) = example
imval = rng.rand(*inputsize)
......@@ -611,7 +611,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng)
def test_DownsampleFactorMaxGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, input sizes
examples = (
((2,), (2,)),
......@@ -649,7 +649,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# avgpool, input sizes
examples = (
((2,), (2,)),
......@@ -691,7 +691,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_DownsampleFactorMaxGrad_grad_stride(self, example, ignore_border):
# checks the gradient of the gradient for
# the case that stride is used
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
(maxpoolshp, stride, inputsize) = example
imval = rng.rand(*inputsize)
grad_shape = Pool.out_shape(
......@@ -699,7 +699,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
ignore_border=ignore_border, stride=stride)
# skip the grad verification when the output is empty
if numpy.prod(grad_shape) != 0:
if np.prod(grad_shape) != 0:
grad_val = rng.rand(*grad_shape)
def mp(input, grad):
......@@ -722,7 +722,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_AveragePoolGrad_grad_stride(self, example, ignore_border, mode):
# checks the gradient of the gradient for
# the case that stride is used
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
(avgpoolshp, stride, inputsize) = example
imval = rng.rand(*inputsize)
grad_shape = Pool.out_shape(
......@@ -731,7 +731,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
ignore_border=ignore_border, stride=stride)
# skip the grad verification when the output is empty
if numpy.prod(grad_shape) != 0:
if np.prod(grad_shape) != 0:
grad_val = rng.rand(*grad_shape)
def mp(input, grad):
......@@ -744,7 +744,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_DownsampleFactorMaxPaddingStride_grad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes
examples = (
((3,), (2,), (2,), (10,)),
......@@ -781,7 +781,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval, grad_val], rng=rng)
def test_AveragePoolPaddingStride_grad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# avgpool, stride, pad, input sizes
examples = (
((3,), (2,), (2,), (10,)),
......@@ -831,10 +831,10 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
# The value has been manually computed from the theoretical gradient,
# and confirmed by the implementation.
assert numpy.allclose(fn_hess([1, 2]), [[0., 0.], [0., 982.7667]])
assert np.allclose(fn_hess([1, 2]), [[0., 0.], [0., 982.7667]])
def test_DownsampleFactorMaxGradGrad_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# maxpool, stride, pad, input sizes
examples = (
((3,), (2,), (2,), (10,)),
......@@ -864,7 +864,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval1, imval2], rng=rng)
def test_max_pool_2d_2D(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (3, 2))
imval = rng.rand(4, 5)
images = tensor.dmatrix()
......@@ -890,7 +890,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_3d_3D(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1, 1), (3, 2, 1))
imval = rng.rand(4, 5, 6)
images = tensor.dtensor3()
......@@ -916,7 +916,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_3d_3D_deprecated_interface(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1, 1), (3, 2, 1))
imval = rng.rand(4, 5, 6)
images = tensor.dtensor3()
......@@ -945,12 +945,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
mode=mode)
def test_max_pool_2d_2D_same_size(self):
rng = numpy.random.RandomState(utt.fetch_seed())
test_input_array = numpy.array([[[
rng = np.random.RandomState(utt.fetch_seed())
test_input_array = np.array([[[
[1., 2., 3., 4.],
[5., 6., 7., 8.]
]]]).astype(theano.config.floatX)
test_answer_array = numpy.array([[[
test_answer_array = np.array([[[
[0., 0., 0., 0.],
[0., 6., 0., 8.]
]]]).astype(theano.config.floatX)
......@@ -965,7 +965,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
utt.verify_grad(mp, [test_input_array], rng=rng)
def test_max_pool_2d_3D(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = [(1, 2)]
imval = rng.rand(2, 3, 4)
images = tensor.dtensor3()
......@@ -992,7 +992,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
# utt.verify_grad(mp, [imval], rng=rng)
def test_max_pool_2d_6D(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = [(3, 2)]
imval = rng.rand(2, 1, 1, 1, 3, 4)
images = tensor.TensorType('float64', [False] * 6)()
......@@ -1022,7 +1022,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
image = tensor.dtensor4()
maxout = tensor.dtensor4()
gz = tensor.dtensor4()
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2))
image_val = rng.rand(4, 6, 7, 9)
......@@ -1078,7 +1078,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
window_size = tensor.ivector()
stride = tensor.ivector()
padding = tensor.ivector()
data = numpy.random.normal(0, 1, (1, 1, 5, 5)).astype('float32')
data = np.random.normal(0, 1, (1, 1, 5, 5)).astype('float32')
# checking variable params vs fixed params
for ignore_border in [True, False]:
......@@ -1110,7 +1110,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
window_size = tensor.ivector()
stride = tensor.ivector()
padding = tensor.ivector()
data = numpy.random.normal(0, 1, (1, 1, 5, 5)).astype('float32')
data = np.random.normal(0, 1, (1, 1, 5, 5)).astype('float32')
# checking variable params vs fixed params
for ignore_border in [True, False]:
......@@ -1172,8 +1172,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
dz_dx = theano.gradient.grad(z.sum(), x)
new_fct = theano.function([x], [y, z, dy_dx, dz_dx])
# 3. Assert that the answer is the same
rng = numpy.random.RandomState(utt.fetch_seed())
image_val = rng.rand(4, 6, 7, 9).astype(numpy.float32)
rng = np.random.RandomState(utt.fetch_seed())
image_val = rng.rand(4, 6, 7, 9).astype(np.float32)
old_out = old_fct(image_val)
new_out = new_fct(image_val)
for o, n in zip(old_out, new_out):
......
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