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提交 c918af6e authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 7039dda4
隐藏空白字符变更
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正在显示 包含 30 行增加 和 30 行删除
theano/tensor/nnet/tests/test_neighbours.py
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from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
import theano
......@@ -25,7 +25,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
for border in ['valid', 'ignore_borders']:
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape))
np.arange(np.prod(shape), dtype=dtype).reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
f = function([],
......@@ -42,13 +42,13 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
for node in f.maker.fgraph.toposort()])
# print g()
assert numpy.allclose(images.get_value(borrow=True), g())
assert np.allclose(images.get_value(borrow=True), g())
def test_neibs_manual(self):
shape = (2, 3, 4, 4)
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape))
np.arange(np.prod(shape), dtype=dtype).reshape(shape))
neib_shape = T.as_tensor_variable((2, 2))
for border in ['valid', 'ignore_borders']:
......@@ -60,7 +60,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
# print images.get_value(borrow=True)
neibs = f()
# print neibs
assert numpy.allclose(neibs, [
assert np.allclose(neibs, [
[0, 1, 4, 5],
[2, 3, 6, 7],
[8, 9, 12, 13],
......@@ -88,12 +88,12 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
assert numpy.allclose(images.get_value(borrow=True), g())
assert np.allclose(images.get_value(borrow=True), g())
def test_neibs_manual_step(self):
shape = (2, 3, 5, 5)
for dtype in self.dtypes:
images = shared(numpy.asarray(numpy.arange(numpy.prod(
images = shared(np.asarray(np.arange(np.prod(
shape)).reshape(shape), dtype=dtype))
neib_shape = T.as_tensor_variable((3, 3))
neib_step = T.as_tensor_variable((2, 2))
......@@ -107,7 +107,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
assert self.op in [type(node.op)
for node in f.maker.fgraph.toposort()]
assert numpy.allclose(neibs, [
assert np.allclose(neibs, [
[0, 1, 2, 5, 6, 7, 10, 11, 12],
[2, 3, 4, 7, 8, 9, 12, 13, 14],
[10, 11, 12, 15, 16, 17, 20, 21, 22],
......@@ -143,8 +143,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_neibs_bad_shape(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(numpy.arange(
numpy.prod(shape), dtype=dtype).reshape(shape))
images = shared(np.arange(
np.prod(shape), dtype=dtype).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
......@@ -212,11 +212,11 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
for dtype in self.dtypes:
images = shared(numpy.asarray(numpy.arange(numpy.prod(
images = shared(np.asarray(np.arange(np.prod(
shape)).reshape(shape), dtype=dtype))
neib_shape = T.as_tensor_variable(neib_shape)
neib_step = T.as_tensor_variable(neib_step)
expected = numpy.asarray(expected)
expected = np.asarray(expected)
f = function([], images2neibs(images, neib_shape, neib_step,
mode="wrap_centered"),
......@@ -225,7 +225,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert numpy.allclose(
assert np.allclose(
neibs[i * expected.shape[0]:(i + 1) * expected.shape[0], :],
expected + 25 * i), "wrap_centered"
......@@ -240,8 +240,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(numpy.arange(
numpy.prod(shape), dtype=dtype
images = shared(np.arange(
np.prod(shape), dtype=dtype
).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
......@@ -253,7 +253,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
self.assertRaises(TypeError, f)
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
images = shared(np.arange(np.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
......@@ -262,7 +262,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
# Test a valid shapes
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
images = shared(np.arange(np.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([],
......@@ -273,7 +273,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_grad_wrap_centered(self):
# It is not implemented for now. So test that we raise an error.
shape = (2, 3, 6, 6)
images_val = numpy.random.rand(*shape).astype('float32')
images_val = np.random.rand(*shape).astype('float32')
def fn(images):
return images2neibs(images, (3, 3), mode='wrap_centered')
......@@ -283,7 +283,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_grad_valid(self):
shape = (2, 3, 6, 6)
images_val = numpy.random.rand(*shape).astype('float32')
images_val = np.random.rand(*shape).astype('float32')
def fn(images):
return images2neibs(images, (2, 2))
......@@ -305,7 +305,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_grad_ignore_border(self):
shape = (2, 3, 5, 5)
images_val = numpy.random.rand(*shape).astype('float32')
images_val = np.random.rand(*shape).astype('float32')
def fn(images):
return images2neibs(images, (2, 2),
......@@ -317,7 +317,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_neibs2images_grad(self):
# say we had images of size (2, 3, 10, 10)
# then we extracted 2x2 neighbors on this, we get (2 * 3 * 5 * 5, 4)
neibs_val = numpy.random.rand(150, 4)
neibs_val = np.random.rand(150, 4)
def fn(neibs):
return neibs2images(neibs, (2, 2), (2, 3, 10, 10))
......@@ -327,7 +327,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_neibs_valid_with_inconsistent_borders(self):
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape),
images_val = np.arange(np.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
......@@ -356,7 +356,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
original_size, mode=mode)
f = theano.function([patsRecovery, original_size], out)
im_val = numpy.ones((1, 3, 320, 320), dtype=numpy.float32)
im_val = np.ones((1, 3, 320, 320), dtype=np.float32)
neibs = extractPatches(im_val)
f(neibs, im_val.shape)
# Wrong number of dimensions
......@@ -368,7 +368,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def speed_neibs(self):
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape),
images = shared(np.arange(np.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
......@@ -380,7 +380,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def speed_neibs_wrap_centered(self):
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape),
images = shared(np.arange(np.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
......@@ -393,7 +393,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_infer_shape(self):
shape = (100, 40, 6, 3)
images = numpy.ones(shape).astype('float32')
images = np.ones(shape).astype('float32')
x = T.ftensor4()
self._compile_and_check(
[x], [images2neibs(x, neib_shape=(2, 1), mode='valid')],
......@@ -402,14 +402,14 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
[x], [images2neibs(x, neib_shape=(2, 3), mode='valid')],
[images], Images2Neibs)
shape = (100, 40, 5, 4)
images = numpy.ones(shape).astype('float32')
images = np.ones(shape).astype('float32')
x = T.ftensor4()
self._compile_and_check(
[x], [images2neibs(
x, neib_shape=(2, 1), mode='ignore_borders')],
[images], Images2Neibs)
shape = (100, 40, 5, 3)
images = numpy.ones(shape).astype('float32')
images = np.ones(shape).astype('float32')
x = T.ftensor4()
self._compile_and_check(
[x], [images2neibs(
......@@ -417,14 +417,14 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
[images], Images2Neibs)
shape = (100, 40, 6, 7)
images = numpy.ones(shape).astype('float32')
images = np.ones(shape).astype('float32')
x = T.ftensor4()
self._compile_and_check(
[x], [images2neibs(
x, neib_shape=(2, 2), mode='ignore_borders')],
[images], Images2Neibs)
shape = (100, 40, 5, 10)
images = numpy.ones(shape).astype('float32')
images = np.ones(shape).astype('float32')
x = T.ftensor4()
self._compile_and_check(
[x], [images2neibs(
......
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