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提交 cf81ce7f authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 e79c4e4c
隐藏空白字符变更
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正在显示 包含 34 行增加 和 35 行删除
theano/tensor/nnet/tests/test_abstract_conv.py
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from __future__ import absolute_import, print_function, division
import unittest
import numpy
import numpy as np
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises, assert_true
......@@ -238,8 +237,8 @@ class TestAssertShape(unittest.TestCase):
expected_shape = [None, s1, s2, None]
f = theano.function([x, s1, s2], assert_shape(x, expected_shape))
v = numpy.zeros((3, 5, 7, 11), dtype='float32')
self.assertEqual(0, numpy.sum(f(v, 5, 7)))
v = np.zeros((3, 5, 7, 11), dtype='float32')
self.assertEqual(0, np.sum(f(v, 5, 7)))
assert_raises(AssertionError, f, v, 5, 0)
assert_raises(AssertionError, f, v, 5, 9)
......@@ -257,12 +256,12 @@ class TestAssertShape(unittest.TestCase):
f = theano.function([input, filters], out)
# mismatched input_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 5, 9, 11), dtype='float32'),
numpy.zeros((7, 5, 3, 3), dtype='float32'))
np.zeros((3, 5, 9, 11), dtype='float32'),
np.zeros((7, 5, 3, 3), dtype='float32'))
# mismatched filter_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 5, 7, 11), dtype='float32'),
numpy.zeros((7, 5, 2, 2), dtype='float32'))
np.zeros((3, 5, 7, 11), dtype='float32'),
np.zeros((7, 5, 2, 2), dtype='float32'))
@change_flags([("conv.assert_shape", True)])
def test_shape_check_conv3d(self):
......@@ -275,12 +274,12 @@ class TestAssertShape(unittest.TestCase):
f = theano.function([input, filters], out)
# mismatched input_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 5, 9, 11, 13), dtype='float32'),
numpy.zeros((7, 5, 3, 3, 3), dtype='float32'))
np.zeros((3, 5, 9, 11, 13), dtype='float32'),
np.zeros((7, 5, 3, 3, 3), dtype='float32'))
# mismatched filter_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 5, 7, 11, 13), dtype='float32'),
numpy.zeros((7, 5, 2, 2, 2), dtype='float32'))
np.zeros((3, 5, 7, 11, 13), dtype='float32'),
np.zeros((7, 5, 2, 2, 2), dtype='float32'))
@change_flags([("conv.assert_shape", True)])
def test_shape_check_conv2d_grad_wrt_inputs(self):
......@@ -293,8 +292,8 @@ class TestAssertShape(unittest.TestCase):
f = theano.function([output_grad, filters], out)
# mismatched filter_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 6, 5, 9), dtype='float32'),
numpy.zeros((7, 6, 3, 3), dtype='float32'))
np.zeros((3, 6, 5, 9), dtype='float32'),
np.zeros((7, 6, 3, 3), dtype='float32'))
@change_flags([("conv.assert_shape", True)])
def test_shape_check_conv3d_grad_wrt_inputs(self):
......@@ -307,8 +306,8 @@ class TestAssertShape(unittest.TestCase):
f = theano.function([output_grad, filters], out)
# mismatched filter_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 6, 5, 9, 11), dtype='float32'),
numpy.zeros((7, 6, 3, 3, 3), dtype='float32'))
np.zeros((3, 6, 5, 9, 11), dtype='float32'),
np.zeros((7, 6, 3, 3, 3), dtype='float32'))
@change_flags([("conv.assert_shape", True)])
def test_shape_check_conv2d_grad_wrt_weights(self):
......@@ -321,8 +320,8 @@ class TestAssertShape(unittest.TestCase):
f = theano.function([input, output_grad], out)
# mismatched filter_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 6, 7, 11), dtype='float32'),
numpy.zeros((3, 7, 5, 9), dtype='float32'))
np.zeros((3, 6, 7, 11), dtype='float32'),
np.zeros((3, 7, 5, 9), dtype='float32'))
@change_flags([("conv.assert_shape", True)])
def test_shape_check_conv3d_grad_wrt_weights(self):
......@@ -335,8 +334,8 @@ class TestAssertShape(unittest.TestCase):
f = theano.function([input, output_grad], out)
# mismatched filter_shape
assert_raises(AssertionError, f,
numpy.zeros((3, 6, 7, 11, 13), dtype='float32'),
numpy.zeros((3, 7, 5, 9, 11), dtype='float32'))
np.zeros((3, 6, 7, 11, 13), dtype='float32'),
np.zeros((3, 7, 5, 9, 11), dtype='float32'))
class BaseTestConv(object):
......@@ -371,8 +370,8 @@ class BaseTestConv(object):
if filter_dilation is None:
filter_dilation = (1,) * (len(inputs_shape) - 2)
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs_val = np.random.random(inputs_shape).astype('float32')
filters_val = np.random.random(filters_shape).astype('float32')
# scale down values to prevent rounding errors
inputs_val /= 10
......@@ -414,8 +413,8 @@ class BaseTestConv(object):
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
res_ref = np.array(f_ref())
res = np.array(f())
utt.assert_allclose(res_ref, res)
if verify_grad and inputs_val.size > 0 and filters_val.size > 0 and res.size > 0:
utt.verify_grad(conv_op(border_mode=border_mode,
......@@ -436,8 +435,8 @@ class BaseTestConv(object):
if filter_dilation is None:
filter_dilation = (1,) * (len(inputs_shape) - 2)
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
inputs_val = np.random.random(inputs_shape).astype('float32')
output_val = np.random.random(output_shape).astype('float32')
inputs = self.shared(inputs_val)
output = self.shared(output_val)
......@@ -473,8 +472,8 @@ class BaseTestConv(object):
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
res_ref = np.array(f_ref())
res = np.array(f())
utt.assert_allclose(res_ref, res)
def abstract_conv_gradweight(inputs_val, output_val):
......@@ -499,8 +498,8 @@ class BaseTestConv(object):
if filter_dilation is None:
filter_dilation = (1,) * (len(inputs_shape) - 2)
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
output_val = np.random.random(output_shape).astype('float32')
filters_val = np.random.random(filters_shape).astype('float32')
output = self.shared(output_val)
filters = self.shared(filters_val)
......@@ -537,10 +536,10 @@ class BaseTestConv(object):
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res = numpy.array(f())
res = np.array(f())
if ref is not None:
res_ref = numpy.array(f_ref())
res_ref = np.array(f_ref())
utt.assert_allclose(res_ref, res)
def abstract_conv_gradinputs(filters_val, output_val):
......@@ -1272,7 +1271,7 @@ class TestConvTypes(unittest.TestCase):
self.filters = tensor.ftensor4()
self.topgrad = tensor.ftensor4()
self.constant_tensor = numpy.zeros((3, 5, 7, 11), dtype='float32')
self.constant_tensor = np.zeros((3, 5, 7, 11), dtype='float32')
def test_grad_types(self):
# This function simply tests the behaviour of the AbstractConv
......@@ -1582,7 +1581,7 @@ class TestConv2dTranspose(unittest.TestCase):
output_shape=(2, 1, 10, 10),
input_dilation=(2, 2)),
mode=mode)()
expected_output = numpy.array(
expected_output = np.array(
[[[[2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
[2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
[4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
......@@ -1593,7 +1592,7 @@ class TestConv2dTranspose(unittest.TestCase):
[4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
[2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
[2, 2, 4, 4, 4, 4, 4, 4, 2, 2]]]] * 2)
numpy.testing.assert_equal(output, expected_output)
np.testing.assert_equal(output, expected_output)
class TestConv2dGrads(unittest.TestCase):
......@@ -1604,7 +1603,7 @@ class TestConv2dGrads(unittest.TestCase):
theano.config.mode == "FAST_COMPILE"):
raise SkipTest("Need blas to test conv2d")
self.random_stream = numpy.random.RandomState(utt.fetch_seed())
self.random_stream = np.random.RandomState(utt.fetch_seed())
self.inputs_shapes = [(8, 1, 12, 12), (1, 1, 5, 5), (1, 1, 5, 6), (1, 1, 6, 6)]
self.filters_shapes = [(5, 1, 2, 2), (1, 1, 3, 3)]
......
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