repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
chainer | chainer-master/tests/chainer_tests/utils_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_walker_alias.py | import unittest
import numpy
from chainer import backend
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
from chainer import utils
class TestWalkerAlias(unittest.TestCase):
def setUp(self):
self.ps = numpy.array([5, 3, 4, 1, 2], dtype=numpy.int32)
... | 1,483 | 27.538462 | 68 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_experimental.py | import unittest
import warnings
import chainer
from chainer import testing
from chainer import utils
def f():
utils.experimental('f')
class C(object):
@staticmethod
def static_method():
utils.experimental('static_method')
@classmethod
def class_method(cls):
utils.experimental(... | 3,064 | 27.119266 | 68 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_argument.py | import unittest
import six
from chainer import testing
from chainer.utils.argument import parse_kwargs
class TestArgument(unittest.TestCase):
def test_parse_kwargs(self):
def test(**kwargs):
return parse_kwargs(kwargs, ('foo', 1), ('bar', 2))
self.assertEqual(test(), (1, 2))
... | 579 | 22.2 | 79 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_utils.py | import unittest
import numpy
from chainer import testing
from chainer import utils
@testing.parameterize(*testing.product({
'dtype': [None, numpy.float16, numpy.float32, numpy.float64],
}))
class TestForceArray(unittest.TestCase):
def test_scalar(self):
x = utils.force_array(numpy.float32(1), dtype... | 1,830 | 30.033898 | 78 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_conv.py | import unittest
import numpy
from six import moves
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv
class TestConv(unittest.TestCase):
def check_conv_outsize(self, size, k, s, p, d):
# When cover_all == False, `outsize` is the... | 5,281 | 32.43038 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_selu.py | import random
import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
... | 1,518 | 25.189655 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_leaky_relu.py | import random
import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'slope': ['random', 0.0],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# ... | 1,690 | 26.721311 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_prelu.py | import numpy
import chainer
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(4, 3, 2), (1,), (1, 2, 3, 4, 5, 6)],
'Wdim': [0, 1, 3],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@chainer.testing.backend.inje... | 1,845 | 29.262295 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_hard_sigmoid.py | import numpy
from chainer import functions
from chainer import testing
from chainer import utils
def _hard_sigmoid(x):
return (x * 0.2 + 0.5).clip(0, 1)
@testing.parameterize(*testing.product({
'shape': [(3, 4), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64]
}))
@testing.fix_random()
@test... | 1,417 | 23.033898 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_tanh.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer import utils
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.flo... | 2,832 | 28.821053 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_log_softmax.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product_dict(
testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}),
test... | 3,333 | 29.587156 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_rrelu.py | import unittest
import numpy
import chainer
from chainer import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.product({
'use_cuda': [True],
... | 3,598 | 27.792 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_crelu.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product_dict(
[
{'shape': (5, 4), 'y_shape': (10, 4), 'axis': 0},
{'shape': (5, 4), 'y_shape': (5, 8), 'axis': 1},
{'shape': (5, 4), 'y_shape': (5, 8), 'axis': -1},
{'shape': (5, ... | 2,144 | 30.544118 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_softplus.py | import numpy
from chainer import functions
from chainer import testing
from chainer import utils
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
... | 1,554 | 26.280702 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_swish.py | import numpy
from chainer import functions
from chainer import testing
def _sigmoid(x):
half = x.dtype.type(0.5)
return numpy.tanh(x * half) * half + half
def _broadcast_to(array, shape):
if hasattr(numpy, 'broadcast_to'):
return numpy.broadcast_to(array, shape)
dummy = numpy.empty(shape, a... | 2,087 | 26.473684 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_clipped_relu.py | import unittest
import mock
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer import utils
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float3... | 3,653 | 28 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_elu.py | import random
import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'alpha_range': [(-2.0, 0.0), 0.0, (0.0, 2.0)],
}))
@testing.fix_random()
@testing.inject_backend_te... | 1,927 | 27.776119 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_relu.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'contiguous': [... | 2,738 | 28.451613 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_sigmoid.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer import utils
def _sigmoid(x):
half = x.dtype.type(0.5)
return numpy.tanh(x * half) * half + half
@testing.parameterize(*test... | 3,029 | 28.417476 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_maxout.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
def _maxout(x, pool_size, axis):
shape = (x.shape[:axis] + (x.shape[axis] // pool_size, pool_size) +
x.shape[axis + 1:])
x ... | 2,835 | 27.938776 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/activation_tests/test_softmax.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product_dict(
[
{'shape': None, 'axis': 1},
{'shape': (5,), 'axis': 0},
{'shape': (2, 3)... | 3,610 | 30.675439 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_triplet.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
... | 6,748 | 36.082418 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_mean_squared_error.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
from chainer import utils
from chainer.utils import type_check
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
{'use_ideep': 'always'},
]
# GPU tests
+ testing.product... | 2,462 | 27.976471 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_mean_absolute_error.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer import utils
from chainer.testing import attr
from chainer.utils import type_check
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
{... | 3,213 | 28.759259 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_ctc.py | import math
import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
class CTCTestBase(object):
de... | 9,219 | 34.057034 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_vae.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions as F
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
@testing.parameterize(
*testing.product({
'wrap_m': [True, False],
'wrap_v': [True, ... | 7,213 | 32.398148 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_decov.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
def _decov(h):
h_mean = h.mean(axis=0)
N, M = h.shape
loss_expect = numpy.zeros((M, M), ... | 2,401 | 25.988764 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_squared_error.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'in_shape': [(5, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'function': [functions.squared_error, functions.squared_difference],
}))
@testing.fix_random()
@testing.inject_... | 1,684 | 28.051724 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_contrastive.py | import math
import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product_dict(
[{'dtype': numpy.float16,
'forward... | 6,422 | 35.913793 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_crf1d.py | import itertools
import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product_dict(
[
{'lengths': [3, 3], 'batches': [2, 2... | 5,482 | 33.702532 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_absolute_error.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer import utils
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'shape': [(), (1... | 2,692 | 30.682353 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_negative_sampling.py | import unittest
import numpy
import pytest
import six
import chainer
from chainer.backend import CpuDevice
from chainer.backends import cuda
from chainer import functions
from chainer.functions.loss import negative_sampling
from chainer import gradient_check
from chainer import testing
from chainer.testing import att... | 6,685 | 32.939086 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_black_out.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
@testing.parameterize(
{'reduce': 'mean'},
{'reduce': 'no'}
)
clas... | 3,392 | 31.314286 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_hinge.py | import unittest
import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product_dict(
[{'dtype': numpy.float16... | 5,596 | 30.801136 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_huber_loss.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer import utils
@testing.parameterize(*testing.product_dict(
[{'dtype': numpy.float16,
'forw... | 5,125 | 34.109589 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_discriminative_margin_based_clustering_loss.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'delta_v': [0.5],
'delta_d': [5],
'alpha': [1],
'beta':... | 4,064 | 32.875 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_cross_covariance.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
def _cross_covariance(y, z, dtype):
row = y.shape[1]
col = z.shape[1]
y, z = cuda.to_cpu(... | 5,427 | 32.925 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_sigmoid_cross_entropy.py | import math
import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer import utils
@testing.parameterize(*(testing.product({
# Test dtype
... | 10,640 | 36.076655 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/loss_tests/test_softmax_cross_entropy.py | import unittest
import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
import chainerx
@testing.inject_backend_tests(
None,
# CPU tests
... | 23,058 | 33.519461 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_spatial_pyramid_pooling_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
from chainer.utils import type_check
from chainer_tests.functions_tests.pool... | 7,222 | 32.133028 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_unpooling_nd.py | import itertools
import unittest
import numpy
import six
import chainer
from chainer import backend
from chainer import functions
from chainer import testing
from chainer.utils import conv
from chainer.utils import type_check
def xs_iter(dims):
return itertools.product(*[range(d) for d in dims])
def kxs_iter(... | 9,058 | 32.18315 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/pooling_nd_helper.py | import itertools
import numpy
import six
from chainer import testing
import chainer.utils
def pooling_patches(dims, ksize, stride, pad, cover_all):
"""Return tuples of slices that indicate pooling patches."""
# Left-top indexes of each pooling patch.
if cover_all:
xss = itertools.product(
... | 1,082 | 29.942857 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_roi_max_pooling_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
def _pair(x):
if isinstance(x, cha... | 4,127 | 33.689076 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_average_pooling_nd.py | import functools
import operator
import unittest
import numpy
import pytest
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv
from chainer_tests.functions_tests.pooling_tests import pool... | 9,162 | 33.318352 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_roi_pooling_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
@testing.parameterize(*testing.product... | 3,199 | 31.989691 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_pooling_nd_kernel.py | import unittest
import chainer
from chainer.functions.pooling import pooling_nd_kernel
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'ndim': [2, 3, 4],
}))
@attr.gpu
class TestPoolingNDKernelMemo(unittest.TestCase):
def setUp(self):
chainer.bac... | 1,601 | 35.409091 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_max_pooling_2d.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
_inject_backend_tests = ba... | 5,784 | 31.683616 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_max_pooling_nd.py | import functools
from operator import mul
import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv
from chainer_tests.functions_tests.pooling_tests import pooling_n... | 7,481 | 32.401786 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_average_pooling_2d.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],... | 3,177 | 31.10101 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_roi_average_align_2d.py | import unittest
import numpy
import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
def _pair... | 4,229 | 31.790698 | 74 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_upsampling_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv
@testing.parameterize(*testing.product({
'in_shape': [(4, 3, 6, 8), (4, 3, 5,... | 4,291 | 34.766667 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_unpooling_2d.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
@testing.parameterize(*test... | 12,806 | 36.778761 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_roi_average_pooling_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.utils import collections_abc
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helpe... | 3,947 | 33.631579 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/pooling_tests/test_roi_max_align_2d.py | import unittest
import numpy
import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
from chainer_tests.functions_tests.pooling_tests import pooling_nd_helper
def _pair... | 4,400 | 32.090226 | 74 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_convolution_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
@testing.parameterize(*(testing.product({
'contiguous': ['C', None],
'cover_all': [True, False],
'x... | 14,148 | 36.136483 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_deformable_convolution_2d_sampler.py | import unittest
import numpy
import chainer
from chainer import cuda
from chainer.functions import convolution_2d
from chainer.functions import deformable_convolution_2d_sampler
from chainer import utils
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'para... | 4,512 | 29.493243 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_convolution_nd.py | import functools
from operator import mul
import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv
@testing.parameterize(*(testing.product({
'dims': [(5,), (4, 3), (3, 4... | 11,475 | 34.310769 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_deconvolution_nd.py | import functools
from operator import mul
import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer.testing import attr
from chainer.testing import parameterize
from chainer.utils import conv
from chainer.utils import type_chec... | 14,050 | 35.591146 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_bilinear.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
def _uniform(*shape):
return numpy.random.uniform(-1, 1, shape).astype(numpy.float32)
@testing.parameterize(*test... | 4,269 | 27.278146 | 74 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_depthwise_convolution_2d.py | import unittest
import numpy
from chainer.backends import cuda
from chainer import functions
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
@testing.parameterize(*(testing.product({
'x_dtype': [numpy.float16, numpy.float32, n... | 3,641 | 33.358491 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_dilated_convolution_2d.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*(testing.product({
'c_contiguous': [True],
'cover_all... | 7,100 | 35.984375 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_embed_id.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer.functions.connection import embed_id
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
@testing.parameterize(*testing.product_dict(
[
... | 3,449 | 27.991597 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_linear.py | import unittest
import numpy
import pytest
import chainer
from chainer import functions
from chainer import testing
from chainer.testing import backend
@testing.parameterize(*testing.product({
'x_dtype': [numpy.float16, numpy.float32, numpy.float64],
'W_dtype': [numpy.float16, numpy.float32, numpy.float64],... | 4,516 | 32.213235 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_local_convolution_2d.py | import unittest
import numpy
from six import moves
from chainer import cuda
from chainer.functions.connection import convolution_2d
from chainer.functions.connection import local_convolution_2d
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import ... | 4,022 | 34.60177 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_deconvolution_2d.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer.testing import array
from chainer.testing import attr
from chainer.testing import parameterize
from chainer.utils import conv
def _pair(x):
if hasattr(x, '__getite... | 13,231 | 34.956522 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/connection_tests/test_shift.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
from chainer.functions.connection import shift
@testing.parameterize(*(test... | 3,094 | 29.343137 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/evaluation_tests/test_classification_summary.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions as F
from chainer import testing
from chainer.testing import attr
def recall(preds, ts, dtype, label_num, ignore_label):
tp = numpy.zeros((label_num,), dtype=numpy.int32)
support = numpy.zeros((label_... | 5,709 | 34.246914 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/evaluation_tests/test_accuracy.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import force_array
from chainer.utils import type_check
def accuracy(x, t, ignore_label):
x_ = numpy.rollaxis(x, ... | 4,114 | 28.818841 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/evaluation_tests/test_r2_score.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import force_array
from chainer.utils import type_check
def r2_score(pred, true, sample_weight=None, multioutput='uniform_averag... | 3,942 | 29.565891 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/evaluation_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/evaluation_tests/test_binary_accuracy.py | import unittest
import numpy
import six
import chainer
from chainer import functions
from chainer import testing
from chainer.utils import force_array
from chainer.utils import type_check
@testing.parameterize(*testing.product({
'shape': [(9, 11), (99,)],
'dtype': [numpy.float16, numpy.float32, numpy.float6... | 3,561 | 26.19084 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/util_tests/test_forget.py | import functools
import unittest
import numpy
import six
import chainer
from chainer import cuda
from chainer import functions
from chainer import gradient_check
from chainer import links
from chainer import testing
from chainer.testing import attr
from chainer import variable
@testing.parameterize(*testing.product... | 8,097 | 34.517544 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/util_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/rnn_tests/test_function_n_step_rnn.py | import numpy
import chainer.functions as F
from chainer import testing
from chainer.testing import backend
def _relu(x):
expected = x.copy()
for i in numpy.ndindex(x.shape):
if x[i] < 0:
expected[i] = 0
return expected
def array(shape, dtype):
return numpy.random.uniform(-1, 1, ... | 9,839 | 30.741935 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/rnn_tests/test_function_lstm.py | import unittest
import numpy
import six
from chainer.backends import cuda
import chainer.functions as F
from chainer.functions.rnn import lstm
from chainer import gradient_check
from chainer import testing
from chainer.testing import backend
def sigmoid(x):
return numpy.tanh(x * 0.5) * 0.5 + 0.5
def _shaped_r... | 7,077 | 27.772358 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/rnn_tests/test_function_tree_lstm.py | import numpy
import six
from chainer import functions
from chainer import testing
from chainer.testing import backend
def _sigmoid(x):
half = x.dtype.type(0.5)
return numpy.tanh(x * half) * half + half
def _shaped_random_array(shape, dtype):
return numpy.random.uniform(-1, 1, shape).astype(dtype)
@te... | 2,970 | 27.295238 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/rnn_tests/test_function_n_step_lstm.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
from chainer.testing import condition
def rand_vector(shape):
# return cuda.cupy.random.randint(-2, 2, shape... | 14,662 | 31.657016 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/rnn_tests/test_function_n_step_gru.py | import numpy
import chainer
import chainer.functions as F
from chainer import testing
from chainer.testing import backend
def sigmoid(x):
return numpy.tanh(x * 0.5) * 0.5 + 0.5
def array(shape, dtype):
return numpy.random.uniform(-1, 1, shape).astype(dtype)
@testing.parameterize(*testing.product_dict(
... | 9,476 | 30.380795 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/rnn_tests/test_function_slstm.py | import unittest
import numpy
import six
from chainer.backends import cuda
from chainer import functions
from chainer.functions.rnn import slstm
from chainer import gradient_check
from chainer import testing
from chainer.testing import backend
def _sigmoid(x):
half = x.dtype.type(0.5)
return numpy.tanh(x * h... | 6,206 | 31.328125 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_floor.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU t... | 1,223 | 21.666667 | 60 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_sqrt.py | import unittest
import numpy
import chainer.functions as F
from chainer import testing
# sqrt
def make_data(shape, dtype):
x = numpy.random.uniform(0.1, 5, shape).astype(dtype)
gy = numpy.random.uniform(-1, 1, shape).astype(dtype)
ggx = numpy.random.uniform(-1, 1, shape).astype(dtype)
return x, gy,... | 992 | 20.12766 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_average.py | import unittest
import numpy
import six
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer import utils
@testing.parameterize(*(
testing.product({
'shape': [(3, 2, 4)],
'axis': [None, 0, 1, 2, -1, (0, 1), (1, -1)],
'dtype': [numpy.floa... | 6,055 | 30.378238 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_minimum.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
from chainer.utils import type_check
@testing.parameterize(*testing.product({
'shape': [
# x1, x2, y
((3, 2), (3, 2), (3, 2)),
((), (), ()),
((3, 2), (3, 1), (3, 2)),
((2... | 3,220 | 29.67619 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_sign.py | import numpy
from chainer import functions
from chainer import testing
from chainer import utils
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
... | 1,375 | 22.724138 | 71 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_exponential_m1.py | import numpy
from chainer import functions
from chainer import testing
from chainer import utils
@testing.parameterize(*testing.product({
'dtype': [numpy.float32],
'shape': [(), (3, 2)],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests... | 1,234 | 21.87037 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_bias.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
from chainer import utils
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.product({
'use_cuda': [True],
'cuda_dev... | 1,580 | 23.703125 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_logarithm_1p.py | import numpy
from chainer import functions
from chainer import testing
from chainer.utils import force_array
@testing.parameterize(*testing.product({
'shape': [(), (3, 2)],
'dtype': [numpy.float32]
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
... | 1,038 | 20.645833 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_minmax.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
from chainer import utils
@testing.parameterize(*testing.product({
'function_name': ['max', 'min'],
'shape': [(3, 2, 4)],
'dtype': [numpy.float32],
'axis': [
None,
0, 1, 2, # axis
... | 5,027 | 27.247191 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_digamma.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
def _digamma_cpu(x, dtype):
from scipy import special
return numpy.vectorize(special.digamma, otypes=[dtype])(x)
def _digamma_gpu(x, dtype):
... | 1,801 | 25.115942 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_sparse_matmul.py | import unittest
import numpy
import chainer
from chainer import cuda
import chainer.functions as F
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer import utils
from chainer.utils import type_check
_scipy_available = True
try:
from scipy import sparse ... | 10,693 | 34.528239 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_trigonometric.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
import chainer.functions as F
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'func_name': ['cos', 'sin', 'tan'],
'shape': [(3, 2), ()],
... | 6,529 | 32.316327 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_square.py | import unittest
import chainer.functions as F
from chainer import testing
@testing.unary_math_function_unittest(F.square)
class TestSquare(unittest.TestCase):
pass
testing.run_module(__name__, __file__)
| 212 | 15.384615 | 47 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_cholesky.py | import numpy
import chainer.functions as F
from chainer import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64],
'shape': [(5, 5), (1, 1)]
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.product({
... | 2,420 | 28.52439 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_fix.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU t... | 1,217 | 21.555556 | 60 | py |
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