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
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chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_lgamma.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 _lgamma_cpu(x, dtype):
from scipy import special
return numpy.vectorize(special.gammaln, otypes=[dtype])(x)
def _lgamma_gpu(x, dtype):
... | 1,790 | 24.956522 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_ndtr.py | import math
import unittest
import numpy
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer import utils
def _ndtr_cpu(x, dtype):
erfc = numpy.vectorize(
lambda x: 0.5 * math.erfc(-x / 2 ** 0.5))
return utils.force_arr... | 760 | 18.512821 | 56 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_maximum.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... | 2,553 | 28.022727 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_log_ndtr.py | import unittest
import numpy
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
def _log_ndtr_cpu(x, dtype):
from scipy import special
return special.log_ndtr(x).astype(dtype)
def _log_ndtr_gpu(x, dtype):
return cuda.to_gpu(_log_ndtr... | 735 | 18.891892 | 60 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_cumsum.py | import unittest
import numpy
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
@testing.parameterize(*testing.product_dict(
[
{'shape': (1,), 'axis': 0},
... | 2,858 | 25.472222 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_sum.py | import unittest
import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'axis': [None, 0, 1, 2, -1, (0, 1), (1, 0), (0, -1), (-2, 0)],
'keepdims': [True, False],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@tes... | 2,332 | 27.45122 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_arctanh.py | import unittest
import numpy
import chainer.functions as F
from chainer import testing
def make_data(shape, dtype):
# Input values close to -1 or 1 would make tests unstable
x = numpy.random.uniform(-0.9, 0.9, shape).astype(dtype, copy=False)
gy = numpy.random.uniform(-1, 1, shape).astype(dtype, copy=Fa... | 668 | 22.892857 | 72 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_ndtri.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 _ndtri_cpu(x, dtype):
from scipy import special
return numpy.vectorize(special.ndtri, otypes=[dtype])(x)
def _ndtri_gpu(x, dtype):
ret... | 1,669 | 23.558824 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_scale.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.product({
'use_cuda': [True],
'cuda_device': [0, 1],
})
# ChainerX tests
+ ... | 1,505 | 23.290323 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_fmod.py | import math
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
[
{},
... | 1,882 | 26.691176 | 74 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_fft.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(4,), (2, 3), (2, 3, 2)],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'method': ['fft', 'ifft']
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CP... | 1,497 | 24.827586 | 71 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_clip.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({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, ... | 4,350 | 30.302158 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_erfinv.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 _erfinv_cpu(x, dtype):
from scipy import special
return numpy.vectorize(special.erfinv, otypes=[dtype])(x)
def _erfinv_gpu(x, dtype):
... | 1,682 | 23.75 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_cumprod.py | import unittest
import numpy
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
@testing.parameterize(*(testing.product_dict(
[
{'shape': (1,), 'axis': 0},... | 3,202 | 26.612069 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_erfcx.py | import unittest
import numpy
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
def _erfcx_cpu(x, dtype):
from scipy import special
return special.erfcx(x).astype(dtype)
def _erfcx_gpu(x, dtype):
return cuda.to_gpu(_erfcx_cpu(cuda.to... | 706 | 18.108108 | 57 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_einsum.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer.functions.math import einsum
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer import utils
def _tuple_to_gpu(xs):
return tuple(cuda.to_gpu(x) for x in xs)
def ... | 10,446 | 33.707641 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_logsumexp.py | import unittest
import numpy
from chainer import functions
from chainer import testing
from chainer.utils import force_array
@testing.parameterize(
*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'shape': [(), (3, 2, 4)],
'axis': [None, 0, 1, 2, -1, (0, 1), (1,... | 2,689 | 28.23913 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_det.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions as F
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(*testing.product({
'dtype': [nump... | 10,333 | 32.121795 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_prod.py | import unittest
import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*(testing.product({
'axis': [None, 0, 1, 2, -1, (0, 1), (1, 0), (0, -1), (-2, 0)],
'keepdims': [True, False],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'contain_zero': [True, Fa... | 2,750 | 28.265957 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_exponential.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
[
... | 2,992 | 26.712963 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_polygamma.py | import unittest
import numpy
import chainer
import chainer.functions as F
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(), (3, 2)],
'dtype': [numpy.float16, numpy.float32, numpy.float64]
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
... | 2,324 | 27.012048 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_erfc.py | import math
import unittest
import numpy
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
def _erfc_cpu(x, dtype):
return numpy.vectorize(math.erfc, otypes=[dtype])(x)
def _erfc_gpu(x, dtype):
return cuda.to_gpu(_erfc_cpu(cuda.to_cpu(x... | 662 | 17.416667 | 56 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_inv.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 type_check
def _inv(x):
if x.ndim == 2:
return numpy.linalg.inv(x)
return numpy.array([numpy.linalg.inv(i... | 6,604 | 30.303318 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_basic_math.py | import operator
import sys
import unittest
import numpy
import pytest
import chainer
from chainer.backends import cuda
from chainer import basic_math
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
from chainer.utils import type_check... | 58,895 | 30.512039 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_tensordot.py | import unittest
import numpy
import chainer
import chainer.functions as F
from chainer import testing
@testing.parameterize(*testing.product_dict(
[
{'a_shape': (4, 3, 2), 'b_shape': (3, 2, 5), 'axes': 2, 'gc_shape': (4, 5)}, # NOQA
{'a_shape': (4, 3, 2), 'b_shape': (3, 2, 5), 'axes': ([1, 2], ... | 8,068 | 50.724359 | 119 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_erfcinv.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 _erfcinv_cpu(x, dtype):
from scipy import special
return numpy.vectorize(special.erfcinv, otypes=[dtype])(x)
def _erfcinv_gpu(x, dtype):
... | 1,693 | 23.911765 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_linear_interpolate.py | import numpy
from chainer import functions
from chainer import testing
from chainer import utils
@testing.parameterize(*testing.product({
'shape': [(3, 4), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.fix_random()
@testing.inject_backend_tests(
None,
# CPU tests
[
... | 1,685 | 26.639344 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_matmul.py | import unittest
import numpy
import chainer
from chainer.backend import CpuDevice
import chainer.functions as F
from chainer import testing
from chainer.utils import type_check
@testing.parameterize(*testing.product_dict(
[
# matmul
{'x1_shape': (2, 5), 'x2_shape': (5, 10),
'transa': Fa... | 7,773 | 30.860656 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_ceil.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... | 1,220 | 21.611111 | 60 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_erf.py | import math
import unittest
import numpy
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
def _erf_cpu(x, dtype):
return numpy.vectorize(math.erf, otypes=[dtype])(x)
def _erf_gpu(x, dtype):
return cuda.to_gpu(_erf_cpu(cuda.to_cpu(x), d... | 652 | 17.138889 | 55 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_zeta.py | import unittest
import numpy
import chainer
import chainer.functions as F
from chainer import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'shape': [(), (3, 2)],
'x_range': [(1.1, 2), (2, 50)],
'q_range': [(1.1, 2), (2, 50)],
}))
@testing.i... | 2,622 | 27.824176 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_hyperbolic.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.float16, numpy.float32, numpy.float64],
'function_name': ['cosh', 'sinh'],
}))
@testing.fix_random()
@testing.inject... | 1,743 | 26.25 | 71 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/math_tests/test_batch_l2_norm_squared.py | import unittest
import numpy as np
import six
import chainer
from chainer import functions
from chainer import testing
from chainer.utils import type_check
def _as_two_dim(x):
if x.ndim == 2:
return x
return x.reshape((len(x), -1))
@testing.parameterize(*testing.product({
'dtype': [np.float16,... | 1,924 | 24.666667 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_batch_renormalization.py | import numpy
import six
import chainer
from chainer.functions.normalization import batch_renormalization
from chainer import testing
import chainerx
# naive implementation of differentiable batch renormalization
def _naive_batch_renormalization(
x, gamma, beta, # variables
mean, var, # variables
... | 9,263 | 33.438662 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_l2_normalization.py | import functools
import itertools
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 _skip_if(cond, reason):
"""Skip test if cond(self) is True"... | 7,483 | 34.469194 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_batch_normalization.py | import unittest
import warnings
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.testing import backend
def _as_noncontiguous_array(array):
# TODO(ni... | 20,347 | 36.681481 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_group_normalization.py | import numpy
import six
from chainer import functions
import chainer.functions.normalization.group_normalization as gn_module
from chainer import testing
def _simple_group_normalization(x, groups, gamma, beta, eps=1e-5):
batch_size, channels = x.shape[:2]
x_reshape = x.reshape(batch_size, groups, channels //... | 4,610 | 30.367347 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_local_response_normalization.py | import numpy
import six
from chainer import functions
from chainer import testing
from chainer.testing import backend
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@backend.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tes... | 1,845 | 27.84375 | 72 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_decorrelated_batch_normalization.py | import numpy
from chainer import functions
from chainer import testing
def _decorrelated_batch_normalization(x, mean, projection, groups):
xs = numpy.split(x, groups, axis=1)
assert mean.shape[0] == groups
assert projection.shape[0] == groups
ys = [
_decorrelated_batch_normalization_1group(xi... | 7,586 | 32.570796 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/test_layer_normalization.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*(testing.product({
'batchsize': [1, 5],
'size': [10, 20],
'dtype': [numpy.float32],
'eps': [1e-5, 1e-1],
})))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
... | 2,172 | 30.955882 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/normalization_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/noise_tests/test_zoneout.py | import unittest
import mock
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
def _zoneout(h, x, creator):
h_next = h * creator.flag_h + x * creat... | 3,672 | 31.219298 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/noise_tests/test_gaussian.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import gradient_check
from chainer import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'shape': [(3, 2), ()],
}))
@testing.backend.inject_backend_tests(
None... | 3,832 | 36.213592 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/noise_tests/test_dropout.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(
{'dtype': numpy.float16, 'ratio': 0.1},
{'dtype': numpy.float32, 'ratio': 0.3},
... | 7,443 | 32.836364 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/noise_tests/test_simplified_dropconnect.py | 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
@testing.parameterize(*testing.product({
'x_dtype': [numpy.float16, numpy.float3... | 6,462 | 35.721591 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/noise_tests/test_gumbel_softmax.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
@testing.parameterize(*testing.product({
'shape': [(3, 2), ()],
'dtype': [numpy.float16, numpy.float32, numpy.f... | 1,228 | 26.931818 | 71 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/noise_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_repeat.py | import unittest
import numpy
from chainer import functions
from chainer import testing
def _repeat(arr, repeats, axis=None):
# Workaround NumPy 1.9 issue.
if isinstance(repeats, tuple) and len(repeats) == 1:
repeats = repeats[0]
return numpy.repeat(arr, repeats, axis)
@testing.parameterize(*te... | 3,655 | 27.341085 | 75 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_pad.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product_dict(
[
{'shape': (), 'pad_width': 1, 'mode': 'constant'},
{'shape': (2, 3), 'pad_width': 0, 'mode': 'constant'},
{'shape': (2, 3), 'pad_width': 1, 'mode': 'constant'},
{'... | 3,560 | 27.717742 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_im2col.py | import unittest
import numpy
from six import moves
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.conv import get_conv_outsize
def _pair(x):
if hasattr(x, '__getitem__'):
... | 5,631 | 31.554913 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_separate.py | import numpy
from chainer import functions
from chainer import testing
from chainer.utils import force_array
@testing.parameterize(*testing.product_dict(
[
{'shape': (2, 3, 4), 'axis': 0},
{'shape': (2, 3, 4), 'axis': 1},
{'shape': (2, 3, 4), 'axis': 2},
{'shape': (2, 3, 4), 'axis... | 1,543 | 22.753846 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_spatial_transformer_sampler.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 import Variable
def _identiy_grid(in_shape):
mesh = numpy... | 8,708 | 33.152941 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_rollaxis.py | import unittest
import numpy
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(
{'axis': 0, 'start': 2, 'out_shape': (3, 2, 4)},
{'axis': 2, 'start': 0, 'out_shape': (4, 2, 3... | 2,753 | 26 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_swapaxes.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'in_shape': [(3, 4, 2)],
'axis1': [0],
'axis2': [1],
'dtype': [numpy.float16, numpy.float32, numpy.float32],
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
... | 1,126 | 21.54 | 60 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_pad_sequence.py | import contextlib
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
@contextlib.contextmanager
def disable_debug_mode_if(disable):
if disable:
... | 3,985 | 30.888 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_spatial_transformer_grid.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({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
'use_cu... | 2,621 | 32.615385 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_concat.py | import unittest
import numpy
from chainer import functions
from chainer import testing
from chainer.testing import backend
@backend.inject_backend_tests(
None,
# CPU tests
testing.product({
'use_cuda': [False],
'use_ideep': ['never', 'always'],
})
# GPU tests
+ [{'use_cuda': ... | 3,200 | 34.175824 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_select_item.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_dict(
[
{'in_shape': (10, 5), 'out_shape': (10,)},
{'... | 4,229 | 31.538462 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_split_axis.py | import unittest
import numpy
import six
import chainer
from chainer import functions
from chainer import testing
from chainer.testing import backend
def inject_backend_tests():
decorator = backend.inject_backend_tests(
None,
# CPU tests
testing.product({
'use_cuda': [False],
... | 9,026 | 33.193182 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_get_item.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
_backend_params = (
# CPU tests
testing.product({
'use_cuda': [False],
'use_ideep': ['ne... | 7,353 | 30.973913 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_expand_dims.py | import numpy
from chainer.backends import cuda
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product_dict(
[
{'in_shape': (3, 2), 'out_shape': (1, 3, 2), 'axis': 0},
{'in_shape': (3, 2), 'out_shape': (3, 1, 2), 'axis': 1},
{'in_shape': (3, 2), 'o... | 1,672 | 26.42623 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_flipud.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3,), (3, 4)],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.p... | 992 | 20.586957 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_broadcast.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 type_check
import chainerx
@testing.parameterize(*testing.product_dict(
[
... | 7,210 | 31.628959 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_space_2_depth.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({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
class T... | 4,171 | 38.358491 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_fliplr.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 4), (3, 4, 2)],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ test... | 983 | 20.866667 | 69 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_depth_2_space.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({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
class T... | 4,171 | 38.358491 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_transpose_sequence.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(
{'shape': (), 'lengths': [4, 2, 1], 'trans_lengths': [3, 2, 1, 1]},
{'... | 2,143 | 30.072464 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_reshape.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'in_shape': [(4, 3, 2)],
'out_shape': [(2, 2, 6), (2, -1, 6)],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.inject_backend_tests(
Non... | 1,578 | 22.220588 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_dstack.py | import unittest
import numpy
import six
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(*testing.product_dict(
[
{'shape': (2, 3, 4), 'y_shape': (2, 3, 8), 'xs_length': 2},
{'shape': (3, 4), 'y_... | 3,262 | 28.93578 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_permutate.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
@testing.parameterize(*testing.product_dict(
[{'shape': (3,), 'dtype': 'f', 'axis': 0, 'inv': False},
... | 2,760 | 26.888889 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_stack.py | 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
import chainerx
@testing.parameterize(*testing.product_dict(
[
{'shape':... | 3,692 | 30.836207 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_transpose.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'in_shape': [(4, 3, 2)],
'axes': [(-1, 0, 1), None],
'dtype': [numpy.float16, numpy.float32, numpy.float32],
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]... | 1,052 | 21.404255 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_tile.py | import unittest
import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'in_shape': [(), 2, (2, 3)],
'reps': [(), 0, 2, (0, 0), (1, 2), (2, 2), (2, 0)],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.inject_backend_tests(
... | 2,118 | 24.841463 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_moveaxis.py | import unittest
import numpy
import six
from chainer import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
def _normalize_axis_tuple(axis, ndim):
if numpy.isscalar(axis):
axis = (axis,)
ret = []
for ax in axis... | 4,143 | 25.909091 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_flatten.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product({
'shape': [(3, 4), ()],
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.pro... | 989 | 20.521739 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_scatter_add.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_dict(
[{'dtype': numpy.float16},
{'dtype': numpy.float32},
{'dtype': numpy.float64},
],
[{'slices': (0, slice(0, 1)... | 2,786 | 27.731959 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_as_strided.py | import unittest
import numpy as np
import chainer
from chainer import cuda
import chainer.functions as F
from chainer.functions.array.as_strided import _stride_array
from chainer import testing
def _broadcast_to(xp, x, shape):
if hasattr(xp, 'broadcast_to'):
return xp.broadcast_to(x, shape)
else:
... | 10,239 | 31.507937 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_copy.py | import unittest
import numpy
import pytest
import chainer
from chainer import backend
from chainer.backends import _cpu
from chainer.backends import cuda
from chainer import functions
from chainer import testing
import chainerx
def _to_gpu(x, device_id):
if device_id >= 0:
return cuda.to_gpu(x, device_i... | 8,705 | 32.744186 | 78 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_flip.py | import unittest
import numpy
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(*testing.product_dict(
[
{'shape': (1,), 'axis': 0},
{'shape': (2, 3, 4), 'axis': 0... | 3,218 | 24.752 | 73 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_hstack.py | import unittest
import numpy
import six
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(*testing.product_dict(
[
{'shape': (2, 3, 4), 'y_shape': (2, 6, 4), 'xs_length': 2},
{'shape': (3, 4), 'y_... | 2,840 | 28.28866 | 76 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_vstack.py | import unittest
import numpy
import pytest
import six
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.parameterize(*testing.product_dict(
[
{'shape': (2, 3, 4), 'y_shape': (4, 3, 4), 'xs_length': 2},
{'shape... | 3,112 | 27.824074 | 77 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_resize_images.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
@testing.parameterize(*testing.product({
'in_shape': [(2, 3, 8, 6), (2, 1, 4, 6)],
'mode': ['bilinear', 'nearest'],
... | 6,798 | 27.809322 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_where.py | import unittest
import numpy
import pytest
import chainer
from chainer import functions
from chainer import testing
from chainer.utils import type_check
@testing.parameterize(*testing.product({
'shape': [
# c, x, y, output
((3, 2, 4),) * 4,
((4,), (3, 1, 1), (2, 1), (3, 2, 4)),
],
... | 2,560 | 26.537634 | 67 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_diagonal.py | import numpy
from chainer import functions
from chainer import testing
@testing.parameterize(*testing.product_dict(
[
{'shape': (2, 4, 6), 'args': (1, 2, 0)},
{'shape': (2, 4, 6), 'args': (-1, 2, 0)},
{'shape': (2, 4, 6), 'args': (0, -1, -2)},
{'shape': (2, 4, 6), 'args': (0, -1, ... | 1,186 | 22.74 | 70 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_cast.py | import unittest
import numpy
import chainer
from chainer import functions
from chainer import testing
from chainer.testing import attr
import chainerx
if chainerx.is_available():
import chainerx.testing
@testing.parameterize(*testing.product_dict(
[
{'shape': (3, 4)},
{'shape': ()},
],... | 4,719 | 30.677852 | 79 | py |
chainer | chainer-master/tests/chainer_tests/functions_tests/array_tests/test_squeeze.py | import unittest
import numpy
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
@testing.inject_backend_tests(
None,
# CPU tests
[
{},
]
# GPU tests
+ testing.product({
... | 3,111 | 25.151261 | 75 | py |
chainer | chainer-master/tests/chainer_tests/serializers_tests/test_npz.py | import os
import tempfile
import unittest
import mock
import numpy
import pytest
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import link
from chainer import links
from chainer import optimizers
from chainer.serializers impor... | 22,252 | 31.486131 | 79 | py |
chainer | chainer-master/tests/chainer_tests/serializers_tests/test_hdf5.py | import os
import sys
import tempfile
import unittest
import mock
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import link
from chainer import links
from chainer import optimizers
from chainer.serializers import hdf5
from ch... | 17,165 | 32.461988 | 78 | py |
chainer | chainer-master/tests/chainer_tests/serializers_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/function_hooks_tests/test_cuda_profile.py | import unittest
import mock
import numpy
import chainer
from chainer.backends import cuda
from chainer import function_hooks
from chainer import testing
from chainer.testing import attr
@attr.gpu
@unittest.skipUnless(
cuda.available and cuda.cupy.cuda.nvtx_enabled, 'nvtx is not installed')
class TestCUDAProfile... | 2,124 | 27.333333 | 76 | py |
chainer | chainer-master/tests/chainer_tests/function_hooks_tests/test_debug_print.py | import re
import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import function_hooks
from chainer import testing
from chainer.testing import attr
class DummyFunction(chainer.Function):
def forward(self, inputs):
self.retain_inputs((0,))
return in... | 3,892 | 26.807143 | 79 | py |
chainer | chainer-master/tests/chainer_tests/function_hooks_tests/test_timer.py | import os
import time
import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import function_hooks
from chainer import functions
from chainer.functions.math import basic_math
from chainer import testing
from chainer.testing import attr
try:
_get_time = time.perf_cou... | 6,276 | 29.177885 | 79 | py |
chainer | chainer-master/tests/chainer_tests/function_hooks_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/function_hooks_tests/test_cupy_memory_profile.py | import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import function_hooks
from chainer import functions
from chainer.functions.math import basic_math
from chainer import testing
from chainer.testing import attr
def check_history(self, t, function_type, used_bytes_ty... | 8,189 | 32.565574 | 79 | py |
chainer | chainer-master/tests/chainer_tests/graph_optimization_tests/test_static_graph_models.py | import unittest
import numpy
import chainer
from chainer import configuration
from chainer import cuda
import chainer.functions as F
from chainer import gradient_check
from chainer.graph_optimizations.static_graph import static_graph
import chainer.links as L
from chainer import links
from chainer import testing
from... | 18,125 | 35.10757 | 79 | py |
chainer | chainer-master/tests/chainer_tests/graph_optimization_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/datasets_tests/test_text_dataset.py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
import pickle
import unittest
import six
from chainer import datasets
from chainer import testing
class TestTextDataset(unittest.TestCase):
def setUp(self):
self.root = os.path.join(os.path.dirname(__file__), 'text_dataset')
... | 4,703 | 29.947368 | 79 | py |
chainer | chainer-master/tests/chainer_tests/datasets_tests/test_tuple_dataset.py | import unittest
import numpy
from chainer.backends import cuda
from chainer import datasets
from chainer import testing
from chainer.testing import attr
class TestTupleDataset(unittest.TestCase):
def setUp(self):
self.x0 = numpy.random.rand(3, 4)
self.x1 = numpy.random.rand(3, 5)
self.z... | 1,737 | 28.457627 | 76 | py |
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