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/chainermn/datasets/empty_dataset.py | def create_empty_dataset(dataset):
"""Creates an empty dataset for models with no inputs and outputs.
This function generates an empty dataset, i.e., ``__getitem__()`` only
returns ``None``. Its dataset is compatible with the original one.
Such datasets used for models which do not take any inputs,
... | 709 | 36.368421 | 75 | py |
chainer | chainer-master/chainermn/datasets/scatter.py | import warnings
import chainer.datasets
import numpy
class DataSizeError(RuntimeError):
pass
def scatter_dataset(dataset, comm, root=0, shuffle=False,
seed=None, max_buf_len=256 * 1024 * 1024,
*, force_equal_length=True):
"""Scatter the given dataset to the workers i... | 7,316 | 34.347826 | 79 | py |
chainer | chainer-master/chainermn/datasets/__init__.py | from chainermn.datasets.empty_dataset import create_empty_dataset # NOQA
from chainermn.datasets.scatter import DataSizeError # NOQA
from chainermn.datasets.scatter import scatter_index # NOQA
from chainermn.datasets.scatter import scatter_dataset # NOQA
| 259 | 51 | 73 | py |
chainer | chainer-master/chainermn/extensions/checkpoint.py | import errno
import os
import shutil
import tempfile
import time
import warnings
import chainer
from chainer.training import extension
def create_multi_node_checkpointer(name, comm, cp_interval=5,
gc_interval=5, path=None):
'''Create multi-node checkpointer object
Generati... | 11,492 | 33.002959 | 79 | py |
chainer | chainer-master/chainermn/extensions/multi_node_evaluator.py | import copy
import six
from chainer.training import extension
from chainer import backend
from chainer.dataset import convert
from chainer import function
from chainer.utils import argument
import chainerx as chx
class GenericMultiNodeEvaluator(extension.Extension):
'''Generic multi-node evaluator for non-allred... | 9,083 | 34.484375 | 79 | py |
chainer | chainer-master/chainermn/extensions/allreduce_persistent.py | import chainer
import chainer.training.extension
def _namedpersistents(model):
assert isinstance(model, chainer.Link)
for lname, link in model.namedlinks():
for pname in link._persistent:
yield lname + '/' + pname, link.__dict__[pname]
class AllreducePersistent(chainer.training.extensio... | 1,616 | 32.6875 | 74 | py |
chainer | chainer-master/chainermn/extensions/multi_node_early_stopping_trigger.py | from chainer.training.triggers import EarlyStoppingTrigger
from chainermn.extensions import ObservationAggregator
class MultiNodeEarlyStoppingTrigger(object):
"""__init__(\
self, comm, check_trigger=(1, 'epoch'), monitor='main/loss', \
patience=3, mode='auto', verbose=False, \
max_trigger=... | 3,334 | 41.21519 | 79 | py |
chainer | chainer-master/chainermn/extensions/_multi_node_snapshot.py | import io
from chainer.serializers import load_npz
from chainer.serializers import save_npz
from chainer.training.extension import Extension
from chainer.training.extensions._snapshot import _find_latest_snapshot
def multi_node_snapshot(comm, snapshot, replica_sets):
'''Create trainer extension for multi-node sn... | 7,435 | 33.910798 | 79 | py |
chainer | chainer-master/chainermn/extensions/observation_aggregator.py | from __future__ import division
from chainer.training import extension, util
from chainer import Variable
import chainerx as chx
class ObservationAggregator(extension.Extension):
"""Trainer extension to aggregate an observation in the trainer.
Args:
comm: ChainerMN communicator
original_key... | 2,974 | 36.1875 | 79 | py |
chainer | chainer-master/chainermn/extensions/__init__.py | from chainermn.extensions.allreduce_persistent import AllreducePersistent # NOQA
from chainermn.extensions.checkpoint import create_multi_node_checkpointer # NOQA
from chainermn.extensions.multi_node_evaluator import create_multi_node_evaluator # NOQA
from chainermn.extensions.multi_node_evaluator import GenericMult... | 616 | 76.125 | 104 | py |
chainer | chainer-master/chainermn/iterators/synchronized_iterator.py | import chainer
import numpy
class _SynchronizedIterator(chainer.dataset.iterator.Iterator):
def __init__(self, actual_iterator, communicator):
if not hasattr(actual_iterator, 'order_sampler'):
raise ValueError('actual_iterator must have order_sampler')
else:
super(_Synchro... | 2,898 | 33.927711 | 74 | py |
chainer | chainer-master/chainermn/iterators/__init__.py | from chainermn.iterators.multi_node_iterator import create_multi_node_iterator # NOQA
from chainermn.iterators.synchronized_iterator import create_synchronized_iterator # NOQA
| 178 | 58.666667 | 90 | py |
chainer | chainer-master/chainermn/iterators/multi_node_iterator.py | import chainer
import numpy
def _is_valid_type(element):
if isinstance(element, tuple) and len(element) == 2 \
and hasattr(element[0], 'dtype') \
and hasattr(element[1], 'dtype'):
return True
elif hasattr(element, 'dtype'):
return True
return False
def _build_ctrl... | 9,602 | 36.956522 | 79 | py |
chainer | chainer-master/chainermn/communicators/non_cuda_aware_communicator.py | import warnings
import chainer.cuda
import chainerx
import math
import mpi4py.MPI
import numpy as np
from chainermn.communicators import _communication_utility
from chainermn.communicators import _memory_utility
from chainermn.communicators import mpi_communicator_base
from chainermn import nccl
class NonCudaAwareC... | 6,101 | 38.882353 | 79 | py |
chainer | chainer-master/chainermn/communicators/pure_nccl_communicator.py | import warnings
import chainer.cuda
from chainermn.communicators import _communication_utility
from chainermn.communicators import _memory_utility
from chainermn.communicators import mpi_communicator_base
from chainermn import nccl
import numpy as np
class PureNcclCommunicator(mpi_communicator_base.MpiCommunicator... | 7,919 | 39.824742 | 79 | py |
chainer | chainer-master/chainermn/communicators/naive_communicator.py | from chainermn.communicators import _memory_utility
from chainermn.communicators import mpi_communicator_base
class NaiveCommunicator(mpi_communicator_base.MpiCommunicatorBase):
def __init__(self, mpi_comm):
super(NaiveCommunicator, self).__init__(mpi_comm)
def multi_node_mean_grad(self, model, zero... | 668 | 36.166667 | 74 | py |
chainer | chainer-master/chainermn/communicators/dummy_communicator.py | from chainermn.communicators import _memory_utility
from chainermn.communicators import mpi_communicator_base
import numpy as np
class DummyCommunicator(mpi_communicator_base.MpiCommunicatorBase):
"""Dummy communicator that does not communicate at all.
This class is intended to measure the overhead of packi... | 1,430 | 38.75 | 76 | py |
chainer | chainer-master/chainermn/communicators/_communication_utility.py | from chainermn import nccl
import collections
import numpy as np
import pickle
import mpi4py.MPI
def init_ranks(mpi_comm):
"""Returns rank information of the local process in `mpi_comm`.
Args:
mpi_comm (type:TODO)
MPI Communicator from mpi4py
Returns:
rank_info (list):
... | 5,957 | 30.860963 | 76 | py |
chainer | chainer-master/chainermn/communicators/mpi_communicator_base.py | import mpi4py
import numpy
import chainer
import chainer.backends
import chainer.utils
from chainer.utils import collections_abc
from chainermn.communicators import _communication_utility
from chainermn.communicators._communication_utility import chunked_bcast_obj
from chainermn.communicators import _memory_utility
fr... | 30,778 | 36.719363 | 88 | py |
chainer | chainer-master/chainermn/communicators/_memory_utility.py | import ctypes
import mpi4py.MPI
import numpy as np
from chainermn.communicators import _communication_utility
import chainer.backends
import chainerx as chx
try:
import cupy as cp
_cupy_avail = True
except Exception:
cp = None
_cupy_avail = False
def _get_memory_pointer_from_chainerx(array):
# ... | 14,945 | 33.75814 | 79 | py |
chainer | chainer-master/chainermn/communicators/communicator_base.py | from abc import ABCMeta
from abc import abstractmethod
import contextlib
import six
import warnings
class CommunicatorBase(six.with_metaclass(ABCMeta)):
'''Interface definition of all communicators.
All communicators that have compatible set of methods with this
class is supposed to work in ChainerMN's p... | 14,214 | 31.160633 | 79 | py |
chainer | chainer-master/chainermn/communicators/__init__.py | import warnings
from chainer.utils import argument
from chainermn.communicators.communicator_base import CommunicatorBase # NOQA
def create_communicator(
communicator_name='pure_nccl', mpi_comm=None, **kwargs):
"""Create a ChainerMN communicator.
Different communicators provide different approache... | 5,933 | 43.616541 | 78 | py |
chainer | chainer-master/chainermn/communicators/flat_communicator.py | import numpy as np
from chainermn.communicators import _memory_utility
from chainermn.communicators import mpi_communicator_base
class FlatCommunicator(mpi_communicator_base.MpiCommunicatorBase):
def __init__(self, mpi_comm):
super(FlatCommunicator, self).__init__(mpi_comm)
self.gpu_buffer_a = ... | 1,399 | 39 | 78 | py |
chainer | chainer-master/onnx_chainer/export.py | from __future__ import print_function
from collections import OrderedDict
import warnings
import chainer
try:
import onnx
from onnx import checker
from onnx import helper
from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
from onnx import numpy_helper
from onnx import shape_inference
from o... | 22,516 | 42.21881 | 79 | py |
chainer | chainer-master/onnx_chainer/context.py | import chainer
import onnx
from onnx import numpy_helper
from onnx_chainer import onnx_helper
def _tensor_from_array_for_constant(array, name):
tensor = numpy_helper.from_array(array, name=name)
# Avoid `raw_data` for better debuggability. This would be OK
# since constants are usually small.
field_... | 4,408 | 33.716535 | 78 | py |
chainer | chainer-master/onnx_chainer/graph.py | import collections
from collections import OrderedDict
import heapq
import chainer
from onnx_chainer.functions.converter import FunctionConverterParams
from onnx_chainer import onnx_helper
class Graph(object):
def __init__(self, context, converters, opset_version,
explicit_input_names, network... | 5,263 | 37.144928 | 79 | py |
chainer | chainer-master/onnx_chainer/replace_func.py | import inspect
import chainer
class WrappedFunctionNode(chainer.FunctionNode):
"""Wrap the target function and operate as ``FunctionNode``
Arguments:
name (str): name of the function node
func (func): the target function
args (list): args for the function
kwargs (dict): kwarg... | 9,133 | 35.390438 | 79 | py |
chainer | chainer-master/onnx_chainer/__init__.py | from onnx_chainer.export import convert_parameter # NOQA
from onnx_chainer.export import export # NOQA
from onnx_chainer.export import MAXIMUM_OPSET_VERSION # NOQA
from onnx_chainer.export import MINIMUM_OPSET_VERSION # NOQA
from onnx_chainer.export_testcase import export_testcase # NOQA
| 296 | 36.125 | 64 | py |
chainer | chainer-master/onnx_chainer/onnx_helper.py | import onnx
__func_name = None # not care the name is unique on whole graph
def set_func_name(func_name):
"""Set the name of Chainer function being converted.
Args:
func_name (str): The name of Chainer function.
"""
global __func_name
__func_name = func_name
def get_func_name():
... | 4,521 | 30.402778 | 79 | py |
chainer | chainer-master/onnx_chainer/export_testcase.py | import os
import warnings
import chainer
from onnx_chainer.export import _available
if _available:
from onnx_chainer.export import export
from onnx_chainer.onnx_helper import cleanse_param_name
from onnx_chainer.onnx_helper import write_tensor_pb
def export_testcase(model, args, out_dir, output_grad=F... | 2,741 | 38.73913 | 79 | py |
chainer | chainer-master/onnx_chainer/mapping.py | from contextlib import contextmanager
import chainer.functions as F
from onnx_chainer import functions
from onnx_chainer.functions.converter import FunctionConverter
from onnx_chainer.replace_func import fake_as_funcnode
_supported_function_node_set = {
# Activation
'ClippedReLU',
'ELU',
'HardSigmoi... | 2,980 | 16.231214 | 74 | py |
chainer | chainer-master/onnx_chainer/functions/pooling.py | import warnings
from chainer.utils import conv
import numpy as np
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
@support((1, 7))
def convert_AveragePooling2D(
func, opset_version, input_names, output_names, context):
pad = [func.ph, func.pw]
stride = [... | 6,863 | 33.149254 | 79 | py |
chainer | chainer-master/onnx_chainer/functions/activation.py | import numpy as np
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
def _convert_softmax_impl(op_type, func, input_names, output_names):
axis = func.axis
ndim = len(func.inputs[0].shape)
if axis == ndim - 1:
return onnx_helper.make_node(
op... | 5,706 | 32.970238 | 79 | py |
chainer | chainer-master/onnx_chainer/functions/math.py | import numpy as np
from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
@support((1, 6, 7))
def convert_Add(func, opset_version, input_names, output_names, context):
if opset_version == 1:
return onnx_helper.make_nod... | 14,447 | 34.940299 | 79 | py |
chainer | chainer-master/onnx_chainer/functions/array.py | import warnings
import chainer
import numpy as np
import onnx
from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
TENSOR_TYPE_TO_NAME = {
0: 'UNDEFINED',
1: 'FLOAT',
2: 'UINT8',
3: 'INT8',
4: 'UINT16',
... | 23,717 | 34.085799 | 79 | py |
chainer | chainer-master/onnx_chainer/functions/connection.py | import numpy as np
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
def convert_Convolution2DFunction(
func, opset_version, input_names, output_names, context):
if hasattr(func, 'dy') and hasattr(func, 'dx'):
node = onnx_helper.make_node(
'... | 3,652 | 30.491379 | 70 | py |
chainer | chainer-master/onnx_chainer/functions/loss.py | import chainer
import numpy as np
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
@support((9,))
def convert_SoftmaxCrossEntropy(
func, opset_version, input_names, output_names, context):
# obtain input variable
if not isinstance(func, chainer.FunctionNod... | 1,798 | 38.977778 | 79 | py |
chainer | chainer-master/onnx_chainer/functions/opset_version.py | def support(opset_versions):
"""Detect lowest supported version of the target converter
A simple wrap function for convert functions to detect lowest number of
supported opset version. For example, the target ONNX operater is added
from 6 and updated on 8, add this function as decorator like the below.... | 1,571 | 33.173913 | 76 | py |
chainer | chainer-master/onnx_chainer/functions/noise.py | import chainer
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
@support((1, 6, 7))
def convert_Dropout(func, opset_version, input_names, output_names, context):
if opset_version == 1:
return onnx_helper.make_node(
'Dropout', input_names, output_na... | 846 | 30.37037 | 77 | py |
chainer | chainer-master/onnx_chainer/functions/converter.py | class FunctionConverterParams(object):
def __init__(
self, func=None, opset_version=None, input_names=None,
output_names=None, context=None):
"""Wrapper of converter parameters
Exporter set this parameters to the target converter's argument.
>>> def own_converter(p... | 1,697 | 31.653846 | 74 | py |
chainer | chainer-master/onnx_chainer/functions/__init__.py | from onnx_chainer.functions.activation import convert_ClippedReLU # NOQA
from onnx_chainer.functions.activation import convert_ELU # NOQA
from onnx_chainer.functions.activation import convert_HardSigmoid # NOQA
from onnx_chainer.functions.activation import convert_LeakyReLU # NOQA
from onnx_chainer.functions.activa... | 6,902 | 61.189189 | 91 | py |
chainer | chainer-master/onnx_chainer/functions/rnn.py | import chainer
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
@support((1, 6, 7))
def convert_n_step_gru(func, opset_version, input_names, output_names,
context):
n_layers, dropout_ratio, hx, ws, bs, xs = func.args
assert n_layers >= 1
... | 3,642 | 34.028846 | 79 | py |
chainer | chainer-master/onnx_chainer/functions/normalization.py | import sys
import chainer
import numpy as np
from onnx_chainer.functions.array import get_slice_node
from onnx_chainer.functions.opset_version import support
from onnx_chainer import onnx_helper
@support((1, 6, 7))
def convert_BatchNormalization(
func, opset_version, input_names, output_names, context):
... | 6,532 | 35.294444 | 115 | py |
chainer | chainer-master/onnx_chainer/testing/test_onnxruntime.py | import glob
import os
import warnings
import numpy as np
import onnx
try:
import onnxruntime as rt
ONNXRUNTIME_AVAILABLE = True
except ImportError:
warnings.warn(
'ONNXRuntime is not installed. Please install it to use '
' the testing utility for ONNX-Chainer\'s converters.',
Impor... | 2,228 | 34.380952 | 76 | py |
chainer | chainer-master/onnx_chainer/testing/test_mxnet.py | import collections
import os
import warnings
import chainer
import numpy as np
from onnx_chainer.testing.test_onnxruntime import load_test_data
try:
import mxnet
MXNET_AVAILABLE = True
except ImportError:
warnings.warn(
'MXNet is not installed. Please install mxnet to use '
'testing utili... | 2,113 | 36.087719 | 77 | py |
chainer | chainer-master/onnx_chainer/testing/input_generator.py | import numpy as np
def shaped_range(*shape, dtype=np.float32):
r = np.arange(np.prod(shape))
r = r.reshape(shape)
return r
def _increasing_impl(*shape, dtype=np.float32, negative=True, bias=0):
r = shaped_range(*shape, dtype=dtype)
if negative:
r -= r.size // 2
if dtype in (np.float3... | 2,876 | 29.935484 | 77 | py |
chainer | chainer-master/onnx_chainer/testing/get_test_data_set.py | import os
import onnx_chainer
TEST_OUT_DIR = 'out'
def gen_test_data_set(model, args, name, opset_version, **kwargs):
model.xp.random.seed(42)
test_path = os.path.join(
TEST_OUT_DIR, 'opset{}'.format(opset_version), name)
onnx_chainer.export_testcase(
model, args, test_path, opset_versi... | 369 | 22.125 | 70 | py |
chainer | chainer-master/onnx_chainer/testing/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/onnx_chainer/examples/resnet50/export.py | """Example for exporting ResNet50 model to ONNX graph.
$ pwd
/path/to/onnx-chainer
$ python examples/resnet50/export.py -I target.jpg -O onnx_model
'model.onnx' will be output under 'onnx_model' directory.
"""
import argparse
import os
import chainer.cuda
import chainercv.links as C
from chainercv.transforms i... | 2,402 | 32.84507 | 84 | py |
chainer | chainer-master/onnx_chainer/examples/yolov2tiny/export.py | """Example for exporting YOLOv2 Tiny model to ONNX graph.
$ pwd
/path/to/onnx-chainer
$ python examples/yolov2tiny/export.py -I target.jpg -O onnx_model
'model.onnx' will be output under 'onnx_model' directory.
NOTE: Outputs are required postprocessing to draw bbox on the target.jpg.
See ChainerCV's exam... | 2,494 | 33.178082 | 84 | py |
chainer | chainer-master/tests/conftest.py | import os
import subprocess
import sys
from chainer import testing
from chainer.testing import parameterized
_pairwise_parameterize = (
os.environ.get('CHAINER_TEST_PAIRWISE_PARAMETERIZATION', 'never'))
assert _pairwise_parameterize in ('never', 'always')
def _is_pip_installed():
try:
import pip #... | 1,329 | 26.708333 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_variable.py | import copy
import inspect
import platform
import re
import sys
import unittest
import warnings
import mock
import numpy as np
import pytest
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.backends import intel64
import chainer.functions as F
from chainer import in... | 103,385 | 32.264479 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_optimizer.py | import copy
import unittest
import warnings
import mock
import numpy as np
import pytest
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import optimizer
from chainer import optimizers
from chainer import serializer
from chainer import testing
from chainer.testing import attr... | 27,955 | 32.400239 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_chainer_objects.py | import importlib
import inspect
import pkgutil
import types
import unittest
import six
import chainer
from chainer import testing
module_prefix = 'chainer.'
def walk_modules():
root = chainer.__path__
for loader, modname, ispkg in pkgutil.walk_packages(root, module_prefix):
# Skip modules generate... | 2,776 | 27.628866 | 79 | py |
chainer | chainer-master/tests/chainer_tests/conftest.py | import pytest
from chainer.backends import cuda
import chainerx
if not chainerx.is_available():
# Skip all ChainerX tests if ChainerX is unavailable.
# TODO(kmaehashi) This is an tentative fix. This file should be removed
# once chainer-test supports ChainerX.
pytest.mark.chainerx = pytest.mark.skip
... | 488 | 23.45 | 75 | py |
chainer | chainer-master/tests/chainer_tests/test_runtime_info.py | import unittest
import six
import chainer
from chainer import _runtime_info
from chainer import testing
class TestRuntimeInfo(unittest.TestCase):
def test_get_runtime_info(self):
info = _runtime_info._get_runtime_info()
assert chainer.__version__ in str(info)
def test_print_runtime_info(sel... | 512 | 22.318182 | 71 | py |
chainer | chainer-master/tests/chainer_tests/test_sequential.py | import functools
import os
import tempfile
import unittest
import mock
import numpy
import pytest
import six
import chainer
from chainer import cuda
from chainer import functions
from chainer import links
from chainer import testing
from chainer.testing import attr
from chainer import variable
class TestSequential(... | 24,937 | 35.945185 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_link.py | import copy
import unittest
import warnings
import mock
import numpy
import pytest
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import initializers
from chainer import testing
from chainer.testing import attr
import chainerx
def _asse... | 103,809 | 36.194554 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_backprop_utils.py | import unittest
import mock
import numpy
import six
import chainer
from chainer import _backprop_utils
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
def make_array(start, shape, dtype):
size = numpy.product(shape, dtype='i')
a = numpy.arange(start, start + si... | 7,801 | 36.873786 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_gradient_check.py | import math
import unittest
import warnings
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import gradient_check
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
from chainer.testing import condition
import chainerx
def _uniform(... | 28,541 | 31.507973 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_backend.py | import unittest
import numpy
import pytest
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer.backends import intel64
from chainer import testing
from chainer.testing import attr
import chainerx
if chainerx.is_available():
import chainerx.testing
class _TestCopyToBase(ob... | 18,285 | 31.080702 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_runnable.py | import io
import os
import re
import unittest
from chainer import testing
class TestRunnable(unittest.TestCase):
def test_runnable(self):
cwd = os.path.dirname(__file__)
regex = re.compile(r'^test_.*\.py$')
for dirpath, dirnames, filenames in os.walk(cwd):
for filename in fil... | 825 | 28.5 | 71 | py |
chainer | chainer-master/tests/chainer_tests/test_function.py | import threading
import unittest
import mock
import numpy
import six
import chainer
from chainer import backend
from chainer.backends import cuda
import chainer.functions as F
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check
def make_array(start, shape, dtype):
s... | 16,047 | 30.22179 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_computational_graph.py | import unittest
import numpy as np
import six
from chainer import computational_graph as c
from chainer import function
from chainer import testing
from chainer import variable
class MockFunction(function.Function):
def __init__(self, n_in, n_out):
self.n_in = n_in
self.n_out = n_out
def f... | 8,849 | 30.161972 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_init_docstring.py | import importlib
import inspect
import pkgutil
import unittest
import chainer
from chainer import testing
def get_init_doc(klass):
for attr in inspect.classify_class_attrs(klass):
if attr.name == '__init__':
if attr.defining_class is klass:
return attr.object.__doc__
... | 1,890 | 29.015873 | 78 | py |
chainer | chainer-master/tests/chainer_tests/test_function_node.py | from __future__ import print_function
import threading
import unittest
import mock
import numpy
import pytest
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
# TODO(hvy): Remove the following import once testing.backe... | 43,917 | 30.550287 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_init.py | import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
class TestUseCuDNN(unittest.TestCase):
@attr.cudnn
def test_valid_case_combination(self):
with chainer.using_config('use_cudnn', 'always'):
self.ass... | 3,140 | 36.392857 | 78 | py |
chainer | chainer-master/tests/chainer_tests/test_function_hook.py | import unittest
import mock
import numpy
import chainer
from chainer import testing
class TestFunctionHook(unittest.TestCase):
def setUp(self):
self.h = chainer.FunctionHook()
def test_name(self):
self.assertEqual(self.h.name, 'FunctionHook')
def test_forward_preprocess(self):
... | 1,627 | 29.148148 | 75 | py |
chainer | chainer-master/tests/chainer_tests/test_function_and_function_node.py | import unittest
import numpy
import chainer
from chainer import testing
import chainer.testing.backend
import chainerx
def _get_expected_xp(backend_config, is_function):
# Returns a pair of xp's expected in forward() and backward() respectively.
xp = backend_config.xp
if xp is chainerx:
forward... | 17,057 | 33.321932 | 79 | py |
chainer | chainer-master/tests/chainer_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/test_reporter.py | import contextlib
import tempfile
import threading
import time
import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import configuration
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.testing import backend
class TestRepo... | 16,729 | 33.494845 | 79 | py |
chainer | chainer-master/tests/chainer_tests/test_configuration.py | import io
import threading
import unittest
import chainer
from chainer import configuration
from chainer import testing
class TestLocalConfig(unittest.TestCase):
def setUp(self):
self.global_config = configuration.GlobalConfig()
self.config = configuration.LocalConfig(self.global_config)
... | 2,789 | 32.614458 | 73 | py |
chainer | chainer-master/tests/chainer_tests/test_initializer.py | import unittest
from chainer import initializer
from chainer import testing
@testing.parameterize(
{'shape': (2, 1), 'expect': (1, 2)},
{'shape': (2, 3, 4), 'expect': (12, 8)},
{'shape': (2, 3, 4, 5), 'expect': (60, 40)})
class TestGetFans(unittest.TestCase):
def test_get_fans(self):
actual ... | 675 | 22.310345 | 50 | py |
chainer | chainer-master/tests/chainer_tests/test_link_hook.py | import time
import unittest
import numpy
import chainer
from chainer import testing
try:
_process_time = time.process_time
except AttributeError:
_process_time = time.clock
class MyLinkHook(chainer.LinkHook):
name = 'MyLinkHook'
def __init__(self):
self.added_args = []
self.delete... | 7,011 | 30.872727 | 69 | py |
chainer | chainer-master/tests/chainer_tests/test_backprop.py | import unittest
import mock
import numpy as np
import pytest
import chainer
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
import chainerx
class TestBackward(unittest.TestCase):
def test_no_output(self):
chainer.backward([])
chainer.backward([], [... | 5,140 | 31.745223 | 75 | py |
chainer | chainer-master/tests/chainer_tests/backends_tests/test_chainerx.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
import chainerx
@testing.inject_backend_tests(
None,
[
{'use_chainerx': True, 'chainerx_device': 'native:0'},
{'use_chainerx'... | 7,449 | 33.651163 | 77 | py |
chainer | chainer-master/tests/chainer_tests/backends_tests/test_intel64.py | import unittest
import numpy
from chainer import backend
from chainer.backends import intel64
from chainer import testing
@testing.attr.ideep
class TestIntel64Device(unittest.TestCase):
def check_device(self, device):
assert device.xp is numpy
assert device.supported_array_types == (numpy.ndarr... | 2,676 | 27.478723 | 79 | py |
chainer | chainer-master/tests/chainer_tests/backends_tests/test_cpu.py | import unittest
import numpy
from chainer import backend
from chainer import testing
class TestCpuDevice(unittest.TestCase):
def test_hashable(self):
assert isinstance(hash(backend.CpuDevice()), int)
class TestCpuDeviceFromArray(unittest.TestCase):
def check_device(self, device):
assert ... | 2,524 | 25.861702 | 75 | py |
chainer | chainer-master/tests/chainer_tests/backends_tests/test_cuda.py | import os
import shutil
import subprocess
import sys
import tempfile
import unittest
import warnings
import numpy
import pytest
import six
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
class TestDummyDeviceType(unittest.Test... | 19,875 | 30.8016 | 77 | py |
chainer | chainer-master/tests/chainer_tests/initializer_tests/test_normal.py | import math
import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import initializers
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
default_scale = {
initializers.Normal: 0.05,
}
default_coeff ... | 4,513 | 31.710145 | 73 | py |
chainer | chainer-master/tests/chainer_tests/initializer_tests/test_orthogonal.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import initializers
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
@testing.parameterize(*testing.product({
'shape,dim_in,dim_out': [
... | 6,108 | 30.328205 | 77 | py |
chainer | chainer-master/tests/chainer_tests/initializer_tests/test_sampling.py | import unittest
import numpy
from chainer.backends import cuda
from chainer import initializers
from chainer import testing
from chainer.testing import attr
@testing.parameterize(*testing.product({
'target': [
initializers.UpsamplingDeconvFilter,
initializers.DownsamplingConvFilter,
],
'... | 1,941 | 31.366667 | 77 | py |
chainer | chainer-master/tests/chainer_tests/initializer_tests/test_constant.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer import initializers
from chainer import testing
@testing.parameterize(*testing.product({
'dtype': [numpy.float16, numpy.float32, numpy.float64],
}))
@testing.backend.inject_backend_tests(
None,
[
{},
{'u... | 4,417 | 31.014493 | 71 | py |
chainer | chainer-master/tests/chainer_tests/initializer_tests/test_init.py | import os
import unittest
import numpy
import chainer
from chainer.backends import cuda
from chainer import initializers
from chainer import testing
from chainer.testing import attr
import chainerx
class TestGenerateArray(unittest.TestCase):
def _generate_array(self, xp, dtype=None, device=None):
initi... | 3,692 | 29.775 | 78 | py |
chainer | chainer-master/tests/chainer_tests/initializer_tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/tests/chainer_tests/initializer_tests/test_uniform.py | import math
import unittest
import numpy
import chainer
from chainer import backend
from chainer.backends import cuda
from chainer import initializers
from chainer import testing
from chainer.testing import attr
from chainer.testing import condition
default_scale = {
initializers.Uniform: 0.05,
}
default_coeff... | 4,629 | 31.605634 | 73 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/test_weight_decay.py | import unittest
import numpy as np
import chainer
import chainer.functions as F
from chainer import optimizer_hooks
from chainer import optimizers
from chainer import testing
import utils
_backend_params = [
# NumPy
{},
{'use_ideep': 'always'},
# CuPy
{'use_cuda': True, 'cuda_device': 0},
{... | 3,058 | 28.413462 | 74 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/test_gradient_lars.py | import unittest
import numpy as np
import chainer
from chainer import optimizer_hooks
from chainer import optimizers
from chainer import testing
import utils
_backend_params = [
# NumPy
{},
{'use_ideep': 'always'},
# CuPy
{'use_cuda': True, 'cuda_device': 0},
{'use_cuda': True, 'cuda_device... | 2,840 | 33.228916 | 76 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/test_gradient_noise.py | import itertools
import unittest
import mock
import numpy as np
from chainer import optimizer_hooks
from chainer import optimizers
from chainer import testing
import utils
_backend_params = [
# NumPy
{},
{'use_ideep': 'always'},
# CuPy
{'use_cuda': True, 'cuda_device': 0},
{'use_cuda': True... | 2,820 | 30.696629 | 76 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/utils.py | import numpy
import chainer
class ParametersLink(chainer.Link):
'''Link with specific parameters.'''
def __init__(self, params):
super(ParametersLink, self).__init__()
with self.init_scope():
for i, p in enumerate(params):
setattr(self, 'p{}'.format(i), p)
@s... | 1,163 | 28.1 | 70 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/test_gradient_hard_clipping.py | import unittest
import numpy as np
from chainer import optimizer_hooks
from chainer import optimizers
from chainer import testing
import utils
_backend_params = [
# NumPy
{},
{'use_ideep': 'always'},
# CuPy
{'use_cuda': True, 'cuda_device': 0},
{'use_cuda': True, 'cuda_device': 1},
# Ch... | 2,000 | 28.865672 | 75 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/test_gradient_clipping.py | import math
import unittest
import numpy as np
import chainer
from chainer import optimizer_hooks
from chainer import optimizers
from chainer import testing
import utils
_backend_params = [
# NumPy
{},
{'use_ideep': 'always'},
# CuPy
{'use_cuda': True, 'cuda_device': 0},
{'use_cuda': True, ... | 2,411 | 27.714286 | 76 | py |
chainer | chainer-master/tests/chainer_tests/optimizer_hooks_tests/test_lasso.py | import unittest
import numpy as np
from chainer import optimizer_hooks
from chainer import optimizers
from chainer import testing
import utils
_backend_params = [
# NumPy
{},
{'use_ideep': 'always'},
# CuPy
{'use_cuda': True, 'cuda_device': 0},
{'use_cuda': True, 'cuda_device': 1},
# Ch... | 1,841 | 27.78125 | 77 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_type_check.py | import pickle
import sys
import unittest
import warnings
import numpy
from chainer.backends import cuda
from chainer import testing
from chainer.testing import attr
from chainer.utils import type_check as T
class TestConstant(unittest.TestCase):
def setUp(self):
self.x = T.Constant(10)
def test_st... | 12,347 | 29.117073 | 75 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_cache.py | import unittest
import numpy
import chainer
from chainer import testing
from chainer.utils import cache
class MockDistribution(object):
def __init__(self, x):
self.x = x
self.h_call_count = 0
self.y_call_count = 0
@cache.cached_property
def h(self):
self.h_call_count +=... | 2,502 | 25.62766 | 69 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_conv_nd.py | import itertools
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_nd
class TestAsTuple(unittest.TestCase):
def test_scalar(self):
actual = conv_nd.as_tuple(1, 3)
expec... | 10,391 | 32.960784 | 79 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_conv_nd_kernel.py | import unittest
import mock
import chainer
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv_nd_kernel
@testing.parameterize(*testing.product({
'ndim': [2, 3, 4],
}))
@attr.gpu
class TestIm2colNDKernelMemo(unittest.TestCase):
def setUp(self):
chainer.backen... | 1,324 | 25.5 | 78 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_sparse.py | import unittest
import numpy
from chainer import testing
from chainer import utils
def _setup_tensor(_min, _max, shape, dtype, threshold=None):
y = numpy.random.uniform(_min, _max, shape).astype(dtype)
if threshold is not None:
y[y < threshold] = 0
return y
@testing.parameterize(*testing.produ... | 4,614 | 30.182432 | 70 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_array.py | import unittest
import numpy
from chainer import testing
from chainer.utils import array
@testing.parameterize(
{'shape': ()},
{'shape': (2, 3)},
{'shape': (1,)},
{'shape': (0,)},
{'shape': (5, 6, 7)},
{'shape': (0, 3)},
{'shape': (2, 0)},
{'shape': (5, 0, 7)},
)
class TestSizeOfShap... | 1,601 | 27.105263 | 77 | py |
chainer | chainer-master/tests/chainer_tests/utils_tests/test_precision.py | import unittest
import numpy
from chainer import function_node
from chainer import testing
from chainer.utils import precision
class F(function_node.FunctionNode):
@precision._fp16_mixed_precision_helper
def forward(self, x):
self.x = x
return x
class G(function_node.FunctionNode):
@... | 1,990 | 25.905405 | 57 | py |
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