def test_load_twice():
from nnabla.ext_utils import list_extensions, get_extension_context
exts = list_extensions()
if "cudnn" not in exts:
pytest.skip("This test is only for cudnn context!")
with create_temp_with_dir("network.nntxt") as fn:
with open(fn, "w") as f:
f.write(nntxt)
ctx = get_extension_context('cudnn')
nn.set_default_context(ctx)
nnp = nnp_graph.NnpLoader(fn)
for network_name in sorted(nnp.get_network_names()):
print(network_name)
network = nnp.get_network(network_name, batch_size=32)
for network_name in sorted(nnp.get_network_names()):
print(network_name)
network = nnp.get_network(network_name, batch_size=32)
import pytest
import nnabla as nn
import nnabla.utils.dlpack
import numpy as np
from nnabla.ext_utils import list_extensions, \
get_extension_context
from nnabla.testing import assert_allclose
# Try to import PyTorch
try:
import torch
import torch.utils.dlpack
except ImportError:
pytest.skip('PyTorch is not installed.', allow_module_level=True)
ext_names = list_extensions()
types = [[np.float32, torch.float32]]
# PyTorch to NNabla
@pytest.mark.parametrize("ext_name", ext_names)
@pytest.mark.parametrize("numpy_type, torch_type", types)
def test_from_dlpack_new(ext_name, numpy_type, torch_type):
ctx = get_extension_context(ext_name)
device_name = ctx.backend[0].split(':')[0]
if device_name == 'cudnn':
device_name = 'cuda' # for PyTorch
nn.set_default_context(ctx)
# Init PyTorch Tensor
t = torch.ones((5, 5), dtype=torch_type, device=torch.device(device_name))
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from nnabla import ext_utils
ext_names = ext_utils.list_extensions()
@pytest.mark.parametrize('ext_name', ext_names)
def test_import_extension_module(ext_name):
ext = ext_utils.import_extension_module(ext_name)
@pytest.mark.parametrize('ext_name', ext_names)
def test_get_extension_context(ext_name):
ctx = ext_utils.get_extension_context(ext_name)
@pytest.mark.parametrize('ext_name', ext_names)
def test_ext_utils_misc(ext_name):
ext = ext_utils.import_extension_module(ext_name)
import pytest
import nnabla as nn
import numpy as np
from nnabla.ext_utils import get_extension_context, list_extensions
from nnabla.utils.inspection import TimeProfiler
from .models import simple_cnn
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("n_class", [5])
@pytest.mark.parametrize(
"ext_name", [x for x in list_extensions() if x in ["cpu", "cudnn"]])
def test_time_profiler(batch_size, n_class, ext_name, tmpdir):
nn.clear_parameters()
ctx = get_extension_context(ext_name)
nn.set_default_context(ctx)
x = nn.Variable.from_numpy_array(
np.random.normal(size=(batch_size, 3, 16, 16)))
t = nn.Variable.from_numpy_array(
np.random.randint(low=0, high=n_class, size=(batch_size, 1)))
y = simple_cnn(x, t, n_class)
tp = TimeProfiler(ext_name, device_id=ctx.device_id)
for i in range(5):
import nnabla.functions as F
from nnabla.ext_utils import get_extension_context, list_extensions
from nnabla.utils.inspection import NanInfTracer
from .models import simple_cnn
def _refresh_inputs_grad(f):
for i in f.inputs:
i.grad.zero()
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("n_class", [5])
@pytest.mark.parametrize("ext_name", list_extensions())
@pytest.mark.parametrize("trace_nan", [False, True])
@pytest.mark.parametrize("trace_inf", [False, True])
def test_nan_inf_tracer(batch_size, n_class, ext_name, trace_nan, trace_inf):
nn.clear_parameters()
ctx = get_extension_context(ext_name)
nn.set_default_context(ctx)
x = nn.Variable.from_numpy_array(
np.random.normal(size=(batch_size, 3, 16, 16)))
t = nn.Variable.from_numpy_array(
np.random.randint(low=0, high=n_class, size=(batch_size, 1)))
y = simple_cnn(x, t, n_class)
