def inject_backend_tests(): decorator = backend.inject_backend_tests( None, # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) # GPU tests + [{ 'use_cuda': True }] # ChainerX tests + [ { 'use_chainerx': True, 'chainerx_device': 'native:0' }, { 'use_chainerx': True, 'chainerx_device': 'cuda:0' }, { 'use_chainerx': True, 'chainerx_device': 'cuda:1' }, ]) return decorator
def inject_backend_tests(method_names): decorator = backend.inject_backend_tests( method_names, # CPU tests [{'use_cuda': False}] # GPU tests + [{'use_cuda': True}]) return decorator
def inject_backend_tests(method_names): decorator = backend.inject_backend_tests( method_names, # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) + # GPU tests [{'use_cuda': True}]) return decorator
def inject_backend_tests(method_names): decorator = backend.inject_backend_tests( method_names, # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) # GPU tests + [{'use_cuda': True}]) return decorator
def inject_backend_tests(method_names): decorator = backend.inject_backend_tests( method_names, # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) # GPU tests + [{'use_cuda': True}] # ChainerX tests + [ {'use_chainerx': True, 'chainerx_device': 'native:0'}, {'use_chainerx': True, 'chainerx_device': 'cuda:0'}, ]) return decorator
msg = 'loss_scaling must be False, \'dynamic\' or \'static\'.' raise ValueError(msg) if loss_scaling == 'dynamic': optimizer.loss_scaling() elif loss_scaling == 'static': optimizer.loss_scaling(scale=10.0) _inject_backend_tests = ( backend.inject_backend_tests( ['test_linear_model'], # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) # GPU tests + [{'use_cuda': True}] # ChainerX tests + [ {'use_chainerx': True, 'chainerx_device': 'native:0'}, {'use_chainerx': True, 'chainerx_device': 'cuda:0'}, ])) class OptimizerTestBase(object): loss_scaling = False def create(self): raise NotImplementedError()
elif loss_scaling is 'static': optimizer.loss_scaling(scale=10.0) _inject_backend_tests = ( backend.inject_backend_tests( ['test_linear_model'], # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) # GPU tests + [{ 'use_cuda': True }] # ChainerX tests + [ { 'use_chainerx': True, 'chainerx_device': 'native:0' }, { 'use_chainerx': True, 'chainerx_device': 'cuda:0' }, ])) class OptimizerTestBase(object): loss_scaling = False
from chainer.backends import cuda from chainer import functions from chainer import testing from chainer.testing import attr from chainer.testing import backend _inject_backend_tests = backend.inject_backend_tests( None, # CPU tests testing.product({ 'use_cuda': [False], 'use_ideep': ['never', 'always'], }) # GPU tests + testing.product({ 'use_cuda': [True], 'use_cudnn': ['never', 'always'], }) # ChainerX tests + testing.product({ 'use_chainerx': [True], 'chainerx_device': ['native:0', 'cuda:0'], }) ) @_inject_backend_tests @testing.parameterize(*testing.product({ 'cover_all': [True, False], 'dtype': [numpy.float16, numpy.float32, numpy.float64], 'contiguous': [None, 'C'],