def __init__(self, network, types, shapes, output_num, strategy3=None, strategy4=None, axis=-1):
super(NetWithLoss, self).__init__()
self.get_next = P.GetNext(types, shapes, output_num, "")
self.one_hot = P.OneHot(axis=axis).shard(strategy3)
self.on_value = Tensor(1.0, ms.float32)
self.off_value = Tensor(0.0, ms.float32)
self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy4)
self.network = network
tensor = Tensor(input_np)
ms_types.append(tensor.dtype())
return ms_types
if __name__ == '__main__':
data_set = test_me_de_train_dataset()
dataset_size = data_set.get_dataset_size()
batch_size = data_set.get_batch_size()
dataset_shapes = data_set.output_shapes()
np_types = data_set.output_types()
dataset_types = convert_type(dataset_shapes, np_types)
ds1 = data_set.device_que()
get_next = P.GetNext(dataset_types, dataset_shapes, 2, ds1.queue_name)
tadd = P.ReLU()
class dataiter(nn.Cell):
def __init__(self):
super(dataiter, self).__init__()
def construct(self):
input_, _ = get_next()
return tadd(input_)
net = dataiter()
net.set_train()
_executor.init_dataset(ds1.queue_name, 39, batch_size, dataset_types,
dataset_shapes, (), 'dataset')
# 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 mindspore as ms
from mindspore.ops import Primitive
from mindspore.ops import operations as P
get_next = P.GetNext([ms.float32], [[1, 64, 112, 112]], 1, "")
tensor_move_attr = Primitive('TensorMove')
tensor_move_attr.add_prim_attr("label_for_insert_stream_active", True)
tensor_move = Primitive('tensor_move')
cast = P.Cast()
add = P.Add()
class FnDict:
def __init__(self):
self.fnDict = {}
def __call__(self, fn):
self.fnDict[fn.__name__] = fn
def __getitem__(self, name):
#
# 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 mindspore as ms
from mindspore.ops import Primitive
from mindspore.ops import _constants as Constants
from mindspore.ops import operations as P
get_next = P.GetNext([ms.float32, ms.int32], [[32, 64], [32]], 2, "")
memcpy_async = Primitive('memcpy_async')
make_tuple = Primitive('make_tuple')
tuple_getitem = Primitive(Constants.kTupleGetItem)
class FnDict:
def __init__(self):
self.fnDict = {}
def __call__(self, fn):
self.fnDict[fn.__name__] = fn
def __getitem__(self, name):
return self.fnDict[name]
def __init__(self):
super().__init__()
self.get_next = P.GetNext([ms.float32, ms.int32], [[32, 64], [32]],
2, "")
def __init__(self, network, dataset_types, dataset_shapes, shared_name=''):
super(NetWithTDT, self).__init__()
self.get_next = P.GetNext(dataset_types, dataset_shapes, len(dataset_shapes), shared_name)
self.Op_network = network
