def _set_shape(graph, value, shape):
if isinstance(value, nnef.Identifier):
tensor = graph.tensors[value]
graph.tensors[value] = nnef.Tensor(tensor.name, tensor.dtype, shape, tensor.data, tensor.compression, tensor.quantization)
elif isinstance(value, list):
for v, s in zip(value, shape):
_set_shape(graph, v, s)
# 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 nnef
import numpy as np
from collections import OrderedDict
input = nnef.Tensor('input', dtype='scalar')
filter = nnef.Tensor('filter',
dtype='scalar',
data=np.random.randn(32, 3, 5, 5))
output = nnef.Tensor('output', dtype='scalar')
external = nnef.Operation('external',
attribs={'shape': [1, 3, 224, 224]},
inputs=OrderedDict(),
outputs=OrderedDict([('output',
nnef.Identifier('input'))]))
variable = nnef.Operation('variable',
attribs={
'shape': [32, 3, 5, 5],
'label': 'conv/filter'
},