def _nnefdog_to_source_impl(nnefdog,
file_handle,
custom_fragments="",
enable_shape_of=False):
# type: (NnefGraph, TextIO, str, bool)->TextIO
f = file_handle
fix_str = utils.ensure_not_unicode_in_python2
indent = 4 * " "
print(nnef.format_version((1, 0)), file=f)
extensions = []
if enable_shape_of:
extensions.append("KHR_enable_operator_expressions")
print(nnef.format_extensions(extensions), file=f)
print(file=f)
if custom_fragments:
print(custom_fragments, file=f)
print(file=f)
graph_params = nnef.format_graph(
name=fix_str(nnefdog.name),
inputs=[fix_str(name) for name in nnefdog.input_dn_names],
outputs=[fix_str(name) for name in nnefdog.output_dn_names])
print("graph {}".format(graph_params), file=f)
print("{", file=f)
for op in nnefdog.ops:
dtype = op.result.dtype if op.name in [
"external", "constant", "variable"
] else None
invocation = nnef.format_invocation(name=fix_str(op.name),
args=[],
kwargs=_preprocess_args(op.args),
results=_results_to_result_names(
op.results.values()),
dtype=dtype)
comments = utils.without_nones(
[dn.extra.get(dog.EXTRA_COMMENT) for dn in op.get_result_nodes()])
comment = " # {}".format(", ".join(comments)) if comments else ""
print("{}{};{}".format(indent, invocation, comment), file=f)
print("}", file=f)
return file_handle
if args.input_shape:
input_shape = eval(args.input_shape)
if not isinstance(input_shape, (list, dict)):
print("input-shape must be Python list or dict expression")
exit(-1)
for op in graph.operations:
if op.name == 'external':
if isinstance(input_shape, dict):
name = op.outputs['output']
if name in input_shape:
op.attribs['shape'] = input_shape[name]
else:
op.attribs['shape'] = input_shape
if args.shapes:
try:
nnef.infer_shapes(graph)
except nnef.Error as err:
print('Shape error: ' + str(err))
exit(-1)
print(
nnef.format_graph(graph.name,
graph.inputs,
graph.outputs,
graph.operations,
graph.tensors,
annotate_shapes=args.shapes))
print('Validation succeeded')
# See the License for the specific language governing permissions and
# limitations under the License.
import nnef
def shuffle_shape(op, args, shapes):
shapes[args['output']] = shapes[args['input']]
nnef._register_custom_ops(
"shuffle",
"fragment shuffle<?>( input: tensor<?>, groups: integer ) -> ( output: tensor<?> );"
)
nnef._register_custom_shapes({"shuffle": shuffle_shape})
graph = nnef.parse_string("""
version 1.0;
graph Net( input ) -> ( output )
{
input = external(shape = [1,3,224,224]);
filter = variable(shape = [32,3,5,5], label = 'conv/filter');
conv = conv(input, filter);
output = shuffle(conv, groups = 4);
}
""")
print(
nnef.format_graph(graph.name, graph.inputs, graph.outputs,
graph.operations))
# Copyright (c) 2017 The Khronos Group Inc.
#
# 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 nnef
graph = nnef.parse_string(
"""
version 1.0;
graph Net( input ) -> ( output )
{
input = external(shape = [1,3,224,224]);
filter = variable(shape = [32,3,5,5], label = 'conv/filter');
output = conv(input, filter);
}
"""
)
print(nnef.format_graph(graph.name, graph.inputs, graph.outputs, graph.operations, graph.tensors))