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https://github.com/zebrajr/pytorch.git
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cc7e61c88d
* Move onnx-caffe2 inside caffe2 * Update to the lastest onnx-caffe2 and update jenkins env * Rename onnx_caffe2 to onnx * Add __init__.py to caffe2/python/onnx * Change CI check variable to JENKINS_URL * Cherrypick recent onnx-caffe2 update
78 lines
3.5 KiB
Python
78 lines
3.5 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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## @package onnx
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# Module caffe2.python.onnx.backend_rep
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core, workspace
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from caffe2.proto import caffe2_pb2
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from onnx.backend.base import BackendRep, namedtupledict
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class Caffe2Rep(BackendRep):
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def __init__(self, init_net, predict_net, workspace, uninitialized):
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super(Caffe2Rep, self).__init__()
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self.init_net = init_net
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self.predict_net = predict_net
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self.workspace = workspace
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# The list of uninitialized external_inputs in workspace, we need this to
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# pair the name with given sequence inputs.
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self.uninitialized = uninitialized
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self.nets_created = False
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self.ran_init_net = False
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@property
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def _name_scope(self):
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if self.predict_net.device_option.device_type == caffe2_pb2.CUDA:
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return 'gpu_{}'.format(self.predict_net.device_option.cuda_gpu_id)
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return ''
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def run(self, inputs, **kwargs):
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super(Caffe2Rep, self).run(inputs, **kwargs)
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with self.workspace:
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with core.DeviceScope(self.predict_net.device_option):
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if isinstance(inputs, dict):
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with core.NameScope(self._name_scope):
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for key, value in inputs.items():
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workspace.FeedBlob(key, value)
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elif isinstance(inputs, list) or isinstance(inputs, tuple):
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if len(self.uninitialized) != len(inputs):
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raise RuntimeError('Expected {} values for uninitialized '
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'graph inputs ({}), but got {}.'.format(
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len(self.uninitialized),
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', '.join(self.uninitialized),
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len(inputs)))
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for i, value in enumerate(inputs):
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# namescope already baked into protobuf
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workspace.FeedBlob(self.uninitialized[i], value)
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else:
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# single input
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workspace.FeedBlob(self.uninitialized[0], inputs)
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if not self.nets_created:
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workspace.CreateNet(self.init_net)
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workspace.CreateNet(self.predict_net)
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self.nets_created = True
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if not self.ran_init_net:
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workspace.RunNet(self.init_net.name)
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self.ran_init_net = True
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workspace.RunNet(self.predict_net.name)
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output_values = [workspace.FetchBlob(name)
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for name in self.predict_net.external_output]
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return namedtupledict('Outputs',
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self.predict_net.external_output)(*output_values)
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