82d70085e0ad4a355acfd486884a3dc28af1b6b9,chainer_/models/squeezenet.py,SqueezeNet,__init__,#SqueezeNet#Any#Any#Any#Any#Any#Any#,157

Before Change


            in_channels = init_block_channels
            for i, channels_per_stage in enumerate(channels):
                //stage = Sequential()
                self.features.append(partial(
                    F.max_pooling_2d,
                    ksize=3,
                    stride=2))
                for j, out_channels in enumerate(channels_per_stage):
                    expand_channels = out_channels // 2
                    squeeze_channels = out_channels // 8
                    self.features.append(FireUnit(

After Change


                    out_channels=init_block_channels,
                    ksize=init_block_kernel_size))
                in_channels = init_block_channels
                for i, channels_per_stage in enumerate(channels):
                    stage = SimpleSequential()
                    with stage.init_scope():
                        setattr(stage, "pool{}".format(i + 1), partial(
                            F.max_pooling_2d,
                            ksize=3,
                            stride=2))
                        for j, out_channels in enumerate(channels_per_stage):
                            expand_channels = out_channels // 2
                            squeeze_channels = out_channels // 8
                            setattr(stage, "unit{}".format(j + 1), FireUnit(
                                in_channels=in_channels,
                                squeeze_channels=squeeze_channels,
                                expand1x1_channels=expand_channels,
                                expand3x3_channels=expand_channels,
                                residual=((residuals is not None) and (residuals[i][j] == 1))))
                            in_channels = out_channels
                    setattr(self.features, "stage{}".format(i + 1), stage)
                setattr(self.features, "dropout", partial(
                    F.dropout,
                    ratio=0.5))
Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 3

Instances


Project Name: osmr/imgclsmob
Commit Name: 82d70085e0ad4a355acfd486884a3dc28af1b6b9
Time: 2018-09-02
Author: osemery@gmail.com
File Name: chainer_/models/squeezenet.py
Class Name: SqueezeNet
Method Name: __init__


Project Name: explosion/thinc
Commit Name: 3140a57326e533548df00f0f128cd356932d400b
Time: 2017-01-11
Author: honnibal+gh@gmail.com
File Name: thinc/neural/_classes/model.py
Class Name: Model
Method Name: __init__


Project Name: WZBSocialScienceCenter/tmtoolkit
Commit Name: b1453b0d225f78553f08dc3e4f3ad393e32f2343
Time: 2020-01-21
Author: markus.konrad@wzb.eu
File Name: tmtoolkit/preprocess/_preprocworker.py
Class Name: PreprocWorker
Method Name: _task_add_metadata_per_token