340094b32576bf6dce50dbfdf82df14a5f6c043e,gluon/gluoncv2/models/efficientnet.py,EfficientNet,__init__,#EfficientNet#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,184

Before Change

        self.in_size = in_size
        self.classes = classes

        with self.name_scope():
            self.features = nn.HybridSequential(prefix="")
            self.features.add(conv3x3_block(
                in_channels=in_channels,
                out_channels=init_block_channels,
                strides=2,
                bn_use_global_stats=bn_use_global_stats,
                activation="swish"))
            in_channels = init_block_channels
            for i, channels_per_stage in enumerate(channels):
                kernel_sizes_per_stage = kernel_sizes[i]
                expansion_factors_per_stage = expansion_factors[i]
                stage = nn.HybridSequential(prefix="stage{}_".format(i + 1))
                with stage.name_scope():
                    for j, out_channels in enumerate(channels_per_stage):
                        kernel_size = kernel_sizes_per_stage[j]
                        expansion_factor = expansion_factors_per_stage[j]
                        strides = strides_per_stage[i] if (j == 0) else 1
                        if i == 0:
                            stage.add(EffiDwsConvUnit(
                                in_channels=in_channels,
                                out_channels=out_channels,
                                bn_use_global_stats=bn_use_global_stats))
                        else:
                            stage.add(EffiInvResUnit(
                                in_channels=in_channels,
                                out_channels=out_channels,
                                kernel_size=kernel_size,
                                strides=strides,
                                expansion_factor=expansion_factor,
                                bn_use_global_stats=bn_use_global_stats))
                        in_channels = out_channels
                self.features.add(stage)
            self.features.add(conv1x1_block(
                in_channels=in_channels,
                out_channels=final_block_channels,
                bn_use_global_stats=bn_use_global_stats,
                activation="swish"))
            in_channels = final_block_channels
            self.features.add(nn.GlobalAvgPool2D())

            self.output = nn.HybridSequential(prefix="")
            self.output.add(nn.Flatten())
            if dropout_rate > 0.0:
                self.output.add(nn.Dropout(rate=dropout_rate))
            self.output.add(nn.Dense(
                units=classes,
                in_units=in_channels))

    def hybrid_forward(self, F, x):
        x = self.features(x)
        x = self.output(x)
        return x

After Change

        Small float added to variance in Batch norm.
    bn_use_global_stats : bool, default False
        Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
        Useful for fine-tuning.
    in_channels : int, default 3
        Number of input channels.
    in_size : tuple of two ints, default (224, 224)
        Spatial size of the expected input image.
    classes : int, default 1000
        Number of classification classes.
    
    def __init__(self,
                 channels,
                 init_block_channels,
                 final_block_channels,
                 kernel_sizes,
                 strides_per_stage,
                 expansion_factors,
                 dropout_rate=0.2,
                 bn_epsilon=1e-3,
                 bn_use_global_stats=False,
                 in_channels=3,
                 in_size=(224, 224),
                 classes=1000,
                 **kwargs):
        super(EfficientNet, self).__init__(**kwargs)
        self.in_size = in_size
        self.classes = classes
        activation = "swish"

        with self.name_scope():
            self.features = nn.HybridSequential(prefix="")
            self.features.add(conv3x3_block(
                in_channels=in_channels,
                out_channels=init_block_channels,
                strides=2,
                bn_epsilon=bn_epsilon,
                bn_use_global_stats=bn_use_global_stats,
                activation=activation))
            in_channels = init_block_channels
            for i, channels_per_stage in enumerate(channels):
                kernel_sizes_per_stage = kernel_sizes[i]
                expansion_factors_per_stage = expansion_factors[i]

Instances