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@ -1706,6 +1706,7 @@ def conv2d_transpose(input,
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padding=0,
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stride=1,
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dilation=1,
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groups=None,
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param_attr=None,
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bias_attr=None,
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use_cudnn=True,
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@ -1776,6 +1777,12 @@ def conv2d_transpose(input,
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dilation(int|tuple): The dilation size. If dilation is a tuple, it must
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contain two integers, (dilation_H, dilation_W). Otherwise, the
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dilation_H = dilation_W = dilation. Default: dilation = 1.
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groups(int): The groups number of the Conv2d transpose layer. Inspired by
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grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
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when group=2, the first half of the filters is only connected to the
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first half of the input channels, while the second half of the
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filters is only connected to the second half of the input channels.
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Default: groups=1
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param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
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Default: None
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bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
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@ -1830,7 +1837,8 @@ def conv2d_transpose(input,
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filter_size = utils.convert_to_list(filter_size, 2,
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'conv2d_transpose.filter_size')
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filter_shape = [input_channel, num_filters] + filter_size
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groups = 1 if groups is None else groups
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filter_shape = [input_channel, num_filters / groups] + filter_size
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img_filter = helper.create_parameter(
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dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
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