def __init__(self, filter_size=(3,3),
input_feature=None, output_feature=None,
feature_map_multiplier=1,
subsample=(1,1), border='half', need_bias=False, dc=0.0):
"""
This 2d convolution deals with 4d tensor:
(batch_size, feature map/channel, filter_row, filter_col)
feature_map_multiplier always has a ligher priority
than input_feature/output_feature
"""
super(Conv2d, self).__init__()
self.filterSize = filter_size
self.inputFeature = input_feature
self.outputFeature = output_feature
self.mapMulti = feature_map_multiplier
self.border = border
self.subsample = subsample
self.need_bias = need_bias
self.dc = dc
self.w = None
self.b = None
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