Open yaoliUoA opened 4 years ago
The EfficientDet paper uses depth-wise convolution of BiFPN, however, in this implementation, clearly depth-wise convolution is not used (in module.py).
`conv_cfg = { 'Conv': nn.Conv2d, 'ConvWS': ConvWS2d,
} ' ' def build_convlayer(cfg, *args, **kwargs): """ Build convolution layer Args: cfg (None or dict): cfg should contain: type (str): identify conv layer type. layer args: args needed to instantiate a conv layer. Returns: layer (nn.Module): created conv layer """ if cfg is None: cfg = dict(type='Conv') else: assert isinstance(cfg, dict) and 'type' in cfg cfg_ = cfg.copy()
layer_type = cfg_.pop('type') if layer_type not in conv_cfg: raise KeyError('Unrecognized norm type {}'.format(layer_type)) else: conv_layer = conv_cfg[layer_type] layer = conv_layer(*args, **kwargs, **cfg_) return layer`
i have the same issue
jiangnanLZP
commented
4 years ago
The EfficientDet paper uses depth-wise convolution of BiFPN, however, in this implementation, clearly depth-wise convolution is not used (in module.py).
`conv_cfg = { 'Conv': nn.Conv2d, 'ConvWS': ConvWS2d,
TODO: octave conv
} ' ' def build_convlayer(cfg, *args, **kwargs): """ Build convolution layer Args: cfg (None or dict): cfg should contain: type (str): identify conv layer type. layer args: args needed to instantiate a conv layer. Returns: layer (nn.Module): created conv layer """ if cfg is None: cfg = dict(type='Conv') else: assert isinstance(cfg, dict) and 'type' in cfg cfg_ = cfg.copy()