def BiLinear(repin, repout):
""" Cheap bilinear layer (adds parameters for each part of the input which can be
interpreted as a linear map from a part of the input to the output representation)."""
Wdim, weight_proj = bilinear_weights(repout, repin)
#self.w = TrainVar(objax.random.normal((Wdim,)))#xavier_normal((Wdim,))) #TODO: revert to xavier
logging.info(f"BiW components: dim:{Wdim}")
return _BiLinear(Wdim, weight_proj)
def BiLinear(repin, repout):
""" Cheap bilinear layer (adds parameters for each part of the input which can be
interpreted as a linear map from a part of the input to the output representation)."""
Wdim, weight_proj = bilinear_weights(repout, repin)
return lambda x: hkBiLinear(weight_proj, Wdim)(x)
def __init__(self, repin, repout):
super().__init__()
Wdim, weight_proj = bilinear_weights(repout, repin)
self.weight_proj = torchify_fn(jit(weight_proj))
self.bi_params = nn.Parameter(torch.randn(Wdim))
logging.info(f"BiW components: dim:{Wdim}")
def __init__(self, repin, repout):
super().__init__()
Wdim, weight_proj = bilinear_weights(repout,repin)
self.weight_proj = jit(weight_proj)
self.w = TrainVar(objax.random.normal((Wdim,)))#xavier_normal((Wdim,))) #TODO: revert to xavier
logging.info(f"BiW components: dim:{Wdim}")