def __init__(
self,
input_dims,
output_dim,
mm_dim=1600,
chunks=20,
rank=15,
shared=False,
dropout_input=0.0,
dropout_pre_lin=0.0,
dropout_output=0.0,
pos_norm="before_cat",
):
super().__init__()
self.input_dims = input_dims
self.output_dim = output_dim
self.mm_dim = mm_dim
self.chunks = chunks
self.rank = rank
self.shared = shared
self.dropout_input = dropout_input
self.dropout_pre_lin = dropout_pre_lin
self.dropout_output = dropout_output
assert pos_norm in ["before_cat", "after_cat"]
self.pos_norm = pos_norm
# Modules
self.linear0 = nn.Linear(input_dims[0], mm_dim)
if shared:
self.linear1 = self.linear0
else:
self.linear1 = nn.Linear(input_dims[1], mm_dim)
merge_linears0, merge_linears1 = [], []
self.sizes_list = get_sizes_list(mm_dim, chunks)
for size in self.sizes_list:
ml0 = nn.Linear(size, size * rank)
merge_linears0.append(ml0)
if self.shared:
ml1 = ml0
else:
ml1 = nn.Linear(size, size * rank)
merge_linears1.append(ml1)
self.merge_linears0 = nn.ModuleList(merge_linears0)
self.merge_linears1 = nn.ModuleList(merge_linears1)
self.linear_out = nn.Linear(mm_dim, output_dim)
self.n_params = sum(p.numel() for p in self.parameters()
if p.requires_grad)
log_class_usage("Fusion", self.__class__)
def __init__(
self,
input_dims,
output_dim,
mm_dim=1600,
chunks=20,
shared=False,
dropout_input=0.0,
dropout_pre_lin=0.0,
dropout_output=0.0,
pos_norm="before_cat",
):
super().__init__()
self.input_dims = input_dims
self.output_dim = output_dim
self.mm_dim = mm_dim
self.chunks = chunks
self.shared = shared
self.dropout_input = dropout_input
self.dropout_pre_lin = dropout_pre_lin
self.dropout_output = dropout_output
assert pos_norm in ["before_cat", "after_cat"]
self.pos_norm = pos_norm
# Modules
self.linear0 = nn.Linear(input_dims[0], mm_dim)
if self.shared:
self.linear1 = self.linear0
else:
self.linear1 = nn.Linear(input_dims[1], mm_dim)
self.sizes_list = get_sizes_list(mm_dim, chunks)
bilinears = []
for size in self.sizes_list:
bilinears.append(nn.Bilinear(size, size, size))
self.bilinears = nn.ModuleList(bilinears)
self.linear_out = nn.Linear(self.mm_dim, self.output_dim)
self.n_params = sum(p.numel() for p in self.parameters()
if p.requires_grad)
log_class_usage("Fusion", self.__class__)