Loading zoo/monochrome/transformer.py +2 −2 Original line number Diff line number Diff line Loading @@ -41,13 +41,13 @@ class CNNHead(nn.Module): class SigTransformer(nn.Module): __model_name__ = 'transformer' def __init__(self, in_ch=3, n_cls=2, hidden=512, nlayers=5, dropout=0.1): def __init__(self, in_ch=3, n_cls=2, hidden=512, nlayers=5, dropout=0.1, seq_len=90): super(SigTransformer, self).__init__() nhead = hidden // 64 self.head = CNNHead(in_ch, hidden) #self.pos_encoder = PositionalEncoding(hidden, dropout) self.pos_embedding = nn.Parameter(torch.randn(90 + 1, 1, hidden)) self.pos_embedding = nn.Parameter(torch.randn(seq_len + 1, 1, hidden)) self.cls_token = nn.Parameter(torch.randn(1, 1, hidden) * 0.02) Loading Loading
zoo/monochrome/transformer.py +2 −2 Original line number Diff line number Diff line Loading @@ -41,13 +41,13 @@ class CNNHead(nn.Module): class SigTransformer(nn.Module): __model_name__ = 'transformer' def __init__(self, in_ch=3, n_cls=2, hidden=512, nlayers=5, dropout=0.1): def __init__(self, in_ch=3, n_cls=2, hidden=512, nlayers=5, dropout=0.1, seq_len=90): super(SigTransformer, self).__init__() nhead = hidden // 64 self.head = CNNHead(in_ch, hidden) #self.pos_encoder = PositionalEncoding(hidden, dropout) self.pos_embedding = nn.Parameter(torch.randn(90 + 1, 1, hidden)) self.pos_embedding = nn.Parameter(torch.randn(seq_len + 1, 1, hidden)) self.cls_token = nn.Parameter(torch.randn(1, 1, hidden) * 0.02) Loading