snn_eval.py [303:325]:
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    encoder.fc = torch.nn.Sequential(OrderedDict([
        ('fc1', torch.nn.Linear(hidden_dim, hidden_dim)),
        ('bn1', torch.nn.BatchNorm1d(hidden_dim)),
        ('relu1', torch.nn.ReLU(inplace=True)),
        ('fc2', torch.nn.Linear(hidden_dim, hidden_dim)),
        ('bn2', torch.nn.BatchNorm1d(hidden_dim)),
        ('relu2', torch.nn.ReLU(inplace=True)),
        ('fc3', torch.nn.Linear(hidden_dim, output_dim))
    ]))

    # -- prediction head
    encoder.pred = None
    if use_pred:
        mx = 4  # 4x bottleneck prediction head
        pred_head = OrderedDict([])
        pred_head['bn1'] = torch.nn.BatchNorm1d(output_dim)
        pred_head['fc1'] = torch.nn.Linear(output_dim, output_dim//mx)
        pred_head['bn2'] = torch.nn.BatchNorm1d(output_dim//mx)
        pred_head['relu'] = torch.nn.ReLU(inplace=True)
        pred_head['fc2'] = torch.nn.Linear(output_dim//mx, output_dim)
        encoder.pred = torch.nn.Sequential(pred_head)

    encoder.to(device)
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src/suncet_train.py [397:419]:
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    encoder.fc = torch.nn.Sequential(OrderedDict([
        ('fc1', torch.nn.Linear(hidden_dim, hidden_dim)),
        ('bn1', torch.nn.BatchNorm1d(hidden_dim)),
        ('relu1', torch.nn.ReLU(inplace=True)),
        ('fc2', torch.nn.Linear(hidden_dim, hidden_dim)),
        ('bn2', torch.nn.BatchNorm1d(hidden_dim)),
        ('relu2', torch.nn.ReLU(inplace=True)),
        ('fc3', torch.nn.Linear(hidden_dim, output_dim))
    ]))

    # -- prediction head
    encoder.pred = None
    if use_pred:
        mx = 4  # 4x bottleneck prediction head
        pred_head = OrderedDict([])
        pred_head['bn1'] = torch.nn.BatchNorm1d(output_dim)
        pred_head['fc1'] = torch.nn.Linear(output_dim, output_dim//mx)
        pred_head['bn2'] = torch.nn.BatchNorm1d(output_dim//mx)
        pred_head['relu'] = torch.nn.ReLU(inplace=True)
        pred_head['fc2'] = torch.nn.Linear(output_dim//mx, output_dim)
        encoder.pred = torch.nn.Sequential(pred_head)

    encoder.to(device)
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