self_supervision_benchmark/modeling/colorization/alexnet_colorize_finetune_linear.py [193:220]:
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    )
    bn_c4 = model.SpatialBN(
        resize_c4, resize_c4 + '_bn', 384, epsilon=cfg.MODEL.BN_EPSILON,
        momentum=cfg.MODEL.BN_MOMENTUM, is_test=test_mode
    )
    if cfg.MODEL.BN_NO_SCALE_SHIFT:
        model.param_init_net.ConstantFill(
            [bn_c4 + '_s'], bn_c4 + '_s', value=1.0
        )
        model.param_init_net.ConstantFill(
            [bn_c4 + '_b'], bn_c4 + '_b', value=0.0
        )
    fc_conv4 = model.FC(
        bn_c4, 'fc_c4', 384 * 5 * 5, num_classes,
        weight_init=('GaussianFill', {'std': 0.01}),
        bias_init=('ConstantFill', {'value': 0.0}),
    )
    model.net.Alias(fc_conv4, 'pred_c4')
    if not cfg.MODEL.EXTRACT_FEATURES_ONLY:
        model.Accuracy([fc_conv4, labels], 'accuracy_c4')
        if split == 'train':
            softmax, loss_c4 = model.SoftmaxWithLoss(
                ['pred_c4', labels], ['softmax_c4', 'loss_c4'], scale=scale
            )
        elif split in ['test', 'val']:
            softmax = model.Softmax('pred_c4', 'softmax_c4', engine='CUDNN')
            loss_c4 = None
        losses.append(loss_c4)
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self_supervision_benchmark/modeling/jigsaw/alexnet_jigsaw_finetune_linear.py [249:276]:
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    )
    bn_c4 = model.SpatialBN(
        resize_c4, resize_c4 + '_bn', 384, epsilon=cfg.MODEL.BN_EPSILON,
        momentum=cfg.MODEL.BN_MOMENTUM, is_test=test_mode
    )
    if cfg.MODEL.BN_NO_SCALE_SHIFT:
        model.param_init_net.ConstantFill(
            [bn_c4 + '_s'], bn_c4 + '_s', value=1.0
        )
        model.param_init_net.ConstantFill(
            [bn_c4 + '_b'], bn_c4 + '_b', value=0.0
        )
    fc_conv4 = model.FC(
        bn_c4, 'fc_c4', 384 * 5 * 5, num_classes,
        weight_init=('GaussianFill', {'std': 0.01}),
        bias_init=('ConstantFill', {'value': 0.0}),
    )
    model.net.Alias(fc_conv4, 'pred_c4')
    if not cfg.MODEL.EXTRACT_FEATURES_ONLY:
        model.Accuracy([fc_conv4, labels], 'accuracy_c4')
        if split == 'train':
            softmax, loss_c4 = model.SoftmaxWithLoss(
                ['pred_c4', labels], ['softmax_c4', 'loss_c4'], scale=scale
            )
        elif split in ['test', 'val']:
            softmax = model.Softmax('pred_c4', 'softmax_c4', engine='CUDNN')
            loss_c4 = None
        losses.append(loss_c4)
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