Training not converging on DenseNet-bottleneck

[lizhenstat]

Hi, I want to test your method on DenseNet with bottleneck structure on CIFAR100 (conv1x1 --> conv3x3)
I follow the code of densenet_svd.py and hinge_resnet_bottleneck.py,
mainly changing the following function

def compress_module_param(module, percentage, threshold):
    # Bias in None. So things becomes easier.
    # get the body
    '''
    # transition 
    (0): BatchNorm2d(168, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (1): ReLU()
    (2): Conv2d(168, 84, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (3): Conv2d(84, 84, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (4): AvgPool2d(kernel_size=2, stride=2, padding=0)
    '''
    if isinstance(module, Transition):
        body = module

        conv1 = body._modules['2']
        conv2 = body._modules['3']

        ws1 = conv1.weight.data.shape
        weight1 = conv1.weight.data.squeeze().t()

        ws2 = conv2.weight.data.shape
        weight2 = conv2.weight.data.squeeze().t()

        # calculate pindex
        _, pindex = get_nonzero_index(weight1, dim='output', counter=1, percentage=percentage, threshold=threshold)

        pl = pindex.shape[0]
        weight1 = torch.index_select(weight1, dim=1, index=pindex) 
        conv1.weight = nn.Parameter(weight1.t().view(pl, ws1[1], ws1[2], ws1[3]))
        conv1.out_channels = pl

        # compress conv2
        conv2.weight = nn.Parameter(torch.index_select(weight2, dim=0, index=pindex).t().view(ws2[0], pl, ws2[2], ws2[3]))
        conv2.in_channels = pl

    elif isinstance(module, BottleNeck):
        # with torchsnooper.snoop():
        body = module._modules['body']
        # conv1x1
        conv1 = body._modules['2']
        batchnorm1 = body._modules['3'] # conv1-output 对应的 batchnorm
        conv2 = body._modules['5']

        
        # get conv weights
        ws1 = conv1.weight.data.shape
        weight1 = conv1.weight.data.squeeze().t()
        
        bn_weight1 = batchnorm1.weight.data
        bn_bias1 = batchnorm1.bias.data
        bn_mean1 = batchnorm1.running_mean.data
        bn_var1 = batchnorm1.running_var.data
        
        ws2 = conv2.weight.data.shape
        weight2 = conv2.weight.data.view(ws2[0], ws2[1] * ws2[2] * ws2[3]).t()
        
        # selection compressed channels
        _, pindex1 = get_nonzero_index(weight1, dim='output', counter=1, percentage=percentage, threshold=threshold)
        pl1 = len(pindex1)
        conv1.weight = nn.Parameter(torch.index_select(weight1, dim=1, index=pindex1).t().view(pl1, -1, 1, 1))
        conv1.out_channels = pl1

        # batchnorm1
        batchnorm1.weight = nn.Parameter(torch.index_select(bn_weight1, dim=0, index=pindex1)) 
        batchnorm1.bias = nn.Parameter(torch.index_select(bn_bias1, dim=0, index=pindex1))
        batchnorm1.running_mean = torch.index_select(bn_mean1, dim=0, index=pindex1)
        batchnorm1.running_var = torch.index_select(bn_var1, dim=0, index=pindex1)
        batchnorm1.num_features = pl1
        
        # conv2
        index = torch.repeat_interleave(pindex1, ws2[2] * ws2[3]) * ws2[2] * ws2[3] + \
                torch.tensor(range(0, ws2[2] * ws2[3])).repeat(pindex1.shape[0]).cuda()
        weight2 = torch.index_select(weight2, dim=0, index=index)
        # weight2 = torch.index_select(weight2, dim=1, index=pindex3)
        conv2.weight = nn.Parameter(weight2.view(ws2[0], pl1, 3, 3))
        conv2.in_channels = pl1
        # exit(0)
    else:
        raise NotImplementedError('Do not need to compress the layer ' + module.__class__.__name__)

while testing the model using default parameters, the top-1 test error change as follows:

Did you test your model on DenseNet-bottleck during experiment?
I was wondering is there something wrong with my code, if not, why the testing loss behavior like this?

Thanks for your time and looking forward to your reply.