jianghaojun / condensenetv2 Goto Github PK

Is it only include weights or it is full model and weights?

size mismatch for features.denseblock_1.denselayer_1.conv_1.conv.weight: copying a param with shape torch.Size([32, 2, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 16, 1, 1]).

您好，请问我能用作者训练好imagenet 模型进行迁移学习吗，我并没有发现该模型，因为我简单的把该结构拿出来进行图像检索，具体操作为去掉最后的分类层，添加l连接层(1024,128)发现结果并不是很好，大概是我做错了，而且我发现收敛的很慢，运行同样的epoch和损失函数，该网络65个epoch时只有50%,而imagenet预训练下的alexnet都有70%，实验设备有限，我希望能快速得到结果，希望作者能给合适imagenet模型，邮箱是[email protected],万分感谢。这是我修改的地方

class CondenseNetV2(nn.Module):
def init(self, args, bit):

    super(CondenseNetV2, self).__init__()

    self.stages = args.stages
    self.growth = args.growth
    assert len(self.stages) == len(self.growth)
    self.args = args
    self.progress = 0.0
    if args.dataset in ['cifar10', 'cifar100']:
        self.init_stride = 1
        self.pool_size = 8
    else:
        self.init_stride = 2
        self.pool_size = 7

    self.features = nn.Sequential()
    ### Initial nChannels should be 3
    self.num_features = 2 * self.growth[0]
    ### Dense-block 1 (224x224)
    self.features.add_module('init_conv', nn.Conv2d(3, self.num_features,
                                                    kernel_size=3,
                                                    stride=self.init_stride,
                                                    padding=1,
                                                    bias=False))
    for i in range(len(self.stages)):
        activation = 'HS' if i >= args.HS_start_block else 'ReLU'
        use_se = True if i >= args.SE_start_block else False
        ### Dense-block i
        self.add_block(i, activation, use_se)

#只修改了这里
#self.fc = nn.Linear(self.num_features, args.fc_channel)
#self.fc_act = HS()
self.hash = nn.Linear(self.num_features, bit)
self.fc_act = HS()
### Classifier layer
#self.classifier = nn.Linear(args.fc_channel, args.num_classes)
self._initialize()
#************************

def cdnv2_b(self,args, bit):#这里没改，就加个bit
args.stages = '2-4-6-8-6'
args.growth = '6-12-24-48-96'
print('Stages: {}, Growth: {}'.format(args.stages, args.growth))
args.stages = list(map(int, args.stages.split('-')))
args.growth = list(map(int, args.growth.split('-')))
args.condense_factor = 6
args.trans_factor = 6
args.group_1x1 = 6
args.group_3x3 = 6
args.group_trans = 6
args.bottleneck = 4
args.last_se_reduction = 16
args.HS_start_block = 2
args.SE_start_block = 3
args.fc_channel = 1024
return CondenseNetV2(args, bit)

#************************************************args参数只传了这些，其他都没改
import os
import argparse
import warnings
warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser(description='PyTorch Condensed Convolutional Networks')
args, unknown = parser.parse_known_args()
args.dataset = 'cifar100'
args.num_classes = 100
cdnv2_b(args, 64):

Hello, can you provide pre training weights for CIFAR-100, [email protected]

Awesome job!
Recently, I have read your paper and have a question about the way you prune the weight. In the paper, you write as following

M^g_{i,j} is set to zero for all j in g-th group for each pruned output feature map i.

Maybe it should be implemented as
self._mask[d, i:i+d_in, :, :].fill_(0)
rather than

Is there anything wrong of my understanding?

您好，我看源代码中参考Pytorch Image Models的代码，但是数据集训练的代码有所修改。请问训练时，关于imagenet数据集的文件结构以及目录是怎样的呢？标签文件怎么放置？

Sfr-shufflenetv2 is mentioned in the paper, but I can't find its code. Is Sfr-shufflenetv2 add or concat 1 * 1 convolution and the other half channel of the input?
And the paper mentioned that you shuffle the channel after using the SFR module in condensenetv2, but I didn't see the use of channel shuffle in the corresponding condensenetv2.py file, so channel shuffle is not necessary, right? thanks!!

hi，thanks for your best contributions!
When i converted the trained model on cifar10, i find the converted model get more larger,e.g. 10723->24418, how can i converted the model right, or the fact is that.

the train arguments on cifar10 is :
--model condensenetv2 -b 64 -j 12 --data cifar10 --stages 14-14-14 --growth 8-16-32

thanks!