Open ShuhanYe opened 1 month ago
作者您好!我在复现论文时遇到了一些问题: 1、我安装tonic库,但tonic.dataset.ncaltech101似乎不支持本项目,比如它并没有cls_count等一系列参数,我在Brain-cog-x中找到了以下似乎可以使用的代码,不知道是不是因为tonic版本的问题?
`# encoding: utf-8
Author : FloyedFloyed_Shen@outlook.com
Datetime : 2023/1/30 21:28
User : yu
Product : PyCharm
Project : BrainCog
File : ncaltech101.py
explain :
import os import numpy as np
from tonic.io import read_mnist_file from tonic.dataset import Dataset from tonic.download_utils import extract_archive
class NCALTECH101(Dataset): """N-CALTECH101 dataset https://www.garrickorchard.com/datasets/n-caltech101. Events have (xytp) ordering. ::
@article{orchard2015converting, title={Converting static image datasets to spiking neuromorphic datasets using saccades}, author={Orchard, Garrick and Jayawant, Ajinkya and Cohen, Gregory K and Thakor, Nitish}, journal={Frontiers in neuroscience}, volume={9}, pages={437}, year={2015}, publisher={Frontiers} } Parameters: save_to (string): Location to save files to on disk. transform (callable, optional): A callable of transforms to apply to the data. target_transform (callable, optional): A callable of transforms to apply to the targets/labels. """ url = "https://data.mendeley.com/public-files/datasets/cy6cvx3ryv/files/36b5c52a-b49d-4853-addb-a836a8883e49/file_downloaded" filename = "N-Caltech101-archive.zip" file_md5 = "66201824eabb0239c7ab992480b50ba3" data_filename = "N-Caltech101-archive.zip" folder_name = "Caltech101" cls_count = [467, 435, 200, 798, 55, 800, 42, 42, 47, 54, 46, 33, 128, 98, 43, 85, 91, 50, 43, 123, 47, 59, 62, 107, 47, 69, 73, 70, 50, 51, 57, 67, 52, 65, 68, 75, 64, 53, 64, 85, 67, 67, 45, 34, 34, 51, 99, 100, 42, 54, 88, 80, 31, 64, 86, 114, 61, 81, 78, 41, 66, 43, 40, 87, 32, 76, 55, 35, 39, 47, 38, 45, 53, 34, 57, 82, 59, 49, 40, 63, 39, 84, 57, 35, 64, 45, 86, 59, 64, 35, 85, 49, 86, 75, 239, 37, 59, 34, 56, 39, 60] # length = 8242 length = 8709 sensor_size = None # all recordings are of different size dtype = np.dtype([("x", int), ("y", int), ("t", int), ("p", int)]) ordering = dtype.names def __init__(self, save_to, transform=None, target_transform=None): super(NCALTECH101, self).__init__( save_to, transform=transform, target_transform=target_transform ) classes = { 'BACKGROUND_Google': 0, 'Faces_easy': 1, 'Leopards': 2, 'Motorbikes': 3, 'accordion': 4, 'airplanes': 5, 'anchor': 6, 'ant': 7, 'barrel': 8, 'bass': 9, 'beaver': 10, 'binocular': 11, 'bonsai': 12, 'brain': 13, 'brontosaurus': 14, 'buddha': 15, 'butterfly': 16, 'camera': 17, 'cannon': 18, 'car_side': 19, 'ceiling_fan': 20, 'cellphone': 21, 'chair': 22, 'chandelier': 23, 'cougar_body': 24, 'cougar_face': 25, 'crab': 26, 'crayfish': 27, 'crocodile': 28, 'crocodile_head': 29, 'cup': 30, 'dalmatian': 31, 'dollar_bill': 32, 'dolphin': 33, 'dragonfly': 34, 'electric_guitar': 35, 'elephant': 36, 'emu': 37, 'euphonium': 38, 'ewer': 39, 'ferry': 40, 'flamingo': 41, 'flamingo_head': 42, 'garfield': 43, 'gerenuk': 44, 'gramophone': 45, 'grand_piano': 46, 'hawksbill': 47, 'headphone': 48, 'hedgehog': 49, 'helicopter': 50, 'ibis': 51, 'inline_skate': 52, 'joshua_tree': 53, 'kangaroo': 54, 'ketch': 55, 'lamp': 56, 'laptop': 57, 'llama': 58, 'lobster': 59, 'lotus': 60, 'mandolin': 61, 'mayfly': 62, 'menorah': 63, 'metronome': 64, 'minaret': 65, 'nautilus': 66, 'octopus': 67, 'okapi': 68, 'pagoda': 69, 'panda': 70, 'pigeon': 71, 'pizza': 72, 'platypus': 73, 'pyramid': 74, 'revolver': 75, 'rhino': 76, 'rooster': 77, 'saxophone': 78, 'schooner': 79, 'scissors': 80, 'scorpion': 81, 'sea_horse': 82, 'snoopy': 83, 'soccer_ball': 84, 'stapler': 85, 'starfish': 86, 'stegosaurus': 87, 'stop_sign': 88, 'strawberry': 89, 'sunflower': 90, 'tick': 91, 'trilobite': 92, 'umbrella': 93, 'watch': 94, 'water_lilly': 95, 'wheelchair': 96, 'wild_cat': 97, 'windsor_chair': 98, 'wrench': 99, 'yin_yang': 100, } # if not self._check_exists(): # self.download() # extract_archive(os.path.join(self.location_on_system, self.data_filename)) file_path = os.path.join(self.location_on_system, self.folder_name) for path, dirs, files in os.walk(file_path): dirs.sort() # if 'BACKGROUND_Google' in path: # continue for file in files: if file.endswith("bin"): self.data.append(path + "/" + file) label_name = os.path.basename(path) if isinstance(label_name, bytes): label_name = label_name.decode() self.targets.append(classes[label_name]) def __getitem__(self, index): """ Returns: a tuple of (events, target) where target is the index of the target class. """ events = read_mnist_file(self.data[index], dtype=self.dtype) target = self.targets[index] events["x"] -= events["x"].min() events["y"] -= events["y"].min() if self.transform is not None: events = self.transform(events) if self.target_transform is not None: target = self.target_transform(target) return events, target def __len__(self): return len(self.data) def _check_exists(self): return self._is_file_present() and self._folder_contains_at_least_n_files_of_type( 8709, ".bin" )
`
2、在使用上述的ncaltech101类替代tonic.datasets.ncaltehc101后,我还碰到了该问题,DVS/NCALTECH101/train_cache路径能生成但是似乎无法缓存进去 Data 3407: D:/datasets\DVS/NCALTECH101/train_cache_10\3407_2.hdf5 not in cache, generating it now Traceback (most recent call last): File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\tonic\cached_dataset.py", line 145, in getitem data, targets = load_from_disk_cache(file_path) File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\tonic\cached_dataset.py", line 220, in load_from_disk_cache with h5py.File(file_path, "r") as f: File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\h5py_hl\files.py", line 533, in init fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr) File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\h5py_hl\files.py", line 226, in make_fid fid = h5f.open(name, flags, fapl=fapl) File "h5py_objects.pyx", line 54, in h5py._objects.with_phil.wrapper File "h5py_objects.pyx", line 55, in h5py._objects.with_phil.wrapper File "h5py\h5f.pyx", line 106, in h5py.h5f.open FileNotFoundError: [Errno 2] Unable to open file (unable to open file: name = 'D:/datasets\DVS/NCALTECH101/train_cache_10\3407_2.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
3、能否上传下CEPDVS数据集
感谢您的工作,期待您的回复
您好,感谢你对我们工作的关注,以下是一些回复:
以上预计在2小时左右完成,谢谢
非常感谢!祝您工作顺利
非常感谢!祝您工作顺利
您好,抱歉回复的晚了一些。
非常感谢!祝您工作顺利
您好,抱歉回复的晚了一些。
- CEP-DVS数据集(带生成的缓存文件),可以在这里找到:链接:https://pan.baidu.com/s/1_fUF4XvNeQuP1TzkQmhF3w 提取码:r465
- NCALTECH101数据集(带生成的缓存文件),可以在这里找到:链接:https://pan.baidu.com/s/189IlBE17CwtQmkZ4_qoqTg 提取码:q257
- tonic-0.0.1版本(之前回复的0.1.0版本有误,应该是0.0.1),可以在这里找到:链接:https://pan.baidu.com/s/1LCimoFgbfAweYu-uJ-WyUA 提取码:3x4r
感谢您的回复!我已经成功跑通了代码,但是精度差异较大在NCALTECH101运用DVS迁移学习只有不到85的top1,我注意到有两个有些疑惑的地方:
1、我在调试代码时报错了source_input_list下标越阶的错误,原因是NCALTECH101删除了faces类而保留了BACKGROUND_GOOGLE类有8709张图片,tmp_sampler_list可能会生成0-8709的随机数,而在braincog.dataset.dataset中的caltech101相关操作是移除BACKGROUND_GOOGLE类与Ncaltech101似乎不一样,以至于最后整个caltech101只有8677张图片造成下标越界,我取消类移除BACKGROUND_GOOGLE的操作,并且将build_dataset中的对应num_classes改成102,成功跑通了代码,但我不知道您在工作中是怎么处理这部分的,也许是此部分影响了最终复现精度。
2、我注意到论文中您是将RGB图像转换到HSV空间,进行类匹配并使RGB数据可以进VGG_SNN的backbone,但看代码中注释了复制V通道与步长,而是直接取了RGB的B通道进行通道复制和步长复制输入VGG_SNN的backbone。我想请教下您在工作中使用的是哪一种方法,如果取Value通道似乎与我们为了利用RGB数据的颜色等信息指导DVS数据的初衷有所偏差,跑出来的结果也较差,但是如果是取用蓝色通道并复制,是出于什么样的动机呢?
我设置的参数如下,仅有batchsize和workers由于显存大小设置不同: model:Transfer_VGG_SNN step: 10 batch_size: 16 act_fun: QGateGrad # activation function device: 0 seed: 42 num_classes: 101 traindata_ratio: 1.0 domain_loss: True # store true domain_loss_coefficient: 0.5 TET_loss: True smoothing: 0.0 no_use_hsv: True regularization: True workers: 0
期待得到您的回复!
在代码实现中,需要对Caltech101修改两处,以使得类别数和少一类的NCALTECH101对齐。
A)手动的对Caltech101文件进行修改(即braincog.dataset.datasets,build_dataset
中用到的datasets.Caltech101
),,该文件的位置处于/usr/local/anaconda3/envs/all_hx/lib/python3.7/site-packages/torchvision/datasets/caltech.py
, 注释掉remove背景类这一行
B)对caltech101数据集,手动删除"Faces'这一类(一个文件夹)
上述两处修改后的文件可以在这里查看:链接:https://pan.baidu.com/s/1n3eAw0uLbBpYA1jnoVO2QA 提取码:38hh
确实忘记在仓库中说这一改动了,但是他很重要,谢谢你的提醒,后续加到readme.md的说明中
DVS数据是由事件摄像机在设定阈值的情况下,对物体亮度变化超过阈值做出反应的数据,因此他只有1和-1两个通道,每个通道每个像素点的值都是0、1,同时带有时间戳。而常见的RGB数据是3通道的,我们文章的初衷是用RGB指导DVS,更好地让网络模型进行学习。而RGB颜色空间不能显式地反映数据的强度,因此我们将其转换到HSV空间,并只取value维度,复制两份儿以和DVS数据对齐输入。我们没有直接取RGB的B通道,尽管代码中注释了复制V通道与步长,但转换到HSV空间的操作是在读取rgb数据集中完成的,即braincog.datasets.datasets
中的get_transfer_CALTECH101_data,use_hsv参数,如果use_hsv=True,那么他会在build_dataset函数中执行:
if use_hsv:
print("Used V-channel!")
t.append(ConvertHSV())
这一步完成了RGB到HSV颜色空间的转换。关于hsv颜色空间对于指导dvs数据集的优势,相关的实验可以在arxiv版本中的附录中找到相关实验结果。
您的配置没有问题,workers设为>0的数可以保证数据读取和主进程分开,如果硬件允许他会获得更好的性能;batch_size对实验结果的影响不大
作者您好!我在复现论文时遇到了一些问题: 1、我安装tonic库,但tonic.dataset.ncaltech101似乎不支持本项目,比如它并没有cls_count等一系列参数,我在Brain-cog-x中找到了以下似乎可以使用的代码,不知道是不是因为tonic版本的问题?
`# encoding: utf-8
Author : FloyedFloyed_Shen@outlook.com
Datetime : 2023/1/30 21:28
User : yu
Product : PyCharm
Project : BrainCog
File : ncaltech101.py
explain :
import os import numpy as np
from tonic.io import read_mnist_file from tonic.dataset import Dataset from tonic.download_utils import extract_archive
class NCALTECH101(Dataset): """N-CALTECH101 dataset https://www.garrickorchard.com/datasets/n-caltech101. Events have (xytp) ordering. ::
`
2、在使用上述的ncaltech101类替代tonic.datasets.ncaltehc101后,我还碰到了该问题,DVS/NCALTECH101/train_cache路径能生成但是似乎无法缓存进去 Data 3407: D:/datasets\DVS/NCALTECH101/train_cache_10\3407_2.hdf5 not in cache, generating it now Traceback (most recent call last): File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\tonic\cached_dataset.py", line 145, in getitem data, targets = load_from_disk_cache(file_path) File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\tonic\cached_dataset.py", line 220, in load_from_disk_cache with h5py.File(file_path, "r") as f: File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\h5py_hl\files.py", line 533, in init fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr) File "C:\Users\86158\anaconda3\envs\pytorch-gpu\lib\site-packages\h5py_hl\files.py", line 226, in make_fid fid = h5f.open(name, flags, fapl=fapl) File "h5py_objects.pyx", line 54, in h5py._objects.with_phil.wrapper File "h5py_objects.pyx", line 55, in h5py._objects.with_phil.wrapper File "h5py\h5f.pyx", line 106, in h5py.h5f.open FileNotFoundError: [Errno 2] Unable to open file (unable to open file: name = 'D:/datasets\DVS/NCALTECH101/train_cache_10\3407_2.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
3、能否上传下CEPDVS数据集
感谢您的工作,期待您的回复