Decompressed Data Too Large

[ai-agi]

Hi Tianhong. There are always errors when training main_rdm.py regardless of the value setting of batch size, e.g. , from 32 to 512.
What' s the problem and how to solve it?
the following is collapse information:
File "main_rdm.py", line 186, in main
train_stats = train_one_epoch(
File "/home/fengjiw/project/rcg/engine_rdm.py", line 29, in train_one_epoch
for data_iter_step, (samples, class_label) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
File "/home/fengjiw/project/rcg/util/misc.py", line 134, in log_every
for obj in iterable:
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 530, in next
data = self._next_data()
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1224, in _next_data
return self._process_data(data)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1250, in _process_data
data.reraise()
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/_utils.py", line 457, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torchvision/datasets/folder.py", line 230, in getitem
sample = self.loader(path)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torchvision/datasets/folder.py", line 269, in default_loader
return pil_loader(path)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/torchvision/datasets/folder.py", line 248, in pil_loader
img = Image.open(f)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/Image.py", line 3172, in open
im = _open_core(fp, filename, prefix, formats)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/Image.py", line 3158, in _open_core
im = factory(fp, filename)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/ImageFile.py", line 116, in init
self._open()
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/PngImagePlugin.py", line 734, in open
s = self.png.call(cid, pos, length)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/PngImagePlugin.py", line 202, in call
return getattr(self, "chunk" + cid.decode("ascii"))(pos, length)
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/PngImagePlugin.py", line 412, in chunk_iCCP
icc_profile = _safe_zlib_decompress(s[i + 2 :])
File "/home/fengjiw/miniconda3/envs/rcg/lib/python3.8/site-packages/PIL/PngImagePlugin.py", line 148, in _safe_zlib_decompress
raise ValueError("Decompressed Data Too Large")

[LTH14]

Do you use customized data? The code should work fine with the ImageNet dataset. Maybe try to make each of your own data smaller?

[gzhuinjune]

我也遇到了类似的问题。我用的是自定义的数据集，但是还是把它放在imagenet的train下面，请问可以帮我看看吗：

Traceback (most recent call last):
File "main_mage.py", line 296, in <module>
main(args)
File "main_mage.py", line 269, in main
gen_img(model, args, epoch, batch_size=16, log_writer=log_writer, cfg=0)
File "/home/user/sdb2/rcg-main/engine_mage.py", line 124, in gen_img
metrics_dict = torch_fidelity.calculate_metrics(
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/metrics.py", line 341, in calculate_metrics
return calculate_metrics_one_feature_extractor(**kwargs)
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/metrics.py", line 80, in calculate_metrics_one_feature_extractor
featuresdict_2 = extract_featuresdict_from_input_id_cached(2, feat_extractor, **kwargs)
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/utils.py", line 424, in extract_featuresdict_from_input_id_cached
featuresdict = fn_recompute()
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/utils.py", line 412, in fn_recompute
return extract_featuresdict_from_input_id(input_id, feat_extractor, **kwargs)
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/utils.py", line 394, in extract_featuresdict_from_input_id
input = prepare_input_from_id(input_id, **kwargs)
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/utils.py", line 317, in prepare_input_from_id
return prepare_input_from_descriptor(input_desc, **kwargs)In June:
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/utils.py", line 317, in prepare_input_from_id
return prepare_input_from_descriptor(input_desc, **kwargs)
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/utils.py", line 293, in prepare_input_from_descriptor
vassert(
File "/home/user/sdb2/rcg-main/src/torch-fidelity/torch_fidelity/helpers.py", line 13, in vassert
raise ValueError(message)
ValueError: Input descriptor "input" field can be either an instance of Dataset, GenerativeModelBase class, or a string, such as a path to a name of a registered dataset (cifar10-train, cifar10-val, cifar100-train, cifar100-val, stl10-train, stl10-test, stl10-unlabeled), a directory with file samples, or a path to an ONNX or PTH (JIT) module
[2024-04-05 01:20:50,214] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 2369647) of binary: /home/user/anaconda3/envs/rcg/bin/python
Traceback (most recent call last):
File "/home/user/anaconda3/envs/rcg/bin/torchrun", line 8, in <module>
sys.exit(main())
File "/home/user/anaconda3/envs/rcg/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/init.py", line 347, in wrapper
return f(*args, **kwargs)
File "/home/user/anaconda3/envs/rcg/lib/python3.8/site-packages/torch/distributed/run.py", line 812, in main
run(args)
File "/home/user/anaconda3/envs/rcg/lib/python3.8/site-packages/torch/distributed/run.py", line 803, in run
elastic_launch(
File "/home/user/anaconda3/envs/rcg/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 135, in call

In June:
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/user/anaconda3/envs/rcg/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 268, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

main_mage.py FAILED

Failures:
<NO_OTHER_FAILURES>

Root Cause (first observed failure):
[0]:
time : 2024-04-05_01:20:50
host : SYS-740GP-TNRT
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 2369647)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html

[LTH14]

您好，您的这个问题是由于torch-fidelity可能处理不了自定义数据，可能在这里要将torch_fidelity.calculate_metrics换成你自己数据上的metric

[gzhuinjune]

谢谢大哥，是不是改这个路径就行。这个路径就是我的val的路径，里面有一个叫做building的分类的文件夹（我的任务只有一个类别），文件夹里面有我的图片

[LTH14]

那你把这个input2设置成/home/user/sdb2/rcg-main/data/imagenet/val/building就行，他这里的input2需要是一个里面全是图片的文件夹。另一个需要注意的是，如果要evaluate你自己数据集上的FID，请不要使用修改过的torch fidelity，而是直接安装原版pip install torch-fidelity

[gzhuinjune]

谢谢大哥，祝你科研顺利

[gzhuinjune]

那你把这个input2设置成/home/user/sdb2/rcg-main/data/imagenet/val/building就行，他这里的input2需要是一个里面全是图片的文件夹。另一个需要注意的是，如果要evaluate你自己数据集上的FID，请不要使用修改过的torch fidelity，而是直接安装原版pip install torch-fidelity
我现在用了自己的数据集，那么我的engine_mage里面的class_num=1000也做对应的修改对嘛，请问我还要改别的哪里嘛
。“一个里面全是图片的文件夹”指的是我自己所有类别图片混在一起的文件夹，还是我其中一个类别的图片的文件夹呢，谢谢！

[LTH14]

那你把这个input2设置成/home/user/sdb2/rcg-main/data/imagenet/val/building就行，他这里的input2需要是一个里面全是图片的文件夹。另一个需要注意的是，如果要evaluate你自己数据集上的FID，请不要使用修改过的torch fidelity，而是直接安装原版pip install torch-fidelity
我现在用了自己的数据集，那么我的engine_mage里面的class_num=1000也做对应的修改对嘛，请问我还要改别的哪里嘛
。“一个里面全是图片的文件夹”指的是我自己所有类别图片混在一起的文件夹，还是我其中一个类别的图片的文件夹呢，谢谢！

所有图片混在一起的