h5py

问题 I'm working with huge sattelite data that i'm splitting into small tiles to feed a deep learning model. I'm using pytorch, which means the data loader can work with multiple thread. [settings : python, Ubuntu 18.04] I can't find any answer of which is the best in term of data accessing and storage between : registering all the data in one huge HDF5 file (over 20Go) splitting it into multiple (over 16 000) small HDF5 files (approx 1.4Mo). Is there any problem of multiple access of one file by

问题 I have installed raspbian os on raspberry pi 3 model b. I have to perform a project which involves use of h5py. The os already came preinstalled with python 2.7 and 3.5 With the help of pip, I installed h5py and it was successful, for python 3.5. ImportError: libhdf5_serial.so.100: cannot open shared object file: No such file or directory I don't know how to proceed with this error, can somebody please point out an appropriate way to handle this error? 回答1: I met the same question as yours,

问题 I have installed raspbian os on raspberry pi 3 model b. I have to perform a project which involves use of h5py. The os already came preinstalled with python 2.7 and 3.5 With the help of pip, I installed h5py and it was successful, for python 3.5. ImportError: libhdf5_serial.so.100: cannot open shared object file: No such file or directory I don't know how to proceed with this error, can somebody please point out an appropriate way to handle this error? 回答1: I met the same question as yours,

问题 I tried to extract data from tar.gz file which contains digitStruct.mat file. I used the following code snippet: train_dataset = h5py.File('./train/digitStruct.mat') I want to access the bbox and name details from this object itself. for eg: train_dataset[0] Should output something like: {'boxes': [{'height': 219.0, 'label': 1.0, 'left': 246.0, 'top': 77.0, 'width': 81.0}, {'height': 219.0, 'label': 9.0, 'left': 323.0, 'top': 81.0, 'width': 96.0}], 'filename': '1.png'} I searched for it and

问题 I'm tring to run mbin for methylation analysis. But get error message: h5py.h5py_warnings.H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead. for several attempts, while trying to extract control IPDs with buildcontrols. Environment: mbin version: 1.1.1 Python version:2.7.12 Operating System: centOS running under virtualenv I thought it was, again, caused by version. What I've tried: I tried on a server with both python 3 and 2. And specified virtualenv to use

问题 This is what my dataframe looks like. The first column is a single int. The second column is a single list of 512 ints. IndexID Ids 1899317 [0, 47715, 1757, 9, 38994, 230, 12, 241, 12228... 22861131 [0, 48156, 154, 6304, 43611, 11, 9496, 8982, 1... 2163410 [0, 26039, 41156, 227, 860, 3320, 6673, 260, 1... 15760716 [0, 40883, 4086, 11, 5, 18559, 1923, 1494, 4, ... 12244098 [0, 45651, 4128, 227, 5, 10397, 995, 731, 9, 3... I saved it to hdf and tried opening it using df.to_hdf('test.h5', key=

问题 This is what my dataframe looks like. The first column is a single int. The second column is a single list of 512 ints. IndexID Ids 1899317 [0, 47715, 1757, 9, 38994, 230, 12, 241, 12228... 22861131 [0, 48156, 154, 6304, 43611, 11, 9496, 8982, 1... 2163410 [0, 26039, 41156, 227, 860, 3320, 6673, 260, 1... 15760716 [0, 40883, 4086, 11, 5, 18559, 1923, 1494, 4, ... 12244098 [0, 45651, 4128, 227, 5, 10397, 995, 731, 9, 3... I saved it to hdf and tried opening it using df.to_hdf('test.h5', key=

问题 I have several big HDF5 file stored on an SSD (lzf compressed file size is 10–15 GB, uncompressed size would be 20–25 GB). Reading the contents from such a file into RAM for further processing takes roughly 2 minutes per file. During that time only one core is utilized (but to 100%). So I guess the decompression part running on CPU is the bottleneck and not the IO throughput of the SSD. At the start of my program it reads multiple files of that kind into RAM, which takes quite some time. I

问题 I have several big HDF5 file stored on an SSD (lzf compressed file size is 10–15 GB, uncompressed size would be 20–25 GB). Reading the contents from such a file into RAM for further processing takes roughly 2 minutes per file. During that time only one core is utilized (but to 100%). So I guess the decompression part running on CPU is the bottleneck and not the IO throughput of the SSD. At the start of my program it reads multiple files of that kind into RAM, which takes quite some time. I

问题 The below code is how I save the numpy array and it is about 27GB after saved. There are more than 200K images data and each shape is (224,224,3) hf = h5py.File('cropped data/features_train.h5', 'w') for i,each in enumerate(features_train): hf.create_dataset(str(i), data=each) hf.close() This is the method I used to load the data, and it takes hours for loading. features_train = np.zeros(shape=(1,224,224,3)) hf = h5py.File('cropped data/features_train.h5', 'r') for key in hf.keys(): x = hf