Prepare your dataset in ImageRecord format¶
Raw images are natural data format for computer vision tasks. However, when loading data from image files for training, disk IO might be a bottleneck.
For instance, when training a ResNet50 model with ImageNet on an AWS p3.16xlarge instance, The parallel training on 8 GPUs makes it so fast, with which even reading images from ramdisk can’t catch up.
To boost the performance on top-configured platform, we suggest users to train with MXNet’s ImageRecord format.
It is as simple as a few lines of code to create ImageRecord file for your own images.
Assuming we have a folder ./example, in which images are places in different subfolders representing classes:
./example/class_A/1.jpg ./example/class_A/2.jpg ./example/class_A/3.jpg ./example/class_B/4.jpg ./example/class_B/5.jpg ./example/class_B/6.jpg ./example/class_C/100.jpg ./example/class_C/1024.jpg ./example/class_D/65535.jpg ./example/class_D/0.jpg ...
First, we need to generate a .lst file, i.e. a list of these images containing label and filename information.
python im2rec.py ./example_rec ./example/ --recursive --list --num-thread 8
After the execution, you may find a file ./example_rec.lst generated. With this file, the next step is:
python im2rec.py ./example_rec ./example/ --recursive --pass-through --pack-label --num-thread 8
It gives you two more files: example_rec.idx and example_rec.rec. Now, you can use them to train!
For validation set, we usually don’t shuffle the order of images, thus the corresponding command would be
python im2rec.py ./example_rec_val ./example_val --recursive --list --num-thread 8 python im2rec.py ./example_rec_val ./example_val --recursive --pass-through --pack-label --no-shuffle --num-thread 8
ImageRecord file for ImageNet¶
As mentioned previously, ImageNet training can benefit from the improved IO speed with ImageRecord format.
We use the same script in our tutorial “Prepare the ImageNet dataset” , with different arguments. Please read through it and download the imagenet files in advance.
Assuming the tar files are saved in folder
~/ILSVRC2012. We can use the
following command to prepare the dataset automatically.
python imagenet.py --download-dir ~/ILSVRC2012 --with-rec
Extracting the images may take a while. For example, it takes about 30min on an AWS EC2 instance with EBS.
imagenet.py will extract the images into
can specify a different target folder by setting
Read with ImageRecordIter¶
The prepared dataset can be loaded with utility class
directly. Here is an example that randomly reads 128 images each time and
performs randomized resizing and cropping.
import os from mxnet import nd from mxnet.io import ImageRecordIter rec_path = os.path.expanduser('~/.mxnet/datasets/imagenet/rec/') # You need to specify ``root`` for ImageNet if you extracted the images into # a different folder train_data = ImageRecordIter( path_imgrec = os.path.join(rec_path, 'train.rec'), path_imgidx = os.path.join(rec_path, 'train.idx'), data_shape = (3, 224, 224), batch_size = 32, shuffle = True )
for batch in train_data: print(batch.data.shape, batch.label.shape) break
Plot some validation images
from gluoncv.utils import viz val_data = ImageRecordIter( path_imgrec = os.path.join(rec_path, 'val.rec'), path_imgidx = os.path.join(rec_path, 'val.idx'), data_shape = (3, 224, 224), batch_size = 32, shuffle = False ) for batch in val_data: viz.plot_image(nd.transpose(batch.data, (1, 2, 0))) viz.plot_image(nd.transpose(batch.data, (1, 2, 0))) break
Total running time of the script: ( 0 minutes 0.000 seconds)