"""5. Train Your Own Model on ImageNet ========================================== ``ImageNet`` is the most well-known dataset for image classification. Since it was published, most of the research that advances the state-of-the-art of image classification was based on this dataset. Although there are a lot of available models, it is still a non-trivial task to train a state-of-the-art model on ``ImageNet`` from scratch. In this tutorial, we will smoothly walk you through the process of training a model on ``ImageNet``. .. note:: The rest of the tutorial walks you through the details of ``ImageNet`` training. If you want a quick start without knowing the details, try downloading this script and start training with just one command. :download:`Download train_imagenet.py<../../../scripts/classification/imagenet/train_imagenet.py>` The commands used to reproduce results from papers are given in our `Model Zoo <../../model_zoo/index.html>`__. .. note:: Since real training is extremely resource consuming, we don't actually execute code blocks in this tutorial. Prerequisites ------------- **Expertise** We assume readers have a basic understanding of ``Gluon``, we suggest you start with `Gluon Crash Course `__ . Also, we assume that readers have gone through previous tutorials on `CIFAR10 Training `_ and `ImageNet Demo `_ . **Data Preparation** Unlike ``CIFAR10``, we need to prepare the data manually. If you haven't done so, please go through our tutorial on `Prepare ImageNet Data <../examples_datasets/imagenet.html>`_ . **Hardware** Training deep learning models on a dataset of over one million images is very resource demanding. Two main bottlenecks are tensor computation and data IO. For tensor computation, it is recommended to use a GPU, preferably a high-end one. Using multiple GPUs together will further reduce training time. For data IO, we recommend a fast CPU and a SSD disk. Data loading can greatly benefit from multiple CPU threads, and a fast SSD disk. Note that in total the compressed and extracted ``ImageNet`` data could occupy around 300GB disk space, thus a SSD with at least 300GB is required. Network structure ----------------- Finished preparation? Let's get started! First, import the necessary libraries into python. .. code-block:: python import argparse, time import numpy as np import mxnet as mx from mxnet import gluon, nd from mxnet import autograd as ag from mxnet.gluon import nn from mxnet.gluon.data.vision import transforms from gluoncv.model_zoo import get_model from gluoncv.utils import makedirs, TrainingHistory In this tutorial we use ``ResNet50_v2``, a network with balanced prediction accuracy and computational cost. .. code-block:: python # number of GPUs to use num_gpus = 4 ctx = [mx.gpu(i) for i in range(num_gpus)] # Get the model ResNet50_v2, with 10 output classes net = get_model('ResNet50_v2', classes=1000) net.initialize(mx.init.Xavier(magnitude=2), ctx = ctx) Note that the ResNet model we use here for ``ImageNet`` is different in structure from the one we used to train ``CIFAR10``. Please refer to the original paper or GluonCV codebase for details. Data Augmentation and Data Loader --------------------------------- Data augmentation is essential for a good result. It is similar to what we have in the ``CIFAR10`` training tutorial, just different in the parameters. We compose our transform functions as following: .. code-block:: python jitter_param = 0.4 lighting_param = 0.1 transform_train = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomFlipLeftRight(), transforms.RandomColorJitter(brightness=jitter_param, contrast=jitter_param, saturation=jitter_param), transforms.RandomLighting(lighting_param), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) Since ``ImageNet`` images have much higher resolution and quality than ``CIFAR10``, we can crop a larger image (224x224) as input to the model. For prediction, we still need deterministic results. The transform function is: .. code-block:: python transform_test = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) Notice that it is important to keep the normalization consistent, since trained model only works well on test data from the same distribution. With the transform functions, we can define data loaders for our training and validation datasets. .. code-block:: python # Batch Size for Each GPU per_device_batch_size = 64 # Number of data loader workers num_workers = 32 # Calculate effective total batch size batch_size = per_device_batch_size * num_gpus data_path = '~/.mxnet/datasets/imagenet' # Set train=True for training data # Set shuffle=True to shuffle the training data train_data = gluon.data.DataLoader( imagenet.classification.ImageNet(data_path, train=True).transform_first(transform_train), batch_size=batch_size, shuffle=True, last_batch='discard', num_workers=num_workers) # Set train=False for validation data val_data = gluon.data.DataLoader( imagenet.classification.ImageNet(data_path, train=False).transform_first(transform_test), batch_size=batch_size, shuffle=False, num_workers=num_workers) Note that we set ``per_device_batch_size=64``, which may not suit GPUs with Memory smaller than 12GB. Please tune the value according to your specific configuration. Path ``'~/.mxnet/datasets/imagenet'`` is the default path if you prepared the data `with our script <../examples_datasets/imagenet.html>`_. Optimizer, Loss and Metric -------------------------- Optimizer is what improves the model during training. We use the popular Nesterov accelerated gradient descent algorithm. .. code-block:: python # Learning rate decay factor lr_decay = 0.1 # Epochs where learning rate decays lr_decay_epoch = [30, 60, 90, np.inf] # Nesterov accelerated gradient descent optimizer = 'nag' # Set parameters optimizer_params = {'learning_rate': 0.1, 'wd': 0.0001, 'momentum': 0.9} # Define our trainer for net trainer = gluon.Trainer(net.collect_params(), optimizer, optimizer_params) For classification tasks, we usually use softmax cross entropy as the loss function. .. code-block:: python loss_fn = gluon.loss.SoftmaxCrossEntropyLoss() With 1000 classes the model may not always rate the correct answer with the highest rank. Besides top-1 accuracy, we also consider top-5 accuracy as a measurement of how well the model is doing. At the end of every epoch, we record and print the metric scores. .. code-block:: python acc_top1 = mx.metric.Accuracy() acc_top5 = mx.metric.TopKAccuracy(5) train_history = TrainingHistory(['training-top1-err', 'training-top5-err', 'validation-top1-err', 'validation-top5-err']) Validation ---------- At the end of every training epoch, we evaluate it on the validation data set, and report the top-1 and top-5 error rate. .. code-block:: python def test(ctx, val_data): acc_top1_val = mx.metric.Accuracy() acc_top5_val = mx.metric.TopKAccuracy(5) for i, batch in enumerate(val_data): data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0) label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0) outputs = [net(X) for X in data] acc_top1_val.update(label, outputs) acc_top5_val.update(label, outputs) _, top1 = acc_top1_val.get() _, top5 = acc_top5_val.get() return (1 - top1, 1 - top5) Training -------- After all these preparation, we can finally start training! Following is the main training loop: .. code-block:: python epochs = 120 lr_decay_count = 0 log_interval = 50 num_batch = len(train_data) for epoch in range(epochs): tic = time.time() btic = time.time() acc_top1.reset() acc_top5.reset() train_loss = 0 if lr_decay_period == 0 and epoch == lr_decay_epoch[lr_decay_count]: trainer.set_learning_rate(trainer.learning_rate*lr_decay) lr_decay_count += 1 for i, batch in enumerate(train_data): data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0) label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0) with ag.record(): outputs = [net(X) for X in data] loss = [L(yhat, y) for yhat, y in zip(outputs, label)] for l in loss: l.backward() trainer.step(batch_size) acc_top1.update(label, outputs) acc_top5.update(label, outputs) train_loss += sum([l.sum().asscalar() for l in loss]) if log_interval and not (i + 1) % log_interval: _, top1 = acc_top1.get() _, top5 = acc_top5.get() err_top1, err_top5 = (1-top1, 1-top5) print('Epoch[%d] Batch [%d]\tSpeed: %f samples/sec\ttop1-err=%f\ttop5-err=%f'%( epoch, i, batch_size*opt.log_interval/(time.time()-btic), err_top1, err_top5)) btic = time.time() _, top1 = acc_top1.get() _, top5 = acc_top5.get() err_top1, err_top5 = (1-top1, 1-top5) train_loss /= num_batch * batch_size err_top1_val, err_top5_val = test(ctx, val_data) train_history.update([err_top1, err_top5, err_top1_val, err_top5_val]) print('[Epoch %d] training: err-top1=%f err-top5=%f loss=%f'%(epoch, err_top1, err_top5, train_loss)) print('[Epoch %d] time cost: %f'%(epoch, time.time()-tic)) print('[Epoch %d] validation: err-top1=%f err-top5=%f'%(epoch, err_top1_val, err_top5_val)) We can plot the top-1 error rates with: .. code-block:: python train_history.plot(['training-top1-err', 'validation-top1-err']) If you train the model with ``epochs=120``, the plot may look like: |image-imagenet-curve| Next Step --------- `Model Zoo <../../model_zoo/index.html>`_ provides scripts and commands for training models on ``ImageNet``. If you want to know what you can do with the model you just trained, please read the tutorial on `Transfer learning `__. Besides classification, deep learning models nowadays can do other exciting tasks like `object detection <../examples_detection/index.html>`_ and `semantic segmentation <../examples_segmentation/index.html>`_. .. |image-imagenet-curve| image:: https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/classification/resnet50_v2_top1.png """