Alternate optimizers.
Note that we have made almost no attempt to optimise the 96% time and we would expect it to come down considerably from here. We use the original learning rate schedule rescaled by a range of factors between 1/8 and 16. Perhaps we should try something more radical and move max-pooling before batch norm. FFT based convolutions via CuDNN-4 : Using the CuDNN Torch bindings, one can select the fastest convolution kernels by setting cudnn.fastest and cudnn.benchmark to true. Using 4 NVIDIA Kepler GPUs and optimizations described below, training took from 3.5 days for the 18-layer model to 14 days for the 101-layer model. through further algorithmic developments.
So how can it be that we are simultaneously at the speed limit of training and able to increase batch size without sustaining instability from curvature effects? Multi-threaded kernel launching : The FFT-based convolutions require multiple smaller kernels that are launched rapidly in succession. It is straightforward to implement proper mixed precision training but this adds about a second to overall training time and we found it to have no effect on final accuracy, so we continue to do without it below. These experiments help verify the model’s correctness and uncover some interesting directions for future work. This is less than the time taken to transfer the dataset once to the CPU! The idea of recomputing losses in this way is to compare the loss of the model on batches seen most recently versus ones seen longer ago to test the model’s memory. In other cases, inference time is also a constraint and a sensible approach is to maximise accuracy subject to such constraints. More extensive forms of TTA are of course possible for other symmetries (such as translational symmetry, variations in brightness/colour etc.) We will follow this with a learnable 1×1 convolution. Code for training ResNets on ImageNet is at https://github.com/facebook/fb.resnet.torch. Training to 94% test accuracy took 341s and with some minor adjustments to network and data loading we had reduced this to 297s. This implies that training is not able to extract information from the full dataset and that 50% of the unaugmented dataset already contains (almost) as much information as the model can learn in this regime. How to Train Your ResNet 3: Regularisation. This suggests that the learnable biases are indeed doing something useful – either learning appropriate levels of sparsity or perhaps just adding regularisation noise. At the very largest batch sizes, curvature effects dominate once again and for this reason there is substantial overlap between the techniques used in large batch training of ImageNet and fast single GPU training of CIFAR10. We also used the in-place variants of the ReLU and CAddTable modules.
Our training thus far uses a batch size of 128. At the end of last year, Microsoft Research Asia released a paper titled “Deep Residual Learning for Image Recognition”, authored by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. We are releasing the ResNet-18, 34, 50 and 101 models for use by everyone in the community. Batch norm does a good job at controlling distributions of individual channels but doesn’t tackle covariance between channels and pixels. Many of them appear to converge faster initially (see the training curve below), but ultimately, SGD+momentum has 0.7% lower test error than the second-best strategy.
If it is the case that the same techniques which speed up training time to 94% accuracy on CIFAR10 also improve converged accuracy on ImageNet, then this suggests a rather effective way to accelerate research on the latter problem! This can be problematic: there are paths that allow data to pass through several successive batch normalization layers without any other processing. Data parallelism over 4 GPUs : This is a standard way of speeding up training deep learning models. The main goal of today’s post is to provide a well-tuned baseline on which to test novel techniques, allowing one to complete a statistically significant number of training runs within minutes on a single GPU. So without further ado, let’s train with batch size 512. I am using the randomPatchExtractionDatastore to feed the network with training data. Actually we can fix the batch norm scales to 1 instead if we rescale the $\alpha$ parameter of CELU by a compensating factor of 4 and the learning rate and weight decay for the batch norm biases by $4^2$ and $1/4^2$ respectively.
Kaiming He for discussing ambiguous and missing details in the original paper and helping us reproduce the results. We will argue that this something else is the alarmingly named Catastrophic Forgetting and that this, rather than curvature of the loss, is what limits learning rates at small batch sizes. In some applications, classification accuracy is all that is desired and in that case TTA should most definitely be used. Deep Residual Learning for Image Recognition, For a more in-depth report of the ablation studies, read here, includes instructions for fine-tuning on your own datasets. [1] He, Kaiming, et al. I hope that the reader will find this useful in their work and believe that training times have a long way to fall yet (or accuracies improve if that’s your thing!) What’s notable is that we achieved error rates that were better than the published results by using a different data augmentation method. We compare training runs on two different datasets: a) 50% of the full training set with no data augmentation and b) the full dataset with our standard augmentation.
Now let’s replace the first 3×3 convolution of the network with a fixed 3×3 whitening convolution to equalise the scales of the eigenpatches above, followed by a learnable 1×1 convolution and see the effect on training. This dropout-training viewpoint makes it clear that any attempt to introduce a rule disallowing TTA from a benchmark is going to be fraught with difficulties. An alternative, would be to use the same procedure at training time as at test time and present each image along with its mirror. This sped up the time per-mini-batch by about 40% on a single GPU, ... when training Resnet-101 this amounts to a saving of 13 hours.
We released optimized training code, as well as pre-trained models, in the hope that this benefits the community. The classic way to remove input correlations is to perform global PCA (or ZCA) whitening.
There is much scope for improvement on that front as well. If gradients are being averaged over mini-batches, then learning rates should be scaled to undo the effect and weight decay left alone since our weight decay update incorporates a factor of the learning rate. 15 epochs brings a test accuracy of 94.1% in 39s, closing in on the 4-GPU, test-time-augmentation assisted DAWNBench leader! The authors of the ResNet paper argue that this underfitting is unlikely to be caused by vanishing gradients, since this difficulty occurs even with batch normalized networks. From this point of view, we have just introduced a larger network for which we have an efficient stochastic training methodology. The result is a small negative effect on test accuracy which moves to 94.0% (mean of 50 runs) compared to our baseline of 94.1%. using larger models with sparse updates or perhaps natural gradient descent), or we should push batch sizes higher. Indeed we can improve things slightly by increasing the learning rate of the biases by another factor of 4 and dividing weight decay by a corresponding factor.
All in all, I estimate that a machine with a single consumer GPU, such as 1080 or 1080ti, can train ~100 epochs of ResNet … SGD with mini-batches is similar to training one example at a time, with the difference being that parameter updates are delayed until the end of a batch. © Myrtle.ai | Company No: 4978210 | VAT Reg No: 831979590 | Privacy Policy. Conversely, in a large range around the original learning rate (learning rate factor=1 in the plots) training and test losses are stable. Our main weapon is statistical significance. This has a harmful effect: we found that putting batch normalization after the addition significantly hurts test error on CIFAR, which is in line with the original paper’s recommendations. We trained variants of the 18, 34, 50, and 101-layer ResNet models on the ImageNet classification dataset.
The 5s gain from a more efficient network more than compensates the 2.5s loss from the extra training epoch. If we rerun the network and training from our DAWNBench submission with the new GPU data processing, training time drops just under 70s, moving us up two places on the leaderboard! GPU memory usage when using the baseline, network-wide allocation policy (left axis).
Figure 1. In our case, the tasks in question are different parts of the same training set and forgetfulness can occur within a single epoch at high enough learning rates. GPU memory optimizations. Natalia Gimelshein, Nicolas Vasilache, Jeff Johnson for code and discussions around multi-GPU optimization. Moreover, since we are no longer racing CPU preprocessing queues against the GPU, we can stop worrying about data loading altogether, even as training gets faster. This is still a long time, but critical bugs that prevent convergence are often immediately apparent because if the code is bug-free the training loss should decrease quickly (within minutes after training starts). More exploration is needed. For a larger dataset such as ImageNet-1K, which consists of about 20× as many training examples as CIFAR10, the effects of forgetfulness are likely to be much more severe. By the end of the post our single-GPU implementation surpasses the top multi-GPU times comfortably, reclaiming the coveted DAWNBench crown with a time of 34s and achieving a 10× improvement over the single-GPU state-of-the-art at the start of the series! In this blog post we implement Deep Residual Networks (ResNets) and investigate ResNets from a model-selection and optimization perspective. We shall discuss the validity of this approach towards the end of the post (our conclusion is that any reasonable restriction should be based on total inference cost and that the form of mild TTA used here, along with a lightweight network, passes on that front.) On the other hand, if channel scales vary substantially this might reduce the effective number of channels and introduce a bottleneck. If we increase the maximum learning rate by a further ~50% and reduce the amount of cutout augmentation, from 8×8 to 5×5 patches, to compensate for the extra regularisation that the higher learning rate brings, we can remove a further epoch and reach 94.1% test accuracy in 36s, moving us narrowly into top spot on the leaderboard!! We are going to apply PCA whitening to 3×3 patches of inputs as an initial 3×3 convolution with fixed (non-learnable) weights. The net effect brings our time to 64s, up to third place on the leaderboard. Test accuracy improves to 94.2% (mean of 50 runs.) © Myrtle.ai | Company No: 4978210 | VAT Reg No: 831979590 | Privacy Policy. It is not at all clear that this limit applies. We reduce the warmup period – during which learning rates increase linearly – in proportion to the overall number of epochs. Thus, second order differences between small and large batch training could accumulate over time and lead to substantially different training trajectories. We’ve reached the end of the series and a few words are in order. As we might expect, variations in local brightness dominate. We shall investigate exponential moving averaging of parameters which is a standard approach. The bulk of the time is spent transferring the preprocessed dataset back to the CPU which takes nearly half-a-second. This is probably a good approach for training benchmarks too. In short, if higher order effects can be neglected, you are not training fast enough. Label smoothing is a well-established trick for improving the training speed and generalization of neural nets in classification problems.
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