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hessian_penalty.py
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hessian_penalty.py
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"""
## Adapted to work with our "batches"
Official PyTorch implementation of the Hessian Penalty regularization term from https://arxiv.org/pdf/2008.10599.pdf
Author: Bill Peebles
TensorFlow Implementation (GPU + Multi-Layer): hessian_penalty_tf.py
Simple Pure NumPy Implementation: hessian_penalty_np.py
Simple use case where you want to apply the Hessian Penalty to the output of net w.r.t. net_input:
>>> from hessian_penalty_pytorch import hessian_penalty
>>> net = MyNeuralNet()
>>> net_input = sample_input()
>>> loss = hessian_penalty(net, z=net_input) # Compute hessian penalty of net's output w.r.t. net_input
>>> loss.backward() # Compute gradients w.r.t. net's parameters
If your network takes multiple inputs, simply supply them to hessian_penalty as you do in the net's forward pass. In the
following example, we assume BigGAN.forward takes a second input argument "y". Note that we always take the Hessian
Penalty w.r.t. the z argument supplied to hessian_penalty:
>>> from hessian_penalty_pytorch import hessian_penalty