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algorithms.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
from torch.autograd import Variable
import copy
import numpy as np
from collections import defaultdict
from domainbed import networks
from domainbed.lib.misc import random_pairs_of_minibatches, ParamDict
ALGORITHMS = [
'ERM',
'Fish',
'IRM',
'GroupDRO',
'Mixup',
'MLDG',
'CORAL',
'MMD',
'DANN',
'CDANN',
'MTL',
'SagNet',
'ARM',
'VREx',
'RSC',
'SD',
'ANDMask',
'IGA',
'SelfReg'
]
def get_algorithm_class(algorithm_name):
"""Return the algorithm class with the given name."""
if algorithm_name not in globals():
raise NotImplementedError("Algorithm not found: {}".format(algorithm_name))
return globals()[algorithm_name]
class Algorithm(torch.nn.Module):
"""
A subclass of Algorithm implements a domain generalization algorithm.
Subclasses should implement the following:
- update()
- predict()
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(Algorithm, self).__init__()
self.hparams = hparams
def update(self, minibatches, unlabeled=None):
"""
Perform one update step, given a list of (x, y) tuples for all
environments.
Admits an optional list of unlabeled minibatches from the test domains,
when task is domain_adaptation.
"""
raise NotImplementedError
def predict(self, x):
raise NotImplementedError
class ERM(Algorithm):
"""
Empirical Risk Minimization (ERM)
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(ERM, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.featurizer = networks.Featurizer(input_shape, self.hparams)
self.classifier = networks.Classifier(
self.featurizer.n_outputs,
num_classes,
self.hparams['nonlinear_classifier'])
self.network = nn.Sequential(self.featurizer, self.classifier)
self.optimizer = torch.optim.Adam(
self.network.parameters(),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay']
)
def update(self, minibatches, unlabeled=None):
all_x = torch.cat([x for x,y in minibatches])
all_y = torch.cat([y for x,y in minibatches])
loss = F.cross_entropy(self.predict(all_x), all_y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'loss': loss.item()}
def predict(self, x):
return self.network(x)
class Fish(Algorithm):
"""
Implementation of Fish, as seen in Gradient Matching for Domain
Generalization, Shi et al. 2021.
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(Fish, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.input_shape = input_shape
self.num_classes = num_classes
self.network = networks.WholeFish(input_shape, num_classes, hparams)
self.optimizer = torch.optim.Adam(
self.network.parameters(),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay']
)
self.optimizer_inner_state = None
def create_clone(self, device):
self.network_inner = networks.WholeFish(self.input_shape, self.num_classes, self.hparams,
weights=self.network.state_dict()).to(device)
self.optimizer_inner = torch.optim.Adam(
self.network_inner.parameters(),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay']
)
if self.optimizer_inner_state is not None:
self.optimizer_inner.load_state_dict(self.optimizer_inner_state)
def fish(self, meta_weights, inner_weights, lr_meta):
meta_weights = ParamDict(meta_weights)
inner_weights = ParamDict(inner_weights)
meta_weights += lr_meta * (inner_weights - meta_weights)
return meta_weights
def update(self, minibatches, unlabeled=None):
self.create_clone(minibatches[0][0].device)
for x, y in minibatches:
loss = F.cross_entropy(self.network_inner(x), y)
self.optimizer_inner.zero_grad()
loss.backward()
self.optimizer_inner.step()
self.optimizer_inner_state = self.optimizer_inner.state_dict()
meta_weights = self.fish(
meta_weights=self.network.state_dict(),
inner_weights=self.network_inner.state_dict(),
lr_meta=self.hparams["meta_lr"]
)
self.network.reset_weights(meta_weights)
return {'loss': loss.item()}
def predict(self, x):
return self.network(x)
class ARM(ERM):
""" Adaptive Risk Minimization (ARM) """
def __init__(self, input_shape, num_classes, num_domains, hparams):
original_input_shape = input_shape
input_shape = (1 + original_input_shape[0],) + original_input_shape[1:]
super(ARM, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.context_net = networks.ContextNet(original_input_shape)
self.support_size = hparams['batch_size']
def predict(self, x):
batch_size, c, h, w = x.shape
if batch_size % self.support_size == 0:
meta_batch_size = batch_size // self.support_size
support_size = self.support_size
else:
meta_batch_size, support_size = 1, batch_size
context = self.context_net(x)
context = context.reshape((meta_batch_size, support_size, 1, h, w))
context = context.mean(dim=1)
context = torch.repeat_interleave(context, repeats=support_size, dim=0)
x = torch.cat([x, context], dim=1)
return self.network(x)
class AbstractDANN(Algorithm):
"""Domain-Adversarial Neural Networks (abstract class)"""
def __init__(self, input_shape, num_classes, num_domains,
hparams, conditional, class_balance):
super(AbstractDANN, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.register_buffer('update_count', torch.tensor([0]))
self.conditional = conditional
self.class_balance = class_balance
# Algorithms
self.featurizer = networks.Featurizer(input_shape, self.hparams)
self.classifier = networks.Classifier(
self.featurizer.n_outputs,
num_classes,
self.hparams['nonlinear_classifier'])
self.discriminator = networks.MLP(self.featurizer.n_outputs,
num_domains, self.hparams)
self.class_embeddings = nn.Embedding(num_classes,
self.featurizer.n_outputs)
# Optimizers
self.disc_opt = torch.optim.Adam(
(list(self.discriminator.parameters()) +
list(self.class_embeddings.parameters())),
lr=self.hparams["lr_d"],
weight_decay=self.hparams['weight_decay_d'],
betas=(self.hparams['beta1'], 0.9))
self.gen_opt = torch.optim.Adam(
(list(self.featurizer.parameters()) +
list(self.classifier.parameters())),
lr=self.hparams["lr_g"],
weight_decay=self.hparams['weight_decay_g'],
betas=(self.hparams['beta1'], 0.9))
def update(self, minibatches, unlabeled=None):
device = "cuda" if minibatches[0][0].is_cuda else "cpu"
self.update_count += 1
all_x = torch.cat([x for x, y in minibatches])
all_y = torch.cat([y for x, y in minibatches])
all_z = self.featurizer(all_x)
if self.conditional:
disc_input = all_z + self.class_embeddings(all_y)
else:
disc_input = all_z
disc_out = self.discriminator(disc_input)
disc_labels = torch.cat([
torch.full((x.shape[0], ), i, dtype=torch.int64, device=device)
for i, (x, y) in enumerate(minibatches)
])
if self.class_balance:
y_counts = F.one_hot(all_y).sum(dim=0)
weights = 1. / (y_counts[all_y] * y_counts.shape[0]).float()
disc_loss = F.cross_entropy(disc_out, disc_labels, reduction='none')
disc_loss = (weights * disc_loss).sum()
else:
disc_loss = F.cross_entropy(disc_out, disc_labels)
disc_softmax = F.softmax(disc_out, dim=1)
input_grad = autograd.grad(disc_softmax[:, disc_labels].sum(),
[disc_input], create_graph=True)[0]
grad_penalty = (input_grad**2).sum(dim=1).mean(dim=0)
disc_loss += self.hparams['grad_penalty'] * grad_penalty
d_steps_per_g = self.hparams['d_steps_per_g_step']
if (self.update_count.item() % (1+d_steps_per_g) < d_steps_per_g):
self.disc_opt.zero_grad()
disc_loss.backward()
self.disc_opt.step()
return {'disc_loss': disc_loss.item()}
else:
all_preds = self.classifier(all_z)
classifier_loss = F.cross_entropy(all_preds, all_y)
gen_loss = (classifier_loss +
(self.hparams['lambda'] * -disc_loss))
self.disc_opt.zero_grad()
self.gen_opt.zero_grad()
gen_loss.backward()
self.gen_opt.step()
return {'gen_loss': gen_loss.item()}
def predict(self, x):
return self.classifier(self.featurizer(x))
class DANN(AbstractDANN):
"""Unconditional DANN"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(DANN, self).__init__(input_shape, num_classes, num_domains,
hparams, conditional=False, class_balance=False)
class CDANN(AbstractDANN):
"""Conditional DANN"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(CDANN, self).__init__(input_shape, num_classes, num_domains,
hparams, conditional=True, class_balance=True)
class IRM(ERM):
"""Invariant Risk Minimization"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(IRM, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.register_buffer('update_count', torch.tensor([0]))
@staticmethod
def _irm_penalty(logits, y):
device = "cuda" if logits[0][0].is_cuda else "cpu"
scale = torch.tensor(1.).to(device).requires_grad_()
loss_1 = F.cross_entropy(logits[::2] * scale, y[::2])
loss_2 = F.cross_entropy(logits[1::2] * scale, y[1::2])
grad_1 = autograd.grad(loss_1, [scale], create_graph=True)[0]
grad_2 = autograd.grad(loss_2, [scale], create_graph=True)[0]
result = torch.sum(grad_1 * grad_2)
return result
def update(self, minibatches, unlabeled=None):
device = "cuda" if minibatches[0][0].is_cuda else "cpu"
penalty_weight = (self.hparams['irm_lambda'] if self.update_count
>= self.hparams['irm_penalty_anneal_iters'] else
1.0)
nll = 0.
penalty = 0.
all_x = torch.cat([x for x,y in minibatches])
all_logits = self.network(all_x)
all_logits_idx = 0
for i, (x, y) in enumerate(minibatches):
logits = all_logits[all_logits_idx:all_logits_idx + x.shape[0]]
all_logits_idx += x.shape[0]
nll += F.cross_entropy(logits, y)
penalty += self._irm_penalty(logits, y)
nll /= len(minibatches)
penalty /= len(minibatches)
loss = nll + (penalty_weight * penalty)
if self.update_count == self.hparams['irm_penalty_anneal_iters']:
# Reset Adam, because it doesn't like the sharp jump in gradient
# magnitudes that happens at this step.
self.optimizer = torch.optim.Adam(
self.network.parameters(),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay'])
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.update_count += 1
return {'loss': loss.item(), 'nll': nll.item(),
'penalty': penalty.item()}
class VREx(ERM):
"""V-REx algorithm from http://arxiv.org/abs/2003.00688"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(VREx, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.register_buffer('update_count', torch.tensor([0]))
def update(self, minibatches, unlabeled=None):
if self.update_count >= self.hparams["vrex_penalty_anneal_iters"]:
penalty_weight = self.hparams["vrex_lambda"]
else:
penalty_weight = 1.0
nll = 0.
all_x = torch.cat([x for x, y in minibatches])
all_logits = self.network(all_x)
all_logits_idx = 0
losses = torch.zeros(len(minibatches))
for i, (x, y) in enumerate(minibatches):
logits = all_logits[all_logits_idx:all_logits_idx + x.shape[0]]
all_logits_idx += x.shape[0]
nll = F.cross_entropy(logits, y)
losses[i] = nll
mean = losses.mean()
penalty = ((losses - mean) ** 2).mean()
loss = mean + penalty_weight * penalty
if self.update_count == self.hparams['vrex_penalty_anneal_iters']:
# Reset Adam (like IRM), because it doesn't like the sharp jump in
# gradient magnitudes that happens at this step.
self.optimizer = torch.optim.Adam(
self.network.parameters(),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay'])
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.update_count += 1
return {'loss': loss.item(), 'nll': nll.item(),
'penalty': penalty.item()}
class Mixup(ERM):
"""
Mixup of minibatches from different domains
https://arxiv.org/pdf/2001.00677.pdf
https://arxiv.org/pdf/1912.01805.pdf
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(Mixup, self).__init__(input_shape, num_classes, num_domains,
hparams)
def update(self, minibatches, unlabeled=None):
objective = 0
for (xi, yi), (xj, yj) in random_pairs_of_minibatches(minibatches):
lam = np.random.beta(self.hparams["mixup_alpha"],
self.hparams["mixup_alpha"])
x = lam * xi + (1 - lam) * xj
predictions = self.predict(x)
objective += lam * F.cross_entropy(predictions, yi)
objective += (1 - lam) * F.cross_entropy(predictions, yj)
objective /= len(minibatches)
self.optimizer.zero_grad()
objective.backward()
self.optimizer.step()
return {'loss': objective.item()}
class GroupDRO(ERM):
"""
Robust ERM minimizes the error at the worst minibatch
Algorithm 1 from [https://arxiv.org/pdf/1911.08731.pdf]
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(GroupDRO, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.register_buffer("q", torch.Tensor())
def update(self, minibatches, unlabeled=None):
device = "cuda" if minibatches[0][0].is_cuda else "cpu"
if not len(self.q):
self.q = torch.ones(len(minibatches)).to(device)
losses = torch.zeros(len(minibatches)).to(device)
for m in range(len(minibatches)):
x, y = minibatches[m]
losses[m] = F.cross_entropy(self.predict(x), y)
self.q[m] *= (self.hparams["groupdro_eta"] * losses[m].data).exp()
self.q /= self.q.sum()
loss = torch.dot(losses, self.q)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'loss': loss.item()}
class MLDG(ERM):
"""
Model-Agnostic Meta-Learning
Algorithm 1 / Equation (3) from: https://arxiv.org/pdf/1710.03463.pdf
Related: https://arxiv.org/pdf/1703.03400.pdf
Related: https://arxiv.org/pdf/1910.13580.pdf
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(MLDG, self).__init__(input_shape, num_classes, num_domains,
hparams)
def update(self, minibatches, unlabeled=None):
"""
Terms being computed:
* Li = Loss(xi, yi, params)
* Gi = Grad(Li, params)
* Lj = Loss(xj, yj, Optimizer(params, grad(Li, params)))
* Gj = Grad(Lj, params)
* params = Optimizer(params, Grad(Li + beta * Lj, params))
* = Optimizer(params, Gi + beta * Gj)
That is, when calling .step(), we want grads to be Gi + beta * Gj
For computational efficiency, we do not compute second derivatives.
"""
num_mb = len(minibatches)
objective = 0
self.optimizer.zero_grad()
for p in self.network.parameters():
if p.grad is None:
p.grad = torch.zeros_like(p)
for (xi, yi), (xj, yj) in random_pairs_of_minibatches(minibatches):
# fine tune clone-network on task "i"
inner_net = copy.deepcopy(self.network)
inner_opt = torch.optim.Adam(
inner_net.parameters(),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay']
)
inner_obj = F.cross_entropy(inner_net(xi), yi)
inner_opt.zero_grad()
inner_obj.backward()
inner_opt.step()
# The network has now accumulated gradients Gi
# The clone-network has now parameters P - lr * Gi
for p_tgt, p_src in zip(self.network.parameters(),
inner_net.parameters()):
if p_src.grad is not None:
p_tgt.grad.data.add_(p_src.grad.data / num_mb)
# `objective` is populated for reporting purposes
objective += inner_obj.item()
# this computes Gj on the clone-network
loss_inner_j = F.cross_entropy(inner_net(xj), yj)
grad_inner_j = autograd.grad(loss_inner_j, inner_net.parameters(),
allow_unused=True)
# `objective` is populated for reporting purposes
objective += (self.hparams['mldg_beta'] * loss_inner_j).item()
for p, g_j in zip(self.network.parameters(), grad_inner_j):
if g_j is not None:
p.grad.data.add_(
self.hparams['mldg_beta'] * g_j.data / num_mb)
# The network has now accumulated gradients Gi + beta * Gj
# Repeat for all train-test splits, do .step()
objective /= len(minibatches)
self.optimizer.step()
return {'loss': objective}
# This commented "update" method back-propagates through the gradients of
# the inner update, as suggested in the original MAML paper. However, this
# is twice as expensive as the uncommented "update" method, which does not
# compute second-order derivatives, implementing the First-Order MAML
# method (FOMAML) described in the original MAML paper.
# def update(self, minibatches, unlabeled=None):
# objective = 0
# beta = self.hparams["beta"]
# inner_iterations = self.hparams["inner_iterations"]
# self.optimizer.zero_grad()
# with higher.innerloop_ctx(self.network, self.optimizer,
# copy_initial_weights=False) as (inner_network, inner_optimizer):
# for (xi, yi), (xj, yj) in random_pairs_of_minibatches(minibatches):
# for inner_iteration in range(inner_iterations):
# li = F.cross_entropy(inner_network(xi), yi)
# inner_optimizer.step(li)
#
# objective += F.cross_entropy(self.network(xi), yi)
# objective += beta * F.cross_entropy(inner_network(xj), yj)
# objective /= len(minibatches)
# objective.backward()
#
# self.optimizer.step()
#
# return objective
class AbstractMMD(ERM):
"""
Perform ERM while matching the pair-wise domain feature distributions
using MMD (abstract class)
"""
def __init__(self, input_shape, num_classes, num_domains, hparams, gaussian):
super(AbstractMMD, self).__init__(input_shape, num_classes, num_domains,
hparams)
if gaussian:
self.kernel_type = "gaussian"
else:
self.kernel_type = "mean_cov"
def my_cdist(self, x1, x2):
x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
res = torch.addmm(x2_norm.transpose(-2, -1),
x1,
x2.transpose(-2, -1), alpha=-2).add_(x1_norm)
return res.clamp_min_(1e-30)
def gaussian_kernel(self, x, y, gamma=[0.001, 0.01, 0.1, 1, 10, 100,
1000]):
D = self.my_cdist(x, y)
K = torch.zeros_like(D)
for g in gamma:
K.add_(torch.exp(D.mul(-g)))
return K
def mmd(self, x, y):
if self.kernel_type == "gaussian":
Kxx = self.gaussian_kernel(x, x).mean()
Kyy = self.gaussian_kernel(y, y).mean()
Kxy = self.gaussian_kernel(x, y).mean()
return Kxx + Kyy - 2 * Kxy
else:
mean_x = x.mean(0, keepdim=True)
mean_y = y.mean(0, keepdim=True)
cent_x = x - mean_x
cent_y = y - mean_y
cova_x = (cent_x.t() @ cent_x) / (len(x) - 1)
cova_y = (cent_y.t() @ cent_y) / (len(y) - 1)
mean_diff = (mean_x - mean_y).pow(2).mean()
cova_diff = (cova_x - cova_y).pow(2).mean()
return mean_diff + cova_diff
def update(self, minibatches, unlabeled=None):
objective = 0
penalty = 0
nmb = len(minibatches)
features = [self.featurizer(xi) for xi, _ in minibatches]
classifs = [self.classifier(fi) for fi in features]
targets = [yi for _, yi in minibatches]
for i in range(nmb):
objective += F.cross_entropy(classifs[i], targets[i])
for j in range(i + 1, nmb):
penalty += self.mmd(features[i], features[j])
objective /= nmb
if nmb > 1:
penalty /= (nmb * (nmb - 1) / 2)
self.optimizer.zero_grad()
(objective + (self.hparams['mmd_gamma']*penalty)).backward()
self.optimizer.step()
if torch.is_tensor(penalty):
penalty = penalty.item()
return {'loss': objective.item(), 'penalty': penalty}
class MMD(AbstractMMD):
"""
MMD using Gaussian kernel
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(MMD, self).__init__(input_shape, num_classes,
num_domains, hparams, gaussian=True)
class CORAL(AbstractMMD):
"""
MMD using mean and covariance difference
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(CORAL, self).__init__(input_shape, num_classes,
num_domains, hparams, gaussian=False)
class MTL(Algorithm):
"""
A neural network version of
Domain Generalization by Marginal Transfer Learning
(https://arxiv.org/abs/1711.07910)
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(MTL, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.featurizer = networks.Featurizer(input_shape, self.hparams)
self.classifier = networks.Classifier(
self.featurizer.n_outputs * 2,
num_classes,
self.hparams['nonlinear_classifier'])
self.optimizer = torch.optim.Adam(
list(self.featurizer.parameters()) +\
list(self.classifier.parameters()),
lr=self.hparams["lr"],
weight_decay=self.hparams['weight_decay']
)
self.register_buffer('embeddings',
torch.zeros(num_domains,
self.featurizer.n_outputs))
self.ema = self.hparams['mtl_ema']
def update(self, minibatches, unlabeled=None):
loss = 0
for env, (x, y) in enumerate(minibatches):
loss += F.cross_entropy(self.predict(x, env), y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'loss': loss.item()}
def update_embeddings_(self, features, env=None):
return_embedding = features.mean(0)
if env is not None:
return_embedding = self.ema * return_embedding +\
(1 - self.ema) * self.embeddings[env]
self.embeddings[env] = return_embedding.clone().detach()
return return_embedding.view(1, -1).repeat(len(features), 1)
def predict(self, x, env=None):
features = self.featurizer(x)
embedding = self.update_embeddings_(features, env).normal_()
return self.classifier(torch.cat((features, embedding), 1))
class SagNet(Algorithm):
"""
Style Agnostic Network
Algorithm 1 from: https://arxiv.org/abs/1910.11645
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(SagNet, self).__init__(input_shape, num_classes, num_domains,
hparams)
# featurizer network
self.network_f = networks.Featurizer(input_shape, self.hparams)
# content network
self.network_c = networks.Classifier(
self.network_f.n_outputs,
num_classes,
self.hparams['nonlinear_classifier'])
# style network
self.network_s = networks.Classifier(
self.network_f.n_outputs,
num_classes,
self.hparams['nonlinear_classifier'])
# # This commented block of code implements something closer to the
# # original paper, but is specific to ResNet and puts in disadvantage
# # the other algorithms.
# resnet_c = networks.Featurizer(input_shape, self.hparams)
# resnet_s = networks.Featurizer(input_shape, self.hparams)
# # featurizer network
# self.network_f = torch.nn.Sequential(
# resnet_c.network.conv1,
# resnet_c.network.bn1,
# resnet_c.network.relu,
# resnet_c.network.maxpool,
# resnet_c.network.layer1,
# resnet_c.network.layer2,
# resnet_c.network.layer3)
# # content network
# self.network_c = torch.nn.Sequential(
# resnet_c.network.layer4,
# resnet_c.network.avgpool,
# networks.Flatten(),
# resnet_c.network.fc)
# # style network
# self.network_s = torch.nn.Sequential(
# resnet_s.network.layer4,
# resnet_s.network.avgpool,
# networks.Flatten(),
# resnet_s.network.fc)
def opt(p):
return torch.optim.Adam(p, lr=hparams["lr"],
weight_decay=hparams["weight_decay"])
self.optimizer_f = opt(self.network_f.parameters())
self.optimizer_c = opt(self.network_c.parameters())
self.optimizer_s = opt(self.network_s.parameters())
self.weight_adv = hparams["sag_w_adv"]
def forward_c(self, x):
# learning content network on randomized style
return self.network_c(self.randomize(self.network_f(x), "style"))
def forward_s(self, x):
# learning style network on randomized content
return self.network_s(self.randomize(self.network_f(x), "content"))
def randomize(self, x, what="style", eps=1e-5):
device = "cuda" if x.is_cuda else "cpu"
sizes = x.size()
alpha = torch.rand(sizes[0], 1).to(device)
if len(sizes) == 4:
x = x.view(sizes[0], sizes[1], -1)
alpha = alpha.unsqueeze(-1)
mean = x.mean(-1, keepdim=True)
var = x.var(-1, keepdim=True)
x = (x - mean) / (var + eps).sqrt()
idx_swap = torch.randperm(sizes[0])
if what == "style":
mean = alpha * mean + (1 - alpha) * mean[idx_swap]
var = alpha * var + (1 - alpha) * var[idx_swap]
else:
x = x[idx_swap].detach()
x = x * (var + eps).sqrt() + mean
return x.view(*sizes)
def update(self, minibatches, unlabeled=None):
all_x = torch.cat([x for x, y in minibatches])
all_y = torch.cat([y for x, y in minibatches])
# learn content
self.optimizer_f.zero_grad()
self.optimizer_c.zero_grad()
loss_c = F.cross_entropy(self.forward_c(all_x), all_y)
loss_c.backward()
self.optimizer_f.step()
self.optimizer_c.step()
# learn style
self.optimizer_s.zero_grad()
loss_s = F.cross_entropy(self.forward_s(all_x), all_y)
loss_s.backward()
self.optimizer_s.step()
# learn adversary
self.optimizer_f.zero_grad()
loss_adv = -F.log_softmax(self.forward_s(all_x), dim=1).mean(1).mean()
loss_adv = loss_adv * self.weight_adv
loss_adv.backward()
self.optimizer_f.step()
return {'loss_c': loss_c.item(), 'loss_s': loss_s.item(),
'loss_adv': loss_adv.item()}
def predict(self, x):
return self.network_c(self.network_f(x))
class RSC(ERM):
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(RSC, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.drop_f = (1 - hparams['rsc_f_drop_factor']) * 100
self.drop_b = (1 - hparams['rsc_b_drop_factor']) * 100
self.num_classes = num_classes
def update(self, minibatches, unlabeled=None):
device = "cuda" if minibatches[0][0].is_cuda else "cpu"
# inputs
all_x = torch.cat([x for x, y in minibatches])
# labels
all_y = torch.cat([y for _, y in minibatches])
# one-hot labels
all_o = torch.nn.functional.one_hot(all_y, self.num_classes)
# features
all_f = self.featurizer(all_x)
# predictions
all_p = self.classifier(all_f)
# Equation (1): compute gradients with respect to representation
all_g = autograd.grad((all_p * all_o).sum(), all_f)[0]
# Equation (2): compute top-gradient-percentile mask
percentiles = np.percentile(all_g.cpu(), self.drop_f, axis=1)
percentiles = torch.Tensor(percentiles)
percentiles = percentiles.unsqueeze(1).repeat(1, all_g.size(1))
mask_f = all_g.lt(percentiles.to(device)).float()
# Equation (3): mute top-gradient-percentile activations
all_f_muted = all_f * mask_f
# Equation (4): compute muted predictions
all_p_muted = self.classifier(all_f_muted)
# Section 3.3: Batch Percentage
all_s = F.softmax(all_p, dim=1)
all_s_muted = F.softmax(all_p_muted, dim=1)
changes = (all_s * all_o).sum(1) - (all_s_muted * all_o).sum(1)
percentile = np.percentile(changes.detach().cpu(), self.drop_b)
mask_b = changes.lt(percentile).float().view(-1, 1)
mask = torch.logical_or(mask_f, mask_b).float()
# Equations (3) and (4) again, this time mutting over examples
all_p_muted_again = self.classifier(all_f * mask)
# Equation (5): update
loss = F.cross_entropy(all_p_muted_again, all_y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'loss': loss.item()}
class SD(ERM):
"""
Gradient Starvation: A Learning Proclivity in Neural Networks
Equation 25 from [https://arxiv.org/pdf/2011.09468.pdf]
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(SD, self).__init__(input_shape, num_classes, num_domains,
hparams)
self.sd_reg = hparams["sd_reg"]
def update(self, minibatches, unlabeled=None):
all_x = torch.cat([x for x,y in minibatches])
all_y = torch.cat([y for x,y in minibatches])
all_p = self.predict(all_x)
loss = F.cross_entropy(all_p, all_y)
penalty = (all_p ** 2).mean()
objective = loss + self.sd_reg * penalty
self.optimizer.zero_grad()
objective.backward()
self.optimizer.step()
return {'loss': loss.item(), 'penalty': penalty.item()}
class ANDMask(ERM):
"""
Learning Explanations that are Hard to Vary [https://arxiv.org/abs/2009.00329]
AND-Mask implementation from [https://github.com/gibipara92/learning-explanations-hard-to-vary]
"""
def __init__(self, input_shape, num_classes, num_domains, hparams):
super(ANDMask, self).__init__(input_shape, num_classes, num_domains, hparams)
self.tau = hparams["tau"]
def update(self, minibatches, unlabeled=None):
total_loss = 0
param_gradients = [[] for _ in self.network.parameters()]
all_x = torch.cat([x for x,y in minibatches])
all_logits = self.network(all_x)
all_logits_idx = 0
for i, (x, y) in enumerate(minibatches):
logits = all_logits[all_logits_idx:all_logits_idx + x.shape[0]]
all_logits_idx += x.shape[0]
env_loss = F.cross_entropy(logits, y)
total_loss += env_loss
env_grads = autograd.grad(env_loss, self.network.parameters(), retain_graph=True)
for grads, env_grad in zip(param_gradients, env_grads):
grads.append(env_grad)
mean_loss = total_loss / len(minibatches)
self.optimizer.zero_grad()
self.mask_grads(self.tau, param_gradients, self.network.parameters())
self.optimizer.step()
return {'loss': mean_loss.item()}
def mask_grads(self, tau, gradients, params):
for param, grads in zip(params, gradients):
grads = torch.stack(grads, dim=0)
grad_signs = torch.sign(grads)
mask = torch.mean(grad_signs, dim=0).abs() >= self.tau
mask = mask.to(torch.float32)
avg_grad = torch.mean(grads, dim=0)
mask_t = (mask.sum() / mask.numel())
param.grad = mask * avg_grad
param.grad *= (1. / (1e-10 + mask_t))
return 0
class IGA(ERM):
"""
Inter-environmental Gradient Alignment
From https://arxiv.org/abs/2008.01883v2
"""
def __init__(self, in_features, num_classes, num_domains, hparams):
super(IGA, self).__init__(in_features, num_classes, num_domains, hparams)
def update(self, minibatches, unlabeled=False):
all_x = torch.cat([x for x,y in minibatches])
all_logits = self.network(all_x)
total_loss = 0