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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
####################
# Critic Model #
####################
# Part of the code is adapted from here: https://github.com/yaohungt/Pointwise_Dependency_Neural_Estimation
def probabilistic_classifier_obj(f):
criterion = nn.BCEWithLogitsLoss()
batch_size = f.shape[0]
labels = [0.]*(batch_size*batch_size)
labels[::(batch_size+1)] = [1.]*batch_size
labels = torch.tensor(labels).type_as(f)
labels = labels.view(-1,1)
logits = f.contiguous().view(-1,1)
Loss = -1.*criterion(logits, labels)
return Loss
def probabilistic_classifier_eval(f):
batch_size = f.shape[0]
joint_feat = f.contiguous().view(-1)[::(batch_size+1)]
joint_logits = torch.clamp(torch.sigmoid(joint_feat), min=1e-6, max=1-1e-6)
MI = torch.mean(torch.log((batch_size-1)*joint_logits/(1.-joint_logits)))
# we have batch_size*(batch_size-1) product of marginal samples
# we have batch_size joint density samples
return MI
def infonce_lower_bound_obj(scores):
nll = scores.diag().mean() - scores.logsumexp(dim=1)
# Alternative implementation:
# nll = -tf.nn.sparse_softmax_cross_entropy_with_logits(logits=scores, labels=tf.range(batch_size))
mi = torch.tensor(scores.size(0)).float().log() + nll
mi = mi.mean()
return mi
def mlp(dim, hidden_dim, output_dim, layers, activation):
activation = {
'relu': nn.ReLU,
'tanh': nn.Tanh,
}[activation]
seq = [nn.Linear(dim, hidden_dim), activation()]
for _ in range(layers):
seq += [nn.Linear(hidden_dim, hidden_dim), activation()]
seq += [nn.Linear(hidden_dim, output_dim)]
return nn.Sequential(*seq)
class SeparableCritic(nn.Module):
def __init__(self, x1_dim, x2_dim, hidden_dim, embed_dim,
layers, activation):
super(SeparableCritic, self).__init__()
self._g = mlp(x1_dim, hidden_dim, embed_dim, layers, activation)
self._h = mlp(x2_dim, hidden_dim, embed_dim, layers, activation)
def transformed_x(self, x):
return self._g(x)
def transformed_y(self, y):
return self._h(y)
def forward(self, x, y):
scores = torch.matmul(self._h(y), self._g(x).t())
return scores
def pointwise_mi(self, x, y, estimator):
scores = torch.matmul(self._h(y), self._g(x).t())
if estimator == 'probabilistic_classifier':
# the prob of being a pair
# PMI = torch.sigmoid(scores.diag())
# PMI
batch_size = scores.shape[0]
# N_pxpy / N_pxy = (batch_size - 1.) * batch_size / batch_size
PMI = scores.diag() + np.log(batch_size - 1.)
else:
raise NotImplementedError("not supporting our PMI!")
return PMI
class ConcatCritic(nn.Module):
def __init__(self, A_dim, B_dim, hidden_dim, layers, activation, **extra_kwargs):
super(ConcatCritic, self).__init__()
# output is scalar score
self._f = mlp(A_dim + B_dim, hidden_dim, 1, layers, activation)
def forward(self, x, y):
batch_size = x.shape[0]
# Tile all possible combinations of x and y
x_tiled = torch.stack([x] * batch_size, dim=0)
y_tiled = torch.stack([y] * batch_size, dim=1)
# xy is [batch_size * batch_size, x_dim + y_dim]
xy_pairs = torch.reshape(torch.cat((x_tiled, y_tiled), dim=2), [batch_size * batch_size, -1])
# Compute scores for each x_i, y_j pair.
scores = self._f(xy_pairs)
return torch.reshape(scores, [batch_size, batch_size]).t()
# Concat critic with the InfoNCE (NCE) objective
class InfoNCECritic(nn.Module):
def __init__(self, A_dim, B_dim, hidden_dim, layers, activation, **extra_kwargs):
super(InfoNCECritic, self).__init__()
# output is scalar score
self._f = mlp(A_dim + B_dim, hidden_dim, 1, layers, activation)
def forward(self, x_samples, y_samples):
sample_size = y_samples.shape[0]
#x_samples = F.normalize(x_samples, dim=-1)
#y_samples = F.normalize(y_samples, dim=-1)
x_tile = x_samples.unsqueeze(0).repeat((sample_size, 1, 1))
y_tile = y_samples.unsqueeze(1).repeat((1, sample_size, 1))
T0 = self._f(torch.cat([x_samples,y_samples], dim = -1))
T1 = self._f(torch.cat([x_tile, y_tile], dim = -1))
lower_bound = T0.mean() - (T1.logsumexp(dim = 1).mean() - np.log(sample_size))
return -lower_bound
# Concat critic with the CLUBInfoNCE (NCE-CLUB) objective
class CLUBInfoNCECritic(nn.Module):
def __init__(self, A_dim, B_dim, hidden_dim, layers, activation, **extra_kwargs):
super(CLUBInfoNCECritic, self).__init__()
self._f = mlp(A_dim + B_dim, hidden_dim, 1, layers, activation)
# CLUB loss
def forward(self, x_samples, y_samples):
sample_size = y_samples.shape[0]
#x_samples = F.normalize(x_samples, dim=-1)
#y_samples = F.normalize(y_samples, dim=-1)
x_tile = x_samples.unsqueeze(0).repeat((sample_size, 1, 1))
y_tile = y_samples.unsqueeze(1).repeat((1, sample_size, 1))
T0 = self._f(torch.cat([y_samples,x_samples], dim = -1))
T1 = self._f(torch.cat([y_tile, x_tile], dim = -1))
return -(T0.mean() - T1.mean())
# InfoNCE loss
def learning_loss(self, x_samples, y_samples):
sample_size = y_samples.shape[0]
#x_samples = F.normalize(x_samples, dim=-1)
#y_samples = F.normalize(y_samples, dim=-1)
x_tile = x_samples.unsqueeze(0).repeat((sample_size, 1, 1))
y_tile = y_samples.unsqueeze(1).repeat((1, sample_size, 1))
T0 = self._f(torch.cat([y_samples,x_samples], dim = -1))
T1 = self._f(torch.cat([y_tile, x_tile], dim = -1))
lower_bound = T0.mean() - (T1.logsumexp(dim = 1).mean() - np.log(sample_size))
return -lower_bound
class SupConLoss(nn.Module):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(self, temperature=0.07, contrast_mode='all',
base_temperature=0.07):
super(SupConLoss, self).__init__()
self.temperature = temperature
self.contrast_mode = contrast_mode
self.base_temperature = base_temperature
def forward(self, features, labels=None, mask=None):
"""Compute loss for model. If both `labels` and `mask` are None,
it degenerates to SimCLR unsupervised loss:
https://arxiv.org/pdf/2002.05709.pdf
Args:
features: hidden vector of shape [bsz, n_views, ...].
labels: ground truth of shape [bsz].
mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
has the same class as sample i. Can be asymmetric.
Returns:
A loss scalar.
"""
device = (torch.device('cuda')
if features.is_cuda
else torch.device('cpu'))
if len(features.shape) < 3:
raise ValueError('`features` needs to be [bsz, n_views, ...],'
'at least 3 dimensions are required')
if len(features.shape) > 3:
features = features.view(features.shape[0], features.shape[1], -1)
batch_size = features.shape[0]
if labels is not None and mask is not None:
raise ValueError('Cannot define both `labels` and `mask`')
elif labels is None and mask is None:
mask = torch.eye(batch_size, dtype=torch.float32).to(device)
elif labels is not None:
labels = labels.contiguous().view(-1, 1)
if labels.shape[0] != batch_size:
raise ValueError('Num of labels does not match num of features')
mask = torch.eq(labels, labels.T).float().to(device)
else:
mask = mask.float().to(device)
contrast_count = features.shape[1]
contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
if self.contrast_mode == 'one':
anchor_feature = features[:, 0]
anchor_count = 1
elif self.contrast_mode == 'all':
anchor_feature = contrast_feature
anchor_count = contrast_count
else:
raise ValueError('Unknown mode: {}'.format(self.contrast_mode))
# compute logits
anchor_dot_contrast = torch.div(
torch.matmul(anchor_feature, contrast_feature.T),
self.temperature)
# for numerical stability
logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
logits = anchor_dot_contrast - logits_max.detach()
# tile mask
mask = mask.repeat(anchor_count, contrast_count)
# mask-out self-contrast cases
logits_mask = torch.scatter(
torch.ones_like(mask),
1,
torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
0
)
mask = mask * logits_mask
# compute log_prob
exp_logits = torch.exp(logits) * logits_mask
log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
# compute mean of log-likelihood over positive
mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
# loss
loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
loss = loss.view(anchor_count, batch_size).mean()
return loss
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