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random_walk_loss.py
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random_walk_loss.py
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import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import torchvision.transforms as transforms
from model import Discriminator
class DeNormalize(object):
def __init__(self, mean, std):
self.mean = torch.tensor(mean)
self.std = torch.tensor(std)
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized image.
"""
self.std = self.std.to(tensor.device)
self.mean = self.mean.to(tensor.device)
tensor.mul_(self.std[:, None, None]).add_(self.mean[:, None, None])
return tensor
wikiart_denormalize = DeNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
imagenet_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std = [0.229, 0.224, 0.225])
def get_nearest_mean_examples(dset, key, module, examples_per_class=10, batch_size=64, verbose=False, diverse=False):
"""
1. Get samples nearest to the the actual mean of the class
2. Get samples nearest to the the actual mean of the class but are diverse.
"""
proto_files = {}
## Changing resolution to 256 for proper calculation of protos
current_res = dset.resolution
dset.resolution = 256
labels = getattr(dset, key)
classes, counts = np.unique(labels, return_counts=True)
for itr, (clas, count) in enumerate(zip(classes, counts)):
low = 0
labels = torch.tensor(labels)
indexes, = (labels[:, 0] == clas).nonzero(as_tuple=True)
output_tensor = []
high = count
for i in range(low, count, batch_size):
range_value = indexes[i:i+batch_size if i+batch_size < high else high]
input_tensor = [imagenet_normalize(wikiart_denormalize(dset[k]['img']))[None] for k in range_value]
with torch.no_grad():
output_tensor.append(module(torch.cat(input_tensor, dim=0)))
class_features = torch.cat(output_tensor, dim=0)
mean_class_features = class_features.mean(dim=0)
dist = torch.norm(class_features - mean_class_features[None], dim=1, p=None)
if diverse:
samples = get_diverse_samples(class_features, mean_class_features, count, examples_per_class, verbose=verbose)
proto_files[itr] = indexes[samples].tolist()
else:
knn = dist.topk(examples_per_class, largest=False)
if verbose:
print("norm: ", torch.norm(class_features[knn.indices].mean(dim=0) - mean_class_features, dim=0, p=None))
proto_files[itr] = indexes[knn.indices].tolist()
if verbose:
print('\n', itr, proto_files[itr])
print('\n')
## setting the current resolution back.
dset.resolution = current_res
return proto_files
def get_diverse_samples(class_features, mean_class_features, count, examples_per_class, top_k=100, verbose=False):
total_features, feature_size = class_features.shape
K = 5000
indices = torch.cat([torch.tensor(np.random.choice(np.arange(total_features), size=examples_per_class, replace=False)) for i in range(K)])
Y = class_features[indices].view(K, examples_per_class, feature_size).contiguous()
indices = indices.view(K, examples_per_class)
mean_Y = Y.mean(dim=1)
dist = torch.norm(mean_Y - mean_class_features[None], dim=1, p=None)
topk = dist.topk(top_k, largest=False)
Ys = Y[topk.indices]
indices = indices[topk.indices]
if verbose:
print(indices)
print(dist[topk.indices])
return indices[torch.randint(0, len(indices), size=(1,))][0]
def get_random_examples(dset, key, examples_per_class=10):
low = 0
proto_files = {}
labels = getattr(dset, key)
classes, counts = np.unique(labels, return_counts=True)
for itr, (clas, count) in enumerate(zip(classes, counts)):
labels = torch.tensor(labels)
indexes, = (labels[:, 0] == clas).nonzero(as_tuple=True)
proto_files[itr] = indexes[torch.randint(0, int(count), (examples_per_class,))].tolist()
return proto_files
def compute_protos(proto_files, key, module, dset, grad_proto, module_kwargs, verbose=False):
protos = []
for clas, idxs in proto_files.items():
input_tensors = []
for idx in idxs:
item = dset[idx]
if verbose:
print(item['img'].shape, item['img'].min(), item['img'].max(), item['img'].mean((1,2)))
input_tensors.append(item['img'][None])
input_batch = torch.cat(input_tensors, dim=0).cuda()
with torch.no_grad():
if key == 'pruned_style_class':
### 10 images on single GPU.
if type(module) == Discriminator:
_, features = module(input_batch, **module_kwargs)
else:
_, features = module.module(input_batch, **module_kwargs)
protos.append(features.mean(0)[None])
return torch.cat(protos, dim=0)
def imitative_loss(input, binary=False, unique_cv=None):
current_device = input.device
eye = torch.eye(input.shape[0], input.shape[0]).to(current_device)
if unique_cv is not None:
if binary:
return F.binary_cross_entropy(input[unique_cv].view(-1, 1), eye[unique_cv].view(-1, 1))
else:
return F.kl_div(input[unique_cv].log(), eye[unique_cv])
else:
if binary:
return F.binary_cross_entropy(input.view(-1, 1), eye.view(-1, 1))
else:
return F.kl_div(input.log(), eye)
def creative_loss(input, binary=False):
current_device = input.device
target = torch.ones(input.shape[0], input.shape[0]).to(current_device) / input.shape[0]
if binary:
return F.binary_cross_entropy(input.view(-1, 1), target.view(-1, 1))
else:
return F.kl_div(input.log(), target)
def visiting_loss(input):
current_device = input.device
target = torch.ones_like(input).to(current_device)/ input.shape[0]
return F.kl_div(input.log().view(1,-1), target.view(1,-1))
class RWLoss(nn.Module):
def __init__(self,
tau=3,
alpha=0.7,
binary=False,
graph_smoothing=0.1,
proto_method='random_once',
running_mean_factor=None,
feature_extractor=None,
opt=None
):
"""
n_classes: total number of classes will be required for mean proto.
tau: number of hops on unlabelled points
alpha: decay factor for loss
binary: If true, it will comput binary cross entropy loss.
graph_smoothing: As in TF implementation.
proto_method:'random_once', 'random_all', 'nearest_mean'
"""
super().__init__()
self.tau = tau
self.alpha = alpha
self.binary = binary
self.c = graph_smoothing
self.proto_method = proto_method
self.running_mean_factor = running_mean_factor
self.opt = opt
self.feature_extractor = feature_extractor
self.proto_examples = {}
self.protos = {}
if opt.normalize_protos_scale is not None:
print(f"Prototypes and features will be normalized with value {opt.normalize_protos_scale}")
self.normalize_protos = True
self.normalize_scale = opt.normalize_protos_scale
else:
print("Prototypes and features will not be normalized")
self.normalize_protos = False
def forward(self,
features,
labels,
discriminator,
mode,
dataset,
key,
**kwargs):
"""
features: BXF
labels: N (tensor of integers)
discriminator: nn.Module to compute protos
mode: string "creative" or "imitative"
dataset: dataset object
"""
verbose = kwargs.get('verbose', False)
self.mode = mode
N, F = features.shape
if labels is not None:
unique_cv = labels.unique()
else:
unique_cv = None
module_kwargs = kwargs.get('module_kwargs', {})
if self.proto_method == 'random_once':
if hasattr(self, 'proto_examples'):
if key not in self.proto_examples.keys():
self.proto_examples[key] = get_random_examples(dataset, key, 10)
elif self.proto_method == 'random_all':
self.proto_examples = get_random_examples(dataset, 10)
elif self.proto_method == 'nearest_mean_once':
if hasattr(self, 'proto_examples'): # compute best options using self.feature_extractor
if key not in self.proto_examples.keys():
self.proto_examples[key] = get_nearest_mean_examples(dataset, key, self.feature_extractor, 10, verbose=verbose)
elif self.proto_method == 'nearest_mean_once_diverse':
if hasattr(self, 'proto_examples'): # compute best options using self.feature_extractor
if key not in self.proto_examples.keys():
self.proto_examples[key] = get_nearest_mean_examples(dataset, key, self.feature_extractor, 10, verbose=verbose, diverse=True)
if verbose:
print('computing protos')
protos = compute_protos(self.proto_examples[key], key, discriminator, dataset, self.opt.RW_grad_proto, module_kwargs)
self.protos[key] = protos
if verbose:
print('protos shape ', protos.shape)
current_device = self.protos[key].device
if self.normalize_protos:
features = (features * self.normalize_scale) / torch.norm(features, dim=-1, keepdim=True).detach()
self.protos[key] = (self.protos[key] * self.normalize_scale) / torch.norm(self.protos[key], dim=-1, keepdim=True).detach()
A = torch.norm(self.protos[key].unsqueeze(1) - features, dim=-1).pow(2).mul(-1)
A_t = (1 - self.c) * A.t().softmax(-1) + (self.c * torch.ones_like(A.t())/len(self.protos[key]))
A = A.softmax(-1)
A = (1 - self.c) * A + (self.c * torch.ones_like(A) / len(self.protos[key]))
A_t = (1 - self.c) * A_t + (self.c * torch.ones_like(A_t) / N)
B = ((torch.norm(features.unsqueeze(1) - features, dim=-1).pow(2).mul(-1)) - torch.eye(N,N).to(current_device) * 1000).softmax(-1) ## Adding -1000 at diagonals to avoid self loops
## Random Walk.
landing_probs = []
T0 = A@A_t
landing_probs.append(T0)
T = A
for i in range(self.tau - 1):
T = T@B
landing_probs.append(T@A_t)
loss = 0.
for i, p in enumerate(landing_probs):
if self.mode == 'creative':
loss += (self.alpha ** i) * creative_loss(p, self.binary)
elif self.mode == 'imitative':
loss += (self.alpha ** i) * imitative_loss(p, self.binary, unique_cv)
else:
raise Exception("Wrong Mode")
if verbose:
print(loss, visiting_loss(A))
loss += visiting_loss(A)
return loss