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Source code for mmselfsup.models.algorithms.odc
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.nn as nn
from ..builder import (ALGORITHMS, build_backbone, build_head, build_memory,
build_neck)
from ..utils import Sobel
from .base import BaseModel
[docs]@ALGORITHMS.register_module()
class ODC(BaseModel):
"""ODC.
Official implementation of `Online Deep Clustering for Unsupervised
Representation Learning <https://arxiv.org/abs/2006.10645>`_.
The operation w.r.t. memory bank and loss re-weighting is in
`core/hooks/odc_hook.py`.
Args:
backbone (dict): Config dict for module of backbone.
with_sobel (bool): Whether to apply a Sobel filter on images.
Defaults to False.
neck (dict): Config dict for module of deep features to compact feature
vectors. Defaults to None.
head (dict): Config dict for module of loss functions.
Defaults to None.
memory_bank (dict): Module of memory banks. Defaults to None.
"""
def __init__(self,
backbone,
with_sobel=False,
neck=None,
head=None,
memory_bank=None,
init_cfg=None):
super(ODC, self).__init__(init_cfg)
self.with_sobel = with_sobel
if with_sobel:
self.sobel_layer = Sobel()
self.backbone = build_backbone(backbone)
if neck is not None:
self.neck = build_neck(neck)
assert head is not None
self.head = build_head(head)
assert memory_bank is not None
self.memory_bank = build_memory(memory_bank)
# set re-weight tensors
self.num_classes = self.head.num_classes
self.loss_weight = torch.ones((self.num_classes, ),
dtype=torch.float32).cuda()
self.loss_weight /= self.loss_weight.sum()
[docs] def extract_feat(self, img):
"""Function to extract features from backbone.
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
Returns:
tuple[Tensor]: backbone outputs.
"""
if self.with_sobel:
img = self.sobel_layer(img)
x = self.backbone(img)
return x
[docs] def forward_train(self, img, idx, **kwargs):
"""Forward computation during training.
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
idx (Tensor): Index corresponding to each image.
kwargs: Any keyword arguments to be used to forward.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
# forward & backward
feature = self.extract_feat(img)
if self.with_neck:
feature = self.neck(feature)
outs = self.head(feature)
if self.memory_bank.label_bank.is_cuda:
loss_inputs = (outs, self.memory_bank.label_bank[idx])
else:
loss_inputs = (outs, self.memory_bank.label_bank[idx.cpu()].cuda())
losses = self.head.loss(*loss_inputs)
# update samples memory
change_ratio = self.memory_bank.update_samples_memory(
idx, feature[0].detach())
losses['change_ratio'] = change_ratio
return losses
[docs] def forward_test(self, img, **kwargs):
"""Forward computation during test.
Args:
img (Tensor): Input images of shape (N, C, H, W).
Typically these should be mean centered and std scaled.
Returns:
dict[str, Tensor]: A dictionary of output features.
"""
feature = self.extract_feat(img) # tuple
if self.with_neck:
feature = self.neck(feature)
outs = self.head(feature)
keys = [f'head{i}' for i in range(len(outs))]
out_tensors = [out.cpu() for out in outs] # NxC
return dict(zip(keys, out_tensors))
[docs] def set_reweight(self, labels=None, reweight_pow=0.5):
"""Loss re-weighting.
Re-weighting the loss according to the number of samples in each class.
Args:
labels (numpy.ndarray): Label assignments. Defaults to None.
reweight_pow (float): The power of re-weighting. Defaults to 0.5.
"""
if labels is None:
if self.memory_bank.label_bank.is_cuda:
labels = self.memory_bank.label_bank.cpu().numpy()
else:
labels = self.memory_bank.label_bank.numpy()
histogram = np.bincount(
labels, minlength=self.num_classes).astype(np.float32)
inv_histogram = (1. / (histogram + 1e-10))**reweight_pow
weight = inv_histogram / inv_histogram.sum()
self.loss_weight.copy_(torch.from_numpy(weight))
self.head.criterion = nn.CrossEntropyLoss(weight=self.loss_weight)