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Source code for mmselfsup.models.algorithms.deepcluster
# 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_neck
from ..utils import Sobel
from .base import BaseModel
[docs]@ALGORITHMS.register_module()
class DeepCluster(BaseModel):
"""DeepCluster.
Implementation of `Deep Clustering for Unsupervised Learning
of Visual Features <https://arxiv.org/abs/1807.05520>`_.
The clustering operation is in `core/hooks/deepcluster_hook.py`.
Args:
backbone (dict): Config dict for module of backbone.
with_sobel (bool): Whether to apply a Sobel filter on images.
Defaults to True.
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.
"""
def __init__(self,
backbone,
with_sobel=True,
neck=None,
head=None,
init_cfg=None):
super(DeepCluster, 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)
# re-weight
self.num_classes = self.head.num_classes
self.loss_weight = torch.ones((self.num_classes, ),
dtype=torch.float32)
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, pseudo_label, **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.
pseudo_label (Tensor): Label assignments.
kwargs: Any keyword arguments to be used to forward.
Returns:
dict[str, Tensor]: A dictionary of loss components.
"""
x = self.extract_feat(img)
if self.with_neck:
x = self.neck(x)
outs = self.head(x)
loss_inputs = (outs, pseudo_label)
losses = self.head.loss(*loss_inputs)
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.
"""
x = self.extract_feat(img) # tuple
if self.with_neck:
x = self.neck(x)
outs = self.head(x)
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, 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.
reweight_pow (float): The power of re-weighting. Defaults to 0.5.
"""
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)