mmselfsup.models.algorithms.densecl 源代码
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from mmengine.structures import BaseDataElement
from mmselfsup.registry import MODELS
from mmselfsup.structures import SelfSupDataSample
from mmselfsup.utils import (batch_shuffle_ddp, batch_unshuffle_ddp,
concat_all_gather)
from .base import BaseModel
[文档]@MODELS.register_module()
class DenseCL(BaseModel):
"""DenseCL.
Implementation of `Dense Contrastive Learning for Self-Supervised Visual
Pre-Training <https://arxiv.org/abs/2011.09157>`_.
Borrowed from the authors' code: `<https://github.com/WXinlong/DenseCL>`_.
The loss_lambda warmup is in `engine/hooks/densecl_hook.py`.
Args:
backbone (dict): Config dict for module of backbone.
neck (dict): Config dict for module of deep features to compact
feature vectors.
head (dict): Config dict for module of head functions.
queue_len (int): Number of negative keys maintained in the queue.
Defaults to 65536.
feat_dim (int): Dimension of compact feature vectors. Defaults to 128.
momentum (float): Momentum coefficient for the momentum-updated
encoder. Defaults to 0.999.
loss_lambda (float): Loss weight for the single and dense contrastive
loss. Defaults to 0.5.
pretrained (str, optional): The pretrained checkpoint path, support
local path and remote path. Defaults to None.
data_preprocessor (dict, optional): The config for preprocessing
input data. If None or no specified type, it will use
"SelfSupDataPreprocessor" as type.
See :class:`SelfSupDataPreprocessor` for more details.
Defaults to None.
init_cfg (Union[List[dict], dict], optional): Config dict for weight
initialization. Defaults to None.
"""
def __init__(self,
backbone: dict,
neck: dict,
head: dict,
queue_len: int = 65536,
feat_dim: int = 128,
momentum: float = 0.999,
loss_lambda: float = 0.5,
pretrained: Optional[str] = None,
data_preprocessor: Optional[dict] = None,
init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
super().__init__(
backbone=backbone,
neck=neck,
head=head,
pretrained=pretrained,
data_preprocessor=data_preprocessor,
init_cfg=init_cfg)
# create momentum model
self.encoder_k = ExponentialMovingAverage(
nn.Sequential(self.backbone, self.neck), 1 - momentum)
self.queue_len = queue_len
self.loss_lambda = loss_lambda
# create the queue
self.register_buffer('queue', torch.randn(feat_dim, queue_len))
self.queue = nn.functional.normalize(self.queue, dim=0)
self.register_buffer('queue_ptr', torch.zeros(1, dtype=torch.long))
# create the second queue for dense output
self.register_buffer('queue2', torch.randn(feat_dim, queue_len))
self.queue2 = nn.functional.normalize(self.queue2, dim=0)
self.register_buffer('queue2_ptr', torch.zeros(1, dtype=torch.long))
@torch.no_grad()
def _dequeue_and_enqueue(self, keys: torch.Tensor) -> None:
"""Update queue."""
# gather keys before updating queue
keys = concat_all_gather(keys)
batch_size = keys.shape[0]
ptr = int(self.queue_ptr)
assert self.queue_len % batch_size == 0 # for simplicity
# replace the keys at ptr (dequeue and enqueue)
self.queue[:, ptr:ptr + batch_size] = keys.transpose(0, 1)
ptr = (ptr + batch_size) % self.queue_len # move pointer
self.queue_ptr[0] = ptr
@torch.no_grad()
def _dequeue_and_enqueue2(self, keys: torch.Tensor) -> None:
"""Update queue2."""
# gather keys before updating queue
keys = concat_all_gather(keys)
batch_size = keys.shape[0]
ptr = int(self.queue2_ptr)
assert self.queue_len % batch_size == 0 # for simplicity
# replace the keys at ptr (dequeue and enqueue)
self.queue2[:, ptr:ptr + batch_size] = keys.transpose(0, 1)
ptr = (ptr + batch_size) % self.queue_len # move pointer
self.queue2_ptr[0] = ptr
[文档] def extract_feat(self, inputs: List[torch.Tensor],
**kwargs) -> Tuple[torch.Tensor]:
"""Function to extract features from backbone.
Args:
inputs (List[torch.Tensor]): The input images.
data_samples (List[SelfSupDataSample]): All elements required
during the forward function.
Returns:
Tuple[torch.Tensor]: Backbone outputs.
"""
x = self.backbone(inputs[0])
return x
[文档] def loss(self, inputs: List[torch.Tensor],
data_samples: List[SelfSupDataSample],
**kwargs) -> Dict[str, torch.Tensor]:
"""The forward function in training.
Args:
inputs (List[torch.Tensor]): The input images.
data_samples (List[SelfSupDataSample]): All elements required
during the forward function.
Returns:
Dict[str, torch.Tensor]: A dictionary of loss components.
"""
assert isinstance(inputs, list)
im_q = inputs[0]
im_k = inputs[1]
# compute query features
q_b = self.backbone(im_q) # backbone features
q, q_grid, q2 = self.neck(q_b) # queries: NxC; NxCxS^2
q_b = q_b[0]
q_b = q_b.view(q_b.size(0), q_b.size(1), -1)
q = nn.functional.normalize(q, dim=1)
q2 = nn.functional.normalize(q2, dim=1)
q_grid = nn.functional.normalize(q_grid, dim=1)
q_b = nn.functional.normalize(q_b, dim=1)
# compute key features
with torch.no_grad(): # no gradient to keys
# update the key encoder
self.encoder_k.update_parameters(
nn.Sequential(self.backbone, self.neck))
# shuffle for making use of BN
im_k, idx_unshuffle = batch_shuffle_ddp(im_k)
k_b = self.encoder_k.module[0](im_k) # backbone features
k, k_grid, k2 = self.encoder_k.module[1](k_b) # keys: NxC; NxCxS^2
k_b = k_b[0]
k_b = k_b.view(k_b.size(0), k_b.size(1), -1)
k = nn.functional.normalize(k, dim=1)
k2 = nn.functional.normalize(k2, dim=1)
k_grid = nn.functional.normalize(k_grid, dim=1)
k_b = nn.functional.normalize(k_b, dim=1)
# undo shuffle
k = batch_unshuffle_ddp(k, idx_unshuffle)
k2 = batch_unshuffle_ddp(k2, idx_unshuffle)
k_grid = batch_unshuffle_ddp(k_grid, idx_unshuffle)
k_b = batch_unshuffle_ddp(k_b, idx_unshuffle)
# compute logits
# Einstein sum is more intuitive
# positive logits: Nx1
l_pos = torch.einsum('nc,nc->n', [q, k]).unsqueeze(-1)
# negative logits: NxK
l_neg = torch.einsum('nc,ck->nk', [q, self.queue.clone().detach()])
# feat point set sim
backbone_sim_matrix = torch.matmul(q_b.permute(0, 2, 1), k_b)
densecl_sim_ind = backbone_sim_matrix.max(dim=2)[1] # NxS^2
indexed_k_grid = torch.gather(k_grid, 2,
densecl_sim_ind.unsqueeze(1).expand(
-1, k_grid.size(1), -1)) # NxCxS^2
densecl_sim_q = (q_grid * indexed_k_grid).sum(1) # NxS^2
# dense positive logits: NS^2X1
l_pos_dense = densecl_sim_q.view(-1).unsqueeze(-1)
q_grid = q_grid.permute(0, 2, 1)
q_grid = q_grid.reshape(-1, q_grid.size(2))
# dense negative logits: NS^2xK
l_neg_dense = torch.einsum(
'nc,ck->nk', [q_grid, self.queue2.clone().detach()])
loss_single = self.head(l_pos, l_neg)
loss_dense = self.head(l_pos_dense, l_neg_dense)
losses = dict()
losses['loss_single'] = loss_single * (1 - self.loss_lambda)
losses['loss_dense'] = loss_dense * self.loss_lambda
self._dequeue_and_enqueue(k)
self._dequeue_and_enqueue2(k2)
return losses
[文档] def predict(self, inputs: List[torch.Tensor],
data_samples: List[SelfSupDataSample],
**kwargs) -> SelfSupDataSample:
"""Predict results from the extracted features.
Args:
batch_inputs (List[torch.Tensor]): The input images.
data_samples (List[SelfSupDataSample]): All elements required
during the forward function.
Returns:
SelfSupDataSample: The prediction from model.
"""
q_grid = self.extract_feat(inputs)[0]
q_grid = q_grid.view(q_grid.size(0), q_grid.size(1), -1)
q_grid = nn.functional.normalize(q_grid, dim=1)
test_results = SelfSupDataSample()
q_grid = dict(value=q_grid)
q_grid = BaseDataElement(**q_grid)
test_results.q_grid = q_grid
return test_results