import torch
import torch.nn as nn
import torch.nn.functional as F
from mmaction.core.bbox import bbox_target
try:
from mmdet.models.builder import HEADS as MMDET_HEADS
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
[docs]class BBoxHeadAVA(nn.Module):
"""Simplest RoI head, with only two fc layers for classification and
regression respectively.
Args:
temporal_pool_type (str): The temporal pool type. Choices are 'avg' or
'max'. Default: 'avg'.
spatial_pool_type (str): The spatial pool type. Choices are 'avg' or
'max'. Default: 'max'.
in_channels (int): The number of input channels. Default: 2048.
num_classes (int): The number of classes. Default: 81.
dropout_ratio (float): A float in [0, 1], indicates the dropout_ratio.
Default: 0.
dropout_before_pool (bool): Dropout Feature before spatial temporal
pooling. Default: True.
topk (int or tuple[int]): Parameter for evaluating multilabel accuracy.
Default: (3, 5)
multilabel (bool): Whether used for a multilabel task. Default: True.
(Only support multilabel == True now).
"""
def __init__(
self,
temporal_pool_type='avg',
spatial_pool_type='max',
in_channels=2048,
# The first class is reserved, to classify bbox as pos / neg
num_classes=81,
dropout_ratio=0,
dropout_before_pool=True,
topk=(3, 5),
multilabel=True):
super(BBoxHeadAVA, self).__init__()
assert temporal_pool_type in ['max', 'avg']
assert spatial_pool_type in ['max', 'avg']
self.temporal_pool_type = temporal_pool_type
self.spatial_pool_type = spatial_pool_type
self.in_channels = in_channels
self.num_classes = num_classes
self.dropout_ratio = dropout_ratio
self.dropout_before_pool = dropout_before_pool
self.multilabel = multilabel
if topk is None:
self.topk = ()
elif isinstance(topk, int):
self.topk = (topk, )
elif isinstance(topk, tuple):
assert all([isinstance(k, int) for k in topk])
self.topk = topk
else:
raise TypeError('topk should be int or tuple[int], '
f'but get {type(topk)}')
# Class 0 is ignored when calculaing multilabel accuracy,
# so topk cannot be equal to num_classes
assert all([k < num_classes for k in self.topk])
# Handle AVA first
assert self.multilabel
in_channels = self.in_channels
# Pool by default
if self.temporal_pool_type == 'avg':
self.temporal_pool = nn.AdaptiveAvgPool3d((1, None, None))
else:
self.temporal_pool = nn.AdaptiveMaxPool3d((1, None, None))
if self.spatial_pool_type == 'avg':
self.spatial_pool = nn.AdaptiveAvgPool3d((None, 1, 1))
else:
self.spatial_pool = nn.AdaptiveMaxPool3d((None, 1, 1))
if dropout_ratio > 0:
self.dropout = nn.Dropout(dropout_ratio)
self.fc_cls = nn.Linear(in_channels, num_classes)
self.debug_imgs = None
def init_weights(self):
nn.init.normal_(self.fc_cls.weight, 0, 0.01)
nn.init.constant_(self.fc_cls.bias, 0)
[docs] def forward(self, x):
if self.dropout_before_pool and self.dropout_ratio > 0:
x = self.dropout(x)
x = self.temporal_pool(x)
x = self.spatial_pool(x)
if not self.dropout_before_pool and self.dropout_ratio > 0:
x = self.dropout(x)
x = x.view(x.size(0), -1)
cls_score = self.fc_cls(x)
# We do not predict bbox, so return None
return cls_score, None
def get_targets(self, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
cls_reg_targets = bbox_target(pos_proposals, neg_proposals,
pos_gt_labels, rcnn_train_cfg)
return cls_reg_targets
[docs] def recall_prec(self, pred_vec, target_vec):
"""
Args:
pred_vec (tensor[N x C]): each element is either 0 or 1
target_vec (tensor[N x C]): each element is either 0 or 1
"""
correct = pred_vec & target_vec
# Seems torch 1.5 has no auto type conversion
recall = correct.sum(1) / target_vec.sum(1).float()
prec = correct.sum(1) / (pred_vec.sum(1) + 1e-6)
return recall.mean(), prec.mean()
def multilabel_accuracy(self, pred, target, thr=0.5):
pred = pred.sigmoid()
pred_vec = pred > thr
# Target is 0 or 1, so using 0.5 as the borderline is OK
target_vec = target > 0.5
recall_thr, prec_thr = self.recall_prec(pred_vec, target_vec)
recalls, precs = [], []
for k in self.topk:
_, pred_label = pred.topk(k, 1, True, True)
pred_vec = pred.new_full(pred.size(), 0, dtype=torch.bool)
num_sample = pred.shape[0]
for i in range(num_sample):
pred_vec[i, pred_label[i]] = 1
recall_k, prec_k = self.recall_prec(pred_vec, target_vec)
recalls.append(recall_k)
precs.append(prec_k)
return recall_thr, prec_thr, recalls, precs
def loss(self,
cls_score,
bbox_pred,
rois,
labels,
label_weights,
bbox_targets=None,
bbox_weights=None,
reduce=True):
losses = dict()
if cls_score is not None:
# Only use the cls_score
labels = labels[:, 1:]
pos_inds = torch.sum(labels, dim=-1) > 0
cls_score = cls_score[pos_inds, 1:]
labels = labels[pos_inds]
bce_loss = F.binary_cross_entropy_with_logits
losses['loss_action_cls'] = bce_loss(cls_score, labels)
recall_thr, prec_thr, recall_k, prec_k = self.multilabel_accuracy(
cls_score, labels, thr=0.5)
losses['recall@thr=0.5'] = recall_thr
losses['prec@thr=0.5'] = prec_thr
for i, k in enumerate(self.topk):
losses[f'recall@top{k}'] = recall_k[i]
losses[f'prec@top{k}'] = prec_k[i]
return losses
def get_det_bboxes(self,
rois,
cls_score,
img_shape,
flip=False,
crop_quadruple=None,
cfg=None):
# might be used by testing w. augmentation
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
assert self.multilabel
scores = cls_score.sigmoid() if cls_score is not None else None
bboxes = rois[:, 1:]
assert bboxes.shape[-1] == 4
# First reverse the flip
img_h, img_w = img_shape
if flip:
bboxes_ = bboxes.clone()
bboxes_[:, 0] = img_w - 1 - bboxes[:, 2]
bboxes_[:, 2] = img_w - 1 - bboxes[:, 0]
bboxes = bboxes_
# Then normalize the bbox to [0, 1]
bboxes[:, 0::2] /= img_w
bboxes[:, 1::2] /= img_h
def _bbox_crop_undo(bboxes, crop_quadruple):
decropped = bboxes.clone()
if crop_quadruple is not None:
x1, y1, tw, th = crop_quadruple
decropped[:, 0::2] = bboxes[..., 0::2] * tw + x1
decropped[:, 1::2] = bboxes[..., 1::2] * th + y1
return decropped
bboxes = _bbox_crop_undo(bboxes, crop_quadruple)
return bboxes, scores
if mmdet_imported:
MMDET_HEADS.register_module()(BBoxHeadAVA)