DETR代码学习笔记（三）
前两篇博文已经将DETR中的数据输入以及transformer大致记录完，感兴趣的可以往回看：
DETR代码学习笔记（一）
DETR代码学习笔记（二）
整个算法流程中最难的应该是损失函数的设计，很有意思的是DETR中使用了匈牙利算法，该算法的一般理解是在某个任务中，比如打扫卫生，找到耗时最少的工作分配方式，在DETR中就是找到最优匹配。大致的理解可以看我的另一篇博文理解匈牙利算法。
这里偷个懒，要注意的都注释在代码中了，直接看代码
class HungarianMatcher(nn.Module):
"""This class computes an assignment between the targets and the predictions of the network
For efficiency reasons, the targets don't include the no_object. Because of this, in general,
there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
while the others are un-matched (and thus treated as non-objects).
"""
def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):
"""Creates the matcher
Params:
cost_class: This is the relative weight of the classification error in the matching cost
cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
"""
super().__init__()
self.cost_class = cost_class
self.cost_bbox = cost_bbox
self.cost_giou = cost_giou
assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0"
@torch.no_grad()
def forward(self, outputs, targets):
""" Performs the matching
Params:
outputs: This is a dict that contains at least these entries:
"pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
"pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
"labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
objects in the target) containing the class labels
"boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
Returns:
A list of size batch_size, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds:
len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
"""
bs, num_queries = outputs["pred_logits"].shape[:2]
# We flatten to compute the cost matrices in a batch
# 将batch维度合并,out_prob shape为[200,92],out_bbox shape为[200,4]
out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes]
out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4]
# Also concat the target labels and boxes
# 将目标的ground truth id和bbox的batch维度合并,假设此处有22个类
# 即假设第一张图像有20个类,第二张图像有2个类
# 那么tgt_ids的shape为22,tgt_bbox的shape为[22,4]
tgt_ids = torch.cat([v["labels"] for v in targets])
tgt_bbox = torch.cat([v["boxes"] for v in targets])
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
# 取出out_prob每一行对应索引中的元素,此时cost_class的shape为[200,22]
cost_class = -out_prob[:, tgt_ids]
# Compute the L1 cost between boxes
# 计算out_bbox和tgt_bbox的L1距离,此时cost_bbox的shape为[200,22]
cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
# Compute the giou cost betwen boxes
# 计算giou,此时cost_giou的shape为[200,22]
cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))
# Final cost matrix
# C [200,22]->[2,100,22]
C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
C = C.view(bs, num_queries, -1).cpu()
# size [20,2]
sizes = [len(v["boxes"]) for v in targets]
# 匈牙利算法的实现,指派最优的目标索引,输出一个二维列表,第一维是batch为0,即一个batch中第一张图像通过匈
# 牙利算法计算得到的最优解的横纵坐标,第二维是batch为1,即一个batch中第二张图像,后面的batch维度以此类推
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
for i, c in enumerate(C.split(sizes, -1)):
import numpy as np
cost_matrix = np.asarray(c[i])
# print('cost_matrix:', cost_matrix)
row_ind, col_ind = linear_sum_assignment(c[i])
for (row, col) in zip(row_ind, col_ind):
print(row, col, '***', cost_matrix[row][col])
print('11:', indices)
# 由于indices调用的是scipy.optimize库,输出的是一个numpy数组,最后输出的indices需要转换为torch tensor
now = [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
return now
上面的代码为了方便理解做了一点点改动
整体的损失函数定义在SetCriterion类中，要注意的也都注释在代码中
class SetCriterion(nn.Module):
""" This class computes the loss for DETR.
The process happens in two steps:
1) we compute hungarian assignment between ground truth boxes and the outputs of the model
2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
"""
def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses):
""" Create the criterion.
Parameters:
num_classes: number of object categories, omitting the special no-object category
matcher: module able to compute a matching between targets and proposals
weight_dict: dict containing as key the names of the losses and as values their relative weight.
eos_coef: relative classification weight applied to the no-object category
losses: list of all the losses to be applied. See get_loss for list of available losses.
"""
super().__init__()
self.num_classes = num_classes
self.matcher = matcher
self.weight_dict = weight_dict
self.eos_coef = eos_coef
self.losses = losses
empty_weight = torch.ones(self.num_classes + 1)
empty_weight[-1] = self.eos_coef
self.register_buffer('empty_weight', empty_weight)
def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
"""Classification loss (NLL)
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
"""
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
# _get_src_permutation_idx(indices)返回两个值batch_idx和src_idx
# batch_idx得到的就是匈牙利算法得到的索引是属于哪一张图像,如tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1])
# 前20属于第一张,最后两个属于第二张
# src_idx则表示匈牙利算法得到的横坐标信息,如tensor([14, 20, 24, 28, 32, 37, 42, 46, 50, 52, 60, 64, 67, 70, 79, 87, 91, 93, 94, 97, 6, 31])
# idx = (batch_idx,src_idx)
idx = self._get_src_permutation_idx(indices)
# target_classes_o由targets["labels"] 根据 indices的纵坐标重新排序得到
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(src_logits.shape[:2], self.num_classes,
dtype=torch.int64, device=src_logits.device)
# 根据idx将target_classes_o中的值映射到[2,100]值为91的张量中
target_classes[idx] = target_classes_o
# 计算预测输出的类别损失
loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)
losses = {'loss_ce': loss_ce}
if log:
# TODO this should probably be a separate loss, not hacked in this one here
losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
return losses
@torch.no_grad()
def loss_cardinality(self, outputs, targets, indices, num_boxes):
""" Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients
"""
pred_logits = outputs['pred_logits']
device = pred_logits.device
tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device)
# Count the number of predictions that are NOT "no-object" (which is the last class)
card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)
card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())
losses = {'cardinality_error': card_err}
return losses
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
assert 'pred_boxes' in outputs
# _get_src_permutation_idx(indices)返回两个值batch_idx和src_idx
# batch_idx得到的就是匈牙利算法得到的索引是属于哪一张图像,如tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1])
# 前20属于第一张,最后两个属于第二张
# src_idx则表示匈牙利算法得到的横坐标信息,如tensor([14, 20, 24, 28, 32, 37, 42, 46, 50, 52, 60, 64, 67, 70, 79, 87, 91, 93, 94, 97, 6, 31])
# idx = (batch_idx,src_idx)
idx = self._get_src_permutation_idx(indices)
# 根据indices的横坐标提取预测输出outputs['pred_boxes']中的对应bbox
src_boxes = outputs['pred_boxes'][idx]
# target_boxes由targets['boxes'] 根据 indices的纵坐标重新排序得到
target_boxes = torch.cat([t['boxes'][i] for t, (_, i) in zip(targets, indices)], dim=0)
loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')
losses = {}
losses['loss_bbox'] = loss_bbox.sum() / num_boxes
loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(
box_ops.box_cxcywh_to_xyxy(src_boxes),
box_ops.box_cxcywh_to_xyxy(target_boxes)))
losses['loss_giou'] = loss_giou.sum() / num_boxes
return losses
def loss_masks(self, outputs, targets, indices, num_boxes):
"""Compute the losses related to the masks: the focal loss and the dice loss.
targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
"""
assert "pred_masks" in outputs
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
src_masks = outputs["pred_masks"]
src_masks = src_masks[src_idx]
masks = [t["masks"] for t in targets]
# TODO use valid to mask invalid areas due to padding in loss
target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
target_masks = target_masks.to(src_masks)
target_masks = target_masks[tgt_idx]
# upsample predictions to the target size
src_masks = interpolate(src_masks[:, None], size=target_masks.shape[-2:],
mode="bilinear", align_corners=False)
src_masks = src_masks[:, 0].flatten(1)
target_masks = target_masks.flatten(1)
target_masks = target_masks.view(src_masks.shape)
losses = {
"loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_boxes),
"loss_dice": dice_loss(src_masks, target_masks, num_boxes),
}
return losses
def _get_src_permutation_idx(self, indices):
# permute predictions following indices
# 输入参数indices是匹配的预测（query）索引与GT的索引，其形式在上述SetCriterion(iv)
# 图中注释已有说明。该方法返回一个tuple，代表所有匹配的预测结果的batch
# index（在当前batch中属于第几张图像）和
# query
# index（图像中的第几个query对象）。
# batch_idx得到的就是匈牙利算法得到的索引是属于哪一张图像,如tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1])
# 前20属于第一张,最后两个属于第二张
batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
# src_idx则表示匈牙利算法得到的横坐标信息,如tensor([14, 20, 24, 28, 32, 37, 42, 46, 50, 52, 60, 64, 67, 70, 79, 87, 91, 93, 94, 97, 6, 31])
src_idx = torch.cat([src for (src, _) in indices])
return batch_idx, src_idx
def _get_tgt_permutation_idx(self, indices):
# permute targets following indices
batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
tgt_idx = torch.cat([tgt for (_, tgt) in indices])
return batch_idx, tgt_idx
def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
loss_map = {
'labels': self.loss_labels,
'cardinality': self.loss_cardinality,
'boxes': self.loss_boxes,
'masks': self.loss_masks
}
assert loss in loss_map, f'do you really want to compute {loss} loss?'
return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs)
def forward(self, outputs, targets):
""" This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}
# Retrieve the matching between the outputs of the last layer and the targets
# 假设这是其中一组输出[(tensor([14, 20, 24, 28, 32, 37, 42, 46, 50, 52, 60, 64, 67, 70, 79, 87, 91, 93,94, 97]),
# tensor([13, 1, 19, 0, 18, 2, 7, 4, 17, 3, 14, 6, 8, 9, 12, 11, 16, 15, 10, 5])),
# (tensor([ 6, 31]), tensor([1, 0]))]
# 其中的(14,13)即为匈牙利算法求出的第一张图像中最优解的横纵坐标
indices = self.matcher(outputs_without_aux, targets)
# Compute the average number of target boxes accross all nodes, for normalization purposes
num_boxes = sum(len(t["labels"]) for t in targets)
num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_boxes)
num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()
# Compute all the requested losses
losses = {}
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if 'aux_outputs' in outputs:
for i, aux_outputs in enumerate(outputs['aux_outputs']):
indices = self.matcher(aux_outputs, targets)
for loss in self.losses:
if loss == 'masks':
# Intermediate masks losses are too costly to compute, we ignore them.
continue
kwargs = {}
if loss == 'labels':
# Logging is enabled only for the last layer
kwargs = {'log': False}
l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs)
l_dict = {k + f'_{i}': v for k, v in l_dict.items()}
losses.update(l_dict)
return losses之后就是反向传播优化参数了，到这里有关DETR的所有内容基本就讲完了，如有错误之处还请大佬指正
如果对纯transformer的目标检测算法感兴趣的话，可以参看我的另一篇博文swin-transformer