mirror of https://github.com/alibaba/EasyCV.git
99 lines
4.9 KiB
Python
99 lines
4.9 KiB
Python
import torch
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import torch.nn as nn
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from scipy.optimize import linear_sum_assignment
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from easycv.models.detection.utils import (box_cxcywh_to_xyxy,
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generalized_box_iou)
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class HungarianMatcher(nn.Module):
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"""This class computes an assignment between the targets and the predictions of the network
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For efficiency reasons, the targets don't include the no_object. Because of this, in general,
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there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
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while the others are un-matched (and thus treated as non-objects).
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"""
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def __init__(self, cost_dict, cost_class_type='ce_cost'):
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"""Creates the matcher
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Params:
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cost_class: This is the relative weight of the classification error in the matching cost
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cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
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cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
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"""
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super().__init__()
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self.cost_class = cost_dict['cost_class']
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self.cost_bbox = cost_dict['cost_bbox']
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self.cost_giou = cost_dict['cost_giou']
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self.cost_class_type = cost_class_type
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assert self.cost_class != 0 or self.cost_bbox != 0 or self.cost_giou != 0, 'all costs cant be 0'
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@torch.no_grad()
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def forward(self, outputs, targets):
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""" Performs the matching
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Params:
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outputs: This is a dict that contains at least these entries:
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"pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
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"pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
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targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
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"labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
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objects in the target) containing the class labels
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"boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
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Returns:
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A list of size batch_size, containing tuples of (index_i, index_j) where:
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- index_i is the indices of the selected predictions (in order)
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- index_j is the indices of the corresponding selected targets (in order)
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For each batch element, it holds:
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len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
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"""
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bs, num_queries = outputs['pred_logits'].shape[:2]
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# We flatten to compute the cost matrices in a batch
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if self.cost_class_type == 'focal_loss_cost':
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out_prob = outputs['pred_logits'].flatten(
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0, 1).sigmoid() # [batch_size * num_queries, num_classes]
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elif self.cost_class_type == 'ce_cost':
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out_prob = outputs['pred_logits'].flatten(0, 1).softmax(
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-1) # [batch_size * num_queries, num_classes]
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out_bbox = outputs['pred_boxes'].flatten(
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0, 1) # [batch_size * num_queries, 4]
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# Also concat the target labels and boxes
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tgt_ids = torch.cat([v['labels'] for v in targets])
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tgt_bbox = torch.cat([v['boxes'] for v in targets])
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# Compute the classification cost.
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if self.cost_class_type == 'focal_loss_cost':
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alpha = 0.25
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gamma = 2.0
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neg_cost_class = (1 - alpha) * (out_prob**gamma)
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neg_cost_class = neg_cost_class * (-(1 - out_prob + 1e-8).log())
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pos_cost_class = alpha * ((1 - out_prob)**gamma)
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pos_cost_class = pos_cost_class * (-(out_prob + 1e-8).log())
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cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:,
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tgt_ids]
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elif self.cost_class_type == 'ce_cost':
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# Compute the classification cost. Contrary to the loss, we don't use the NLL,
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# but approximate it in 1 - proba[target class].
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# The 1 is a constant that doesn't change the matching, it can be ommitted.
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cost_class = -out_prob[:, tgt_ids]
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# Compute the L1 cost between boxes
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cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
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# Compute the giou cost betwen boxes
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cost_giou = -generalized_box_iou(
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box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))
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# Final cost matrix
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C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
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C = C.view(bs, num_queries, -1).cpu()
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sizes = [len(v['boxes']) for v in targets]
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indices = [
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linear_sum_assignment(c[i])
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for i, c in enumerate(C.split(sizes, -1))
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]
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return [(torch.as_tensor(i, dtype=torch.int64),
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torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
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