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# Copyright (c) Tianheng Cheng and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.optimize import linear_sum_assignment
from torch.cuda.amp import autocast
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.utils import reduce_mean
def compute_mask_iou(inputs, targets):
inputs = inputs.sigmoid()
# thresholding
binarized_inputs = (inputs >= 0.4).float()
targets = (targets > 0.5).float()
intersection = (binarized_inputs * targets).sum(-1)
union = targets.sum(-1) + binarized_inputs.sum(-1) - intersection
score = intersection / (union + 1e-6)
return score
def dice_score(inputs, targets):
inputs = inputs.sigmoid()
numerator = 2 * torch.matmul(inputs, targets.t())
denominator = (inputs * inputs).sum(-1)[:,
None] + (targets * targets).sum(-1)
score = numerator / (denominator + 1e-4)
return score
@MODELS.register_module()
class SparseInstCriterion(nn.Module):
"""This part is partially derivated from:
https://github.com/facebookresearch/detr/blob/main/models/detr.py.
"""
def __init__(
self,
num_classes,
assigner,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
alpha=0.25,
gamma=2.0,
reduction='sum',
loss_weight=2.0),
loss_obj=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=1.0),
loss_mask=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='DiceLoss',
use_sigmoid=True,
reduction='sum',
eps=5e-5,
loss_weight=2.0),
):
super().__init__()
self.matcher = TASK_UTILS.build(assigner)
self.num_classes = num_classes
self.loss_cls = MODELS.build(loss_cls)
self.loss_obj = MODELS.build(loss_obj)
self.loss_mask = MODELS.build(loss_mask)
self.loss_dice = MODELS.build(loss_dice)
def _get_src_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = torch.cat(
[torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
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 loss_classification(self, outputs, batch_gt_instances, indices,
num_instances):
assert 'pred_logits' in outputs
src_logits = outputs['pred_logits']
idx = self._get_src_permutation_idx(indices)
target_classes_o = torch.cat(
[gt.labels[J] for gt, (_, J) in zip(batch_gt_instances, indices)])
target_classes = torch.full(
src_logits.shape[:2],
self.num_classes,
dtype=torch.int64,
device=src_logits.device)
target_classes[idx] = target_classes_o
src_logits = src_logits.flatten(0, 1)
target_classes = target_classes.flatten(0, 1)
# comp focal loss.
class_loss = self.loss_cls(
src_logits,
target_classes,
) / num_instances
return class_loss
def loss_masks_with_iou_objectness(self, outputs, batch_gt_instances,
indices, num_instances):
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
# Bx100xHxW
assert 'pred_masks' in outputs
assert 'pred_scores' in outputs
src_iou_scores = outputs['pred_scores']
src_masks = outputs['pred_masks']
with torch.no_grad():
target_masks = torch.cat([
gt.masks.to_tensor(
dtype=src_masks.dtype, device=src_masks.device)
for gt in batch_gt_instances
])
num_masks = [len(gt.masks) for gt in batch_gt_instances]
target_masks = target_masks.to(src_masks)
if len(target_masks) == 0:
loss_dice = src_masks.sum() * 0.0
loss_mask = src_masks.sum() * 0.0
loss_objectness = src_iou_scores.sum() * 0.0
return loss_objectness, loss_dice, loss_mask
src_masks = src_masks[src_idx]
target_masks = F.interpolate(
target_masks[:, None],
size=src_masks.shape[-2:],
mode='bilinear',
align_corners=False).squeeze(1)
src_masks = src_masks.flatten(1)
# FIXME: tgt_idx
mix_tgt_idx = torch.zeros_like(tgt_idx[1])
cum_sum = 0
for num_mask in num_masks:
mix_tgt_idx[cum_sum:cum_sum + num_mask] = cum_sum
cum_sum += num_mask
mix_tgt_idx += tgt_idx[1]
target_masks = target_masks[mix_tgt_idx].flatten(1)
with torch.no_grad():
ious = compute_mask_iou(src_masks, target_masks)
tgt_iou_scores = ious
src_iou_scores = src_iou_scores[src_idx]
tgt_iou_scores = tgt_iou_scores.flatten(0)
src_iou_scores = src_iou_scores.flatten(0)
loss_objectness = self.loss_obj(src_iou_scores, tgt_iou_scores)
loss_dice = self.loss_dice(src_masks, target_masks) / num_instances
loss_mask = self.loss_mask(src_masks, target_masks)
return loss_objectness, loss_dice, loss_mask
def forward(self, outputs, batch_gt_instances, batch_img_metas,
batch_gt_instances_ignore):
# Retrieve the matching between the outputs of
# the last layer and the targets
indices = self.matcher(outputs, batch_gt_instances)
# Compute the average number of target boxes
# across all nodes, for normalization purposes
num_instances = sum(gt.labels.shape[0] for gt in batch_gt_instances)
num_instances = torch.as_tensor([num_instances],
dtype=torch.float,
device=next(iter(
outputs.values())).device)
num_instances = reduce_mean(num_instances).clamp_(min=1).item()
# Compute all the requested losses
loss_cls = self.loss_classification(outputs, batch_gt_instances,
indices, num_instances)
loss_obj, loss_dice, loss_mask = self.loss_masks_with_iou_objectness(
outputs, batch_gt_instances, indices, num_instances)
return dict(
loss_cls=loss_cls,
loss_obj=loss_obj,
loss_dice=loss_dice,
loss_mask=loss_mask)
@TASK_UTILS.register_module()
class SparseInstMatcher(nn.Module):
def __init__(self, alpha=0.8, beta=0.2):
super().__init__()
self.alpha = alpha
self.beta = beta
self.mask_score = dice_score
def forward(self, outputs, batch_gt_instances):
with torch.no_grad():
B, N, H, W = outputs['pred_masks'].shape
pred_masks = outputs['pred_masks']
pred_logits = outputs['pred_logits'].sigmoid()
device = pred_masks.device
tgt_ids = torch.cat([gt.labels for gt in batch_gt_instances])
if tgt_ids.shape[0] == 0:
return [(torch.as_tensor([]).to(pred_logits),
torch.as_tensor([]).to(pred_logits))] * B
tgt_masks = torch.cat([
gt.masks.to_tensor(dtype=pred_masks.dtype, device=device)
for gt in batch_gt_instances
])
tgt_masks = F.interpolate(
tgt_masks[:, None],
size=pred_masks.shape[-2:],
mode='bilinear',
align_corners=False).squeeze(1)
pred_masks = pred_masks.view(B * N, -1)
tgt_masks = tgt_masks.flatten(1)
with autocast(enabled=False):
pred_masks = pred_masks.float()
tgt_masks = tgt_masks.float()
pred_logits = pred_logits.float()
mask_score = self.mask_score(pred_masks, tgt_masks)
# Nx(Number of gts)
matching_prob = pred_logits.view(B * N, -1)[:, tgt_ids]
C = (mask_score**self.alpha) * (matching_prob**self.beta)
C = C.view(B, N, -1).cpu()
# hungarian matching
sizes = [len(gt.masks) for gt in batch_gt_instances]
indices = [
linear_sum_assignment(c[i], maximize=True)
for i, c in enumerate(C.split(sizes, -1))
]
indices = [(torch.as_tensor(i, dtype=torch.int64),
torch.as_tensor(j, dtype=torch.int64))
for i, j in indices]
return indices
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