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# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import YOLACTHead, YOLACTProtonet, YOLACTSegmHead
def test_yolact_head_loss():
"""Tests yolact head losses when truth is empty and non-empty."""
s = 550
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.Config(
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.,
ignore_iof_thr=-1,
gt_max_assign_all=False),
smoothl1_beta=1.,
allowed_border=-1,
pos_weight=-1,
neg_pos_ratio=3,
debug=False,
min_gt_box_wh=[4.0, 4.0]))
bbox_head = YOLACTHead(
num_classes=80,
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=3,
scales_per_octave=1,
base_sizes=[8, 16, 32, 64, 128],
ratios=[0.5, 1.0, 2.0],
strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
centers=[(550 * 0.5 / x, 550 * 0.5 / x)
for x in [69, 35, 18, 9, 5]]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
reduction='none',
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
num_head_convs=1,
num_protos=32,
use_ohem=True,
train_cfg=train_cfg)
segm_head = YOLACTSegmHead(
in_channels=256,
num_classes=80,
loss_segm=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))
mask_head = YOLACTProtonet(
num_classes=80,
in_channels=256,
num_protos=32,
max_masks_to_train=100,
loss_mask_weight=6.125)
feat = [
torch.rand(1, 256, feat_size, feat_size)
for feat_size in [69, 35, 18, 9, 5]
]
cls_score, bbox_pred, coeff_pred = bbox_head.forward(feat)
# Test that empty ground truth encourages the network to predict background
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
gt_masks = [torch.empty((0, 550, 550))]
gt_bboxes_ignore = None
empty_gt_losses, sampling_results = bbox_head.loss(
cls_score,
bbox_pred,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=gt_bboxes_ignore)
# When there is no truth, the cls loss should be nonzero but there should
# be no box loss.
empty_cls_loss = sum(empty_gt_losses['loss_cls'])
empty_box_loss = sum(empty_gt_losses['loss_bbox'])
assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
assert empty_box_loss.item() == 0, (
'there should be no box loss when there are no true boxes')
# Test segm head and mask head
segm_head_outs = segm_head(feat[0])
empty_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels)
mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas,
sampling_results)
empty_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes, img_metas,
sampling_results)
# When there is no truth, the segm and mask loss should be zero.
empty_segm_loss = sum(empty_segm_loss['loss_segm'])
empty_mask_loss = sum(empty_mask_loss['loss_mask'])
assert empty_segm_loss.item() == 0, (
'there should be no segm loss when there are no true boxes')
assert empty_mask_loss == 0, (
'there should be no mask loss when there are no true boxes')
# When truth is non-empty then cls, box, mask, segm loss should be
# nonzero for random inputs.
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
gt_masks = [(torch.rand((1, 550, 550)) > 0.5).float()]
one_gt_losses, sampling_results = bbox_head.loss(
cls_score,
bbox_pred,
gt_bboxes,
gt_labels,
img_metas,
gt_bboxes_ignore=gt_bboxes_ignore)
one_gt_cls_loss = sum(one_gt_losses['loss_cls'])
one_gt_box_loss = sum(one_gt_losses['loss_bbox'])
assert one_gt_cls_loss.item() > 0, 'cls loss should be non-zero'
assert one_gt_box_loss.item() > 0, 'box loss should be non-zero'
one_gt_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels)
mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas,
sampling_results)
one_gt_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes,
img_metas, sampling_results)
one_gt_segm_loss = sum(one_gt_segm_loss['loss_segm'])
one_gt_mask_loss = sum(one_gt_mask_loss['loss_mask'])
assert one_gt_segm_loss.item() > 0, 'segm loss should be non-zero'
assert one_gt_mask_loss.item() > 0, 'mask loss should be non-zero'
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