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# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead
from mmdet.models.roi_heads import PISARoIHead
def test_pisa_retinanet_head_loss():
"""Tests pisa retinanet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg = mmcv.Config(
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
isr=dict(k=2., bias=0.),
carl=dict(k=1., bias=0.2),
allowed_border=0,
pos_weight=-1,
debug=False))
self = PISARetinaHead(num_classes=4, in_channels=1, train_cfg=cfg)
# Anchor head expects a multiple levels of features per image
feat = [
torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
for i in range(len(self.anchor_generator.strides))
]
cls_scores, bbox_preds = self.forward(feat)
# Test that empty ground truth encourages the network to predict background
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
gt_bboxes_ignore = None
empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
img_metas, gt_bboxes_ignore)
# When there is no truth, the cls loss should be nonzero but there should
# be no box loss.
empty_cls_loss = empty_gt_losses['loss_cls'].sum()
empty_box_loss = empty_gt_losses['loss_bbox'].sum()
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')
# When truth is non-empty then both cls and box loss should be nonzero for
# random inputs
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
img_metas, gt_bboxes_ignore)
onegt_cls_loss = one_gt_losses['loss_cls'].sum()
onegt_box_loss = one_gt_losses['loss_bbox'].sum()
assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
def test_pisa_ssd_head_loss():
"""Tests pisa ssd head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg = mmcv.Config(
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.,
ignore_iof_thr=-1,
gt_max_assign_all=False),
isr=dict(k=2., bias=0.),
carl=dict(k=1., bias=0.2),
smoothl1_beta=1.,
allowed_border=-1,
pos_weight=-1,
neg_pos_ratio=3,
debug=False))
ssd_anchor_generator = dict(
type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
strides=[1],
ratios=([2], ),
basesize_ratio_range=(0.15, 0.9))
self = PISASSDHead(
num_classes=4,
in_channels=(1, ),
train_cfg=cfg,
anchor_generator=ssd_anchor_generator)
# Anchor head expects a multiple levels of features per image
feat = [
torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
for i in range(len(self.anchor_generator.strides))
]
cls_scores, bbox_preds = self.forward(feat)
# Test that empty ground truth encourages the network to predict background
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
gt_bboxes_ignore = None
empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
img_metas, 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'])
# SSD is special, #pos:#neg = 1: 3, so empth gt will also lead loss cls = 0
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')
# When truth is non-empty then both cls and box loss should be nonzero for
# random inputs
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
img_metas, gt_bboxes_ignore)
onegt_cls_loss = sum(one_gt_losses['loss_cls'])
onegt_box_loss = sum(one_gt_losses['loss_bbox'])
assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
def test_pisa_roi_head_loss():
"""Tests pisa roi head loss when truth is empty and non-empty."""
train_cfg = mmcv.Config(
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='ScoreHLRSampler',
num=4,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
k=0.5,
bias=0.),
isr=dict(k=2., bias=0.),
carl=dict(k=1., bias=0.2),
allowed_border=0,
pos_weight=-1,
debug=False))
bbox_roi_extractor = dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=1,
featmap_strides=[1])
bbox_head = dict(
type='Shared2FCBBoxHead',
in_channels=1,
fc_out_channels=2,
roi_feat_size=7,
num_classes=4,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))
self = PISARoIHead(bbox_roi_extractor, bbox_head, train_cfg=train_cfg)
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
# Anchor head expects a multiple levels of features per image
feat = [
torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
for i in range(1)
]
proposal_list = [
torch.Tensor([[22.6667, 22.8757, 238.6326, 151.8874], [0, 3, 5, 7]])
]
# Test that empty ground truth encourages the network to predict background
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
gt_bboxes_ignore = None
empty_gt_losses = self.forward_train(feat, img_metas, proposal_list,
gt_bboxes, gt_labels,
gt_bboxes_ignore)
# When there is no truth, the cls loss should be nonzero but there should
# be no box loss.
empty_cls_loss = empty_gt_losses['loss_cls'].sum()
empty_box_loss = empty_gt_losses['loss_bbox'].sum()
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')
# When truth is non-empty then both cls and box loss should be nonzero for
# random inputs
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
one_gt_losses = self.forward_train(feat, img_metas, proposal_list,
gt_bboxes, gt_labels, gt_bboxes_ignore)
onegt_cls_loss = one_gt_losses['loss_cls'].sum()
onegt_box_loss = one_gt_losses['loss_bbox'].sum()
assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
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