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Commit 0e4dea21 authored by liyinhao's avatar liyinhao Committed by zhangwenwei
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Clean parta2 bbox head unittest

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mmdet3d/models/roi_heads/bbox_heads/parta2_bbox_head.py
View file @ 0e4dea21
import numpy as np import numpy as np
import torch import torch
import torch.nn as nn import torch.nn as nn
from mmcv.cnn import ConvModule, build_norm_layer, normal_init, xavier_init from mmcv.cnn import ConvModule, normal_init, xavier_init
import mmdet3d.ops.spconv as spconv import mmdet3d.ops.spconv as spconv
from mmdet3d.core import build_bbox_coder, multi_apply from mmdet3d.core import build_bbox_coder, multi_apply
from mmdet3d.core.bbox import box_torch_ops from mmdet3d.core.bbox import box_torch_ops
from mmdet3d.models.builder import build_loss from mmdet3d.models.builder import build_loss
from mmdet3d.ops import make_sparse_convmodule
from mmdet3d.ops.iou3d.iou3d_utils import (boxes3d_to_bev_torch_lidar, nms_gpu, from mmdet3d.ops.iou3d.iou3d_utils import (boxes3d_to_bev_torch_lidar, nms_gpu,
nms_normal_gpu) nms_normal_gpu)
from mmdet.models import HEADS from mmdet.models import HEADS
...@@ -78,19 +79,18 @@ class PartA2BboxHead(nn.Module): ...@@ -78,19 +79,18 @@ class PartA2BboxHead(nn.Module):
assert down_conv_channels[-1] == shared_fc_channels[0] assert down_conv_channels[-1] == shared_fc_channels[0]
# init layers # init layers
block = self.post_act_block
part_channel_last = part_in_channels part_channel_last = part_in_channels
part_conv = [] part_conv = []
for i, channel in enumerate(part_conv_channels): for i, channel in enumerate(part_conv_channels):
part_conv.append( part_conv.append(
block( make_sparse_convmodule(
part_channel_last, part_channel_last,
channel, channel,
3, 3,
padding=1, padding=1,
norm_cfg=norm_cfg, norm_cfg=norm_cfg,
indice_key=f'rcnn_part{i}')) indice_key=f'rcnn_part{i}',
conv_type='SubMConv3d'))
part_channel_last = channel part_channel_last = channel
self.part_conv = spconv.SparseSequential(*part_conv) self.part_conv = spconv.SparseSequential(*part_conv)
...@@ -98,13 +98,14 @@ class PartA2BboxHead(nn.Module): ...@@ -98,13 +98,14 @@ class PartA2BboxHead(nn.Module):
seg_conv = [] seg_conv = []
for i, channel in enumerate(seg_conv_channels): for i, channel in enumerate(seg_conv_channels):
seg_conv.append( seg_conv.append(
block( make_sparse_convmodule(
seg_channel_last, seg_channel_last,
channel, channel,
3, 3,
padding=1, padding=1,
norm_cfg=norm_cfg, norm_cfg=norm_cfg,
indice_key=f'rcnn_seg{i}')) indice_key=f'rcnn_seg{i}',
conv_type='SubMConv3d'))
seg_channel_last = channel seg_channel_last = channel
self.seg_conv = spconv.SparseSequential(*seg_conv) self.seg_conv = spconv.SparseSequential(*seg_conv)
...@@ -114,26 +115,28 @@ class PartA2BboxHead(nn.Module): ...@@ -114,26 +115,28 @@ class PartA2BboxHead(nn.Module):
merge_conv = [] merge_conv = []
for i, channel in enumerate(merge_conv_channels): for i, channel in enumerate(merge_conv_channels):
merge_conv.append( merge_conv.append(
block( make_sparse_convmodule(
merge_conv_channel_last, merge_conv_channel_last,
channel, channel,
3, 3,
padding=1, padding=1,
norm_cfg=norm_cfg, norm_cfg=norm_cfg,
indice_key=f'rcnn_down0')) indice_key=f'rcnn_down0',
conv_type='SubMConv3d'))
merge_conv_channel_last = channel merge_conv_channel_last = channel
down_conv_channel_last = merge_conv_channel_last down_conv_channel_last = merge_conv_channel_last
conv_down = [] conv_down = []
for i, channel in enumerate(down_conv_channels): for i, channel in enumerate(down_conv_channels):
conv_down.append( conv_down.append(
block( make_sparse_convmodule(
down_conv_channel_last, down_conv_channel_last,
channel, channel,
3, 3,
padding=1, padding=1,
norm_cfg=norm_cfg, norm_cfg=norm_cfg,
indice_key=f'rcnn_down1')) indice_key=f'rcnn_down1',
conv_type='SubMConv3d'))
down_conv_channel_last = channel down_conv_channel_last = channel
self.conv_down.add_module('merge_conv', self.conv_down.add_module('merge_conv',
...@@ -228,69 +231,6 @@ class PartA2BboxHead(nn.Module): ...@@ -228,69 +231,6 @@ class PartA2BboxHead(nn.Module):
normal_init(self.conv_reg[-1].conv, mean=0, std=0.001) normal_init(self.conv_reg[-1].conv, mean=0, std=0.001)
def post_act_block(self,
in_channels,
out_channels,
kernel_size,
indice_key,
stride=1,
padding=0,
conv_type='subm',
norm_cfg=None):
"""Make post activate sparse convolution block.
Args:
in_channels (int): the number of input channels
out_channels (int): the number of out channels
kernel_size (int): kernel size of convolution
indice_key (str): the indice key used for sparse tensor
stride (int): the stride of convolution
padding (int or list[int]): the padding number of input
conv_type (str): conv type in 'subm', 'spconv' or 'inverseconv'
norm_cfg (dict[str]): config of normalization layer
Returns:
spconv.SparseSequential: post activate sparse convolution block.
"""
# TODO: clean post_act_block by existing bottlnecks.
assert conv_type in ['subm', 'spconv', 'inverseconv']
if conv_type == 'subm':
m = spconv.SparseSequential(
spconv.SubMConv3d(
in_channels,
out_channels,
kernel_size,
bias=False,
indice_key=indice_key),
build_norm_layer(norm_cfg, out_channels)[1],
nn.ReLU(inplace=True))
elif conv_type == 'spconv':
m = spconv.SparseSequential(
spconv.SparseConv3d(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
bias=False,
indice_key=indice_key),
build_norm_layer(norm_cfg, out_channels)[1],
nn.ReLU(inplace=True))
elif conv_type == 'inverseconv':
m = spconv.SparseSequential(
spconv.SparseInverseConv3d(
in_channels,
out_channels,
kernel_size,
bias=False,
indice_key=indice_key),
build_norm_layer(norm_cfg, out_channels)[1],
nn.ReLU(inplace=True))
else:
raise NotImplementedError
return m
def forward(self, seg_feats, part_feats): def forward(self, seg_feats, part_feats):
# (B * N, out_x, out_y, out_z, 4) # (B * N, out_x, out_y, out_z, 4)
rcnn_batch_size = part_feats.shape[0] rcnn_batch_size = part_feats.shape[0]
......
mmdet3d/ops/sparse_block.py
View file @ 0e4dea21
...@@ -6,6 +6,21 @@ from mmdet.models.backbones.resnet import BasicBlock, Bottleneck ...@@ -6,6 +6,21 @@ from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
class SparseBottleneck(Bottleneck, spconv.SparseModule): class SparseBottleneck(Bottleneck, spconv.SparseModule):
"""Sparse bottleneck block for PartA^2.
Bottleneck block implemented with submanifold sparse convolution.
Args:
inplanes (int): inplanes of block.
planes (int): planes of block.
stride (int): stride of the first block. Default: 1
downsample (None | Module): down sample module for block.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
"""
expansion = 4 expansion = 4
def __init__(self, def __init__(self,
...@@ -15,10 +30,7 @@ class SparseBottleneck(Bottleneck, spconv.SparseModule): ...@@ -15,10 +30,7 @@ class SparseBottleneck(Bottleneck, spconv.SparseModule):
downsample=None, downsample=None,
conv_cfg=None, conv_cfg=None,
norm_cfg=None): norm_cfg=None):
"""Sparse bottleneck block for PartA^2.
Bottleneck block implemented with submanifold sparse convolution.
"""
spconv.SparseModule.__init__(self) spconv.SparseModule.__init__(self)
Bottleneck.__init__( Bottleneck.__init__(
self, self,
...@@ -53,6 +65,21 @@ class SparseBottleneck(Bottleneck, spconv.SparseModule): ...@@ -53,6 +65,21 @@ class SparseBottleneck(Bottleneck, spconv.SparseModule):
class SparseBasicBlock(BasicBlock, spconv.SparseModule): class SparseBasicBlock(BasicBlock, spconv.SparseModule):
"""Sparse basic block for PartA^2.
Sparse basic block implemented with submanifold sparse convolution.
Args:
inplanes (int): inplanes of block.
planes (int): planes of block.
stride (int): stride of the first block. Default: 1
downsample (None | Module): down sample module for block.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: None
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN')
"""
expansion = 1 expansion = 1
def __init__(self, def __init__(self,
...@@ -62,10 +89,6 @@ class SparseBasicBlock(BasicBlock, spconv.SparseModule): ...@@ -62,10 +89,6 @@ class SparseBasicBlock(BasicBlock, spconv.SparseModule):
downsample=None, downsample=None,
conv_cfg=None, conv_cfg=None,
norm_cfg=None): norm_cfg=None):
"""Sparse basic block for PartA^2.
Sparse basic block implemented with submanifold sparse convolution.
"""
spconv.SparseModule.__init__(self) spconv.SparseModule.__init__(self)
BasicBlock.__init__( BasicBlock.__init__(
self, self,
...@@ -125,6 +148,7 @@ def make_sparse_convmodule(in_channels, ...@@ -125,6 +148,7 @@ def make_sparse_convmodule(in_channels,
spconv.SparseSequential: sparse convolution module. spconv.SparseSequential: sparse convolution module.
""" """
assert isinstance(order, tuple) and len(order) <= 3 assert isinstance(order, tuple) and len(order) <= 3
assert set(order) | {'conv', 'norm', 'act'} == {'conv', 'norm', 'act'}
conv_cfg = dict(type=conv_type, indice_key=indice_key) conv_cfg = dict(type=conv_type, indice_key=indice_key)
......
tests/test_parta2_bbox_head.py 0 → 100644
View file @ 0e4dea21
import pytest
import torch
from mmcv import Config
from torch.nn import BatchNorm1d, ReLU
from mmdet3d.core.bbox.samplers import IoUNegPiecewiseSampler
from mmdet3d.models import PartA2BboxHead
from mmdet3d.ops import make_sparse_convmodule
from mmdet3d.ops.spconv.conv import SubMConv3d
def test_loss():
self = PartA2BboxHead(
num_classes=3,
seg_in_channels=16,
part_in_channels=4,
seg_conv_channels=[64, 64],
part_conv_channels=[64, 64],
merge_conv_channels=[128, 128],
down_conv_channels=[128, 256],
shared_fc_channels=[256, 512, 512, 512],
cls_channels=[256, 256],
reg_channels=[256, 256])
cls_score = torch.Tensor([[-3.6810], [-3.9413], [-5.3971], [-17.1281],
[-5.9434], [-6.2251]])
bbox_pred = torch.Tensor(
[[
-6.3016e-03, -5.2294e-03, -1.2793e-02, -1.0602e-02, -7.4086e-04,
9.2471e-03, 7.3514e-03
],
[
-1.1975e-02, -1.1578e-02, -3.1219e-02, 2.7754e-02, 6.9775e-03,
9.4042e-04, 9.0472e-04
],
[
3.7539e-03, -9.1897e-03, -5.3666e-03, -1.0380e-05, 4.3467e-03,
4.2470e-03, 1.8355e-03
],
[
-7.6093e-02, -1.2497e-01, -9.2942e-02, 2.1404e-02, 2.3750e-02,
1.0365e-01, -1.3042e-02
],
[
2.7577e-03, -1.1514e-02, -1.1097e-02, -2.4946e-03, 2.3268e-03,
1.6797e-03, -1.4076e-03
],
[
3.9635e-03, -7.8551e-03, -3.5125e-03, 2.1229e-04, 9.7042e-03,
1.7499e-03, -5.1254e-03
]])
rois = torch.Tensor([
[0.0000, 13.3711, -12.5483, -1.9306, 1.7027, 4.2836, 1.4283, -1.1499],
[0.0000, 19.2472, -7.2655, -10.6641, 3.3078, 83.1976, 29.3337, 2.4501],
[0.0000, 13.8012, -10.9791, -3.0617, 0.2504, 1.2518, 0.8807, 3.1034],
[0.0000, 16.2736, -9.0284, -2.0494, 8.2697, 31.2336, 9.1006, 1.9208],
[0.0000, 10.4462, -13.6879, -3.1869, 7.3366, 0.3518, 1.7199, -0.7225],
[0.0000, 11.3374, -13.6671, -3.2332, 4.9934, 0.3750, 1.6033, -0.9665]
])
labels = torch.Tensor([0.7100, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000])
bbox_targets = torch.Tensor(
[[0.0598, 0.0243, -0.0984, -0.0454, 0.0066, 0.1114, 0.1714]])
pos_gt_bboxes = torch.Tensor(
[[13.6686, -12.5586, -2.1553, 1.6271, 4.3119, 1.5966, 2.1631]])
reg_mask = torch.Tensor([1, 0, 0, 0, 0, 0])
label_weights = torch.Tensor(
[0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078])
bbox_weights = torch.Tensor([1., 0., 0., 0., 0., 0.])
loss = self.loss(cls_score, bbox_pred, rois, labels, bbox_targets,
pos_gt_bboxes, reg_mask, label_weights, bbox_weights)
expected_loss_cls = torch.Tensor([
2.0579e-02, 1.5005e-04, 3.5252e-05, 0.0000e+00, 2.0433e-05, 1.5422e-05
])
expected_loss_bbox = torch.as_tensor(0.0622)
expected_loss_corner = torch.Tensor([0.1379])
assert torch.allclose(loss['loss_cls'], expected_loss_cls, 1e-3)
assert torch.allclose(loss['loss_bbox'], expected_loss_bbox, 1e-3)
assert torch.allclose(loss['loss_corner'], expected_loss_corner, 1e-3)
def test_get_targets():
self = PartA2BboxHead(
num_classes=3,
seg_in_channels=16,
part_in_channels=4,
seg_conv_channels=[64, 64],
part_conv_channels=[64, 64],
merge_conv_channels=[128, 128],
down_conv_channels=[128, 256],
shared_fc_channels=[256, 512, 512, 512],
cls_channels=[256, 256],
reg_channels=[256, 256])
sampling_result = IoUNegPiecewiseSampler(
1,
pos_fraction=0.55,
neg_piece_fractions=[0.8, 0.2],
neg_iou_piece_thrs=[0.55, 0.1],
return_iou=True)
sampling_result.pos_bboxes = torch.Tensor(
[[8.1517, 0.0384, -1.9496, 1.5271, 4.1131, 1.4879, 1.2076]])
sampling_result.pos_gt_bboxes = torch.Tensor(
[[7.8417, -0.1405, -1.9652, 1.6122, 3.2838, 1.5331, -2.0835]])
sampling_result.iou = torch.Tensor([
6.7787e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 1.2839e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 7.0261e-04, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 5.8915e-02, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 5.6628e-06,
5.0271e-02, 0.0000e+00, 1.9608e-01, 0.0000e+00, 0.0000e+00, 2.3519e-01,
1.6589e-02, 0.0000e+00, 1.0162e-01, 2.1634e-02, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 5.6326e-02,
1.3810e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,
4.5455e-02, 0.0000e+00, 1.0929e-03, 0.0000e+00, 8.8191e-02, 1.1012e-01,
0.0000e+00, 0.0000e+00, 0.0000e+00, 1.6236e-01, 0.0000e+00, 1.1342e-01,
1.0636e-01, 9.9803e-02, 5.7394e-02, 0.0000e+00, 1.6773e-01, 0.0000e+00,
0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.3464e-03,
0.0000e+00, 2.7977e-01, 0.0000e+00, 3.1252e-01, 2.1642e-01, 2.2945e-01,
0.0000e+00, 1.8297e-01, 0.0000e+00, 2.1908e-01, 1.1661e-01, 1.3513e-01,
1.5898e-01, 7.4368e-03, 1.2523e-01, 1.4735e-04, 0.0000e+00, 0.0000e+00,
0.0000e+00, 1.0948e-01, 2.5889e-01, 4.4585e-04, 8.6483e-02, 1.6376e-01,
0.0000e+00, 2.2894e-01, 2.7489e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00,
1.8334e-01, 1.0193e-01, 2.3389e-01, 1.1035e-01, 3.3700e-01, 1.4397e-01,
1.0379e-01, 0.0000e+00, 1.1226e-01, 0.0000e+00, 0.0000e+00, 1.6201e-01,
0.0000e+00, 1.3569e-01
])
rcnn_train_cfg = Config({
'assigner': [{
'type': 'MaxIoUAssigner',
'iou_calculator': {
'type': 'BboxOverlaps3D',
'coordinate': 'lidar'
},
'pos_iou_thr': 0.55,
'neg_iou_thr': 0.55,
'min_pos_iou': 0.55,
'ignore_iof_thr': -1
}, {
'type': 'MaxIoUAssigner',
'iou_calculator': {
'type': 'BboxOverlaps3D',
'coordinate': 'lidar'
},
'pos_iou_thr': 0.55,
'neg_iou_thr': 0.55,
'min_pos_iou': 0.55,
'ignore_iof_thr': -1
}, {
'type': 'MaxIoUAssigner',
'iou_calculator': {
'type': 'BboxOverlaps3D',
'coordinate': 'lidar'
},
'pos_iou_thr': 0.55,
'neg_iou_thr': 0.55,
'min_pos_iou': 0.55,
'ignore_iof_thr': -1
}],
'sampler': {
'type': 'IoUNegPiecewiseSampler',
'num': 128,
'pos_fraction': 0.55,
'neg_piece_fractions': [0.8, 0.2],
'neg_iou_piece_thrs': [0.55, 0.1],
'neg_pos_ub': -1,
'add_gt_as_proposals': False,
'return_iou': True
},
'cls_pos_thr':
0.75,
'cls_neg_thr':
0.25
})
label, bbox_targets, pos_gt_bboxes, reg_mask, label_weights, bbox_weights\
= self.get_targets([sampling_result], rcnn_train_cfg)
expected_label = torch.Tensor([
0.8557, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0595, 0.0000, 0.1250, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0178, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0498, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.1740, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000
])
expected_bbox_targets = torch.Tensor(
[[0.0805, 0.0130, 0.0047, 0.0542, -0.2252, 0.0299, -0.1495]])
expected_pos_gt_bboxes = torch.Tensor(
[[7.8417, -0.1405, -1.9652, 1.6122, 3.2838, 1.5331, -2.0835]])
expected_reg_mask = torch.LongTensor([
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0
])
expected_label_weights = torch.Tensor([
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078, 0.0078,
0.0078, 0.0078
])
expected_bbox_weights = torch.Tensor([
1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0.
])
assert torch.allclose(label, expected_label, 1e-2)
assert torch.allclose(bbox_targets, expected_bbox_targets, 1e-2)
assert torch.allclose(pos_gt_bboxes, expected_pos_gt_bboxes)
assert torch.all(reg_mask == expected_reg_mask)
assert torch.allclose(label_weights, expected_label_weights, 1e-2)
assert torch.allclose(bbox_weights, expected_bbox_weights)
def test_get_bboxes():
if not torch.cuda.is_available():
pytest.skip()
self = PartA2BboxHead(
num_classes=3,
seg_in_channels=16,
part_in_channels=4,
seg_conv_channels=[64, 64],
part_conv_channels=[64, 64],
merge_conv_channels=[128, 128],
down_conv_channels=[128, 256],
shared_fc_channels=[256, 512, 512, 512],
cls_channels=[256, 256],
reg_channels=[256, 256])
rois = torch.Tensor([[
0.0000e+00, 5.6284e+01, 2.5712e+01, -1.3196e+00, 1.5943e+00,
3.7509e+00, 1.4969e+00, 1.2105e-03
],
[
0.0000e+00, 5.4685e+01, 2.9132e+01, -1.9178e+00,
1.6337e+00, 4.1116e+00, 1.5472e+00, -1.7312e+00
],
[
0.0000e+00, 5.5927e+01, 2.5830e+01, -1.4099e+00,
1.5958e+00, 3.8861e+00, 1.4911e+00, -2.9276e+00
],
[
0.0000e+00, 5.6306e+01, 2.6310e+01, -1.3729e+00,
1.5893e+00, 3.7448e+00, 1.4924e+00, 1.6071e-01
],
[
0.0000e+00, 3.1633e+01, -5.8557e+00, -1.2541e+00,
1.6517e+00, 4.1829e+00, 1.5593e+00, -1.6037e+00
],
[
0.0000e+00, 3.1789e+01, -5.5308e+00, -1.3012e+00,
1.6412e+00, 4.1070e+00, 1.5487e+00, -1.6517e+00
]]).cuda()
cls_score = torch.Tensor([[-2.2061], [-2.1121], [-1.4478], [-2.9614],
[-0.1761], [0.7357]]).cuda()
bbox_pred = torch.Tensor(
[[
-4.7917e-02, -1.6504e-02, -2.2340e-02, 5.1296e-03, -2.0984e-02,
1.0598e-02, -1.1907e-01
],
[
-1.6261e-02, -5.4005e-02, 6.2480e-03, 1.5496e-03, -1.3285e-02,
8.1482e-03, -2.2707e-03
],
[
-3.9423e-02, 2.0151e-02, -2.1138e-02, -1.1845e-03, -1.5343e-02,
5.7208e-03, 8.5646e-03
],
[
6.3104e-02, -3.9307e-02, 2.3005e-02, -7.0528e-03, -9.2637e-05,
2.2656e-02, 1.6358e-02
],
[
-1.4864e-03, 5.6840e-02, 5.8247e-03, -3.5541e-03, -4.9658e-03,
2.5036e-03, 3.0302e-02
],
[
-4.3259e-02, -1.9963e-02, 3.5004e-02, 3.7546e-03, 1.0876e-02,
-3.9637e-04, 2.0445e-02
]]).cuda()
class_labels = [torch.Tensor([2, 2, 2, 2, 2, 2]).cuda()]
class_pred = [
torch.Tensor([[1.0877e-05, 1.0318e-05, 2.6599e-01],
[1.3105e-05, 1.1904e-05, 2.4432e-01],
[1.4530e-05, 1.4619e-05, 2.4395e-01],
[1.3251e-05, 1.3038e-05, 2.3703e-01],
[2.9156e-05, 2.5521e-05, 2.2826e-01],
[3.1665e-05, 2.9054e-05, 2.2077e-01]]).cuda()
]
cfg = Config(
dict(
use_rotate_nms=True,
use_raw_score=True,
nms_thr=0.01,
score_thr=0.1))
result_list = self.get_bboxes(rois, cls_score, bbox_pred, class_labels,
class_pred, None, cfg)
selected_bboxes, selected_scores, selected_label_preds = result_list[0]
expected_selected_bboxes = torch.Tensor(
[[56.2170, 25.9074, -1.3610, 1.6025, 3.6730, 1.5128, -0.1179],
[54.6521, 28.8846, -1.9145, 1.6362, 4.0573, 1.5599, -1.7335],
[31.6179, -5.6004, -1.2470, 1.6458, 4.1622, 1.5632, -1.5734]]).cuda()
expected_selected_scores = torch.Tensor([-2.2061, -2.1121, -0.1761]).cuda()
expected_selected_label_preds = torch.Tensor([2., 2., 2.]).cuda()
assert torch.allclose(selected_bboxes, expected_selected_bboxes, 1e-3)
assert torch.allclose(selected_scores, expected_selected_scores, 1e-3)
assert torch.allclose(selected_label_preds, expected_selected_label_preds)
def test_multi_class_nms():
if not torch.cuda.is_available():
pytest.skip()
self = PartA2BboxHead(
num_classes=3,
seg_in_channels=16,
part_in_channels=4,
seg_conv_channels=[64, 64],
part_conv_channels=[64, 64],
merge_conv_channels=[128, 128],
down_conv_channels=[128, 256],
shared_fc_channels=[256, 512, 512, 512],
cls_channels=[256, 256],
reg_channels=[256, 256])
box_probs = torch.Tensor([[1.0877e-05, 1.0318e-05, 2.6599e-01],
[1.3105e-05, 1.1904e-05, 2.4432e-01],
[1.4530e-05, 1.4619e-05, 2.4395e-01],
[1.3251e-05, 1.3038e-05, 2.3703e-01],
[2.9156e-05, 2.5521e-05, 2.2826e-01],
[3.1665e-05, 2.9054e-05, 2.2077e-01],
[5.5738e-06, 6.2453e-06, 2.1978e-01],
[9.0193e-06, 9.2154e-06, 2.1418e-01],
[1.4004e-05, 1.3209e-05, 2.1316e-01],
[7.9210e-06, 8.1767e-06, 2.1304e-01]]).cuda()
box_preds = torch.Tensor(
[[
5.6217e+01, 2.5908e+01, -1.3611e+00, 1.6025e+00, 3.6730e+00,
1.5129e+00, -1.1786e-01
],
[
5.4653e+01, 2.8885e+01, -1.9145e+00, 1.6362e+00, 4.0574e+00,
1.5599e+00, -1.7335e+00
],
[
5.5809e+01, 2.5686e+01, -1.4457e+00, 1.5939e+00, 3.8270e+00,
1.4997e+00, -2.9191e+00
],
[
5.6107e+01, 2.6082e+01, -1.3557e+00, 1.5782e+00, 3.7444e+00,
1.5266e+00, 1.7707e-01
],
[
3.1618e+01, -5.6004e+00, -1.2470e+00, 1.6459e+00, 4.1622e+00,
1.5632e+00, -1.5734e+00
],
[
3.1605e+01, -5.6342e+00, -1.2467e+00, 1.6474e+00, 4.1519e+00,
1.5481e+00, -1.6313e+00
],
[
5.6211e+01, 2.7294e+01, -1.5350e+00, 1.5422e+00, 3.7733e+00,
1.5140e+00, 9.5846e-02
],
[
5.5907e+01, 2.7155e+01, -1.4712e+00, 1.5416e+00, 3.7611e+00,
1.5142e+00, -5.2059e-02
],
[
5.4000e+01, 3.0585e+01, -1.6874e+00, 1.6495e+00, 4.0376e+00,
1.5554e+00, -1.7900e+00
],
[
5.6007e+01, 2.6300e+01, -1.3945e+00, 1.5716e+00, 3.7064e+00,
1.4715e+00, -2.9639e+00
]]).cuda()
selected = self.multi_class_nms(box_probs, box_preds, 0.1, 0.001)
expected_selected = torch.Tensor([0, 1, 4, 8]).cuda()
assert torch.all(selected == expected_selected)
def test_make_sparse_convmodule():
with pytest.raises(AssertionError):
# assert invalid order setting
make_sparse_convmodule(
in_channels=4,
out_channels=8,
kernel_size=3,
indice_key='rcnn_part2',
norm_cfg=dict(type='BN1d'),
order=('norm', 'act', 'conv', 'norm'))
# assert invalid type of order
make_sparse_convmodule(
in_channels=4,
out_channels=8,
kernel_size=3,
indice_key='rcnn_part2',
norm_cfg=dict(type='BN1d'),
order=['norm', 'conv'])
# assert invalid elements of order
make_sparse_convmodule(
in_channels=4,
out_channels=8,
kernel_size=3,
indice_key='rcnn_part2',
norm_cfg=dict(type='BN1d'),
order=('conv', 'normal', 'activate'))
sparse_convmodule = make_sparse_convmodule(
in_channels=4,
out_channels=64,
kernel_size=3,
padding=1,
indice_key='rcnn_part0',
norm_cfg=dict(type='BN1d', eps=0.001, momentum=0.01))
assert isinstance(sparse_convmodule[0], SubMConv3d)
assert isinstance(sparse_convmodule[1], BatchNorm1d)
assert isinstance(sparse_convmodule[2], ReLU)
assert sparse_convmodule[1].num_features == 64
assert sparse_convmodule[1].eps == 0.001
assert sparse_convmodule[1].affine is True
assert sparse_convmodule[1].track_running_stats is True
assert isinstance(sparse_convmodule[2], ReLU)
assert sparse_convmodule[2].inplace is True
pre_act = make_sparse_convmodule(
in_channels=4,
out_channels=8,
kernel_size=3,
indice_key='rcnn_part1',
norm_cfg=dict(type='BN1d'),
order=('norm', 'act', 'conv'))
assert isinstance(pre_act[0], BatchNorm1d)
assert isinstance(pre_act[1], ReLU)
assert isinstance(pre_act[2], SubMConv3d)
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