【技术文档】HRNet姿态估计
目录
- 模型结构
-
- augment
- cfg
- loss
模型结构
class PoseHighResolutionNet(nn.Module):
def __init__(self, cfg, **kwargs):
self.inplanes = 64
extra = cfg['MODEL']['EXTRA']
super(PoseHighResolutionNet, self).__init__()
# stem net
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(Bottleneck, 64, 4)
self.stage2_cfg = extra['STAGE2']
num_channels = self.stage2_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage2_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))
]
self.transition1 = self._make_transition_layer([256], num_channels)
self.stage2, pre_stage_channels = self._make_stage(
self.stage2_cfg, num_channels)
self.stage3_cfg = extra['STAGE3']
num_channels = self.stage3_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage3_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))
]
self.transition2 = self._make_transition_layer(
pre_stage_channels, num_channels)
self.stage3, pre_stage_channels = self._make_stage(
self.stage3_cfg, num_channels)
self.stage4_cfg = extra['STAGE4']
num_channels = self.stage4_cfg['NUM_CHANNELS']
block = blocks_dict[self.stage4_cfg['BLOCK']]
num_channels = [
num_channels[i] * block.expansion for i in range(len(num_channels))
]
self.transition3 = self._make_transition_layer(
pre_stage_channels, num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels, multi_scale_output=False)
self.final_layer = nn.Conv2d(
in_channels=pre_stage_channels[0],
out_channels=cfg['MODEL']['NUM_JOINTS'],
kernel_size=extra['FINAL_CONV_KERNEL'],
stride=1,
padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0
)
self.pretrained_layers = extra['PRETRAINED_LAYERS']
def _make_transition_layer(
self, num_channels_pre_layer, num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(
nn.Sequential(
nn.Conv2d(
num_channels_pre_layer[i],
num_channels_cur_layer[i],
3, 1, 1, bias=False
),
nn.BatchNorm2d(num_channels_cur_layer[i]),
nn.ReLU(inplace=True)
)
)
else:
transition_layers.append(None)
else:
conv3x3s = []
for j in range(i+1-num_branches_pre):
inchannels = num_channels_pre_layer[-1]
outchannels = num_channels_cur_layer[i] \
if j == i-num_branches_pre else inchannels
conv3x3s.append(
nn.Sequential(
nn.Conv2d(
inchannels, outchannels, 3, 2, 1, bias=False
),
nn.BatchNorm2d(outchannels),
nn.ReLU(inplace=True)
)
)
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False
),
nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_stage(self, layer_config, num_inchannels,
multi_scale_output=True):
num_modules = layer_config['NUM_MODULES']
num_branches = layer_config['NUM_BRANCHES']
num_blocks = layer_config['NUM_BLOCKS']
num_channels = layer_config['NUM_CHANNELS']
block = blocks_dict[layer_config['BLOCK']]
fuse_method = layer_config['FUSE_METHOD']
modules = []
for i in range(num_modules):
# multi_scale_output is only used last module
if not multi_scale_output and i == num_modules - 1:
reset_multi_scale_output = False
else:
reset_multi_scale_output = True
modules.append(
HighResolutionModule(
num_branches,
block,
num_blocks,
num_inchannels,
num_channels,
fuse_method,
reset_multi_scale_output
)
)
num_inchannels = modules[-1].get_num_inchannels()
return nn.Sequential(*modules), num_inchannels
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.layer1(x)
x_list = []
for i in range(self.stage2_cfg['NUM_BRANCHES']):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](x))
else:
x_list.append(x)
y_list = self.stage2(x_list)
x_list = []
for i in range(self.stage3_cfg['NUM_BRANCHES']):
if self.transition2[i] is not None:
x_list.append(self.transition2[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage3(x_list)
x_list = []
for i in range(self.stage4_cfg['NUM_BRANCHES']):
if self.transition3[i] is not None:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage4(x_list)
x = self.final_layer(y_list[0])
augment
旋转，半身增强
cfg
AUTO_RESUME: false
CUDNN:
BENCHMARK: true
DETERMINISTIC: false
ENABLED: true
DATA_DIR: ''
GPUS: (0,1,2,3)
OUTPUT_DIR: 'output'
LOG_DIR: 'log'
WORKERS: 24
PRINT_FREQ: 100
DATASET:
COLOR_RGB: true
DATASET: 'hie'
DATA_FORMAT: jpg
FLIP: true
NUM_JOINTS_HALF_BODY: 8
PROB_HALF_BODY: 0.3
ROOT: '/home/ubuntu/Datasets/HIE20'
ROT_FACTOR: 45
SCALE_FACTOR: 0.35
TEST_SET: train
TRAIN_SET: train
MODEL:
INIT_WEIGHTS: true
NAME: pose_hrnet
NUM_JOINTS: 14
PRETRAINED: '/home/ubuntu/Workspace/deep-high-resolution-net.pytorch/models/hrnet_w48-8ef0771d.pth'
TARGET_TYPE: gaussian
IMAGE_SIZE:
- 288
- 384
HEATMAP_SIZE:
- 72
- 96
SIGMA: 3
EXTRA:
PRETRAINED_LAYERS:
- 'conv1'
- 'bn1'
- 'conv2'
- 'bn2'
- 'layer1'
- 'transition1'
- 'stage2'
- 'transition2'
- 'stage3'
- 'transition3'
- 'stage4'
FINAL_CONV_KERNEL: 1
STAGE2:
NUM_MODULES: 1
NUM_BRANCHES: 2
BLOCK: BASIC
NUM_BLOCKS:
- 4
- 4
NUM_CHANNELS:
- 48
- 96
FUSE_METHOD: SUM
STAGE3:
NUM_MODULES: 4
NUM_BRANCHES: 3
BLOCK: BASIC
NUM_BLOCKS:
- 4
- 4
- 4
NUM_CHANNELS:
- 48
- 96
- 192
FUSE_METHOD: SUM
STAGE4:
NUM_MODULES: 3
NUM_BRANCHES: 4
BLOCK: BASIC
NUM_BLOCKS:
- 4
- 4
- 4
- 4
NUM_CHANNELS:
- 48
- 96
- 192
- 384
FUSE_METHOD: SUM
LOSS:
USE_TARGET_WEIGHT: true
TRAIN:
BATCH_SIZE_PER_GPU: 24
SHUFFLE: true
BEGIN_EPOCH: 0
END_EPOCH: 210
OPTIMIZER: adam
LR: 0.001
LR_FACTOR: 0.1
LR_STEP:
- 170
- 200
WD: 0.0001
GAMMA1: 0.99
GAMMA2: 0.0
MOMENTUM: 0.9
NESTEROV: false
TEST:
BATCH_SIZE_PER_GPU: 12
COCO_BBOX_FILE: '/home/liuhao/seedland/dataset/coco2017/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'
BBOX_THRE: 1.0
IMAGE_THRE: 0.0
IN_VIS_THRE: 0.2
MODEL_FILE: ''
NMS_THRE: 1.0
OKS_THRE: 0.9
USE_GT_BBOX: true
FLIP_TEST: true
POST_PROCESS: true
SHIFT_HEATMAP: true
DEBUG:
DEBUG: true
SAVE_BATCH_IMAGES_GT: true
SAVE_BATCH_IMAGES_PRED: true
SAVE_HEATMAPS_GT: true
SAVE_HEATMAPS_PRED: true
loss
class JointsMSELoss(nn.Module):
def __init__(self, use_target_weight):
super(JointsMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
self.use_target_weight = use_target_weight
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_joints = output.size(1)
heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1)
heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
loss = 0
for idx in range(num_joints):
heatmap_pred = heatmaps_pred[idx].squeeze()
heatmap_gt = heatmaps_gt[idx].squeeze()
if self.use_target_weight:
loss += 0.5 * self.criterion(
heatmap_pred.mul(target_weight[:, idx]),
heatmap_gt.mul(target_weight[:, idx])
)
else:
loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt)
return loss / num_joints