"""
borrowed from https://github.com/vchoutas/expose/blob/master/expose/models/backbone/hrnet.py
"""
import os.path as osp
import torch
import torch.nn as nn
from torchvision.models.resnet import BasicBlock, Bottleneck
BN_MOMENTUM = 0.1
def load_HRNet(pretrained=False):
hr_net_cfg_dict = {
"use_old_impl": False,
"pretrained_layers": ["*"],
"stage1": {
"num_modules": 1,
"num_branches": 1,
"num_blocks": [4],
"num_channels": [64],
"block": "BOTTLENECK",
"fuse_method": "SUM",
},
"stage2": {
"num_modules": 1,
"num_branches": 2,
"num_blocks": [4, 4],
"num_channels": [48, 96],
"block": "BASIC",
"fuse_method": "SUM",
},
"stage3": {
"num_modules": 4,
"num_branches": 3,
"num_blocks": [4, 4, 4],
"num_channels": [48, 96, 192],
"block": "BASIC",
"fuse_method": "SUM",
},
"stage4": {
"num_modules": 3,
"num_branches": 4,
"num_blocks": [4, 4, 4, 4],
"num_channels": [48, 96, 192, 384],
"block": "BASIC",
"fuse_method": "SUM",
},
}
hr_net_cfg = hr_net_cfg_dict
model = HighResolutionNet(hr_net_cfg)
return model
class HighResolutionModule(nn.Module):
def __init__(
self,
num_branches,
blocks,
num_blocks,
num_inchannels,
num_channels,
fuse_method,
multi_scale_output=True,
):
super(HighResolutionModule, self).__init__()
self._check_branches(num_branches, blocks, num_blocks, num_inchannels, num_channels)
self.num_inchannels = num_inchannels
self.fuse_method = fuse_method
self.num_branches = num_branches
self.multi_scale_output = multi_scale_output
self.branches = self._make_branches(num_branches, blocks, num_blocks, num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(True)
def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels):
if num_branches != len(num_blocks):
error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format(num_branches, len(num_blocks))
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format(
num_branches, len(num_channels)
)
raise ValueError(error_msg)
if num_branches != len(num_inchannels):
error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format(
num_branches, len(num_inchannels)
)
raise ValueError(error_msg)
def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
downsample = None
if (
stride != 1 or
self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion
):
downsample = nn.Sequential(
nn.Conv2d(
self.num_inchannels[branch_index],
num_channels[branch_index] * block.expansion,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM),
)
layers = []
layers.append(
block(
self.num_inchannels[branch_index],
num_channels[branch_index],
stride,
downsample,
)
)
self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index]))
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block, num_blocks, num_channels):
branches = []
for i in range(num_branches):
branches.append(self._make_one_branch(i, block, num_blocks, num_channels))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_inchannels[i],
1,
1,
0,
bias=False,
),
nn.BatchNorm2d(num_inchannels[i]),
nn.Upsample(scale_factor=2**(j - i), mode="nearest"),
)
)
elif j == i:
fuse_layer.append(None)
else:
conv3x3s = []
for k in range(i - j):
if k == i - j - 1:
num_outchannels_conv3x3 = num_inchannels[i]
conv3x3s.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_outchannels_conv3x3,
3,
2,
1,
bias=False,
),
nn.BatchNorm2d(num_outchannels_conv3x3),
)
)
else:
num_outchannels_conv3x3 = num_inchannels[j]
conv3x3s.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_outchannels_conv3x3,
3,
2,
1,
bias=False,
),
nn.BatchNorm2d(num_outchannels_conv3x3),
nn.ReLU(True),
)
)
fuse_layer.append(nn.Sequential(*conv3x3s))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def get_num_inchannels(self):
return self.num_inchannels
def forward(self, x):
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i in range(self.num_branches):
x[i] = self.branches[i](x[i])
x_fuse = []
for i in range(len(self.fuse_layers)):
y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
for j in range(1, self.num_branches):
if i == j:
y = y + x[j]
else:
y = y + self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
return x_fuse
blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck}
class HighResolutionNet(nn.Module):
def __init__(self, cfg, **kwargs):
self.inplanes = 64
super(HighResolutionNet, self).__init__()
use_old_impl = cfg.get("use_old_impl")
self.use_old_impl = use_old_impl
# 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.stage1_cfg = cfg.get("stage1", {})
num_channels = self.stage1_cfg["num_channels"][0]
block = blocks_dict[self.stage1_cfg["block"]]
num_blocks = self.stage1_cfg["num_blocks"][0]
self.layer1 = self._make_layer(block, num_channels, num_blocks)
stage1_out_channel = block.expansion * num_channels
self.stage2_cfg = cfg.get("stage2", {})
num_channels = self.stage2_cfg.get("num_channels", (32, 64))
block = blocks_dict[self.stage2_cfg.get("block")]
num_channels = [num_channels[i] * block.expansion for i in range(len(num_channels))]
stage2_num_channels = num_channels
self.transition1 = self._make_transition_layer([stage1_out_channel], num_channels)
self.stage2, pre_stage_channels = self._make_stage(self.stage2_cfg, num_channels)
self.stage3_cfg = cfg.get("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))]
stage3_num_channels = 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 = cfg.get("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)
stage_4_out_channels = num_channels
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels, multi_scale_output=not self.use_old_impl
)
stage4_num_channels = num_channels
self.output_channels_dim = pre_stage_channels
self.pretrained_layers = cfg["pretrained_layers"]
self.init_weights()
self.avg_pooling = nn.AdaptiveAvgPool2d(1)
if use_old_impl:
in_dims = (
2**2 * stage2_num_channels[-1] + 2**1 * stage3_num_channels[-1] +
stage_4_out_channels[-1]
)
else:
# TODO: Replace with parameters
in_dims = 4 * 384
self.subsample_4 = self._make_subsample_layer(
in_channels=stage4_num_channels[0], num_layers=3
)
self.subsample_3 = self._make_subsample_layer(
in_channels=stage2_num_channels[-1], num_layers=2
)
self.subsample_2 = self._make_subsample_layer(
in_channels=stage3_num_channels[-1], num_layers=1
)
self.conv_layers = self._make_conv_layer(in_channels=in_dims, num_layers=5)
def get_output_dim(self):
base_output = {f"layer{idx + 1}": val for idx, val in enumerate(self.output_channels_dim)}
output = base_output.copy()
for key in base_output:
output[f"{key}_avg_pooling"] = output[key]
output["concat"] = 2048
return output
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_conv_layer(self, in_channels=2048, num_layers=3, num_filters=2048, stride=1):
layers = []
for i in range(num_layers):
downsample = nn.Conv2d(in_channels, num_filters, stride=1, kernel_size=1, bias=False)
layers.append(Bottleneck(in_channels, num_filters // 4, downsample=downsample))
in_channels = num_filters
return nn.Sequential(*layers)
def _make_subsample_layer(self, in_channels=96, num_layers=3, stride=2):
layers = []
for i in range(num_layers):
layers.append(
nn.Conv2d(
in_channels=in_channels,
out_channels=2 * in_channels,
kernel_size=3,
stride=stride,
padding=1,
)
)
in_channels = 2 * in_channels
layers.append(nn.BatchNorm2d(in_channels, momentum=BN_MOMENTUM))
layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*layers)
def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True, log=False):
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,
)
)
modules[-1].log = log
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:
if i < self.stage2_cfg["num_branches"]:
x_list.append(self.transition2[i](y_list[i]))
else:
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:
if i < self.stage3_cfg["num_branches"]:
x_list.append(self.transition3[i](y_list[i]))
else:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
if not self.use_old_impl:
y_list = self.stage4(x_list)
output = {}
for idx, x in enumerate(y_list):
output[f"layer{idx + 1}"] = x
feat_list = []
if self.use_old_impl:
x3 = self.subsample_3(x_list[1])
x2 = self.subsample_2(x_list[2])
x1 = x_list[3]
feat_list = [x3, x2, x1]
else:
x4 = self.subsample_4(y_list[0])
x3 = self.subsample_3(y_list[1])
x2 = self.subsample_2(y_list[2])
x1 = y_list[3]
feat_list = [x4, x3, x2, x1]
xf = self.conv_layers(torch.cat(feat_list, dim=1))
xf = xf.mean(dim=(2, 3))
xf = xf.view(xf.size(0), -1)
output["concat"] = xf
# y_list = self.stage4(x_list)
# output['stage4'] = y_list[0]
# output['stage4_avg_pooling'] = self.avg_pooling(y_list[0]).view(
# *y_list[0].shape[:2])
# concat_outputs = y_list + x_list
# output['concat'] = torch.cat([
# self.avg_pooling(tensor).view(*tensor.shape[:2])
# for tensor in concat_outputs],
# dim=1)
return output
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.normal_(m.weight, std=0.001)
for name, _ in m.named_parameters():
if name in ["bias"]:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.ConvTranspose2d):
nn.init.normal_(m.weight, std=0.001)
for name, _ in m.named_parameters():
if name in ["bias"]:
nn.init.constant_(m.bias, 0)
def load_weights(self, pretrained=""):
pretrained = osp.expandvars(pretrained)
if osp.isfile(pretrained):
pretrained_state_dict = torch.load(pretrained, map_location=torch.device("cpu"))
need_init_state_dict = {}
for name, m in pretrained_state_dict.items():
if (
name.split(".")[0] in self.pretrained_layers or self.pretrained_layers[0] == "*"
):
need_init_state_dict[name] = m
missing, unexpected = self.load_state_dict(need_init_state_dict, strict=False)
elif pretrained:
raise ValueError("{} is not exist!".format(pretrained))