WSCL / models /hrnet.py
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"""
This HRNet implementation is modified from the following repository:
https://github.com/HRNet/HRNet-Semantic-Segmentation
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from .lib.nn import SynchronizedBatchNorm2d
from .utils import load_url
BatchNorm2d = SynchronizedBatchNorm2d
BN_MOMENTUM = 0.1
logger = logging.getLogger(__name__)
__all__ = ["hrnetv2"]
model_urls = {
"hrnetv2": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/hrnetv2_w48-imagenet.pth",
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
)
self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False
)
self.bn3 = BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
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(inplace=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)
)
logger.error(error_msg)
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format(
num_branches, len(num_channels)
)
logger.error(error_msg)
raise ValueError(error_msg)
if num_branches != len(num_inchannels):
error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format(
num_branches, len(num_inchannels)
)
logger.error(error_msg)
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,
),
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,
),
BatchNorm2d(num_inchannels[i], momentum=BN_MOMENTUM),
)
)
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,
),
BatchNorm2d(
num_outchannels_conv3x3, momentum=BN_MOMENTUM
),
)
)
else:
num_outchannels_conv3x3 = num_inchannels[j]
conv3x3s.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_outchannels_conv3x3,
3,
2,
1,
bias=False,
),
BatchNorm2d(
num_outchannels_conv3x3, momentum=BN_MOMENTUM
),
nn.ReLU(inplace=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]
elif j > i:
width_output = x[i].shape[-1]
height_output = x[i].shape[-2]
y = y + F.interpolate(
self.fuse_layers[i][j](x[j]),
size=(height_output, width_output),
mode="bilinear",
align_corners=False,
)
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 HRNetV2(nn.Module):
def __init__(self, n_class, **kwargs):
super(HRNetV2, self).__init__()
extra = {
"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",
},
"FINAL_CONV_KERNEL": 1,
}
# stem net
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = BatchNorm2d(64, momentum=BN_MOMENTUM)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn2 = BatchNorm2d(64, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(Bottleneck, 64, 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=True
)
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,
),
BatchNorm2d(
num_channels_cur_layer[i], momentum=BN_MOMENTUM
),
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),
BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True),
)
)
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False,
),
BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
)
layers = []
layers.append(block(inplanes, planes, stride, downsample))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(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, return_feature_maps=False):
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])
x = self.stage4(x_list)
# Upsampling
x0_h, x0_w = x[0].size(2), x[0].size(3)
x1 = F.interpolate(
x[1], size=(x0_h, x0_w), mode="bilinear", align_corners=False
)
x2 = F.interpolate(
x[2], size=(x0_h, x0_w), mode="bilinear", align_corners=False
)
x3 = F.interpolate(
x[3], size=(x0_h, x0_w), mode="bilinear", align_corners=False
)
x = torch.cat([x[0], x1, x2, x3], 1)
# x = self.last_layer(x)
return [x]
def hrnetv2(pretrained=False, **kwargs):
model = HRNetV2(n_class=1000, **kwargs)
if pretrained:
model.load_state_dict(load_url(model_urls["hrnetv2"]), strict=False)
return model