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import torch
import torch.nn as nn
import torch.nn.functional as F
# from torch.nn import BatchNorm2d
from torch.nn import SyncBatchNorm as BatchNorm2d
from functools import partial
import re
from models.base_models.resnet import resnet101, resnet18, resnet50
from utils.seg_opr.conv_2_5d import Conv2_5D_depth, Conv2_5D_disp
class DeepLabV3p_r18(nn.Module):
def __init__(self, num_classes, config):
super(DeepLabV3p_r18, self).__init__()
self.norm_layer = BatchNorm2d
self.backbone = resnet18(config.pretrained_model_r18, norm_layer=self.norm_layer,
bn_eps=config.bn_eps,
bn_momentum=config.bn_momentum,
deep_stem=False, stem_width=64)
self.dilate = 2
for m in self.backbone.layer4.children():
m.apply(partial(self._nostride_dilate, dilate=self.dilate))
self.dilate *= 2
self.head = Head('r18', num_classes, self.norm_layer, config.bn_momentum)
self.business_layer = []
self.business_layer.append(self.head)
self.classifier = nn.Conv2d(256, num_classes, kernel_size=1, bias=True)
self.business_layer.append(self.classifier)
init_weight(self.business_layer, nn.init.kaiming_normal_,
BatchNorm2d, config.bn_eps, config.bn_momentum,
mode='fan_in', nonlinearity='relu')
init_weight(self.classifier, nn.init.kaiming_normal_,
BatchNorm2d, config.bn_eps, config.bn_momentum,
mode='fan_in', nonlinearity='relu')
def forward(self, data, get_sup_loss = False, gt = None, criterion = None):
data = data[0] #rgb is the first element in the list
blocks = self.backbone(data)
v3plus_feature = self.head(blocks) #(b, c, h, w)
b, c, h, w = v3plus_feature.shape
pred = self.classifier(v3plus_feature)
b, c, h, w = data.shape
pred = F.interpolate(pred, size=(h, w), mode='bilinear', align_corners=True)
if not self.training: #return pred for evaluation
return pred
else:
if get_sup_loss:
return pred, self.get_sup_loss(pred, gt, criterion)
else:
return pred
def get_sup_loss(self, pred, gt, criterion):
pred = pred[:gt.shape[0]] #Getting loss for only those examples in batch where gt exists. Won't get sup loss for unlabeled data.
return criterion(pred, gt)
# @staticmethod
def _nostride_dilate(self, m, dilate):
if isinstance(m, nn.Conv2d):
if m.stride == (2, 2):
m.stride = (1, 1)
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
else:
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
def get_params(self):
param_groups = [[], [], []]
enc, enc_no_decay = group_weight(self.backbone, self.norm_layer)
param_groups[0].extend(enc)
param_groups[1].extend(enc_no_decay)
dec, dec_no_decay = group_weight(self.head, self.norm_layer)
param_groups[2].extend(dec)
param_groups[1].extend(dec_no_decay)
classifier, classifier_no_decay = group_weight(self.classifier, self.norm_layer)
param_groups[2].extend(classifier)
param_groups[1].extend(classifier_no_decay)
return param_groups
class DeepLabV3p_r50(nn.Module):
def __init__(self, num_classes, config):
super(DeepLabV3p_r50, self).__init__()
self.norm_layer = BatchNorm2d
self.backbone = resnet50(config.pretrained_model_r50, norm_layer=self.norm_layer,
bn_eps=config.bn_eps,
bn_momentum=config.bn_momentum,
deep_stem=True, stem_width=64)
self.dilate = 2
for m in self.backbone.layer4.children():
m.apply(partial(self._nostride_dilate, dilate=self.dilate))
self.dilate *= 2
self.head = Head('r50', num_classes, self.norm_layer, config.bn_momentum)
self.business_layer = []
self.business_layer.append(self.head)
self.classifier = nn.Conv2d(256, num_classes, kernel_size=1, bias=True)
self.business_layer.append(self.classifier)
init_weight(self.business_layer, nn.init.kaiming_normal_,
BatchNorm2d, config.bn_eps, config.bn_momentum,
mode='fan_in', nonlinearity='relu')
init_weight(self.classifier, nn.init.kaiming_normal_,
BatchNorm2d, config.bn_eps, config.bn_momentum,
mode='fan_in', nonlinearity='relu')
def forward(self, data, get_sup_loss = False, gt = None, criterion = None):
data = data[0] #rgb is the first element in the list
blocks = self.backbone(data)
v3plus_feature = self.head(blocks) #(b, c, h, w)
b, c, h, w = v3plus_feature.shape
pred = self.classifier(v3plus_feature)
b, c, h, w = data.shape
pred = F.interpolate(pred, size=(h, w), mode='bilinear', align_corners=True)
if not self.training: #return pred for evaluation
return pred
else:
if get_sup_loss:
return pred, self.get_sup_loss(pred, gt, criterion)
else:
return pred
def get_sup_loss(self, pred, gt, criterion):
pred = pred[:gt.shape[0]] #Getting loss for only those examples in batch where gt exists. Won't get sup loss for unlabeled data.
return criterion(pred, gt)
def get_params(self):
param_groups = [[], [], []]
enc, enc_no_decay = group_weight(self.backbone, self.norm_layer)
param_groups[0].extend(enc)
param_groups[1].extend(enc_no_decay)
dec, dec_no_decay = group_weight(self.head, self.norm_layer)
param_groups[2].extend(dec)
param_groups[1].extend(dec_no_decay)
classifier, classifier_no_decay = group_weight(self.classifier, self.norm_layer)
param_groups[2].extend(classifier)
param_groups[1].extend(classifier_no_decay)
return param_groups
# @staticmethod
def _nostride_dilate(self, m, dilate):
if isinstance(m, nn.Conv2d):
if m.stride == (2, 2):
m.stride = (1, 1)
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
else:
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
class DeepLabV3p_r101(nn.Module):
def __init__(self, num_classes, config):
super(DeepLabV3p_r101, self).__init__()
self.norm_layer = BatchNorm2d
self.backbone = resnet101(config.pretrained_model_r101, norm_layer=self.norm_layer,
bn_eps=config.bn_eps,
bn_momentum=config.bn_momentum,
deep_stem=True, stem_width=64)
self.dilate = 2
for m in self.backbone.layer4.children():
m.apply(partial(self._nostride_dilate, dilate=self.dilate))
self.dilate *= 2
self.head = Head('r50', num_classes, self.norm_layer, config.bn_momentum)
self.business_layer = []
self.business_layer.append(self.head)
self.classifier = nn.Conv2d(256, num_classes, kernel_size=1, bias=True)
self.business_layer.append(self.classifier)
init_weight(self.business_layer, nn.init.kaiming_normal_,
BatchNorm2d, config.bn_eps, config.bn_momentum,
mode='fan_in', nonlinearity='relu')
init_weight(self.classifier, nn.init.kaiming_normal_,
BatchNorm2d, config.bn_eps, config.bn_momentum,
mode='fan_in', nonlinearity='relu')
def forward(self, data, get_sup_loss = False, gt = None, criterion = None):
data = data[0] #rgb is the first element in the list
blocks = self.backbone(data)
v3plus_feature = self.head(blocks) #(b, c, h, w)
b, c, h, w = v3plus_feature.shape
pred = self.classifier(v3plus_feature)
b, c, h, w = data.shape
pred = F.interpolate(pred, size=(h, w), mode='bilinear', align_corners=True)
if not self.training: #return pred for evaluation
return pred
else:
if get_sup_loss:
return pred, self.get_sup_loss(pred, gt, criterion)
else:
return pred
def get_sup_loss(self, pred, gt, criterion):
pred = pred[:gt.shape[0]] #Getting loss for only those examples in batch where gt exists. Won't get sup loss for unlabeled data.
return criterion(pred, gt)
def get_params(self):
param_groups = [[], [], []]
enc, enc_no_decay = group_weight(self.backbone, self.norm_layer)
param_groups[0].extend(enc)
param_groups[1].extend(enc_no_decay)
dec, dec_no_decay = group_weight(self.head, self.norm_layer)
param_groups[2].extend(dec)
param_groups[1].extend(dec_no_decay)
classifier, classifier_no_decay = group_weight(self.classifier, self.norm_layer)
param_groups[2].extend(classifier)
param_groups[1].extend(classifier_no_decay)
return param_groups
# @staticmethod
def _nostride_dilate(self, m, dilate):
if isinstance(m, nn.Conv2d):
if m.stride == (2, 2):
m.stride = (1, 1)
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
else:
if m.kernel_size == (3, 3):
m.dilation = (dilate, dilate)
m.padding = (dilate, dilate)
class ASPP(nn.Module):
def __init__(self,
in_channels,
out_channels,
dilation_rates=(12, 24, 36),
hidden_channels=256,
norm_act=nn.BatchNorm2d,
pooling_size=None):
super(ASPP, self).__init__()
self.pooling_size = pooling_size
self.map_convs = nn.ModuleList([
nn.Conv2d(in_channels, hidden_channels, 1, bias=False),
nn.Conv2d(in_channels, hidden_channels, 3, bias=False, dilation=dilation_rates[0],
padding=dilation_rates[0]),
nn.Conv2d(in_channels, hidden_channels, 3, bias=False, dilation=dilation_rates[1],
padding=dilation_rates[1]),
nn.Conv2d(in_channels, hidden_channels, 3, bias=False, dilation=dilation_rates[2],
padding=dilation_rates[2])
])
self.map_bn = norm_act(hidden_channels * 4)
self.global_pooling_conv = nn.Conv2d(in_channels, hidden_channels, 1, bias=False)
self.global_pooling_bn = norm_act(hidden_channels)
self.red_conv = nn.Conv2d(hidden_channels * 4, out_channels, 1, bias=False)
self.pool_red_conv = nn.Conv2d(hidden_channels, out_channels, 1, bias=False)
self.red_bn = norm_act(out_channels)
self.leak_relu = nn.LeakyReLU()
def forward(self, x):
# Map convolutions
out = torch.cat([m(x) for m in self.map_convs], dim=1)
out = self.map_bn(out)
out = self.leak_relu(out) # add activation layer
out = self.red_conv(out)
# Global pooling
pool = self._global_pooling(x)
pool = self.global_pooling_conv(pool)
pool = self.global_pooling_bn(pool)
pool = self.leak_relu(pool) # add activation layer
pool = self.pool_red_conv(pool)
if self.training or self.pooling_size is None:
pool = pool.repeat(1, 1, x.size(2), x.size(3))
out += pool
out = self.red_bn(out)
out = self.leak_relu(out) # add activation layer
return out
def _global_pooling(self, x):
pool = x.view(x.size(0), x.size(1), -1).mean(dim=-1)
pool = pool.view(x.size(0), x.size(1), 1, 1)
return pool
class Head(nn.Module):
def __init__(self, base_model, classify_classes, norm_act=nn.BatchNorm2d, bn_momentum=0.0003):
super(Head, self).__init__()
self.classify_classes = classify_classes
if base_model == 'r18':
self.aspp = ASPP(512, 256, [6, 12, 18], norm_act=norm_act)
self.reduce = nn.Sequential(
nn.Conv2d(64, 48, 1, bias=False),
norm_act(48, momentum=bn_momentum),
nn.ReLU(),
)
elif base_model == 'r50':
self.aspp = ASPP(2048, 256, [6, 12, 18], norm_act=norm_act)
self.reduce = nn.Sequential(
nn.Conv2d(256, 48, 1, bias=False),
norm_act(48, momentum=bn_momentum),
nn.ReLU(),
)
else:
raise Exception(f"Head not implemented for {base_model}")
self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
norm_act(256, momentum=bn_momentum),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
norm_act(256, momentum=bn_momentum),
nn.ReLU(),
)
def forward(self, f_list):
f = f_list[-1]
f = self.aspp(f)
low_level_features = f_list[0]
low_h, low_w = low_level_features.size(2), low_level_features.size(3)
low_level_features = self.reduce(low_level_features)
f = F.interpolate(f, size=(low_h, low_w), mode='bilinear', align_corners=True)
f = torch.cat((f, low_level_features), dim=1)
f = self.last_conv(f)
return f
def group_weight(module, norm_layer):
group_decay = []
group_no_decay = []
for m in module.modules():
if isinstance(m, nn.Linear):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d, nn.ConvTranspose3d)):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, Conv2_5D_depth):
group_decay.append(m.weight_0)
group_decay.append(m.weight_1)
group_decay.append(m.weight_2)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, Conv2_5D_disp):
group_decay.append(m.weight_0)
group_decay.append(m.weight_1)
group_decay.append(m.weight_2)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, norm_layer) or isinstance(m, nn.BatchNorm1d) or isinstance(m, nn.BatchNorm2d) \
or isinstance(m, nn.BatchNorm3d) or isinstance(m, nn.GroupNorm):
if m.weight is not None:
group_no_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, nn.Parameter):
group_decay.append(m)
elif isinstance(m, nn.Embedding):
group_decay.append(m)
assert len(list(module.parameters())) == len(group_decay) + len(
group_no_decay)
return group_decay, group_no_decay
def __init_weight(feature, conv_init, norm_layer, bn_eps, bn_momentum,
**kwargs):
for name, m in feature.named_modules():
if isinstance(m, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
conv_init(m.weight, **kwargs)
elif isinstance(m, Conv2_5D_depth):
conv_init(m.weight_0, **kwargs)
conv_init(m.weight_1, **kwargs)
conv_init(m.weight_2, **kwargs)
elif isinstance(m, Conv2_5D_disp):
conv_init(m.weight_0, **kwargs)
conv_init(m.weight_1, **kwargs)
conv_init(m.weight_2, **kwargs)
elif isinstance(m, norm_layer):
m.eps = bn_eps
m.momentum = bn_momentum
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def init_weight(module_list, conv_init, norm_layer, bn_eps, bn_momentum,
**kwargs):
if isinstance(module_list, list):
for feature in module_list:
__init_weight(feature, conv_init, norm_layer, bn_eps, bn_momentum,
**kwargs)
else:
__init_weight(module_list, conv_init, norm_layer, bn_eps, bn_momentum,
**kwargs) |