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# This code clone from https://github.com/ooooverflow/BiSeNet
import torch.nn as nn
import torch
import torch.nn.functional as F
from . import model_util
import warnings
warnings.filterwarnings(action='ignore')
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
# new axis order
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
# Transpose: (N, C, D, H, W) -> (C, N, D, H, W)
transposed = tensor.permute(axis_order)
# Flatten: (C, N, D, H, W) -> (C, N * D * H * W)
return transposed.contiguous().view(C, -1)
class DiceLoss(nn.Module):
def __init__(self):
super().__init__()
self.epsilon = 1e-5
def forward(self, output, target):
assert output.size() == target.size(), "'input' and 'target' must have the same shape"
output = F.softmax(output, dim=1)
output = flatten(output)
target = flatten(target)
# intersect = (output * target).sum(-1).sum() + self.epsilon
# denominator = ((output + target).sum(-1)).sum() + self.epsilon
intersect = (output * target).sum(-1)
denominator = (output + target).sum(-1)
dice = intersect / denominator
dice = torch.mean(dice)
return 1 - dice
# return 1 - 2. * intersect / denominator
class resnet18(torch.nn.Module):
def __init__(self, pretrained=True):
super().__init__()
self.features = model_util.resnet18(pretrained=pretrained)
self.conv1 = self.features.conv1
self.bn1 = self.features.bn1
self.relu = self.features.relu
self.maxpool1 = self.features.maxpool
self.layer1 = self.features.layer1
self.layer2 = self.features.layer2
self.layer3 = self.features.layer3
self.layer4 = self.features.layer4
def forward(self, input):
x = self.conv1(input)
x = self.relu(self.bn1(x))
x = self.maxpool1(x)
feature1 = self.layer1(x) # 1 / 4
feature2 = self.layer2(feature1) # 1 / 8
feature3 = self.layer3(feature2) # 1 / 16
feature4 = self.layer4(feature3) # 1 / 32
# global average pooling to build tail
tail = torch.mean(feature4, 3, keepdim=True)
tail = torch.mean(tail, 2, keepdim=True)
return feature3, feature4, tail
class resnet101(torch.nn.Module):
def __init__(self, pretrained=True):
super().__init__()
self.features = model_util.resnet101(pretrained=pretrained)
self.conv1 = self.features.conv1
self.bn1 = self.features.bn1
self.relu = self.features.relu
self.maxpool1 = self.features.maxpool
self.layer1 = self.features.layer1
self.layer2 = self.features.layer2
self.layer3 = self.features.layer3
self.layer4 = self.features.layer4
def forward(self, input):
x = self.conv1(input)
x = self.relu(self.bn1(x))
x = self.maxpool1(x)
feature1 = self.layer1(x) # 1 / 4
feature2 = self.layer2(feature1) # 1 / 8
feature3 = self.layer3(feature2) # 1 / 16
feature4 = self.layer4(feature3) # 1 / 32
# global average pooling to build tail
tail = torch.mean(feature4, 3, keepdim=True)
tail = torch.mean(tail, 2, keepdim=True)
return feature3, feature4, tail
def build_contextpath(name,pretrained):
model = {
'resnet18': resnet18(pretrained=pretrained),
'resnet101': resnet101(pretrained=pretrained)
}
return model[name]
class ConvBlock(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=2,padding=1):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self, input):
x = self.conv1(input)
return self.relu(self.bn(x))
class Spatial_path(torch.nn.Module):
def __init__(self):
super().__init__()
self.convblock1 = ConvBlock(in_channels=3, out_channels=64)
self.convblock2 = ConvBlock(in_channels=64, out_channels=128)
self.convblock3 = ConvBlock(in_channels=128, out_channels=256)
def forward(self, input):
x = self.convblock1(input)
x = self.convblock2(x)
x = self.convblock3(x)
return x
class AttentionRefinementModule(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.bn = nn.BatchNorm2d(out_channels)
self.sigmoid = nn.Sigmoid()
self.in_channels = in_channels
self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
def forward(self, input):
# global average pooling
x = self.avgpool(input)
assert self.in_channels == x.size(1), 'in_channels and out_channels should all be {}'.format(x.size(1))
x = self.conv(x)
# x = self.sigmoid(self.bn(x))
x = self.sigmoid(x)
# channels of input and x should be same
x = torch.mul(input, x)
return x
class FeatureFusionModule(torch.nn.Module):
def __init__(self, num_classes, in_channels):
super().__init__()
# self.in_channels = input_1.channels + input_2.channels
# resnet101 3328 = 256(from context path) + 1024(from spatial path) + 2048(from spatial path)
# resnet18 1024 = 256(from context path) + 256(from spatial path) + 512(from spatial path)
self.in_channels = in_channels
self.convblock = ConvBlock(in_channels=self.in_channels, out_channels=num_classes, stride=1)
self.conv1 = nn.Conv2d(num_classes, num_classes, kernel_size=1)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(num_classes, num_classes, kernel_size=1)
self.sigmoid = nn.Sigmoid()
self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
def forward(self, input_1, input_2):
x = torch.cat((input_1, input_2), dim=1)
assert self.in_channels == x.size(1), 'in_channels of ConvBlock should be {}'.format(x.size(1))
feature = self.convblock(x)
x = self.avgpool(feature)
x = self.relu(self.conv1(x))
x = self.sigmoid(self.conv2(x))
x = torch.mul(feature, x)
x = torch.add(x, feature)
return x
class BiSeNet(torch.nn.Module):
def __init__(self, num_classes, context_path, train_flag=True):
super().__init__()
# build spatial path
self.saptial_path = Spatial_path()
self.sigmoid = nn.Sigmoid()
# build context path
if train_flag:
self.context_path = build_contextpath(name=context_path,pretrained=True)
else:
self.context_path = build_contextpath(name=context_path,pretrained=False)
# build attention refinement module for resnet 101
if context_path == 'resnet101':
self.attention_refinement_module1 = AttentionRefinementModule(1024, 1024)
self.attention_refinement_module2 = AttentionRefinementModule(2048, 2048)
# supervision block
self.supervision1 = nn.Conv2d(in_channels=1024, out_channels=num_classes, kernel_size=1)
self.supervision2 = nn.Conv2d(in_channels=2048, out_channels=num_classes, kernel_size=1)
# build feature fusion module
self.feature_fusion_module = FeatureFusionModule(num_classes, 3328)
elif context_path == 'resnet18':
# build attention refinement module for resnet 18
self.attention_refinement_module1 = AttentionRefinementModule(256, 256)
self.attention_refinement_module2 = AttentionRefinementModule(512, 512)
# supervision block
self.supervision1 = nn.Conv2d(in_channels=256, out_channels=num_classes, kernel_size=1)
self.supervision2 = nn.Conv2d(in_channels=512, out_channels=num_classes, kernel_size=1)
# build feature fusion module
self.feature_fusion_module = FeatureFusionModule(num_classes, 1024)
else:
print('Error: unspport context_path network \n')
# build final convolution
self.conv = nn.Conv2d(in_channels=num_classes, out_channels=num_classes, kernel_size=1)
self.init_weight()
self.mul_lr = []
self.mul_lr.append(self.saptial_path)
self.mul_lr.append(self.attention_refinement_module1)
self.mul_lr.append(self.attention_refinement_module2)
self.mul_lr.append(self.supervision1)
self.mul_lr.append(self.supervision2)
self.mul_lr.append(self.feature_fusion_module)
self.mul_lr.append(self.conv)
def init_weight(self):
for name, m in self.named_modules():
if 'context_path' not in name:
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
m.eps = 1e-5
m.momentum = 0.1
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, input):
# output of spatial path
sx = self.saptial_path(input)
# output of context path
cx1, cx2, tail = self.context_path(input)
cx1 = self.attention_refinement_module1(cx1)
cx2 = self.attention_refinement_module2(cx2)
cx2 = torch.mul(cx2, tail)
# upsampling
cx1 = torch.nn.functional.interpolate(cx1, size=sx.size()[-2:], mode='bilinear')
cx2 = torch.nn.functional.interpolate(cx2, size=sx.size()[-2:], mode='bilinear')
cx = torch.cat((cx1, cx2), dim=1)
if self.training == True:
cx1_sup = self.supervision1(cx1)
cx2_sup = self.supervision2(cx2)
cx1_sup = torch.nn.functional.interpolate(cx1_sup, size=input.size()[-2:], mode='bilinear')
cx2_sup = torch.nn.functional.interpolate(cx2_sup, size=input.size()[-2:], mode='bilinear')
# output of feature fusion module
result = self.feature_fusion_module(sx, cx)
# upsampling
result = torch.nn.functional.interpolate(result, scale_factor=8, mode='bilinear')
result = self.conv(result)
if self.training == True:
return self.sigmoid(result), self.sigmoid(cx1_sup), self.sigmoid(cx2_sup)
return self.sigmoid(result) |