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import torch.nn as nn
from torch.nn import functional as F
class FCN8s(nn.Module):
def __init__(self, n_classes=4, n_frames=5):
super(FCN8s, self).__init__()
# lt@20240406 reduce resolution
self.pool0 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
# conv1
# The 100 pixel input padding guarantees that the network output can be aligned to the input for any input size
# since the kernel size of fc6 is 7 and its padding equals to 0.
self.conv1_1 = nn.Conv2d(n_frames, 64, 3, padding=100)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/2
# conv2
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/4
# conv3
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/8
# conv4
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/16
# conv5
self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
self.relu5_3 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32
# fc6
self.fc6 = nn.Conv2d(512, 4096, 7)
self.relu6 = nn.ReLU(inplace=True)
self.drop6 = nn.Dropout2d()
# fc7
self.fc7 = nn.Conv2d(4096, 4096, 1)
self.relu7 = nn.ReLU(inplace=True)
self.drop7 = nn.Dropout2d()
self.score_fr = nn.Conv2d(4096, n_classes, 1)
self.score_pool3 = nn.Conv2d(256, n_classes, 1)
self.score_pool4 = nn.Conv2d(512, n_classes, 1)
self.upscore2 = nn.ConvTranspose2d(
n_classes, n_classes, 4, stride=2, bias=False)
self.upscore8 = nn.ConvTranspose2d(
n_classes, n_classes, 16, stride=8, bias=False)
self.upscore_pool4 = nn.ConvTranspose2d(
n_classes, n_classes, 4, stride=2, bias=False)
def forward(self, x):
# lt@20240406 reduce resolution
x = self.pool0(x)
# lt@20240408 add resolution
# x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
h = x
h = self.relu1_1(self.conv1_1(h))
h = self.relu1_2(self.conv1_2(h))
h = self.pool1(h)
h = self.relu2_1(self.conv2_1(h))
h = self.relu2_2(self.conv2_2(h))
h = self.pool2(h)
h = self.relu3_1(self.conv3_1(h))
h = self.relu3_2(self.conv3_2(h))
h = self.relu3_3(self.conv3_3(h))
h = self.pool3(h)
pool3 = h # 1/8
h = self.relu4_1(self.conv4_1(h))
h = self.relu4_2(self.conv4_2(h))
h = self.relu4_3(self.conv4_3(h))
h = self.pool4(h)
pool4 = h # 1/16
h = self.relu5_1(self.conv5_1(h))
h = self.relu5_2(self.conv5_2(h))
h = self.relu5_3(self.conv5_3(h))
h = self.pool5(h)
h = self.relu6(self.fc6(h))
h = self.drop6(h)
h = self.relu7(self.fc7(h))
h = self.drop7(h)
h = self.score_fr(h)
h = self.upscore2(h)
upscore2 = h # 1/16
h = self.score_pool4(pool4)
# crop for alignment
h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]]
score_pool4c = h # 1/16
h = upscore2 + score_pool4c # 1/16
h = self.upscore_pool4(h)
upscore_pool4 = h # 1/8
h = self.score_pool3(pool3)
# crop
h = h[:, :,
9:9 + upscore_pool4.size()[2],
9:9 + upscore_pool4.size()[3]]
score_pool3c = h # 1/8
h = upscore_pool4 + score_pool3c # 1/8
h = self.upscore8(h)
# crop
h = h[:, :, 31:31 + x.size()[2], 31:31 + x.size()[3]].contiguous()
# lt@20240406 recover resolution
h = F.interpolate(h, scale_factor=2, mode='bilinear', align_corners=True)
# lt@20240408 add resolution
# h = F.interpolate(h, scale_factor=0.5, mode='bilinear', align_corners=True)
return h
# from https://github.com/wkentaro/pytorch-fcn
# import torch.nn as nn
# class FCN8s(nn.Module):
# def __init__(self, n_classes=4, n_frames=5):
# super(FCN8s, self).__init__()
# # conv1
# # The 100 pixel input padding guarantees that the network output can be aligned to the input for any input size
# # since the kernel size of fc6 is 7 and its padding equals to 0.
# self.conv1_1 = nn.Conv2d(n_frames, 64, 3, padding=100)
# self.bn1_1 = nn.BatchNorm2d(64)
# self.relu1_1 = nn.ReLU(inplace=True)
# self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
# self.bn1_2 = nn.BatchNorm2d(64)
# self.relu1_2 = nn.ReLU(inplace=True)
# self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/2
# # conv2
# self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
# self.bn2_1 = nn.BatchNorm2d(128)
# self.relu2_1 = nn.ReLU(inplace=True)
# self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
# self.bn2_2 = nn.BatchNorm2d(128)
# self.relu2_2 = nn.ReLU(inplace=True)
# self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/4
# # conv3
# self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
# self.bn3_1 = nn.BatchNorm2d(256)
# self.relu3_1 = nn.ReLU(inplace=True)
# self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
# self.bn3_2 = nn.BatchNorm2d(256)
# self.relu3_2 = nn.ReLU(inplace=True)
# self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)
# self.bn3_3 = nn.BatchNorm2d(256)
# self.relu3_3 = nn.ReLU(inplace=True)
# self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/8
# # conv4
# self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
# self.bn4_1 = nn.BatchNorm2d(512)
# self.relu4_1 = nn.ReLU(inplace=True)
# self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
# self.bn4_2 = nn.BatchNorm2d(512)
# self.relu4_2 = nn.ReLU(inplace=True)
# self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)
# self.bn4_3 = nn.BatchNorm2d(512)
# self.relu4_3 = nn.ReLU(inplace=True)
# self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/16
# # conv5
# self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)
# self.bn5_1 = nn.BatchNorm2d(512)
# self.relu5_1 = nn.ReLU(inplace=True)
# self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)
# self.bn5_2 = nn.BatchNorm2d(512)
# self.relu5_2 = nn.ReLU(inplace=True)
# self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)
# self.bn5_3 = nn.BatchNorm2d(512)
# self.relu5_3 = nn.ReLU(inplace=True)
# self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32
# # fc6
# self.fc6 = nn.Conv2d(512, 4096, 7)
# self.bn6 = nn.BatchNorm2d(4096)
# self.relu6 = nn.ReLU(inplace=True)
# self.drop6 = nn.Dropout2d()
# # fc7
# self.fc7 = nn.Conv2d(4096, 4096, 1)
# self.bn7 = nn.BatchNorm2d(4096)
# self.relu7 = nn.ReLU(inplace=True)
# self.drop7 = nn.Dropout2d()
# self.score_fr = nn.Conv2d(4096, n_classes, 1)
# self.score_pool3 = nn.Conv2d(256, n_classes, 1)
# self.score_pool4 = nn.Conv2d(512, n_classes, 1)
# self.upscore2 = nn.ConvTranspose2d(
# n_classes, n_classes, 4, stride=2, bias=False)
# self.upscore8 = nn.ConvTranspose2d(
# n_classes, n_classes, 16, stride=8, bias=False)
# self.upscore_pool4 = nn.ConvTranspose2d(
# n_classes, n_classes, 4, stride=2, bias=False)
# def forward(self, x):
# h = x
# h = self.relu1_1(self.bn1_1(self.conv1_1(h)))
# h = self.relu1_2(self.bn1_2(self.conv1_2(h)))
# h = self.pool1(h)
# h = self.relu2_1(self.bn2_1(self.conv2_1(h)))
# h = self.relu2_2(self.bn2_2(self.conv2_2(h)))
# h = self.pool2(h)
# h = self.relu3_1(self.bn3_1(self.conv3_1(h)))
# h = self.relu3_2(self.bn3_2(self.conv3_2(h)))
# h = self.relu3_3(self.bn3_3(self.conv3_3(h)))
# h = self.pool3(h)
# pool3 = h # 1/8
# h = self.relu4_1(self.bn4_1(self.conv4_1(h)))
# h = self.relu4_2(self.bn4_2(self.conv4_2(h)))
# h = self.relu4_3(self.bn4_3(self.conv4_3(h)))
# h = self.pool4(h)
# pool4 = h # 1/16
# h = self.relu5_1(self.bn5_1(self.conv5_1(h)))
# h = self.relu5_2(self.bn5_2(self.conv5_2(h)))
# h = self.relu5_3(self.bn5_3(self.conv5_3(h)))
# h = self.pool5(h)
# h = self.relu6(self.bn6(self.fc6(h)))
# h = self.drop6(h)
# h = self.relu7(self.bn7(self.fc7(h)))
# h = self.drop7(h)
# h = self.score_fr(h)
# h = self.upscore2(h)
# upscore2 = h # 1/16
# h = self.score_pool4(pool4)
# # crop for alignment
# h = h[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]]
# score_pool4c = h # 1/16
# h = upscore2 + score_pool4c # 1/16
# h = self.upscore_pool4(h)
# upscore_pool4 = h # 1/8
# h = self.score_pool3(pool3)
# # crop
# h = h[:, :,
# 9:9 + upscore_pool4.size()[2],
# 9:9 + upscore_pool4.size()[3]]
# score_pool3c = h # 1/8
# h = upscore_pool4 + score_pool3c # 1/8
# h = self.upscore8(h)
# # crop
# h = h[:, :, 31:31 + x.size()[2], 31:31 + x.size()[3]].contiguous()
# return h |