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import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
warnings.filterwarnings("ignore")
import torchvision
import rff.layers as rff
import parameters_pvsdnet as params
import helperFunctions as helper
def getLinearLayer(in_feat, out_feat, activation=nn.ReLU(True)):
return nn.Sequential(
nn.Linear(in_features=in_feat, out_features=out_feat, bias=True),
activation
)
def getConvLayer(in_channel,out_channel,stride=1,padding=1,activation=nn.ReLU()):
return nn.Sequential(nn.Conv2d(in_channel,
out_channel,
kernel_size=3,
stride=stride,
padding=padding,
padding_mode='reflect'),
activation)
def getConvTransposeLayer(in_channel, out_channel,kernel=3,stride=1,padding=1,activation=nn.ReLU()):
return nn.Sequential(nn.ConvTranspose2d(in_channel,
out_channel,
kernel_size = kernel,
stride=stride,
padding=padding),
activation)
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
class UnFlatten(nn.Module):
def forward(self, input, size=1):
return input.view(input.size(0), 1, params.params_height//8, params.params_width//8)
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.stride = stride
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.conv2(out)
out = out + self.shortcut(residual)
out = self.relu(out)
return out
class MLPEncoder(nn.Module):
def __init__(self):
super().__init__()
self.m = params.params_m
self.positional_encoding = rff.PositionalEncoding(sigma=1,m=self.m)
self.layer1 = getLinearLayer(2*3*self.m, 1024) # 2*3*m = 12, here m=32, when training with Rotation data, set it to 2*6*m
self.dropout1 = nn.Dropout(0.2)
self.layer2 = getLinearLayer(1024, 2048)
self.dropout2 = nn.Dropout(0.2)
self.layer3 = getLinearLayer(2048, (params.params_height//8)*(params.params_width//8))
self.unflat = UnFlatten()
self.up_layer1 = nn.Upsample(scale_factor=2, mode='nearest')
self.up_layer2 = nn.Upsample(scale_factor=2, mode='nearest')
self.up_layer3 = nn.Upsample(scale_factor=2, mode='nearest')
def forward(self, x):
x = self.positional_encoding(x)
x = self.layer1(x)
x = self.dropout1(x)
x = self.layer2(x)
x = self.dropout2(x)
x = self.layer3(x)
x = self.unflat(x)
x = self.up_layer1(x)
x = self.up_layer2(x)
x = self.up_layer3(x)
return x
class UpperEncoder(nn.Module):
def __init__(self):
super().__init__()
model = torchvision.models.resnet152(pretrained=False)
layers = list(model.children())
self.ResNetEncoder = torch.nn.Sequential(*layers[:5].copy())
del model
def forward(self, x):
x1 = x[:, 0:3, :, :]
x1 = self.ResNetEncoder(x1)
return x1
def apply_resnet_encoder(self, x):
x1 = x[:, 0:3, :, :]
x1 = self.ResNetEncoder(x1)
return x1
class LowerEncoder(nn.Module):
def __init__(self,total_image_input=1):
super().__init__()
self.encoder_pre = ResidualBlock((total_image_input*3)+1, 20)
self.encoder_layer1 = ResidualBlock(20, 30)
self.encoder_layer2 = ResidualBlock(30, 50)
self.encoder_layer3 = nn.Sequential(
ResidualBlock(50, 100),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.encoder_layer4 = ResidualBlock(100, 200)
self.encoder_layer5 = nn.Sequential(
ResidualBlock(200, 400),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.encoder_layer6 = ResidualBlock(400, 600)
self.encoder_layer7 = nn.Sequential(
ResidualBlock(600, 800),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.encoder_layer8 = ResidualBlock(800, 1000)
self.encoder_layer9 = nn.Sequential(
ResidualBlock(1000, 1200),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.encoder_layer10 = ResidualBlock(1200, 1400)
self.encoder_layer11 = ResidualBlock(1400, 1600)
def forward(self, x):
x = self.encoder_pre(x)
x = self.encoder_layer1(x)
x = self.encoder_layer2(x)
skip1 = self.encoder_layer3(x)
x = self.encoder_layer4(skip1)
skip2 = self.encoder_layer5(x)
x = self.encoder_layer6(skip2)
skip3 = self.encoder_layer7(x)
x = self.encoder_layer8(skip3)
skip4 = self.encoder_layer9(x)
x = self.encoder_layer10(skip4)
x = self.encoder_layer11(x)
return x, [skip1, skip2, skip3, skip4]
class MergeDecoder(nn.Module):
def __init__(self):
super().__init__()
self.decoder_layer1 = ResidualBlock(1600, 1400)
self.decoder_layer2 = ResidualBlock(1400, 1200)
self.decoder_layer3 = ResidualBlock(1200, 1000)
self.decoder_layer4 = nn.Sequential(
nn.ConvTranspose2d(1000, 800, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer5 = ResidualBlock(800, 600)
self.decoder_layer6 = nn.Sequential(
nn.ConvTranspose2d(600, 400, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer7 = ResidualBlock(400, 200)
self.decoder_layer8 = nn.Sequential(
nn.ConvTranspose2d(200, 100, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer9 = ResidualBlock(100, 100)
self.decoder_layer10 = nn.Sequential(
nn.ConvTranspose2d(100, 100, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer11 = ResidualBlock(100, 100)
self.decoder_layer12 = ResidualBlock(100, 50)
self.decoder_layer13 = ResidualBlock(50, 40)
self.decoder_layer14 = ResidualBlock(40, 20)
self.decoder_layer15 = nn.Sequential(
nn.Conv2d(20, 8, 3, stride=1, padding=1),
nn.Sigmoid()
)
self.decoder_layer16 = nn.Sequential(
nn.Conv2d(8, 3, 3, stride=1, padding=1),
nn.Sigmoid()
)
def forward(self, x, lower_skip_list, upper_skip_list):
x = self.decoder_layer1(x)
x = self.decoder_layer2(x)
x = x + lower_skip_list[3] + upper_skip_list[1]
x = self.decoder_layer3(x)
x = self.decoder_layer4(x)
x = x + lower_skip_list[2] + upper_skip_list[0]
x = self.decoder_layer5(x)
x = self.decoder_layer6(x)
x = x + lower_skip_list[1]
x = self.decoder_layer7(x)
x = self.decoder_layer8(x)
x = x + lower_skip_list[0]
x = self.decoder_layer9(x)
x = self.decoder_layer10(x)
x = self.decoder_layer11(x)
x = self.decoder_layer12(x)
x = self.decoder_layer13(x)
x = self.decoder_layer14(x)
x = self.decoder_layer15(x)
x = self.decoder_layer16(x)
return x
class DepthDecoder(nn.Module):
def __init__(self):
super().__init__()
self.decoder_layer1 = ResidualBlock(1600, 1400)
self.decoder_layer2 = ResidualBlock(1400, 1200)
self.decoder_layer3 = ResidualBlock(1200, 1000)
self.decoder_layer4 = nn.Sequential(
nn.ConvTranspose2d(1000, 800, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer5 = ResidualBlock(800, 600)
self.decoder_layer6 = nn.Sequential(
nn.ConvTranspose2d(600, 400, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer7 = ResidualBlock(400, 200)
self.decoder_layer8 = nn.Sequential(
nn.ConvTranspose2d(200, 100, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer9 = ResidualBlock(100, 100)
self.decoder_layer10 = nn.Sequential(
nn.ConvTranspose2d(100, 100, 2, stride=2, padding=0),
nn.ReLU(True)
)
self.decoder_layer11 = ResidualBlock(100, 100)
self.decoder_layer12 = ResidualBlock(100, 50)
self.decoder_layer13 = ResidualBlock(50, 40)
self.decoder_layer14 = ResidualBlock(40, 20)
self.decoder_layer15 = nn.Sequential(
nn.Conv2d(20, 8, 3, stride=1, padding=1),
nn.ReLU(True)
)
self.decoder_layer16 = nn.Sequential(
nn.Conv2d(8, 1, 3, stride=1, padding=1),
nn.ReLU(True)
)
def forward(self, x, lower_skip_list, upper_skip_list):
x = self.decoder_layer1(x)
x = self.decoder_layer2(x)
x = x + lower_skip_list[3] + upper_skip_list[1]
x = self.decoder_layer3(x)
x = self.decoder_layer4(x)
x = x + lower_skip_list[2] + upper_skip_list[0]
x = self.decoder_layer5(x)
x = self.decoder_layer6(x)
x = x + lower_skip_list[1]
x = self.decoder_layer7(x)
x = self.decoder_layer8(x)
x = x + lower_skip_list[0]
x = self.decoder_layer9(x)
x = self.decoder_layer10(x)
x = self.decoder_layer11(x)
x = self.decoder_layer12(x)
x = self.decoder_layer13(x)
x = self.decoder_layer14(x)
x = self.decoder_layer15(x)
x = self.decoder_layer16(x)
return x
class PVSNet(nn.Module):
def __init__(self,total_image_input=1):
super().__init__()
self.target_positional_embedding = MLPEncoder()
self.upper_encoder = UpperEncoder()
self.lower_encoder = LowerEncoder(total_image_input)
self.merge_decoder = MergeDecoder()
self.upper_encoder_extra_1 = nn.Sequential(
ResidualBlock(256, 800),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.upper_encoder_extra_2 = nn.Sequential(
ResidualBlock(800, 1200),
nn.MaxPool2d(kernel_size=2, stride=2)
)
def forward(self, x, pos):
target_position_feature = self.target_positional_embedding(pos)
# First Encoder Branch
upper_features_1 = self.upper_encoder.apply_resnet_encoder(x)
upper_features_1 = self.upper_encoder_extra_1(upper_features_1)
upper_features_2 = self.upper_encoder_extra_2(upper_features_1)
# Second Encoder Branch
stacked_tensor = torch.cat((x,target_position_feature),dim=1)
lower_feature, skip_list = self.lower_encoder(stacked_tensor)
# Decoder
merged_feature = self.merge_decoder(lower_feature, skip_list, [upper_features_1, upper_features_2])
return merged_feature
class PVSDNet(nn.Module):
def __init__(self,total_image_input=1):
super().__init__()
self.target_positional_embedding = MLPEncoder()
self.upper_encoder = UpperEncoder()
self.lower_encoder = LowerEncoder(total_image_input)
self.merge_decoder = MergeDecoder()
self.depth_decoder = DepthDecoder()
self.upper_encoder_extra_1 = nn.Sequential(
ResidualBlock(256, 800),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.upper_encoder_extra_2 = nn.Sequential(
ResidualBlock(800, 1200),
nn.MaxPool2d(kernel_size=2, stride=2)
)
print("Loading pre-trained nvs net")
base_net = PVSNet(total_image_input)
#base_net = helper.load_Checkpoint("./checkpoint/checkpoint_init_pvsnet.pth", base_net, load_cpu=True) #uncomment if you want to use the pre-trained pvsnet to train with lora
self.target_positional_embedding = base_net.target_positional_embedding
self.upper_encoder = base_net.upper_encoder
self.lower_encoder = base_net.lower_encoder
self.merge_decoder = base_net.merge_decoder
self.upper_encoder_extra_1 = base_net.upper_encoder_extra_1
self.upper_encoder_extra_2 = base_net.upper_encoder_extra_2
del base_net
print("Loading pre-trained nvs net: Done")
def forward(self, x, pos):
target_position_feature = self.target_positional_embedding(pos)
# First Encoder Branch
upper_features_1 = self.upper_encoder.apply_resnet_encoder(x)
upper_features_1 = self.upper_encoder_extra_1(upper_features_1)
upper_features_2 = self.upper_encoder_extra_2(upper_features_1)
# Second Encoder Branch
stacked_tensor = torch.cat((x,target_position_feature),dim=1)
lower_feature, skip_list = self.lower_encoder(stacked_tensor)
# Decoder
merged_feature = self.merge_decoder(lower_feature, skip_list, [upper_features_1, upper_features_2])
# Depth Decoder
depth_feature = self.depth_decoder(lower_feature, skip_list, [upper_features_1, upper_features_2])
return merged_feature, depth_feature
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