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models.py
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models.py
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"""
Neural network models in DeepVANet
"""
import torch
import torch.nn as nn
import time
# The implementation of CONVLSTM are based on the code from
# https://github.com/ndrplz/ConvLSTM_pytorch/blob/master/convlstm.py
class ConvLSTMCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, padding):
"""
Initialize ConvLSTM cell.
Parameters
----------
input_dim: int
Number of channels of input tensor.
hidden_dim: int
Number of channels of hidden state.
kernel_size: int
Size of the convolutional kernel.
bias: bool
Whether or not to add the bias.
"""
super(ConvLSTMCell, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,
out_channels=4 * self.hidden_dim,
kernel_size=kernel_size,
padding=padding)
def forward(self, input_tensor, cur_state):
h_cur, c_cur = cur_state
combined = torch.cat([input_tensor, h_cur], dim=1) # concatenate along channel axis
combined_conv = self.conv(combined)
cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)
i = torch.sigmoid(cc_i)
f = torch.sigmoid(cc_f)
o = torch.sigmoid(cc_o)
g = torch.tanh(cc_g)
c_next = f * c_cur + i * g
h_next = o * torch.tanh(c_next)
return h_next, c_next
def init_hidden(self, batch_size, height, width):
return (torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device),
torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device))
class ConvLSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size=3, padding=1):
super(ConvLSTM, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.cell = ConvLSTMCell(input_dim=self.input_dim,
hidden_dim=self.hidden_dim,kernel_size=kernel_size, padding=padding)
def forward(self, input_tensor, time=None):
b, _, _, h, w = input_tensor.size()
hidden_state = self.cell.init_hidden(b,h,w)
seq_len = input_tensor.size(1)
h, c = hidden_state
for t in range(seq_len):
h, c = self.cell(input_tensor=input_tensor[:, t, :, :, :], cur_state=[h, c])
return h
class FaceFeatureExtractorCNN(nn.Module):
def __init__(self):
super(FaceFeatureExtractorCNN, self).__init__()
self.net = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(32, 64, kernel_size=3),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(128 * 6 * 6, 1000),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(1000, 2),
nn.Sigmoid()
)
def forward(self,x):
x = self.net(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
def save(self, name=None):
"""
save the model
"""
if name is None:
prefix = 'FaceFeatureExtractorCNN_'
name = time.strftime(prefix + '%m%d_%H:%M:%S.pth')
torch.save(self.state_dict(), name)
return name
def load(self, path):
# self.load_state_dict(torch.load(path))
self.load_state_dict(torch.load(path,map_location=torch.device('cpu')))
class FaceFeatureExtractor(nn.Module):
def __init__(self, feature_size=16, pretrain=True):
super(FaceFeatureExtractor, self).__init__()
cnn = FaceFeatureExtractorCNN()
if pretrain:
cnn.load('./pretrained_cnn.pth')
self.cnn = cnn.net
self.rnn = ConvLSTM(128, 128)
self.fc = nn.Linear(128*6*6, feature_size)
def forward(self, x):
# input should be 5 dimension: (B, T, C, H, W)
b, t, c, h, w = x.shape
x = x.view(b * t, c, h, w)
cnn_output = self.cnn(x)
rnn_input = cnn_output.view(b, t, 128, 6, 6)
rnn_output = self.rnn(rnn_input)
rnn_output = torch.flatten(rnn_output, 1)
output = self.fc(rnn_output)
return output
class BioFeatureExtractor(nn.Module):
def __init__(self, input_size=32, feature_size=40):
super(BioFeatureExtractor, self).__init__()
self.cnn = nn.Sequential(
nn.Conv1d(in_channels=input_size, out_channels=24, kernel_size=5),
nn.BatchNorm1d(num_features=24),
nn.ReLU(inplace=True),
nn.Conv1d(in_channels=24, out_channels=16, kernel_size=3),
nn.BatchNorm1d(num_features=16),
nn.ReLU(inplace=True),
nn.Conv1d(in_channels=16, out_channels=8, kernel_size=3),
nn.BatchNorm1d(num_features=8),
nn.ReLU(inplace=True),
)
self.fc = nn.Linear(8*120, feature_size)
def forward(self,x):
x = self.cnn(x)
x = torch.flatten(x,1)
x = self.fc(x)
return x
class DeepVANetVision(nn.Module):
def __init__(self,feature_size=16,pretrain=True):
super(DeepVANetVision,self).__init__()
self.features = FaceFeatureExtractor(feature_size=feature_size,pretrain=pretrain)
self.classifier = nn.Sequential(
nn.Linear(feature_size, 20),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(20, 1),
nn.Sigmoid()
)
def forward(self,x):
x = self.features(x)
x = self.classifier(x)
x = x.squeeze(-1)
return x
def save(self, name=None):
"""
save the model
"""
if name is None:
prefix = 'checkpoints/' + 'face_classifier_'
name = time.strftime(prefix + '%m%d_%H:%M:%S.pth')
torch.save(self.state_dict(), name)
return name
def load(self, path):
self.load_state_dict(torch.load(path))
# self.load_state_dict(torch.load(path,map_location=torch.device('cpu')))
class DeepVANetBio(nn.Module):
def __init__(self, input_size=32, feature_size=64):
super(DeepVANetBio, self).__init__()
self.features = BioFeatureExtractor(input_size=input_size, feature_size=feature_size)
self.classifier = nn.Sequential(
nn.Linear(feature_size, 20),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(20, 1),
nn.Sigmoid()
)
def forward(self, x):
x = self.features(x)
x = self.classifier(x)
x = x.squeeze(-1)
return x
def save(self, name=None):
"""
save the model
"""
if name is None:
prefix = 'checkpoints/' + 'physiological_classifier_'
name = time.strftime(prefix + '%m%d_%H:%M:%S.pth')
torch.save(self.state_dict(), name)
return name
def load(self, path):
self.load_state_dict(torch.load(path))
# self.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
class DeepVANet(nn.Module):
def __init__(self, bio_input_size=32, face_feature_size=16, bio_feature_size=64,pretrain=True):
super(DeepVANet,self).__init__()
self.face_feature_extractor = FaceFeatureExtractor(feature_size=face_feature_size,pretrain=pretrain)
self.bio_feature_extractor = BioFeatureExtractor(input_size=bio_input_size, feature_size=bio_feature_size)
self.classifier = nn.Sequential(
nn.Linear(face_feature_size + bio_feature_size, 20),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(20, 1),
nn.Sigmoid()
)
def forward(self,x):
img_features = self.face_feature_extractor(x[0])
bio_features = self.bio_feature_extractor(x[1])
features = torch.cat([img_features,bio_features.float()],dim=1)
output = self.classifier(features)
output = output.squeeze(-1)
return output
def save(self, name=None):
"""
save the model
"""
if name is None:
prefix = 'checkpoints/' + 'fusion_classifier_'
name = time.strftime(prefix + '%m%d_%H:%M:%S.pth')
torch.save(self.state_dict(), name)
return name
def load(self, path):
self.load_state_dict(torch.load(path))
# self.load_state_dict(torch.load(path,map_location=torch.device('cpu')))