dspanah / sensor-based-human-activity-recognition-deepconvlstm-pytorch Goto Github PK

def __init__(self, n_hidden=128, n_layers=1, n_filters=64, 
             n_classes=18, filter_size=3, drop_prob=0.6):
    super(HARModel, self).__init__()
    self.drop_prob = drop_prob
    self.n_layers = n_layers
    self.n_hidden = n_hidden
    self.n_filters = n_filters
    self.n_classes = n_classes
    self.filter_size = filter_size
         
    self.conv1 = nn.Conv1d(NB_SENSOR_CHANNELS, n_filters, filter_size)
    self.conv2 = nn.Conv1d(n_filters, n_filters, filter_size)
    self.conv3 = nn.Conv1d(n_filters, n_filters, filter_size)
    
    self.fc1 = nn.Linear(n_filters*filter_size,n_hidden)
    self.fc2 = nn.Linear(n_hidden, n_classes)

    self.dropout = nn.Dropout(drop_prob)

def forward(self, x, hidden, batch_size):
    
    x = x.view(-1, NB_SENSOR_CHANNELS, SLIDING_WINDOW_LENGTH)
    x = F.relu(F.max_pool1d(self.conv1(x),2))
    x = F.relu(F.max_pool1d(self.conv2(x),2))
    x = F.relu(F.max_pool1d(self.conv3(x),2))
    
    x = x.contiguous().view(-1, self.n_hidden)
    x = self.dropout(x)
    x = self.fc1(x)
    x = self.fc2(x)
    
    out = x.view(batch_size, -1, self.n_classes)[:,-1,:]
    
    return out, hidden