def __init__(self, hidden_dim=10):
super(conv_Update, self).__init__()
self.hidden_dim = hidden_dim
dtype = torch.cuda.FloatTensor
self.update = ConvGRU(input_dim=hidden_dim,
hidden_dim=hidden_dim,
kernel_size=(1, 1),
num_layers=1,
dtype=dtype,
batch_first=True,
bias=True,
return_all_layers=False)
def __init__(self, hidden_dim=10, paths_len=3):
super(conv_Update, self).__init__()
self.hidden_dim = hidden_dim
# detect if CUDA is available or not
use_gpu = torch.cuda.is_available()
if use_gpu:
dtype = torch.cuda.FloatTensor # computation in GPU
else:
dtype = torch.FloatTensor
self.conv_update = ConvGRU(input_dim=paths_len * hidden_dim,
hidden_dim=hidden_dim,
kernel_size=(1, 1),
num_layers=1,
dtype=dtype,
batch_first=True,
bias=True,
return_all_layers=False)
def __init__(self, hidden_dim=10, paths_len=3):
super(conv_Update, self).__init__()
self.hidden_dim = hidden_dim
# self.conv_update = nn.Sequential(
# nn.Conv2d((paths_len+1) * hidden_dim, 2 * hidden_dim, kernel_size=3, padding=1, stride=1, bias=False),
# BatchNorm2d(2 * hidden_dim), nn.ReLU(inplace=False),
# nn.Conv2d(2 * hidden_dim, hidden_dim, kernel_size=3, padding=1, stride=1, bias=False),
# BatchNorm2d(hidden_dim), nn.ReLU(inplace=False)
# )
# detect if CUDA is available or not
use_gpu = torch.cuda.is_available()
if use_gpu:
dtype = torch.cuda.FloatTensor # computation in GPU
else:
dtype = torch.FloatTensor
self.conv_update = ConvGRU(input_dim=paths_len* hidden_dim,
hidden_dim=hidden_dim,
kernel_size=(1,1),
num_layers=1,
dtype=dtype,
batch_first=True,
bias=True,
return_all_layers=False)
def __init__(self, in_dim, hidden_dim=10, inputs_num=1):
super(conv_Update, self).__init__()
self.hidden_dim = hidden_dim
self.update = ConvGRU(input_dim=in_dim,
hidden_dim=hidden_dim,
inputs_num=inputs_num)
