def __init__(self, config):
super(UpdaterObj, self).__init__()
self.enc_apc = EncoderBlock(
channel_list=config['dec_apc_channel_rev'],
kernel_list=config['dec_apc_kernel_rev'],
stride_list=config['dec_apc_stride_rev'],
hidden_list=config['dec_apc_hidden_rev'],
in_shape=config['image_shape'],
out_features=None,
)
self.enc_shp = EncoderBlock(
channel_list=config['dec_shp_channel_rev'],
kernel_list=config['dec_shp_kernel_rev'],
stride_list=config['dec_shp_stride_rev'],
hidden_list=config['dec_shp_hidden_rev'],
in_shape=[1, *config['image_shape'][1:]],
out_features=None,
)
self.lstm = nn.LSTMCell(
self.enc_apc.out_features + self.enc_shp.out_features,
config['state_obj_size'])
def __init__(self, config):
super(UpdaterBck, self).__init__()
self.enc = EncoderBlock(
channel_list=config['dec_bck_channel_rev'],
kernel_list=config['dec_bck_kernel_rev'],
stride_list=config['dec_bck_stride_rev'],
hidden_list=config['dec_bck_hidden_rev'],
in_shape=config['image_shape'],
out_features=None,
)
self.lstm = nn.LSTMCell(self.enc.out_features,
config['state_bck_size'])
def __init__(self, channel_list, kernel_list, stride_list, hidden_list,
in_shape, state_size):
super(EncoderLSTM, self).__init__()
self.enc = EncoderBlock(
channel_list=channel_list,
kernel_list=kernel_list,
stride_list=stride_list,
hidden_list=hidden_list,
in_shape=in_shape,
out_features=None,
)
self.lstm = nn.LSTMCell(self.enc.out_features, state_size)
def __init__(self, config):
super(Updater, self).__init__()
self.enc = EncoderBlock(
channel_list=config['upd_channel'],
kernel_list=config['upd_kernel'],
stride_list=config['upd_stride'],
hidden_list=config['upd_hidden'],
in_shape=[
config['image_shape'][0] * 2, *config['image_shape'][1:]
],
out_features=None,
)
self.lstm = nn.LSTMCell(self.enc.out_features, config['state_size'])
def __init__(self, config):
super(NetworkWhat, self).__init__()
latent_size = config['latent_what_size']
self.register_buffer('prior_mu', torch.zeros([latent_size]))
self.register_buffer('prior_logvar', torch.zeros([latent_size]))
self.enc = EncoderBlock(
channel_list=config['enc_what_channel'],
kernel_list=config['enc_what_kernel'],
stride_list=config['enc_what_stride'],
hidden_list=config['enc_what_hidden'],
in_shape=[config['crop_shape'][0] * 2, *config['crop_shape'][1:]],
out_features=latent_size * 2,
)
self.dec = DecoderBlock(
channel_list_rev=config['dec_what_channel_rev'],
kernel_list_rev=config['dec_what_kernel_rev'],
stride_list_rev=config['dec_what_stride_rev'],
hidden_list_rev=config['dec_what_hidden_rev'],
in_features=latent_size,
out_shape=config['crop_shape'],
)
def __init__(self, config):
super(NetworkBack, self).__init__()
latent_size = config['latent_back_size']
self.register_buffer('prior_mu', torch.zeros([latent_size]))
self.register_buffer('prior_logvar', torch.zeros([latent_size]))
self.enc = EncoderBlock(
channel_list=config['enc_back_channel'],
kernel_list=config['enc_back_kernel'],
stride_list=config['enc_back_stride'],
hidden_list=config['enc_back_hidden'],
in_shape=config['image_shape'],
out_features=latent_size * 2,
)
self.dec = DecoderResidual(
avg_hidden_list_rev=config['dec_back_avg_hidden_rev'],
res_channel_list_rev=config['dec_back_res_channel_rev'],
res_kernel_list_rev=config['dec_back_res_kernel_rev'],
res_stride_list_rev=config['dec_back_res_stride_rev'],
res_hidden_list_rev=config['dec_back_res_hidden_rev'],
in_features=latent_size,
image_shape=config['image_shape'],
)