class DynamicAccessor(Accessor):
def __init__(self, args):
super(DynamicAccessor, self).__init__(args)
# logging
self.logger = args.logger
# params
self.use_cuda = args.use_cuda
self.dtype = args.dtype
# dynamic-accessor-specific params
self.read_head_params.num_read_modes = self.write_head_params.num_heads * 2 + 1
self.logger.warning("<--------------------------------===> Accessor: {WriteHead, ReadHead, Memory}")
# functional components
self.usage_vb = None # for dynamic allocation, init in _reset
self.link_vb = None # for temporal link, init in _reset
self.preced_vb = None # for temporal link, init in _reset
self.write_heads = WriteHead(self.write_head_params)
self.read_heads = ReadHead(self.read_head_params)
self.memory = ExternalMemory(self.memory_params)
self._reset()
def _init_weights(self):
pass
def _reset_states(self):
# reset the usage (for dynamic allocation) & link (for temporal link)
self.usage_vb = Variable(self.usage_ts).type(self.dtype)
self.link_vb = Variable(self.link_ts).type(self.dtype)
self.preced_vb = Variable(self.preced_ts).type(self.dtype)
# we reset the write/read weights of heads
self.write_heads._reset_states()
self.read_heads._reset_states()
# we also reset the memory to bias value
self.memory._reset_states()
def _reset(self): # NOTE: should be called at __init__
self._init_weights()
self.type(self.dtype) # put on gpu if possible
# reset internal states
self.usage_ts = torch.zeros(self.batch_size, self.mem_hei)
self.link_ts = torch.zeros(self.batch_size, self.write_head_params.num_heads, self.mem_hei, self.mem_hei)
self.preced_ts = torch.zeros(self.batch_size, self.write_head_params.num_heads, self.mem_hei)
self._reset_states()
def forward(self, hidden_vb):
# 1. first we update the usage using the read/write weights from {t-1}
self.usage_vb = self.write_heads._update_usage(self.usage_vb)
self.usage_vb = self.read_heads._update_usage(hidden_vb, self.usage_vb)
# 2. then write to memory_{t-1} to get memory_{t}
self.memory.memory_vb = self.write_heads.forward(hidden_vb, self.memory.memory_vb, self.usage_vb)
# 3. then we update the temporal link
self.link_vb, self.preced_vb = self.write_heads._temporal_link(self.link_vb, self.preced_vb)
# 4. then read from memory_{t} to get read_vec_{t}
read_vec_vb = self.read_heads.forward(hidden_vb, self.memory.memory_vb, self.link_vb, self.write_head_params.num_heads)
return read_vec_vb
class StaticAccessor(Accessor):
def __init__(self, args):
super(StaticAccessor, self).__init__(args)
# logging
self.logger = args.logger
# params
self.use_cuda = args.use_cuda
self.dtype = args.dtype
self.logger.warning(
"<--------------------------------===> Accessor: {WriteHead, ReadHead, Memory}"
)
# functional components
self.write_heads = WriteHead(self.write_head_params)
self.read_heads = ReadHead(self.read_head_params)
self.memory = ExternalMemory(self.memory_params)
self._reset()
def _init_weights(self):
pass
def _reset_states(self):
# we reset the write/read weights of heads
self.write_heads._reset_states()
self.read_heads._reset_states()
# we also reset the memory to bias value
self.memory._reset_states()
def _reset(self): # NOTE: should be called at __init__
self._init_weights()
self.type(self.dtype) # put on gpu if possible
# reset internal states
self._reset_states()
def forward(self, hidden_vb):
# 1. first write to memory_{t-1} to get memory_{t}
self.memory.memory_vb = self.write_heads.forward(
hidden_vb, self.memory.memory_vb)
# 2. then read from memory_{t} to get read_vec_{t}
read_vec_vb = self.read_heads.forward(hidden_vb, self.memory.memory_vb)
return read_vec_vb
def __init__(self, args):
super(StaticAccessor, self).__init__(args)
# logging
self.logger = args.logger
# params
self.use_cuda = args.use_cuda
self.dtype = args.dtype
self.logger.warning("<--------------------------------===> Accessor: {WriteHead, ReadHead, Memory}")
# functional components
self.write_heads = WriteHead(self.write_head_params)
self.read_heads = ReadHead(self.read_head_params)
self.memory = ExternalMemory(self.memory_params)
self._reset()
def __init__(self, args):
super(DynamicAccessor, self).__init__(args)
# logging
self.logger = args.logger
# params
self.use_cuda = args.use_cuda
self.dtype = args.dtype
# dynamic-accessor-specific params
self.read_head_params.num_read_modes = self.write_head_params.num_heads * 2 + 1
self.logger.warning("<--------------------------------===> Accessor: {WriteHead, ReadHead, Memory}")
# functional components
self.usage_vb = None # for dynamic allocation, init in _reset
self.link_vb = None # for temporal link, init in _reset
self.preced_vb = None # for temporal link, init in _reset
self.write_heads = WriteHead(self.write_head_params)
self.read_heads = ReadHead(self.read_head_params)
self.memory = ExternalMemory(self.memory_params)
self._reset()