class TopDownNet(nn.Module):
def __init__(self,
h_dims=128,
n_classes=10,
filters=[16, 32, 64, 128, 256],
kernel_size=(3, 3),
final_pool_size=(2, 2),
glimpse_type='gaussian',
glimpse_size=(15, 15),
cnn='cnn'):
from networkx.algorithms.traversal.breadth_first_search import bfs_edges
nn.Module.__init__(self)
t = nx.balanced_tree(1, 2)
self.G = DGLGraph(t)
self.root = 0
#self.walk_list = bfs_edges(t, self.root)
self.walk_list = [(0, 1), (1, 2)]
self.h_dims = h_dims
self.n_classes = n_classes
self.update_module = UpdateModule(
h_dims=h_dims,
n_classes=n_classes,
filters=filters,
kernel_size=kernel_size,
final_pool_size=final_pool_size,
glimpse_type=glimpse_type,
glimpse_size=glimpse_size,
cnn='cnn',
)
self.message_module = MessageModule(
h_dims=h_dims, g_dims=self.update_module.glimpse.att_params)
self.readout_module = ReadoutModule(
h_dims=h_dims,
n_classes=n_classes,
)
self.G.register_message_func(self.message_module)
self.G.register_update_func(self.update_module)
self.G.register_readout_func(self.readout_module)
def forward(self, x):
batch_size = x.shape[0]
g_dims = self.update_module.glimpse.att_params
self.update_module.set_image(x)
zero_tensor_x = lambda r, c: \
x.new(r, c).zero_()
init_states = {
's':
zero_tensor_x(batch_size, self.h_dims),
'a': (
zero_tensor_x(batch_size, self.h_dims),
zero_tensor_x(batch_size, g_dims),
),
'g':
None,
'c':
zero_tensor_x(batch_size, 1),
}
for n in self.G.nodes():
self.G.node[n].update(init_states)
self.G.recvfrom(self.root, []) # Update root node
self.G.propagate(self.walk_list)
return self.G.readout()
class DFSGlimpseSingleObjectClassifier(nn.Module):
def __init__(
self,
h_dims=128,
n_classes=10,
filters=[16, 32, 64, 128, 256],
kernel_size=(3, 3),
final_pool_size=(2, 2),
glimpse_type='gaussian',
glimpse_size=(15, 15),
cnn='cnn',
cnn_file='cnn.pt',
):
nn.Module.__init__(self)
#self.T_MAX_RECUR = kwarg['steps']
t = nx.balanced_tree(2, 2)
t_uni = nx.bfs_tree(t, 0)
self.G = DGLGraph(t)
self.root = 0
self.h_dims = h_dims
self.n_classes = n_classes
self.message_module = MessageModule()
self.G.register_message_func(self.message_module) # default: just copy
cnnmodule = CNN(
cnn=cnn,
n_layers=6,
h_dims=h_dims,
n_classes=n_classes,
final_pool_size=final_pool_size,
filters=filters,
kernel_size=kernel_size,
input_size=glimpse_size,
)
if cnn_file is not None:
cnnmodule.load_state_dict(T.load(cnn_file))
#self.update_module = UpdateModule(h_dims, n_classes, glimpse_size)
self.update_module = UpdateModule(
glimpse_type=glimpse_type,
glimpse_size=glimpse_size,
cnn=cnnmodule,
max_recur=1, # T_MAX_RECUR
n_classes=n_classes,
h_dims=h_dims,
)
self.G.register_update_func(self.update_module)
self.readout_module = ReadoutModule(h_dims=h_dims, n_classes=n_classes)
self.G.register_readout_func(self.readout_module)
#self.walk_list = [(0, 1), (1, 2), (2, 1), (1, 0)]
self.walk_list = []
dfs_walk(t_uni, self.root, self.walk_list)
def forward(self, x, pretrain=False):
batch_size = x.shape[0]
self.update_module.set_image(x)
init_states = {
'h':
x.new(batch_size, self.h_dims).zero_(),
'b':
x.new(batch_size, self.update_module.glimpse.att_params).zero_(),
'b_next':
x.new(batch_size, self.update_module.glimpse.att_params).zero_(),
'a':
x.new(batch_size, 1).zero_(),
'y':
x.new(batch_size, self.n_classes).zero_(),
'g':
None,
'b_fix':
None,
'db':
None,
}
for n in self.G.nodes():
self.G.node[n].update(init_states)
#TODO: the following two lines is needed for single object
#TODO: but not useful or wrong for multi-obj
self.G.recvfrom(self.root, [])
if pretrain:
return self.G.readout([self.root], pretrain=True)
else:
#for u, v in self.walk_list:
# self.G.update_by_edge((u, v))
# update local should be inside the update module
#for i in self.T_MAX_RECUR:
# self.G.update_local(u)
self.G.propagate(self.walk_list)
return self.G.readout('all', pretrain=False)
