def __init__(self, **kwargs):
"""
kwargs:
vocab,
dim_hidden,
dim_vision,
mid_size,
glimpses,
dropout_prob,
device,
"""
super(Net, self).__init__()
for k, v in kwargs.items():
setattr(self, k, v)
self.num_inter_head = 8
self.num_intra_head = 8
self.num_block = 6
self.num_classes = len(self.vocab['answer_token_to_idx'])
self.num_token = len(self.vocab['question_token_to_idx'])
self.token_embedding = nn.Embedding(self.num_token, self.dim_word)
for p in self.token_embedding.parameters():
p.require_grad = False
self.dropout = nn.Dropout(self.dropout_prob)
self.text = modulars.BiGRUEncoder_2(
dim_word=self.dim_word,
dim_hidden=self.dim_hidden,
)
# self.vision_to_v = FCNet(self.dim_vision, self.dim_hidden, drop=0.3, bias=False)
# self.map_two_v_to_edge = FCNet(self.dim_hidden*2, self.dim_edge, bias=False)
self.timereasoning = TimeReasoning(hidden_size=self.dim_vision,
mid_size=self.mid_size,
state_size=self.state_size,
num_token=self.num_token,
edge_size=self.dim_edge)
self.interIntraBlocks = MultiBlock(
num_block=self.num_block,
v_size=self.dim_vision,
q_size=self.dim_hidden,
output_size=self.mid_size,
num_inter_head=self.num_inter_head,
num_intra_head=self.num_intra_head,
drop=0.1,
)
self.classifier = Classifier(
in_features=self.mid_size,
mid_features=2048,
out_features=self.num_classes,
drop=0.5,
)
def __init__(self, **kwargs):
"""
kwargs:
vocab,
dim_hidden,
dim_vision,
mid_size,
glimpses,
dropout_prob,
T_ctrl,
stack_len,
device,
"""
super(Net, self).__init__()
for k, v in kwargs.items():
setattr(self, k, v)
self.num_classes = len(self.vocab['answer_token_to_idx'])
self.num_token = len(self.vocab['question_token_to_idx'])
self.classifier = Classifier(
in_features=(self.glimpses * self.dim_vision, self.dim_hidden),
mid_features=self.mid_size,
out_features=self.num_classes,
drop=self.dropout_prob)
self.token_embedding = nn.Embedding(self.num_token, self.dim_word)
self.dropout = nn.Dropout(self.dropout_prob)
self.text = modulars.BiGRUEncoder_2(
dim_word=self.dim_word,
dim_hidden=self.dim_hidden,
)
self.vision_to_v = FCNet(self.dim_vision,
self.dim_hidden,
drop=0.3,
bias=False)
# self.map_two_v_to_edge = FCNet(self.dim_hidden*2, self.dim_edge, bias=False)
self.timereasoning = TimeReasoning(
hidden_size=self.dim_vision,
mid_size=self.dim_hidden,
state_size=self.state_size,
num_token=self.num_token,
edge_size=self.dim_edge,
)
def __init__(self, **kwargs):
"""
kwargs:
vocab,
dim_hidden,
dim_vision,
mid_size,
glimpses,
dropout_prob,
device,
"""
super(Net, self).__init__()
for k, v in kwargs.items():
setattr(self, k, v)
glimpses = 5
objects = 10
self.num_classes = len(self.vocab['answer_token_to_idx'])
self.num_token = len(self.vocab['question_token_to_idx'])
self.classifier = Classifier(in_features=self.mid_size,
mid_features=self.dim_hidden * 2,
out_features=self.num_classes,
drop=self.dropout_prob)
self.token_embedding = nn.Embedding(self.num_token, self.dim_word)
for p in self.token_embedding.parameters():
p.require_grad = False
self.dropout = nn.Dropout(self.dropout_prob)
self.text = modulars.BiGRUEncoder_2(
dim_word=self.dim_word,
dim_hidden=self.dim_hidden,
)
self.vision_to_v = FCNet(self.dim_vision,
self.dim_hidden,
drop=0.3,
bias=False)
self.map_two_v_to_edge = FCNet(self.dim_hidden * 2,
self.dim_edge,
bias=False)
self.timereasoning = TimeReasoning(hidden_size=self.dim_vision,
mid_size=self.mid_size,
state_size=self.state_size,
num_token=self.num_token,
edge_size=self.dim_edge)
self.count = Counter(objects)
self.attention = weight_norm(BiAttention(
v_features=self.dim_vision,
q_features=self.dim_hidden,
mid_features=self.dim_hidden,
glimpses=glimpses,
drop=0.5,
),
name='h_weight',
dim=None)
self.apply_attention = ApplyAttention(
v_features=self.dim_vision,
q_features=self.dim_hidden,
mid_features=self.dim_hidden,
glimpses=glimpses,
num_obj=objects,
drop=0.2,
)