def __init__(self, num_node_features, num_instr_features, dropout=0.1):
super(RecurrentExecutionEngine, self).__init__()
self.num_node_features = num_node_features
self.num_instr_features = num_instr_features
self.engine_one_step_execution_cell = self.get_RecurrentExecutionEngine_layer(
)
self.graph_layer_norm = my_graph_layernorm.LayerNorm(
self.num_node_features)
self.softmax_bitmap_predictor = self.get_softmax_bitmap_predictor()
self.history_vectors_mlp = Seq(
Lin(num_node_features, num_instr_features),
ReLU(),
Lin(num_instr_features, num_instr_features) # output dim
)
def __init__(self):
super(GroundTruth_SceneGraph_Encoder, self).__init__()
from gqa_dataset_entry import GQA_gt_sg_feature_lookup
sg_TEXT = GQA_gt_sg_feature_lookup.SG_ENCODING_TEXT
sg_vocab = GQA_gt_sg_feature_lookup.SG_ENCODING_TEXT.vocab
self.sg_emb_dim = 300 # 300d glove
sg_pad_idx = sg_vocab.stoi[sg_TEXT.pad_token]
self.sg_vocab_embedding = torch.nn.Embedding(len(sg_vocab),
self.sg_emb_dim,
padding_idx=sg_pad_idx)
# self.sg_vocab_embedding.weight.data.copy_(sg_vocab.vectors)
del sg_TEXT, sg_vocab, sg_pad_idx
##################################
# build scene graph encoding layer
##################################
self.scene_graph_encoding_layer = get_gt_scene_graph_encoding_layer(
num_node_features=self.sg_emb_dim,
num_edge_features=self.sg_emb_dim)
self.graph_layer_norm = my_graph_layernorm.LayerNorm(self.sg_emb_dim)