def __init__(self, config: Config, *args, **kwargs):
super().__init__()
self.config = config
model = getattr(torchvision.models, config.name)(
pretrained=config.pretrained, zero_init_residual=config.zero_init_residual
)
# Set avgpool and fc layers in torchvision to Identity.
model.avgpool = Identity()
model.fc = Identity()
self.model = model
self.out_dim = 2048
def __init__(self, emb_type, **kwargs):
super().__init__()
self.model_data_dir = kwargs.get("model_data_dir", None)
self.embedding_dim = kwargs.get("embedding_dim", None)
# Update kwargs here
if emb_type == "identity":
self.module = Identity()
self.module.text_out_dim = self.embedding_dim
elif emb_type == "vocab":
self.module = VocabEmbedding(**kwargs)
self.module.text_out_dim = self.embedding_dim
elif emb_type == "projection":
self.module = ProjectionEmbedding(**kwargs)
self.module.text_out_dim = self.module.out_dim
elif emb_type == "preextracted":
self.module = PreExtractedEmbedding(**kwargs)
elif emb_type == "bilstm":
self.module = BiLSTMTextEmbedding(**kwargs)
elif emb_type == "attention":
self.module = AttentionTextEmbedding(**kwargs)
elif emb_type == "mcan":
self.module = SAEmbedding(**kwargs)
elif emb_type == "torch":
vocab_size = kwargs["vocab_size"]
embedding_dim = kwargs["embedding_dim"]
self.module = nn.Embedding(vocab_size, embedding_dim)
self.module.text_out_dim = self.embedding_dim
else:
raise NotImplementedError("Unknown question embedding '%s'" % emb_type)
self.text_out_dim = self.module.text_out_dim
def __init__(self, config: Config, *args, **kwargs):
super().__init__()
encoder_type = config.type
if isinstance(encoder_type, ImageFeatureEncoderTypes):
encoder_type = encoder_type.value
assert ("in_dim" in config.params
), "ImageFeatureEncoder require 'in_dim' param in config"
params = config.params
if encoder_type == "default" or encoder_type == "identity":
self.module = Identity()
self.module.in_dim = params.in_dim
self.module.out_dim = params.in_dim
elif encoder_type == "projection":
if "module" not in params:
params = deepcopy(params)
params.module = "linear"
self.module = ProjectionEmbedding(**params)
elif encoder_type == "finetune_faster_rcnn_fpn_fc7":
self.module = FinetuneFasterRcnnFpnFc7(params)
elif encoder_type == "spatial":
self.module = VisionSpatialEmbedding(**params)
else:
raise NotImplementedError("Unknown Image Encoder: %s" %
encoder_type)
self.out_dim = self.module.out_dim
def __init__(self, encoder_type, in_dim, **kwargs):
super().__init__()
if encoder_type == "default" or encoder_type == "identity":
self.module = Identity()
self.module.in_dim = in_dim
self.module.out_dim = in_dim
elif encoder_type == "projection":
module_type = kwargs.pop("module", "linear")
self.module = ProjectionEmbedding(module_type, in_dim, **kwargs)
elif encoder_type == "finetune_faster_rcnn_fpn_fc7":
self.module = FinetuneFasterRcnnFpnFc7(in_dim, **kwargs)
else:
raise NotImplementedError("Unknown Image Encoder: %s" % encoder_type)
self.out_dim = self.module.out_dim
