60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
_base_ = [
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'../_base_/datasets/flickr30k_caption.py',
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'../_base_/default_runtime.py',
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]
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# model settings
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model = dict(
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type='BlipCaption',
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vision_encoder=dict(
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type='VisionTransformer',
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arch='b',
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img_size=384,
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patch_size=16,
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out_type='raw',
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),
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tokenizer=dict(type='BlipTokenizer', name_or_path='bert-base-uncased'),
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decoder_head=dict(
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type='SeqGenerationHead',
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decoder=dict(
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type='XBertLMHeadDecoder',
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med_config=dict(
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architectures=['BertModel'],
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attention_probs_dropout_prob=0.1,
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hidden_act='gelu',
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hidden_dropout_prob=0.1,
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hidden_size=768,
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initializer_range=0.02,
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intermediate_size=3072,
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layer_norm_eps=1e-12,
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max_position_embeddings=512,
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model_type='bert',
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num_attention_heads=12,
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num_hidden_layers=12,
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pad_token_id=0,
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add_type_embeddings=False,
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vocab_size=30524,
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encoder_width=768,
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add_cross_attention=True),
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),
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),
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prompt='a picture of ',
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max_txt_len=20,
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)
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# schedule settings
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optim_wrapper = dict(optimizer=dict(type='AdamW', lr=1e-5, weight_decay=0.05))
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param_scheduler = [
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dict(
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type='CosineAnnealingLR',
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by_epoch=True,
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begin=0,
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end=10,
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)
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]
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train_cfg = dict(max_epochs=10)
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val_cfg = dict()
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test_cfg = dict()
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