clip-caption-reward
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64 lines
1.4 KiB
64 lines
1.4 KiB
caption_model: transformer
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noamopt: true
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noamopt_warmup: 20000
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label_smoothing: 0.0
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input_json: data/cocotalk.json
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input_label_h5: data/cocotalk_label.h5
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input_fc_dir: data/cocotalk_clip_RN50_fc
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input_att_dir: data/cocotalk_clip_RN50_att
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input_clipscore_vis_dir: data/cocotalk_clipscore_vis
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seq_per_img: 5
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batch_size: 160
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learning_rate: 0.0005
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checkpoint_path: save/clipRN50_clips_grammar/clipRN50_clips_grammar
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use_multi_rewards: true
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use_grammar: true
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use_grammar_baseline: true
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# clip_load_path: '/scratch-space/retrieval/save/clip_negative_text/clip_negative_text-epoch=10.ckpt'
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clip_load_path: 'retrieval/save/clip_negative_text/clip_negative_text-epoch=12.ckpt'
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# Notice: because I'm to lazy, I reuse the option name for RNNs to set the hyperparameters for transformer:
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# N=num_layers
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# d_model=input_encoding_size
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# d_ff=rnn_size
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# will be ignored
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num_layers: 6
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input_encoding_size: 512
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rnn_size: 2048
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# Transformer config
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N_enc: 6
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N_dec: 6
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d_model: 512
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d_ff: 2048
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num_att_heads: 8
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dropout: 0.1
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learning_rate_decay_start: 0
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scheduled_sampling_start: -1
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save_checkpoint_every: 3000
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language_eval: 1
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val_images_use: 5000
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max_epochs: 15
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train_sample_n: 5
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REFORWARD: false
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# _BASE_: transformer.yml
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reduce_on_plateau: false
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noamopt: false
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learning_rate: 0.000005
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learning_rate_decay_start: -1
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self_critical_after: 15
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max_epochs: 40
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verbose: false
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precision: 32
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use_clipscore: true
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clipscore_reward_weight: 2.0
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