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