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eval-vitb32-ckpt.sh
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data_root="/path"
eval_root="/path"
model_ckpt="/path/model.pth"
rm -rf $eval_root/VITB32-Pretrain
mkdir $eval_root/VITB32-Pretrain
python evals/eval_gs_v1.py \
--model_name ViT-B-32 \
--test_csv $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/query_0_product_id_0.csv \
--doc-meta $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/corpus_1.json \
--weight_key "score_linear" \
--output-dir $eval_root/VITB32-Pretrain/eval_train_e20 \
--pretrained $model_ckpt \
--batch-size 1024 \
--num_workers 4 \
--left-keys "['query']" \
--right-keys "['image_local']" \
--img-or-txt "[['txt'], ['img']]" \
--left-weights "[1]" \
--right-weights "[1]" \
--context-length "[[77], [0]]" \
--top-q 2000
python evals/eval_gs_v1.py \
--model_name ViT-B-32 \
--test_csv $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/query_0_product_id_1.csv \
--doc-meta $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/corpus_2.json \
--weight_key "score_linear" \
--output-dir $eval_root/VITB32-Pretrain/eval_q0d1_e20 \
--pretrained $model_ckpt \
--batch-size 1024 \
--num_workers 4 \
--left-keys "['query']" \
--right-keys "['image_local']" \
--img-or-txt "[['txt'], ['img']]" \
--left-weights "[1]" \
--right-weights "[1]" \
--context-length "[[77], [0]]" \
--top-q 2000
python evals/eval_gs_v1.py \
--model_name ViT-B-32 \
--test_csv $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/query_1_product_id_0.csv \
--doc-meta $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/corpus_1.json \
--weight_key "score_linear" \
--output-dir $eval_root/VITB32-Pretrain/eval_q1d0_e20 \
--pretrained $model_ckpt \
--batch-size 1024 \
--num_workers 4 \
--left-keys "['query']" \
--right-keys "['image_local']" \
--img-or-txt "[['txt'], ['img']]" \
--left-weights "[1]" \
--right-weights "[1]" \
--context-length "[[77], [0]]" \
--top-q 2000
python evals/eval_gs_v1.py \
--model_name ViT-B-32 \
--test_csv $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/query_1_product_id_1.csv \
--doc-meta $data_root/marqo-gs-dataset/marqo_gs_wfash_1m/corpus_2.json \
--weight_key "score_linear" \
--output-dir $eval_root/VITB32-Pretrain/eval_q1d1_e20 \
--pretrained $model_ckpt \
--batch-size 1024 \
--num_workers 4 \
--left-keys "['query']" \
--right-keys "['image_local']" \
--img-or-txt "[['txt'], ['img']]" \
--left-weights "[1]" \
--right-weights "[1]" \
--context-length "[[77], [0]]" \
--top-q 2000
python evals/aggregate_results.py \
--exp-path $eval_root/VITB32-Pretrain \
--evals "['eval_train_e20', 'eval_q0d1_e20', 'eval_q1d0_e20', 'eval_q1d1_e20']"