Evaluation issues encountered while fine-tuning blip2

[fjt12138]

When I use the fine-tuning BLIP_restrivel, I set the path of the file in the running configuration file, but in the evaluation process after training, there are two evaluation processes, which seem to be the evaluation of the original coco dataset, I want to know why there is this result, my configuration is as follows：
_#Copyright (c) 2022, salesforce.com, inc.
#All rights reserved.
#SPDX-License-Identifier: BSD-3-Clause
#For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause

model:
arch: blip2
model_type: coco
load_pretrained: True
load_finetuned: False
freeze_vit: False

#finetune blip2 with clip-vit-large
use_grad_checkpoint: False
pretrained: "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_vitL.pth"
vit_model: clip_L

datasets:
coco_retrieval: # name of the dataset builder
vis_processor:
train:
name: "blip2_image_train"
image_size: 364
eval:
name: "blip_image_eval"
image_size: 364
text_processor:
train:
name: "blip_caption"
eval:
name: "blip_caption"
build_info:
annotations:
train:
url: /data4/coco_train.json
# md5: aa31ac474cf6250ebb81d18348a07ed8
storage: /data4/coco_train.json
val:
url: /data4/coco_val.json
# md5: b273847456ef5580e33713b1f7de52a0
storage: /data4/coco_val.json
test:
url: /data4/coco_test.json
# md5: 3ff34b0ef2db02d01c37399f6a2a6cd1
storage: /data4/coco_test.json
images:
#storage: '/data1/lavis/coco/images/'
storage: ''

run:
task: retrieval
#optimizer
lr_sched: "linear_warmup_cosine_lr"
init_lr: 1e-5
min_lr: 0
warmup_lr: 1e-8
warmup_steps: 1000
weight_decay: 0.05
max_epoch: 5
batch_size_train: 14
batch_size_eval: 16
lr_layer_decay: 0.95 # layer-wise learning rate decay for the ViT
num_workers: 4
accum_grad_iters: 1

seed: 42
output_dir: "output/BLIP2/Retrieval_coco"

amp: True
resume_ckpt_path: null

evaluate: False
train_splits: ["train"]
valid_splits: ["test"]
#test_splits: ["test"]
k_test: 128

device: "cuda"
world_size: 1
dist_url: "env://"
distributed: False
use_dist_eval_sampler: False

Looking forward to your help！