add encoder code + tests code

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
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+*.so
+
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+.Python
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+
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+# Unit test / coverage reports
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+.tox/
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+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

BLIP/eval_nocaps.py

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+'''
+ * Copyright (c) 2022, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+'''
+import argparse
+import os
+import ruamel_yaml as yaml
+import numpy as np
+import random
+import time
+import datetime
+import json
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+from torch.utils.data import DataLoader
+
+from models.blip import blip_decoder
+import utils
+from data import create_dataset, create_sampler, create_loader
+from data.utils import save_result
+
+@torch.no_grad()
+def evaluate(model, data_loader, device, config):
+    # evaluate
+    model.eval() 
+
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Evaluation:'
+    print_freq = 10
+
+    result = []
+    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
+
+        image = image.to(device)       
+
+        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], 
+                                  min_length=config['min_length'], repetition_penalty=1.1)
+
+        for caption, img_id in zip(captions, image_id):
+            result.append({"image_id": img_id.item(), "caption": caption})
+
+    return result
+
+
+def main(args, config):
+    utils.init_distributed_mode(args)    
+
+    device = torch.device(args.device)
+
+    # fix the seed for reproducibility
+    seed = args.seed + utils.get_rank()
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    cudnn.benchmark = True
+
+    #### Dataset #### 
+    print("Creating captioning dataset")
+    val_dataset, test_dataset = create_dataset('nocaps', config)  
+
+    if args.distributed:
+        num_tasks = utils.get_world_size()
+        global_rank = utils.get_rank()            
+        samplers = create_sampler([val_dataset,test_dataset], [False,False], num_tasks, global_rank)         
+    else:
+        samplers = [None,None]
+
+    val_loader, test_loader = create_loader([val_dataset, test_dataset],samplers, 
+                                            batch_size=[config['batch_size']]*2,num_workers=[4,4],
+                                            is_trains=[False, False], collate_fns=[None,None])        
+
+    #### Model #### 
+    print("Creating model")
+    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
+                           prompt=config['prompt'])
+
+    model = model.to(device)   
+
+    model_without_ddp = model
+    if args.distributed:
+        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
+        model_without_ddp = model.module    
+
+    val_result = evaluate(model_without_ddp, val_loader, device, config)  
+    val_result_file = save_result(val_result, args.result_dir, 'val', remove_duplicate='image_id')   
+    test_result = evaluate(model_without_ddp, test_loader, device, config)  
+    test_result_file = save_result(test_result, args.result_dir, 'test', remove_duplicate='image_id') 
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', default='./configs/nocaps.yaml')
+    parser.add_argument('--output_dir', default='output/NoCaps')        
+    parser.add_argument('--device', default='cuda')
+    parser.add_argument('--seed', default=42, type=int)
+    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
+    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
+    parser.add_argument('--distributed', default=True, type=bool)
+    args = parser.parse_args()
+
+    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
+
+    args.result_dir = os.path.join(args.output_dir, 'result')
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
+
+    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
+
+    main(args, config)