Make a folder for seperating blip files, add seed.

finetune/blip/blip.py

@@ -10,8 +10,8 @@ warnings.filterwarnings("ignore")
 
 # from models.vit import VisionTransformer, interpolate_pos_embed
 # from models.med import BertConfig, BertModel, BertLMHeadModel
-from vit import VisionTransformer, interpolate_pos_embed
-from med import BertConfig, BertModel, BertLMHeadModel
+from blip.vit import VisionTransformer, interpolate_pos_embed
+from blip.med import BertConfig, BertModel, BertLMHeadModel
 from transformers import BertTokenizer
 
 import torch

finetune/make_captions.py

@@ -2,6 +2,7 @@ import argparse
 import glob
 import os
 import json
+import random
 
 from PIL import Image
 from tqdm import tqdm
@@ -9,20 +10,31 @@ import numpy as np
 import torch
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
-from blip import blip_decoder
+from blip.blip import blip_decoder
 # from Salesforce_BLIP.models.blip import blip_decoder
 
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 
 def main(args):
+  # fix the seed for reproducibility
+  seed = args.seed # + utils.get_rank()
+  torch.manual_seed(seed)
+  np.random.seed(seed)
+  random.seed(seed)
+    
+  if not os.path.exists("blip"):
+    cwd = os.getcwd()
+    print('Current Working Directory is: ', cwd)
+    os.chdir('finetune')
+
   image_paths = glob.glob(os.path.join(args.train_data_dir, "*.jpg")) + \
       glob.glob(os.path.join(args.train_data_dir, "*.png")) + glob.glob(os.path.join(args.train_data_dir, "*.webp"))
   print(f"found {len(image_paths)} images.")
 
   print(f"loading BLIP caption: {args.caption_weights}")
   image_size = 384
-  model = blip_decoder(pretrained=args.caption_weights, image_size=image_size, vit='large', med_config="./med_config.json")
+  model = blip_decoder(pretrained=args.caption_weights, image_size=image_size, vit='large', med_config="./blip/med_config.json")
   model.eval()
   model = model.to(DEVICE)
   print("BLIP loaded")
@@ -84,6 +96,7 @@ if __name__ == '__main__':
   parser.add_argument("--top_p", type=float, default=0.9, help="top_p in Nucleus sampling / Nucleus sampling時のtop_p")
   parser.add_argument("--max_length", type=int, default=75, help="max length of caption / captionの最大長")
   parser.add_argument("--min_length", type=int, default=5, help="min length of caption / captionの最小長")
+  parser.add_argument('--seed', default=42, type=int, help='seed for reproducibility / 再現性を確保するための乱数seed')
   parser.add_argument("--debug", action="store_true", help="debug mode")
 
   args = parser.parse_args()