Update with md5sum and half precision inference

patch

@@ -1,82 +0,0 @@
-diff --git a/dataset/caption_dataset.py b/dataset/caption_dataset.py
-index 266fdda..0cc5d3f 100644
---- a/dataset/caption_dataset.py
-+++ b/dataset/caption_dataset.py
-@@ -50,7 +50,7 @@ class Caption(Dataset):
-         elif self.dataset == 'demo':
-             img_path_split = self.data_list[index]['image'].split('/')
-             img_name = img_path_split[-2] + '/' + img_path_split[-1]
--            image, labels, labels_info = get_expert_labels('', self.label_path, img_name, 'helpers', self.experts)
-+            image, labels, labels_info = get_expert_labels('prismer', self.label_path, img_name, 'helpers', self.experts)
- 
-         experts = self.transform(image, labels)
-         experts = post_label_process(experts, labels_info)
-diff --git a/dataset/utils.py b/dataset/utils.py
-index b368aac..418358c 100644
---- a/dataset/utils.py
-+++ b/dataset/utils.py
-@@ -5,6 +5,7 @@
- # https://github.com/NVlabs/prismer/blob/main/LICENSE
- 
- import os
-+import pathlib
- import re
- import json
- import torch
-@@ -14,10 +15,12 @@ import torchvision.transforms as transforms
- import torchvision.transforms.functional as transforms_f
- from dataset.randaugment import RandAugment
- 
--COCO_FEATURES = torch.load('dataset/coco_features.pt')['features']
--ADE_FEATURES = torch.load('dataset/ade_features.pt')['features']
--DETECTION_FEATURES = torch.load('dataset/detection_features.pt')['features']
--BACKGROUND_FEATURES = torch.load('dataset/background_features.pt')
-+cur_dir = pathlib.Path(__file__).parent
-+
-+COCO_FEATURES = torch.load(cur_dir / 'coco_features.pt')['features']
-+ADE_FEATURES = torch.load(cur_dir / 'ade_features.pt')['features']
-+DETECTION_FEATURES = torch.load(cur_dir / 'detection_features.pt')['features']
-+BACKGROUND_FEATURES = torch.load(cur_dir / 'background_features.pt')
- 
- 
- class Transform:
-diff --git a/model/prismer.py b/model/prismer.py
-index 080253a..02362a4 100644
---- a/model/prismer.py
-+++ b/model/prismer.py
-@@ -5,6 +5,7 @@
- # https://github.com/NVlabs/prismer/blob/main/LICENSE
- 
- import json
-+import pathlib
- import torch.nn as nn
- 
- from model.modules.vit import load_encoder
-@@ -12,6 +13,9 @@ from model.modules.roberta import load_decoder
- from transformers import RobertaTokenizer, RobertaConfig
- 
- 
-+cur_dir = pathlib.Path(__file__).parent
-+
-+
- class Prismer(nn.Module):
-     def __init__(self, config):
-         super().__init__()
-@@ -26,7 +30,7 @@ class Prismer(nn.Module):
-             elif exp in ['obj_detection', 'ocr_detection']:
-                 self.experts[exp] = 64
- 
--        prismer_config = json.load(open('configs/prismer.json', 'r'))[config['prismer_model']]
-+        prismer_config = json.load(open(f'{cur_dir.parent}/configs/prismer.json', 'r'))[config['prismer_model']]
-         roberta_config = RobertaConfig.from_dict(prismer_config['roberta_model'])
- 
-         self.tokenizer = RobertaTokenizer.from_pretrained(prismer_config['roberta_model']['model_name'])
-@@ -35,7 +39,7 @@ class Prismer(nn.Module):
- 
-         self.prepare_to_train(config['freeze'])
-         self.ignored_modules = self.get_ignored_modules(config['freeze'])
--    
-+
-     def prepare_to_train(self, mode='none'):
-         for name, params in self.named_parameters():
-             if mode == 'freeze_lang':