use our blip

extras/BLIP/models/blip.py

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

extras/BLIP/models/blip_itm.py

@@ -1,11 +1,11 @@
-from models.med import BertConfig, BertModel
+from extras.BLIP.models.med import BertConfig, BertModel
 from transformers import BertTokenizer
 
 import torch
 from torch import nn
 import torch.nn.functional as F
 
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 class BLIP_ITM(nn.Module):
     def __init__(self,                 

extras/BLIP/models/blip_nlvr.py

@@ -1,7 +1,7 @@
-from models.med import BertConfig
-from models.nlvr_encoder import BertModel
-from models.vit import interpolate_pos_embed
-from models.blip import create_vit, init_tokenizer, is_url
+from extras.BLIP.models.med import BertConfig
+from extras.BLIP.models.nlvr_encoder import BertModel
+from extras.BLIP.models.vit import interpolate_pos_embed
+from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url
 
 from timm.models.hub import download_cached_file
 
@@ -10,6 +10,8 @@ from torch import nn
 import torch.nn.functional as F
 from transformers import BertTokenizer
 import numpy as np
+import os
+
 
 class BLIP_NLVR(nn.Module):
     def __init__(self,                 

extras/BLIP/models/blip_pretrain.py

@@ -5,7 +5,7 @@
  * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
  * By Junnan Li
 '''
-from models.med import BertConfig, BertModel, BertLMHeadModel
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
 from transformers import BertTokenizer
 import transformers
 transformers.logging.set_verbosity_error()
@@ -14,7 +14,7 @@ import torch
 from torch import nn
 import torch.nn.functional as F
 
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 class BLIP_Pretrain(nn.Module):
     def __init__(self,                 
@@ -270,7 +270,7 @@ from typing import List
 def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
     uninitialized_encoder_weights: List[str] = []
     if decoder.__class__ != encoder.__class__:
-        logger.info(
+        print(
             f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
         )
 

extras/BLIP/models/blip_retrieval.py

@@ -1,11 +1,11 @@
-from models.med import BertConfig, BertModel
+from extras.BLIP.models.med import BertConfig, BertModel
 from transformers import BertTokenizer
 
 import torch
 from torch import nn
 import torch.nn.functional as F
 
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 class BLIP_Retrieval(nn.Module):
     def __init__(self,                 

extras/BLIP/models/blip_vqa.py

@@ -1,5 +1,5 @@
-from models.med import BertConfig, BertModel, BertLMHeadModel
-from models.blip import create_vit, init_tokenizer, load_checkpoint
+from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel
+from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint
 
 import torch
 from torch import nn

extras/BLIP/models/vit.py

@@ -18,7 +18,10 @@ from timm.models.registry import register_model
 from timm.models.layers import trunc_normal_, DropPath
 from timm.models.helpers import named_apply, adapt_input_conv
 
-from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
+
+def checkpoint_wrapper(x):
+    return x
+
 
 class Mlp(nn.Module):
     """ MLP as used in Vision Transformer, MLP-Mixer and related networks