fix dependency problem in valley

vlmeval/vlm/valley/valley_eagle/model/multimodal_encoder/builder.py

@@ -1,9 +1,9 @@
 import torch
-from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VisionTransformerPretrainedModel
 from ...util.vision_encoder_config import qwen2vl_vit_config
 
 
 def build_vision_tower(vision_tower_cfg, **kwargs):
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VisionTransformerPretrainedModel
     vision_tower = getattr(vision_tower_cfg, "mm_vision_tower", getattr(vision_tower_cfg, "vision_tower", None))
     if getattr(vision_tower_cfg, "language", None) is None:
         vision_tower_cfg.language = "chinese" if "chinese" in vision_tower else "english"

vlmeval/vlm/valley/valley_eagle/model/token_compressor/evo.py

@@ -1,6 +1,5 @@
 import torch
 from torch import nn
-from timm.models.layers import trunc_normal_
 import torch.nn.functional as F
 
 
@@ -43,6 +42,7 @@ def __init__(self, embed_dim=2048, inner_dim=64, prune_ratio=0.25, **kwargs):
         self.apply(self._init_weights)
 
     def _init_weights(self, m):
+        from timm.models.layers import trunc_normal_
         if isinstance(m, nn.Linear):
             trunc_normal_(m.weight, std=.02)
             if isinstance(m, nn.Linear) and m.bias is not None:

vlmeval/vlm/valley/valley_eagle/model/token_compressor/lavit.py

@@ -1,6 +1,5 @@
 import torch
 from torch import nn
-from timm.models.layers import trunc_normal_
 import torch.nn.functional as F
 
 
@@ -40,6 +39,7 @@ def __init__(self, embed_dim=2048, inner_dim=64, **kwargs):
         self.apply(self._init_weights)
 
     def _init_weights(self, m):
+        from timm.models.layers import trunc_normal_
         if isinstance(m, nn.Linear):
             trunc_normal_(m.weight, std=.02)
             if isinstance(m, nn.Linear) and m.bias is not None:

vlmeval/vlm/valley/valley_eagle_chat.py

@@ -6,7 +6,6 @@
 import logging
 from transformers import set_seed
 from transformers import AutoTokenizer, AutoProcessor
-from qwen_vl_utils import fetch_image, fetch_video
 import re
 
 from .valley_eagle.model.language_model.valley_qwen2 import ValleyQwen2ForCausalLM
@@ -240,6 +239,7 @@ def generate_inner(self, message, dataset=None):
         messages_qwen = []
         image_list = []
         for image_file in images:
+            from qwen_vl_utils import fetch_image
             image = fetch_image({"image": image_file})
             image_list.append(image)
         messages_qwen.append({'role': 'user', "content": [{"type": "text", "text": text}]})