fix_processor

lerobot/common/policies/dexvla/qwe2_vla/modeling_qwen2_vla.py

@@ -49,6 +49,7 @@ from transformers.utils import (
 )
 
 from lerobot.common.policies.dexvla.fusion_modules import ActionProjector,FiLM
+from transformers import AutoModelForCausalLM
 
 from .configuration_qwen2_vla import Qwen2VLAConfig, Qwen2VLVisionConfig
 
@@ -2047,6 +2048,6 @@ class Qwen2VLForConditionalGenerationForVLA(Qwen2VLPreTrainedModel, GenerationMi
         return model_inputs
 
 
-from transformers import AutoModelForCausalLM
+
 
 AutoModelForCausalLM.register(Qwen2VLAConfig, Qwen2VLForConditionalGenerationForVLA)

lerobot/common/policies/dexvla/robot_data_processor.py

@@ -30,15 +30,16 @@ class Qwen2VLAProcess:
         len_views = images.shape[0]
         messages = self.construct_chat_data(len_views, raw_lang)
 
-        data_dict = dict(
-            messages=messages,
-        )
+        data_dict = {
+            "messages":messages
+        }
+        
 
         image_data = torch.chunk(images, len_views, 0)
 
         images_list = []
 
-        for i, each in enumerate(image_data):
+        for _i, each in enumerate(image_data):
             img_pil = self.qwen2_image_preprocess(each)
             images_list.append(img_pil)
 
@@ -58,10 +59,7 @@ class Qwen2VLAProcess:
             return model_inputs
 
         input_labels = torch.ones_like(model_inputs["input_ids"]) * -100
-        if use_reasoning:
-            answer = reasoning + "Next action:" + "<|im_end|>"
-        else:
-            answer = "" + "<|im_end|>"
+        answer = reasoning + "Next action:" + "<|im_end|>" if use_reasoning else "" + "<|im_end|>"
 
         output_text = self.tokenizer(answer, padding=True, return_tensors="pt")
         output_labels = output_text["input_ids"]
@@ -119,15 +117,16 @@ class Qwen2VLAProcess:
 
         attention_mask = (input_ids.ne(self.tokenizer.pad_token_id),)
 
-        batch = dict(
-            input_ids=input_ids,
-            attention_mask=attention_mask[0],
-            labels=labels,
-            image_grid_spatiotemporal=image_grid_spatiotemporal,
-            pixel_values_videos=pixel_values_videos,
-            video_grid_spatiotemporal=video_grid_spatiotemporal,
-            pixel_values=pixel_values,
-        )
+        batch = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask[0],
+            "labels": labels,
+            "image_grid_spatiotemporal": image_grid_spatiotemporal,
+            "pixel_values_videos": pixel_values_videos,
+            "video_grid_spatiotemporal": video_grid_spatiotemporal,
+            "pixel_values": pixel_values,
+        }
+
         return batch
 
     def construct_chat_data(self, len_image, raw_lang):
@@ -138,7 +137,7 @@ class Qwen2VLAProcess:
             },
         ]
 
-        for i in range(len_image):
+        for _i in range(len_image):
             messages[0]["content"].append(
                 {
                     "type": "image",