[Fix] Fix Emu3 Inference

vlmeval/vlm/emu.py

@@ -6,6 +6,7 @@
 from .base import BaseModel
 from ..smp import *
 from huggingface_hub import snapshot_download
+from PIL import Image, ImageOps
 
 
 def get_local_root(repo_id):
@@ -19,6 +20,42 @@ def get_local_root(repo_id):
     return cache_path
 
 
+def pad_image_to_aspect_ratio(img, max_aspect_ratio=5):
+    """
+    Pad an image to ensure its aspect ratio (width/height or height/width) is less than the given value.
+    
+    Parameters:
+        img (PIL.Image): The input PIL Image object.
+        max_aspect_ratio (float): The maximum allowed aspect ratio.
+    
+    Returns:
+        PIL.Image: The padded image.
+    """
+    width, height = img.size
+    # Calculate the required minimum dimensions to satisfy the aspect ratio constraint
+    if width > height * max_aspect_ratio:
+        # Width is too large, pad height
+        new_height = int(width / max_aspect_ratio + 1)
+        new_width = width
+    elif height > width * max_aspect_ratio:
+        # Height is too large, pad width
+        new_width = int(height / max_aspect_ratio + 1)
+        new_height = height
+    else:
+        # Aspect ratio is already less than or equal to max_aspect_ratio
+        return img
+
+    # Calculate the padding amounts
+    pad_width = (new_width - width)
+    pad_height = (new_height - height)
+
+    # Pad the image symmetrically
+    padding = (pad_width // 2, pad_height // 2, pad_width - pad_width // 2, pad_height - pad_height // 2)
+    padded_img = ImageOps.expand(img, padding, fill=(0, 0, 0))  # Fill color is black (0, 0, 0) by default
+
+    return padded_img
+
+
 class Emu(BaseModel):
 
     INSTALL_REQ = False
@@ -52,7 +89,6 @@ def __init__(self,
             model = AutoModelForCausalLM.from_pretrained(
                 model_path,  # "BAAI/Emu2-Chat"
                 torch_dtype=torch.bfloat16,
-                low_cpu_mem_usage=True,
                 trust_remote_code=True)
 
         device_map = infer_auto_device_map(
@@ -103,7 +139,7 @@ def generate_inner(self, message, dataset=None):
 
 
 class Emu3_chat(BaseModel):
-    INSTALL_REQ = True
+    INSTALL_REQ = False
     INTERLEAVE = False
 
     def __init__(self, model_path='BAAI/Emu3-Chat', tokenizer_path='BAAI/Emu3-VisionTokenizer', **kwargs):
@@ -132,19 +168,15 @@ def __init__(self, model_path='BAAI/Emu3-Chat', tokenizer_path='BAAI/Emu3-Vision
             tokenizer_path, device_map='cuda', trust_remote_code=True).eval()
         self.processor = Emu3Processor(self.image_processor, self.image_tokenizer, self.tokenizer)
         self.kwargs = kwargs
-        self.cuda = cuda
 
     def generate_inner(self, message, dataset=None):
-        query, images = '', []
-        for item in message:
-            if item['type'] == 'image':
-                images.append(Image.open(item['value']).convert('RGB'))
-            elif item['type'] == 'text':
-                query += item['value']
+        prompt, image = self.message_to_promptimg(message)
+        image = Image.open(image).convert('RGB')
+        image = pad_image_to_aspect_ratio(image, 5)
 
         inputs = self.processor(
-            text=[query],
-            image=images,
+            text=[prompt],
+            image=[image],
             mode='U',
             return_tensors="pt",
             padding="longest",
@@ -159,9 +191,9 @@ def generate_inner(self, message, dataset=None):
         )
         # generate
         outputs = self.model.generate(
-            inputs.input_ids.to(self.cuda),
+            inputs.input_ids.to('cuda'),
             GENERATION_CONFIG,
-            attention_mask=inputs.attention_mask.to(self.cuda),
+            attention_mask=inputs.attention_mask.to('cuda'),
         )
 
         outputs = outputs[:, inputs.input_ids.shape[-1]:]
@@ -170,7 +202,7 @@ def generate_inner(self, message, dataset=None):
 
 
 class Emu3_gen(BaseModel):
-    INSTALL_REQ = True
+    INSTALL_REQ = False
     INTERLEAVE = False
 
     def __init__(self,
@@ -207,7 +239,6 @@ def __init__(self,
             trust_remote_code=True).eval()
         self.processor = Emu3Processor(self.image_processor, self.image_tokenizer, self.tokenizer)
         self.kwargs = kwargs
-        self.cuda = cuda
         self.output_path = output_path
 
     def generate_inner(self, message, dataset=None):