refactor(magic_pdf): adjust VRAM allocation and MFR batch size- Update VRAM allocation logic to use 'VIRTUAL_VRAM_SIZE' environment variable

- Reduce MFR (Math Formula Recognition) batch size from 64 to 32

magic_pdf/model/doc_analyze_by_custom_model.py

@@ -175,7 +175,7 @@ def doc_analyze(
             npu_support = True
 
     if torch.cuda.is_available() and device != 'cpu' or npu_support:
-        gpu_memory = int(os.getenv("virtual_vram_size", round(get_vram(device))))
+        gpu_memory = int(os.getenv("VIRTUAL_VRAM_SIZE", round(get_vram(device))))
         if gpu_memory is not None and gpu_memory >= 8:
             batch_ratio = int(gpu_memory-5)
             if batch_ratio >= 1:

magic_pdf/model/sub_modules/mfr/unimernet/Unimernet.py

@@ -89,7 +89,7 @@ class UnimernetModel(object):
             mf_image_list.append(bbox_img)
 
         dataset = MathDataset(mf_image_list, transform=self.mfr_transform)
-        dataloader = DataLoader(dataset, batch_size=64, num_workers=0)
+        dataloader = DataLoader(dataset, batch_size=32, num_workers=0)
         mfr_res = []
         for mf_img in dataloader:
             mf_img = mf_img.to(self.device)