Expected input batch_size (16) to match target batch_size (262144)
### System Info

ENERATE_JPG_FILES = True   # warning: generation takes ~ 1h
slice_sum=0
if (GENERATE_JPG_FILES):
    
    path = Path(".")

    os.makedirs('train_images',exist_ok=True)
    os.makedirs('train_masks',exist_ok=True)

    for ii in tqdm(range(0,len(df_files))): # take 1/3 nii files for training
        curr_ct        = read_nii(df_files.loc[ii,'dirname']+"/"+df_files.loc[ii,'filename'])
        curr_mask      = read_nii(df_files.loc[ii,'mask_dirname']+"/"+df_files.loc[ii,'mask_filename'])
        curr_file_name = str(df_files.loc[ii,'filename']).split('.')[0]
        curr_dim       = curr_ct.shape[2] # 512, 512, curr_dim
        slice_sum = slice_sum+curr_dim
        
        for curr_slice in range(0,curr_dim,1): # export every 2nd slice for training
            data = tensor(curr_ct[...,curr_slice].astype(np.float32))
            mask = Image.fromarray(curr_mask[...,curr_slice].astype('uint8'), mode="L")
            data.save_jpg(f"train_images/{curr_file_name}_slice_{curr_slice}.jpg", [dicom_windows.liver,dicom_windows.custom])
            mask.save(f"train_masks/{curr_file_name}_slice_{curr_slice}_mask.png")
            
else:
    
    path = Path('C:/AML 2404 AI and ML Lab/Liver Tumor Segmentation/Liver Tumor Segmentation/new_images') # read jpg from saved kernel output
print(slice_sum)


bs = 16
im_size = 128

codes = np.array(["background","liver","tumor"])
    
def get_x(fname:Path): 
    return fname

def label_func(x): 
    return path/'train_masks'/f'{x.stem}_mask.png'

tfms = [IntToFloatTensor(),Normalize()]

db = DataBlock(blocks=(ImageBlock(),MaskBlock(codes)),  #codes = {"Backround": 0,"Liver": 1,"Tumor": 2}
               batch_tfms=tfms,
               splitter=RandomSplitter(),
               item_tfms=[Resize(im_size)],
               get_items=get_image_files,
               get_y=label_func
              )

# ../output/kaggle/working/train_images.zip
# ds = db.datasets(source=path/'train_images.zip')
ds = db.datasets(source='./train_images')
print(len(ds))
print(ds)


dls = db.dataloaders(path/'train_images',bs = bs) # num_workers=0
dls.show_batch()

def foreground_acc(inp, targ, bkg_idx=0, axis=1):  # exclude a background from metric
    "Computes non-background accuracy for multiclass segmentation"
    targ = targ.squeeze(1)
    mask = targ != bkg_idx
    return (inp.argmax(dim=axis)[mask]==targ[mask]).float().mean() 

def cust_foreground_acc(inp, targ):  # # include a background into the metric
    return foreground_acc(inp=inp, targ=targ, bkg_idx=3, axis=1)

learn = vision_learner(dls, resnet34, metrics =([foreground_acc,cust_foreground_acc]))
learn.lr_find()

ValueError: Expected input batch_size (16) to match target batch_size (262144).

### Who can help?

@NielsRogge, @sgugger, @Rocketknight1

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

ENERATE_JPG_FILES = True   # warning: generation takes ~ 1h
slice_sum=0
if (GENERATE_JPG_FILES):
    
    path = Path(".")

    os.makedirs('train_images',exist_ok=True)
    os.makedirs('train_masks',exist_ok=True)

    for ii in tqdm(range(0,len(df_files))): # take 1/3 nii files for training
        curr_ct        = read_nii(df_files.loc[ii,'dirname']+"/"+df_files.loc[ii,'filename'])
        curr_mask      = read_nii(df_files.loc[ii,'mask_dirname']+"/"+df_files.loc[ii,'mask_filename'])
        curr_file_name = str(df_files.loc[ii,'filename']).split('.')[0]
        curr_dim       = curr_ct.shape[2] # 512, 512, curr_dim
        slice_sum = slice_sum+curr_dim
        
        for curr_slice in range(0,curr_dim,1): # export every 2nd slice for training
            data = tensor(curr_ct[...,curr_slice].astype(np.float32))
            mask = Image.fromarray(curr_mask[...,curr_slice].astype('uint8'), mode="L")
            data.save_jpg(f"train_images/{curr_file_name}_slice_{curr_slice}.jpg", [dicom_windows.liver,dicom_windows.custom])
            mask.save(f"train_masks/{curr_file_name}_slice_{curr_slice}_mask.png")
            
else:
    
    path = Path('C:/AML 2404 AI and ML Lab/Liver Tumor Segmentation/Liver Tumor Segmentation/new_images') # read jpg from saved kernel output
print(slice_sum)


bs = 16
im_size = 128

codes = np.array(["background","liver","tumor"])
    
def get_x(fname:Path): 
    return fname

def label_func(x): 
    return path/'train_masks'/f'{x.stem}_mask.png'

tfms = [IntToFloatTensor(),Normalize()]

db = DataBlock(blocks=(ImageBlock(),MaskBlock(codes)),  #codes = {"Backround": 0,"Liver": 1,"Tumor": 2}
               batch_tfms=tfms,
               splitter=RandomSplitter(),
               item_tfms=[Resize(im_size)],
               get_items=get_image_files,
               get_y=label_func
              )

# ../output/kaggle/working/train_images.zip
# ds = db.datasets(source=path/'train_images.zip')
ds = db.datasets(source='./train_images')
print(len(ds))
print(ds)


dls = db.dataloaders(path/'train_images',bs = bs) # num_workers=0
dls.show_batch()

def foreground_acc(inp, targ, bkg_idx=0, axis=1):  # exclude a background from metric
    "Computes non-background accuracy for multiclass segmentation"
    targ = targ.squeeze(1)
    mask = targ != bkg_idx
    return (inp.argmax(dim=axis)[mask]==targ[mask]).float().mean() 

def cust_foreground_acc(inp, targ):  # # include a background into the metric
    return foreground_acc(inp=inp, targ=targ, bkg_idx=3, axis=1)

learn = vision_learner(dls, resnet34, metrics =([foreground_acc,cust_foreground_acc]))
learn.lr_find()

ValueError: Expected input batch_size (16) to match target batch_size (262144).

### Expected behavior

vision_learner modell should predict image processing
Hello, thanks for opening an issue! We try to keep the github issues for bugs/feature requests.
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