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# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for MiniCPMV with Nicheformer integration.
"""
from typing import List, Optional, Union
import torch
import re
from PIL import Image
import anndata as ad
import numpy as np
import os
from transformers import AutoTokenizer
from transformers.image_processing_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import TensorType
from .image_processing_minicpmv import MiniCPMVBatchFeature
from .tokenization_nicheformer import NicheformerTokenizer
class MiniCPMVProcessor(ProcessorMixin):
attributes = ["image_processor", "tokenizer", "gene_tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
gene_tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor=None, tokenizer=None, gene_tokenizer=None, **kwargs):
super().__init__(image_processor, tokenizer, gene_tokenizer, **kwargs)
self.version = kwargs.get("version", 2.6)
self.gene_tokenizer = AutoTokenizer.from_pretrained("your/path/to/gene_tokenizer", trust_remote_code=True)
technology_mean_path = 'your/path/to/gene_tokenizer/xenium_mean_script.npy'
technology_mean = np.load(technology_mean_path)
self.gene_tokenizer._load_technology_mean(technology_mean)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
images: ImageInput = None,
gene_data: Union[ad.AnnData, np.ndarray, List] = None,
max_length: Optional[int] = None,
do_pad: Optional[bool] = True,
max_slice_nums: int = None,
use_image_id: bool = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
**kwargs,
) -> MiniCPMVBatchFeature:
# Step 1: Process images
image_inputs = None
if images is not None and any(img is not None for img in images):
image_inputs = self.image_processor(
images, do_pad=do_pad, max_slice_nums=max_slice_nums, return_tensors=return_tensors
)
# print(f"[DEBUG] 成功获取image_inputs : {image_inputs.keys()}")
# Step 2: Process gene data
gene_inputs = None
if gene_data and len(gene_data) > 0 and len(gene_data[0]) > 0:
adata = gene_data[0][0]
gene_arrays = adata.X
gene_inputs = self.gene_tokenizer(gene_arrays)
# print(f"[DEBUG] 成功获取gene_inputs : {gene_inputs.keys()}")
# Step 3: Merge modalities
return self._convert_all_modalities_to_inputs(
image_inputs=image_inputs,
gene_inputs=gene_inputs,
texts=text,
max_slice_nums=max_slice_nums,
use_image_id=use_image_id,
max_length=max_length,
**kwargs,
)
def _convert_all_modalities_to_inputs(
self,
image_inputs,
gene_inputs,
texts: Union[str, List[str]],
truncation=None,
max_length=None,
max_slice_nums=None,
use_image_id=None,
return_tensors=TensorType.PYTORCH,
**kwargs,
):
if isinstance(texts, str):
texts = [texts]
input_ids_list = []
image_bounds_list = []
gene_bounds_list = []
image_pattern = "(<image>./</image>)"
gene_pattern = "(<gene>./</gene>)"
for index, text in enumerate(texts):
image_tags = re.findall(image_pattern, text)
if image_inputs is not None:
image_sizes = image_inputs["image_sizes"]
assert len(image_tags) == len(
image_sizes[index]
), f"Mismatch between image tags ({len(image_tags)}) and actual images ({len(image_sizes[index])})"
# replace placeholders
final_text = text
if image_inputs is not None:
text_chunks = final_text.split(image_pattern)
final_text = ""
for i in range(len(image_tags)):
final_text += text_chunks[i] + self.image_processor.get_slice_image_placeholder(
image_sizes[index][i], i, max_slice_nums, use_image_id
)
final_text += text_chunks[-1]
# === 处理 gene ===
gene_tags = re.findall(gene_pattern, final_text)
if gene_inputs is not None:
text_chunks = re.split(gene_pattern, final_text)
final_text = ""
for i in range(len(gene_tags)):
gene_tokens = gene_inputs["input_ids"][index]
# gene_token_str = " ".join(map(str, gene_tokens.tolist()))
# final_text += text_chunks[i] + f"<gene_id>{i}</gene_id><gene>{gene_token_str}</gene>"
dummy_placeholder = "<unk>" * 32
final_text += text_chunks[i] + f"<gene_id>{i}</gene_id><gene>{dummy_placeholder}</gene>"
final_text += text_chunks[-1]
# print(f"[DeBUG] final_text: {final_text}")
# 🔑 get input_ids and image_bounds directly
input_ids, image_bounds, gene_bounds = self._convert(final_text, max_length)
input_ids_list.append(input_ids)
image_bounds_list.append(image_bounds) # ✅ keep tensor
gene_bounds_list.append(gene_bounds)
# print(f"[DeBUG] input_ids: {input_ids_list}")
# print(f"[DeBUG] input_ids length: {input_ids.size(0)}")
# print(f"[DeBUG] image_bound: {image_bounds_list}")
# print(f"[DeBUG] gene_bound: {gene_bounds_list}")
# pad input_ids
padded_input_ids, padding_lengths = self.pad(input_ids_list, padding_side="left")
# shift bounds for padding
for i, length in enumerate(padding_lengths):
if image_bounds_list[i].numel() > 0:
image_bounds_list[i] = image_bounds_list[i] + length
if gene_bounds_list[i].numel() > 0:
gene_bounds_list[i] = gene_bounds_list[i] + length
attention_mask = padded_input_ids.ne(self.tokenizer.pad_token_id)
labels = padded_input_ids.clone()
labels[~attention_mask] = -100 # padding 不算loss
# gene span 不算loss
for i, gb in enumerate(gene_bounds_list):
if torch.is_tensor(gb) and gb.numel() > 0:
for (s, e) in gb.tolist():
labels[i, s:e] = -100
# print(f"[DeBUG] padded_input_ids: {padded_input_ids}")
# print(f"[DeBUG] attention_mask: {attention_mask}")
# print(f"[DeBUG] image_bounds_list: {image_bounds_list}")
# print(f"[DeBUG] gene_bounds_list: {gene_bounds_list}")
data = {
"input_ids": padded_input_ids,
"attention_mask": attention_mask,
"labels": labels,
"image_bound": image_bounds_list, # ✅ tensor [N,2]
"gene_bound": gene_bounds_list,
}
if image_inputs:
data.update(
{
"pixel_values": image_inputs["pixel_values"],
"image_sizes": image_inputs["image_sizes"],
"tgt_sizes": image_inputs["tgt_sizes"],
}
)
if gene_inputs:
data.update(
{
"gene_input_ids": gene_inputs["input_ids"],
"gene_attention_mask": gene_inputs["attention_mask"],
}
)
return MiniCPMVBatchFeature(data=data)
def _convert(self, input_str, max_inp_length: Optional[int] = None):
if self.version > 2.5 or not getattr(self.tokenizer, "add_bos_token", False):
input_ids = self.tokenizer.encode(input_str)
else:
input_ids = [self.tokenizer.bos_id] + self.tokenizer.encode(input_str)
if max_inp_length is not None:
input_ids = input_ids[:max_inp_length]
input_ids = torch.tensor(input_ids, dtype=torch.int32)
# 找 image 边界
image_start_tokens = torch.where(
(input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id)
)[0] + 1
image_end_tokens = torch.where(
(input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id)
)[0]
valid_image_nums = min(len(image_start_tokens), len(image_end_tokens))
image_bounds = torch.stack(
[image_start_tokens[:valid_image_nums], image_end_tokens[:valid_image_nums]], dim=1
)
# 找 gene 边界
gene_start_tokens = torch.where(input_ids == self.tokenizer.gene_start_id)[0] + 1
gene_end_tokens = torch.where(input_ids == self.tokenizer.gene_end_id)[0]
valid_gene_nums = min(len(gene_start_tokens), len(gene_end_tokens))
gene_bounds = torch.stack(
[gene_start_tokens[:valid_gene_nums], gene_end_tokens[:valid_gene_nums]], dim=1
) if valid_gene_nums > 0 else torch.zeros((0, 2), dtype=torch.int32)
# print(f"[DETAIL] self.tokenizer.gene_start_id : {self.tokenizer.gene_start_id}")
# print(f"[DETAIL] gene_start_tokens : {gene_start_tokens}")
# print(f"[DETAIL] self.tokenizer.gene_end_id : {self.tokenizer.gene_end_id}")
# print(f"[DETAIL] gene_end_tokens : {gene_end_tokens}")
return input_ids, image_bounds, gene_bounds
def batch_decode(self, *args, **kwargs):
output_ids = args[0]
result_text = []
for result in output_ids:
result = result[result != 0]
if result[0] == self.tokenizer.bos_id:
result = result[1:]
if result[-1] == self.tokenizer.eos_id:
result = result[:-1]
result_text.append(self.tokenizer.decode(result, *args[1:], **kwargs).strip())
return result_text
def decode(self, *args, **kwargs):
result = args[0]
result = result[result != 0]
if result[0] == self.tokenizer.bos_id:
result = result[1:]
if result[-1] == self.tokenizer.eos_id or (
hasattr(self.tokenizer, "eot_id") and result[-1] == self.tokenizer.eot_id
):
result = result[:-1]
return self.tokenizer.decode(result, *args[1:], **kwargs).strip()
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
gene_tokenizer_input_names = self.gene_tokenizer.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names + gene_tokenizer_input_names))
def pad(self, inputs, max_length=None, padding_value=0, padding_side="left"):
items = []
if isinstance(inputs[0], list):
assert isinstance(inputs[0][0], torch.Tensor)
for it in inputs:
for tr in it:
items.append(tr)
else:
assert isinstance(inputs[0], torch.Tensor)
items = inputs
batch_size = len(items)
shape = items[0].shape
dim = len(shape)
assert dim <= 2
if max_length is None:
max_length = 0
max_length = max(max_length, max(item.shape[-1] for item in items))
min_length = min(item.shape[-1] for item in items)
dtype = items[0].dtype
if dim == 0:
return torch.stack([item for item in items], dim=0), [0]
elif dim == 1:
if max_length == min_length:
return torch.stack([item for item in items], dim=0), [0] * batch_size
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
else:
tensor = torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype) + padding_value
padding_length = []
for i, item in enumerate(items):
if dim == 1:
if padding_side == "left":
tensor[i, -len(item) :] = item.clone()
else:
tensor[i, : len(item)] = item.clone()
elif dim == 2:
if padding_side == "left":
tensor[i, -len(item) :, :] = item.clone()
else:
tensor[i, : len(item), :] = item.clone()
padding_length.append(tensor.shape[-1] - len(item))
return tensor, padding_length
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