AlphaFold3  data_transforms 模块的 make_fixed_size 函数的作用是将输入的蛋白质特征字典 protein 中的各个特征张量调整为固定大小。这是为了确保在批量处理时，所有特征张量的形状一致，从而避免形状不匹配的问题。

源代码：

import itertools
import torch
from src.config import NUM_RES, NUM_EXTRA_SEQ, NUM_TEMPLATES, NUM_MSA_SEQ

@curry1
def make_fixed_size(
        protein,
        shape_schema,
        msa_cluster_size,
        extra_msa_size,
        num_res=0,
        num_templates=0,
):
    """Guess at the MSA and sequence dimension to make fixed size."""
    pad_size_map = {
        NUM_RES: num_res,
        NUM_MSA_SEQ: msa_cluster_size,
        NUM_EXTRA_SEQ: extra_msa_size,
        NUM_TEMPLATES: num_templates,
    }

    for k, v in protein.items():
        # Don't transfer this to the accelerator.
        if k == "extra_cluster_assignment":
            continue
        shape = list(v.shape)
        schema = shape_schema[k]
        msg = "Rank mismatch between shape and shape schema for"
        assert len(shape) == len(schema), f"{msg} {k}: {shape} vs {schema}"
        pad_size = [
            pad_size_map.get(s2, None) or s1 for (s1, s2) in zip(shape, schema)
        ]

        padding = [(0, p - v.shape[i]) for i, p in enumerate(pad_size)]
        padding.reverse()
        padding = list(itertools.chain(*padding))
        if padding:
            protein[k] = torch.nn.functional.pad(v, padding)
            protein[k] = torch.reshape(protein[k], pad_size)

    return protein

源码解读：

函数签名

@curry1
def make_fixed_size(
    protein,
    shape_schema,
    msa_cluster_size,
    extra_msa_size,
    num_res=0,
    num_templates=0,
):

@curry1：这是一个装饰器，用于将函数的参数部分绑定。curry1 表示将函数的第一个参数（protein）