funasr_detach/train_utils/load_pretrained_model.py

from typing import Any
from typing import Dict
from typing import Union
from io import BytesIO

import logging
import torch
import torch.nn
import torch.optim


def filter_state_dict(
    dst_state: Dict[str, Union[float, torch.Tensor]],
    src_state: Dict[str, Union[float, torch.Tensor]],
):
    """Filter name, size mismatch instances between dicts.

    Args:
            dst_state: reference state dict for filtering
            src_state: target state dict for filtering

    """
    match_state = {}
    for key, value in src_state.items():
        if key in dst_state and (dst_state[key].size() == src_state[key].size()):
            match_state[key] = value
        else:
            if key not in dst_state:
                logging.warning(
                    f"Filter out {key} from pretrained dict"
                    + " because of name not found in target dict"
                )
            else:
                logging.warning(
                    f"Filter out {key} from pretrained dict"
                    + " because of size mismatch"
                    + f"({dst_state[key].size()}-{src_state[key].size()})"
                )
    return match_state


def assigment_scope_map(dst_state: dict, src_state: dict, scope_map: str = None):
    """Compute the union of the current variables and checkpoint variables."""
    import collections
    import re

    # current model variables
    name_to_variable = collections.OrderedDict()
    for name, var in dst_state.items():
        name_to_variable[name] = var

    scope_map_num = 0
    if scope_map is not None:
        scope_map = scope_map.split(",")
        scope_map_num = len(scope_map) // 2
        for scope_map_idx in range(scope_map_num):
            scope_map_id = scope_map_idx * 2
            logging.info(
                "assignment_map from scope {} to {}".format(
                    scope_map[scope_map_id], scope_map[scope_map_id + 1]
                )
            )

    assignment_map = {}
    for name, var in src_state.items():

        if scope_map:
            for scope_map_idx in range(scope_map_num):
                scope_map_id = scope_map_idx * 2
                try:
                    idx = name.index(scope_map[scope_map_id])
                    new_name = (
                        scope_map[scope_map_id + 1]
                        + name[idx + len(scope_map[scope_map_id]) :]
                    )
                    if new_name in name_to_variable:
                        assignment_map[name] = var
                except:
                    continue
        else:
            if name in name_to_variable:
                assignment_map[name] = var

    return assignment_map


def load_pretrained_model(
    path: str,
    model: torch.nn.Module,
    ignore_init_mismatch: bool,
    map_location: str = "cpu",
    oss_bucket=None,
    scope_map=None,
    excludes=None,
):
    """Load a model state and set it to the model.

    Args:
            init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>

    Examples:

    """

    obj = model
    dst_state = obj.state_dict()
    # import pdb;
    # pdb.set_trace()
    print(f"ckpt: {path}")
    if oss_bucket is None:
        src_state = torch.load(path, map_location=map_location)
    else:
        buffer = BytesIO(oss_bucket.get_object(path).read())
        src_state = torch.load(buffer, map_location=map_location)
    if "state_dict" in src_state:
        src_state = src_state["state_dict"]

    for k in dst_state.keys():
        if not k.startswith("module.") and "module." + k in src_state.keys():
            k_ddp = "module." + k
        else:
            k_ddp = k
        if k_ddp in src_state:
            dst_state[k] = src_state[k_ddp]
        else:
            print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")

    flag = obj.load_state_dict(dst_state, strict=True)
    # print(flag)


# def load_pretrained_model(
# 	path: str,
# 	model: torch.nn.Module,
# 	ignore_init_mismatch: bool,
# 	map_location: str = "cpu",
# 	oss_bucket=None,
# 	scope_map=None,
# 	excludes=None,
# ):
# 	"""Load a model state and set it to the model.
#
# 	Args:
# 		init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
#
# 	Examples:
#
# 	"""
#
# 	obj = model
#
# 	if oss_bucket is None:
# 		src_state = torch.load(path, map_location=map_location)
# 	else:
# 		buffer = BytesIO(oss_bucket.get_object(path).read())
# 		src_state = torch.load(buffer, map_location=map_location)
# 	src_state = src_state["model"] if "model" in src_state else src_state
#
# 	if excludes is not None:
# 		for e in excludes.split(","):
# 			src_state = {k: v for k, v in src_state.items() if not k.startswith(e)}
#
# 	dst_state = obj.state_dict()
# 	src_state = assigment_scope_map(dst_state, src_state, scope_map)
#
# 	if ignore_init_mismatch:
# 		src_state = filter_state_dict(dst_state, src_state)
#
# 	logging.debug("Loaded src_state keys: {}".format(src_state.keys()))
# 	logging.debug("Loaded dst_state keys: {}".format(dst_state.keys()))
# 	dst_state.update(src_state)
# 	obj.load_state_dict(dst_state, strict=True)