from typing import Any
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from typing import Dict
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from typing import Union
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from io import BytesIO
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import logging
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import torch
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import torch.nn
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import torch.optim
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def filter_state_dict(
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dst_state: Dict[str, Union[float, torch.Tensor]],
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src_state: Dict[str, Union[float, torch.Tensor]],
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):
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"""Filter name, size mismatch instances between dicts.
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Args:
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dst_state: reference state dict for filtering
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src_state: target state dict for filtering
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"""
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match_state = {}
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for key, value in src_state.items():
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if key in dst_state and (dst_state[key].size() == src_state[key].size()):
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match_state[key] = value
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else:
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if key not in dst_state:
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logging.warning(
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f"Filter out {key} from pretrained dict"
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+ " because of name not found in target dict"
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)
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else:
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logging.warning(
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f"Filter out {key} from pretrained dict"
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+ " because of size mismatch"
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+ f"({dst_state[key].size()}-{src_state[key].size()})"
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)
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return match_state
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def assigment_scope_map(dst_state: dict, src_state: dict, scope_map: str=None):
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"""Compute the union of the current variables and checkpoint variables."""
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import collections
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import re
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# current model variables
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name_to_variable = collections.OrderedDict()
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for name, var in dst_state.items():
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name_to_variable[name] = var
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scope_map_num = 0
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if scope_map is not None:
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scope_map = scope_map.split(",")
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scope_map_num = len(scope_map) // 2
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for scope_map_idx in range(scope_map_num):
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scope_map_id = scope_map_idx * 2
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logging.info('assignment_map from scope {} to {}'.format(scope_map[scope_map_id], scope_map[scope_map_id+1]))
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assignment_map = {}
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for name, var in src_state.items():
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if scope_map:
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for scope_map_idx in range(scope_map_num):
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scope_map_id = scope_map_idx * 2
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try:
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idx = name.index(scope_map[scope_map_id])
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new_name = scope_map[scope_map_id+1] + name[idx + len(scope_map[scope_map_id]):]
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if new_name in name_to_variable:
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assignment_map[name] = var
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except:
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continue
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else:
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if name in name_to_variable:
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assignment_map[name] = var
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return assignment_map
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def load_pretrained_model(
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path: str,
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model: torch.nn.Module,
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ignore_init_mismatch: bool,
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map_location: str = "cpu",
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oss_bucket=None,
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scope_map=None,
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excludes=None,
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):
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"""Load a model state and set it to the model.
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Args:
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init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
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Examples:
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"""
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obj = model
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if oss_bucket is None:
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src_state = torch.load(path, map_location=map_location)
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else:
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buffer = BytesIO(oss_bucket.get_object(path).read())
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src_state = torch.load(buffer, map_location=map_location)
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src_state = src_state["model"] if "model" in src_state else src_state
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if excludes is not None:
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for e in excludes.split(","):
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src_state = {k: v for k, v in src_state.items() if not k.startswith(e)}
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dst_state = obj.state_dict()
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src_state = assigment_scope_map(dst_state, src_state, scope_map)
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if ignore_init_mismatch:
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src_state = filter_state_dict(dst_state, src_state)
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logging.debug("Loaded src_state keys: {}".format(src_state.keys()))
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logging.debug("Loaded dst_state keys: {}".format(dst_state.keys()))
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# dst_state.update(src_state)
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obj.load_state_dict(dst_state)
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