| | |
| | | ) |
| | | 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, |
| | | ignore_init_mismatch: bool=True, |
| | | map_location: str = "cpu", |
| | | oss_bucket=None, |
| | | scope_map="module.:none", |
| | | excludes=None, |
| | | ignore_mismatch=False, |
| | | **kwargs, |
| | | ): |
| | | """Load a model state and set it to the model. |
| | | |
| | |
| | | scope_map = scope_map.split(",") |
| | | |
| | | 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 |
| | | |
| | | k_src = k |
| | | |
| | | if scope_map is not None: |
| | |
| | | print(f"init param, map: {k} from {k_src} in ckpt") |
| | | |
| | | if k_src in src_state.keys(): |
| | | if ignore_init_mismatch and dst_state[k].shape != src_state[k_src].shape: |
| | | print(f"ignore_mismatch:{ignore_mismatch}, dst: {k, dst_state[k].shape}, src: {k_src, src_state[k_src].shape}") |
| | | else: |
| | | dst_state[k] = src_state[k_src] |
| | | |
| | | # if k_ddp.startswith("audio_encoder"): |
| | | # if k_ddp.replace("audio_encoder", "encoder.model") in src_state.keys(): |
| | | # k_ddp = k_ddp.replace("audio_encoder", "encoder.model") |
| | | # if k_ddp.startswith("adaptor"): |
| | | # if k_ddp.replace("adaptor", "encoder_projector") in src_state.keys(): |
| | | # k_ddp = k_ddp.replace("adaptor", "encoder_projector") |
| | | # if k_ddp in src_state: |
| | | # dst_state[k] = src_state[k_ddp] |
| | | |
| | | else: |
| | | print(f"Warning, miss key in ckpt: {k}, mapped: {k_src}") |
| | | |
| | | flag = obj.load_state_dict(dst_state, strict=False) |
| | | flag = obj.load_state_dict(dst_state, strict=True) |
| | | # print(flag) |