| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| def _remove_bn_statics(state_dict): | |
| layer_keys = sorted(state_dict.keys()) | |
| remove_list = [] | |
| for key in layer_keys: | |
| if 'running_mean' in key or 'running_var' in key or 'num_batches_tracked' in key: | |
| remove_list.append(key) | |
| for key in remove_list: | |
| del state_dict[key] | |
| return state_dict | |
| def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg): | |
| import re | |
| layer_keys = sorted(state_dict.keys()) | |
| for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1): | |
| if not stage_with_dcn: | |
| continue | |
| for old_key in layer_keys: | |
| pattern = ".*layer{}.*conv2.*".format(ix) | |
| r = re.match(pattern, old_key) | |
| if r is None: | |
| continue | |
| for param in ["weight", "bias"]: | |
| if old_key.find(param) is -1: | |
| continue | |
| if 'unit01' in old_key: | |
| continue | |
| new_key = old_key.replace( | |
| "conv2.{}".format(param), "conv2.conv.{}".format(param) | |
| ) | |
| print("pattern: {}, old_key: {}, new_key: {}".format( | |
| pattern, old_key, new_key | |
| )) | |
| state_dict[new_key] = state_dict[old_key] | |
| del state_dict[old_key] | |
| return state_dict | |
| def load_pretrain_format(cfg, f): | |
| model = torch.load(f) | |
| model = _remove_bn_statics(model) | |
| model = _rename_conv_weights_for_deformable_conv_layers(model, cfg) | |
| return dict(model=model) | |