Spaces:
Build error
Build error
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| def tf2th(conv_weights): | |
| """Possibly convert HWIO to OIHW.""" | |
| if conv_weights.ndim == 4: | |
| conv_weights = conv_weights.transpose([3, 2, 0, 1]) | |
| return torch.from_numpy(conv_weights) | |
| 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 = ".*block{}.*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 | |
| )) | |
| # Calculate SD conv weight | |
| w = state_dict[old_key] | |
| v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False) | |
| w = (w - m) / torch.sqrt(v + 1e-10) | |
| state_dict[new_key] = w | |
| del state_dict[old_key] | |
| return state_dict | |
| def load_big_format(cfg, f): | |
| model = OrderedDict() | |
| weights = np.load(f) | |
| cmap = {'a':1, 'b':2, 'c':3} | |
| for key, val in weights.items(): | |
| old_key = key.replace('resnet/', '') | |
| if 'root_block' in old_key: | |
| new_key = 'root.conv.weight' | |
| elif '/proj/standardized_conv2d/kernel' in old_key: | |
| key_pattern = old_key.replace('/proj/standardized_conv2d/kernel', '').replace('resnet/', '') | |
| bname, uname, cidx = key_pattern.split('/') | |
| new_key = '{}.downsample.{}.conv{}.weight'.format(bname,uname,cmap[cidx]) | |
| elif '/standardized_conv2d/kernel' in old_key: | |
| key_pattern = old_key.replace('/standardized_conv2d/kernel', '').replace('resnet/', '') | |
| bname, uname, cidx = key_pattern.split('/') | |
| new_key = '{}.{}.conv{}.weight'.format(bname,uname,cmap[cidx]) | |
| elif '/group_norm/gamma' in old_key: | |
| key_pattern = old_key.replace('/group_norm/gamma', '').replace('resnet/', '') | |
| bname, uname, cidx = key_pattern.split('/') | |
| new_key = '{}.{}.gn{}.weight'.format(bname,uname,cmap[cidx]) | |
| elif '/group_norm/beta' in old_key: | |
| key_pattern = old_key.replace('/group_norm/beta', '').replace('resnet/', '') | |
| bname, uname, cidx = key_pattern.split('/') | |
| new_key = '{}.{}.gn{}.bias'.format(bname,uname,cmap[cidx]) | |
| else: | |
| print('Unknown key {}'.format(old_key)) | |
| continue | |
| print('Map {} -> {}'.format(key, new_key)) | |
| model[new_key] = tf2th(val) | |
| model = _rename_conv_weights_for_deformable_conv_layers(model, cfg) | |
| return dict(model=model) | |