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import numpy as np |
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import torch |
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import torch.nn as nn |
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from collections import OrderedDict |
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def tf2th(conv_weights): |
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"""Possibly convert HWIO to OIHW.""" |
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if conv_weights.ndim == 4: |
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conv_weights = conv_weights.transpose([3, 2, 0, 1]) |
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return torch.from_numpy(conv_weights) |
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def _rename_conv_weights_for_deformable_conv_layers(state_dict, cfg): |
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import re |
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layer_keys = sorted(state_dict.keys()) |
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for ix, stage_with_dcn in enumerate(cfg.MODEL.RESNETS.STAGE_WITH_DCN, 1): |
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if not stage_with_dcn: |
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continue |
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for old_key in layer_keys: |
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pattern = ".*block{}.*conv2.*".format(ix) |
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r = re.match(pattern, old_key) |
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if r is None: |
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continue |
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for param in ["weight", "bias"]: |
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if old_key.find(param) is -1: |
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continue |
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if 'unit01' in old_key: |
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continue |
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new_key = old_key.replace( |
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"conv2.{}".format(param), "conv2.conv.{}".format(param) |
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) |
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print("pattern: {}, old_key: {}, new_key: {}".format( |
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pattern, old_key, new_key |
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)) |
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w = state_dict[old_key] |
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v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False) |
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w = (w - m) / torch.sqrt(v + 1e-10) |
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state_dict[new_key] = w |
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del state_dict[old_key] |
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return state_dict |
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def load_big_format(cfg, f): |
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model = OrderedDict() |
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weights = np.load(f) |
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cmap = {'a':1, 'b':2, 'c':3} |
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for key, val in weights.items(): |
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old_key = key.replace('resnet/', '') |
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if 'root_block' in old_key: |
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new_key = 'root.conv.weight' |
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elif '/proj/standardized_conv2d/kernel' in old_key: |
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key_pattern = old_key.replace('/proj/standardized_conv2d/kernel', '').replace('resnet/', '') |
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bname, uname, cidx = key_pattern.split('/') |
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new_key = '{}.downsample.{}.conv{}.weight'.format(bname,uname,cmap[cidx]) |
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elif '/standardized_conv2d/kernel' in old_key: |
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key_pattern = old_key.replace('/standardized_conv2d/kernel', '').replace('resnet/', '') |
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bname, uname, cidx = key_pattern.split('/') |
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new_key = '{}.{}.conv{}.weight'.format(bname,uname,cmap[cidx]) |
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elif '/group_norm/gamma' in old_key: |
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key_pattern = old_key.replace('/group_norm/gamma', '').replace('resnet/', '') |
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bname, uname, cidx = key_pattern.split('/') |
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new_key = '{}.{}.gn{}.weight'.format(bname,uname,cmap[cidx]) |
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elif '/group_norm/beta' in old_key: |
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key_pattern = old_key.replace('/group_norm/beta', '').replace('resnet/', '') |
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bname, uname, cidx = key_pattern.split('/') |
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new_key = '{}.{}.gn{}.bias'.format(bname,uname,cmap[cidx]) |
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else: |
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print('Unknown key {}'.format(old_key)) |
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continue |
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print('Map {} -> {}'.format(key, new_key)) |
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model[new_key] = tf2th(val) |
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model = _rename_conv_weights_for_deformable_conv_layers(model, cfg) |
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return dict(model=model) |
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