| import fnmatch |
| import re |
| from collections import OrderedDict |
| from typing import Union, Optional, List |
|
|
| import torch |
|
|
|
|
| class AttentionExtract(torch.nn.Module): |
| |
| default_node_names = ['*attn.softmax'] |
| default_module_names = ['*attn_drop'] |
|
|
| def __init__( |
| self, |
| model: Union[torch.nn.Module], |
| names: Optional[List[str]] = None, |
| mode: str = 'eval', |
| method: str = 'fx', |
| hook_type: str = 'forward', |
| use_regex: bool = False, |
| ): |
| """ Extract attention maps (or other activations) from a model by name. |
| |
| Args: |
| model: Instantiated model to extract from. |
| names: List of concrete or wildcard names to extract. Names are nodes for fx and modules for hooks. |
| mode: 'train' or 'eval' model mode. |
| method: 'fx' or 'hook' extraction method. |
| hook_type: 'forward' or 'forward_pre' hooks used. |
| use_regex: Use regex instead of fnmatch |
| """ |
| super().__init__() |
| assert mode in ('train', 'eval') |
| if mode == 'train': |
| model = model.train() |
| else: |
| model = model.eval() |
|
|
| assert method in ('fx', 'hook') |
| if method == 'fx': |
| |
| from timm.models._features_fx import get_graph_node_names, GraphExtractNet |
|
|
| node_names = get_graph_node_names(model)[0 if mode == 'train' else 1] |
| names = names or self.default_node_names |
| if use_regex: |
| regexes = [re.compile(r) for r in names] |
| matched = [g for g in node_names if any([r.match(g) for r in regexes])] |
| else: |
| matched = [g for g in node_names if any([fnmatch.fnmatch(g, n) for n in names])] |
| if not matched: |
| raise RuntimeError(f'No node names found matching {names}.') |
|
|
| self.model = GraphExtractNet(model, matched, return_dict=True) |
| self.hooks = None |
| else: |
| |
| assert hook_type in ('forward', 'forward_pre') |
| from timm.models._features import FeatureHooks |
|
|
| module_names = [n for n, m in model.named_modules()] |
| names = names or self.default_module_names |
| if use_regex: |
| regexes = [re.compile(r) for r in names] |
| matched = [m for m in module_names if any([r.match(m) for r in regexes])] |
| else: |
| matched = [m for m in module_names if any([fnmatch.fnmatch(m, n) for n in names])] |
| if not matched: |
| raise RuntimeError(f'No module names found matching {names}.') |
|
|
| self.model = model |
| self.hooks = FeatureHooks(matched, model.named_modules(), default_hook_type=hook_type) |
|
|
| self.names = matched |
| self.mode = mode |
| self.method = method |
|
|
| def forward(self, x): |
| if self.hooks is not None: |
| self.model(x) |
| output = self.hooks.get_output(device=x.device) |
| else: |
| output = self.model(x) |
| return output |
|
|
