File size: 3,226 Bytes
df763d5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import fnmatch
import re
from collections import OrderedDict
from typing import Union, Optional, List
import torch
class AttentionExtract(torch.nn.Module):
# defaults should cover a significant number of timm models with attention maps.
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':
# names are activation node names
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:
# names are module names
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
|