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""" timm model adapter
Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
"""
import logging
from collections import OrderedDict
import torch
import torch.nn as nn
try:
import timm
from timm.models.layers import Mlp, to_2tuple
try:
# old timm imports < 0.8.1
from timm.models.layers.attention_pool2d import RotAttentionPool2d
from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d
except ImportError:
# new timm imports >= 0.8.1
from timm.layers import RotAttentionPool2d
from timm.layers import AttentionPool2d as AbsAttentionPool2d
except ImportError:
timm = None
from .utils import freeze_batch_norm_2d
class TimmModel(nn.Module):
""" timm model adapter
# FIXME this adapter is a work in progress, may change in ways that break weight compat
"""
def __init__(
self,
model_name,
embed_dim,
image_size=224,
pool='avg',
proj='linear',
proj_bias=False,
drop=0.,
drop_path=None,
pretrained=False,
):
super().__init__()
if timm is None:
raise RuntimeError("Please `pip install timm` to use timm models.")
self.image_size = to_2tuple(image_size)
timm_kwargs = {}
if drop_path is not None:
timm_kwargs['drop_path_rate'] = drop_path
self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs)
feat_size = self.trunk.default_cfg.get('pool_size', None)
feature_ndim = 1 if not feat_size else 2
if pool in ('abs_attn', 'rot_attn'):
assert feature_ndim == 2
# if attn pooling used, remove both classifier and default pool
self.trunk.reset_classifier(0, global_pool='')
else:
# reset global pool if pool config set, otherwise leave as network default
reset_kwargs = dict(global_pool=pool) if pool else {}
self.trunk.reset_classifier(0, **reset_kwargs)
prev_chs = self.trunk.num_features
head_layers = OrderedDict()
if pool == 'abs_attn':
head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)
prev_chs = embed_dim
elif pool == 'rot_attn':
head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)
prev_chs = embed_dim
else:
assert proj, 'projection layer needed if non-attention pooling is used.'
# NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
if proj == 'linear':
head_layers['drop'] = nn.Dropout(drop)
head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)
elif proj == 'mlp':
head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias))
self.head = nn.Sequential(head_layers)
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
""" lock modules
Args:
unlocked_groups (int): leave last n layer groups unlocked (default: 0)
"""
if not unlocked_groups:
# lock full model
for param in self.trunk.parameters():
param.requires_grad = False
if freeze_bn_stats:
freeze_batch_norm_2d(self.trunk)
else:
# NOTE: partial freeze requires latest timm (master) branch and is subject to change
try:
# FIXME import here until API stable and in an official release
from timm.models.helpers import group_parameters, group_modules
except ImportError:
raise RuntimeError(
'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')
matcher = self.trunk.group_matcher()
gparams = group_parameters(self.trunk, matcher)
max_layer_id = max(gparams.keys())
max_layer_id = max_layer_id - unlocked_groups
for group_idx in range(max_layer_id + 1):
group = gparams[group_idx]
for param in group:
self.trunk.get_parameter(param).requires_grad = False
if freeze_bn_stats:
gmodules = group_modules(self.trunk, matcher, reverse=True)
gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}
freeze_batch_norm_2d(self.trunk, gmodules)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
try:
self.trunk.set_grad_checkpointing(enable)
except Exception as e:
logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')
def forward(self, x):
x = self.trunk(x)
x = self.head(x)
return x
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