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import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
from .utils import (activations, forward_adapted_unflatten, get_activation, get_readout_oper,
make_backbone_default, Transpose)
def forward_vit(pretrained, x):
return forward_adapted_unflatten(pretrained, x, "forward_flex")
def _resize_pos_embed(self, posemb, gs_h, gs_w):
posemb_tok, posemb_grid = (
posemb[:, : self.start_index],
posemb[0, self.start_index:],
)
gs_old = int(math.sqrt(len(posemb_grid)))
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
def forward_flex(self, x):
b, c, h, w = x.shape
pos_embed = self._resize_pos_embed(
self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
)
B = x.shape[0]
if hasattr(self.patch_embed, "backbone"):
x = self.patch_embed.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
if getattr(self, "dist_token", None) is not None:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
dist_token = self.dist_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, dist_token, x), dim=1)
else:
if self.no_embed_class:
x = x + pos_embed
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
if not self.no_embed_class:
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
def _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
size=[384, 384],
hooks=[2, 5, 8, 11],
vit_features=768,
use_readout="ignore",
start_index=1,
start_index_readout=1,
):
pretrained = make_backbone_default(model, features, size, hooks, vit_features, use_readout, start_index,
start_index_readout)
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
hooks = [5, 11, 17, 23] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[256, 512, 1024, 1024],
hooks=hooks,
vit_features=1024,
use_readout=use_readout,
)
def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=[0, 1, 8, 11],
vit_features=768,
patch_size=[16, 16],
number_stages=2,
use_vit_only=False,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
used_number_stages = 0 if use_vit_only else number_stages
for s in range(used_number_stages):
pretrained.model.patch_embed.backbone.stages[s].register_forward_hook(
get_activation(str(s + 1))
)
for s in range(used_number_stages, 4):
pretrained.model.blocks[hooks[s]].register_forward_hook(get_activation(str(s + 1)))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
for s in range(used_number_stages):
value = nn.Sequential(nn.Identity(), nn.Identity(), nn.Identity())
exec(f"pretrained.act_postprocess{s + 1}=value")
for s in range(used_number_stages, 4):
if s < number_stages:
final_layer = nn.ConvTranspose2d(
in_channels=features[s],
out_channels=features[s],
kernel_size=4 // (2 ** s),
stride=4 // (2 ** s),
padding=0,
bias=True,
dilation=1,
groups=1,
)
elif s > number_stages:
final_layer = nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
)
else:
final_layer = None
layers = [
readout_oper[s],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[s],
kernel_size=1,
stride=1,
padding=0,
),
]
if final_layer is not None:
layers.append(final_layer)
value = nn.Sequential(*layers)
exec(f"pretrained.act_postprocess{s + 1}=value")
pretrained.model.start_index = start_index
pretrained.model.patch_size = patch_size
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitb_rn50_384(
pretrained, use_readout="ignore", hooks=None, use_vit_only=False
):
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
hooks = [0, 1, 8, 11] if hooks == None else hooks
return _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)
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