You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
491 lines
14 KiB
Python
491 lines
14 KiB
Python
"""Classes and functions for Vision Transformer processing"""
|
|
|
|
import math
|
|
import types
|
|
|
|
import timm
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
|
|
|
|
class Slice(nn.Module):
|
|
def __init__(self, start_index=1):
|
|
super().__init__()
|
|
self.start_index = start_index
|
|
|
|
def forward(self, x):
|
|
return x[:, self.start_index :]
|
|
|
|
|
|
class AddReadout(nn.Module):
|
|
def __init__(self, start_index=1):
|
|
super().__init__()
|
|
self.start_index = start_index
|
|
|
|
def forward(self, x):
|
|
readout = (x[:, 0] + x[:, 1]) / 2 if self.start_index == 2 else x[:, 0]
|
|
return x[:, self.start_index :] + readout.unsqueeze(1)
|
|
|
|
|
|
class ProjectReadout(nn.Module):
|
|
def __init__(self, in_features, start_index=1):
|
|
super().__init__()
|
|
self.start_index = start_index
|
|
|
|
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
|
|
|
|
def forward(self, x):
|
|
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
|
|
features = torch.cat((x[:, self.start_index :], readout), -1)
|
|
|
|
return self.project(features)
|
|
|
|
|
|
class Transpose(nn.Module):
|
|
def __init__(self, dim0, dim1):
|
|
super().__init__()
|
|
self.dim0 = dim0
|
|
self.dim1 = dim1
|
|
|
|
def forward(self, x):
|
|
x = x.transpose(self.dim0, self.dim1)
|
|
return x
|
|
|
|
|
|
def forward_vit(pretrained, x):
|
|
b, c, h, w = x.shape
|
|
|
|
pretrained.model.forward_flex(x)
|
|
|
|
layer_1 = pretrained.activations["1"]
|
|
layer_2 = pretrained.activations["2"]
|
|
layer_3 = pretrained.activations["3"]
|
|
layer_4 = pretrained.activations["4"]
|
|
|
|
layer_1 = pretrained.act_postprocess1[0:2](layer_1)
|
|
layer_2 = pretrained.act_postprocess2[0:2](layer_2)
|
|
layer_3 = pretrained.act_postprocess3[0:2](layer_3)
|
|
layer_4 = pretrained.act_postprocess4[0:2](layer_4)
|
|
|
|
unflatten = nn.Sequential(
|
|
nn.Unflatten(
|
|
2,
|
|
torch.Size(
|
|
[
|
|
h // pretrained.model.patch_size[1],
|
|
w // pretrained.model.patch_size[0],
|
|
]
|
|
),
|
|
)
|
|
)
|
|
|
|
if layer_1.ndim == 3:
|
|
layer_1 = unflatten(layer_1)
|
|
if layer_2.ndim == 3:
|
|
layer_2 = unflatten(layer_2)
|
|
if layer_3.ndim == 3:
|
|
layer_3 = unflatten(layer_3)
|
|
if layer_4.ndim == 3:
|
|
layer_4 = unflatten(layer_4)
|
|
|
|
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
|
|
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
|
|
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
|
|
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
|
|
|
|
return layer_1, layer_2, layer_3, layer_4
|
|
|
|
|
|
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:
|
|
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)
|
|
|
|
x = x + pos_embed
|
|
x = self.pos_drop(x)
|
|
|
|
for blk in self.blocks:
|
|
x = blk(x)
|
|
|
|
x = self.norm(x)
|
|
|
|
return x
|
|
|
|
|
|
activations = {}
|
|
|
|
|
|
def get_activation(name):
|
|
def hook(model, input, output): # noqa
|
|
activations[name] = output
|
|
|
|
return hook
|
|
|
|
|
|
def get_readout_oper(vit_features, features, use_readout, start_index=1):
|
|
if use_readout == "ignore":
|
|
readout_oper = [Slice(start_index)] * len(features)
|
|
elif use_readout == "add":
|
|
readout_oper = [AddReadout(start_index)] * len(features)
|
|
elif use_readout == "project":
|
|
readout_oper = [
|
|
ProjectReadout(vit_features, start_index) for out_feat in features
|
|
]
|
|
else:
|
|
msg = "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
|
|
raise ValueError(msg)
|
|
|
|
return readout_oper
|
|
|
|
|
|
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,
|
|
):
|
|
pretrained = nn.Module()
|
|
|
|
pretrained.model = model
|
|
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
|
|
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
|
|
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
|
|
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
|
|
|
|
pretrained.activations = activations
|
|
|
|
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
|
|
|
|
# 32, 48, 136, 384
|
|
pretrained.act_postprocess1 = nn.Sequential(
|
|
readout_oper[0],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[0],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
nn.ConvTranspose2d(
|
|
in_channels=features[0],
|
|
out_channels=features[0],
|
|
kernel_size=4,
|
|
stride=4,
|
|
padding=0,
|
|
bias=True,
|
|
dilation=1,
|
|
groups=1,
|
|
),
|
|
)
|
|
|
|
pretrained.act_postprocess2 = nn.Sequential(
|
|
readout_oper[1],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[1],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
nn.ConvTranspose2d(
|
|
in_channels=features[1],
|
|
out_channels=features[1],
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=0,
|
|
bias=True,
|
|
dilation=1,
|
|
groups=1,
|
|
),
|
|
)
|
|
|
|
pretrained.act_postprocess3 = nn.Sequential(
|
|
readout_oper[2],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[2],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
)
|
|
|
|
pretrained.act_postprocess4 = nn.Sequential(
|
|
readout_oper[3],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[3],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
nn.Conv2d(
|
|
in_channels=features[3],
|
|
out_channels=features[3],
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
),
|
|
)
|
|
|
|
pretrained.model.start_index = start_index
|
|
pretrained.model.patch_size = [16, 16]
|
|
|
|
# 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 is 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 is None else hooks
|
|
return _make_vit_b16_backbone(
|
|
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
|
|
)
|
|
|
|
|
|
def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
|
|
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
|
|
|
|
hooks = [2, 5, 8, 11] if hooks is None else hooks
|
|
return _make_vit_b16_backbone(
|
|
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
|
|
)
|
|
|
|
|
|
def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
|
|
model = timm.create_model(
|
|
"vit_deit_base_distilled_patch16_384", pretrained=pretrained
|
|
)
|
|
|
|
hooks = [2, 5, 8, 11] if hooks is None else hooks
|
|
return _make_vit_b16_backbone(
|
|
model,
|
|
features=[96, 192, 384, 768],
|
|
hooks=hooks,
|
|
use_readout=use_readout,
|
|
start_index=2,
|
|
)
|
|
|
|
|
|
def _make_vit_b_rn50_backbone(
|
|
model,
|
|
features=(256, 512, 768, 768),
|
|
size=(384, 384),
|
|
hooks=(0, 1, 8, 11),
|
|
vit_features=768,
|
|
use_vit_only=False,
|
|
use_readout="ignore",
|
|
start_index=1,
|
|
):
|
|
pretrained = nn.Module()
|
|
|
|
pretrained.model = model
|
|
|
|
if use_vit_only is True:
|
|
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
|
|
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
|
|
else:
|
|
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
|
|
get_activation("1")
|
|
)
|
|
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
|
|
get_activation("2")
|
|
)
|
|
|
|
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
|
|
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
|
|
|
|
pretrained.activations = activations
|
|
|
|
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
|
|
|
|
if use_vit_only is True:
|
|
pretrained.act_postprocess1 = nn.Sequential(
|
|
readout_oper[0],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[0],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
nn.ConvTranspose2d(
|
|
in_channels=features[0],
|
|
out_channels=features[0],
|
|
kernel_size=4,
|
|
stride=4,
|
|
padding=0,
|
|
bias=True,
|
|
dilation=1,
|
|
groups=1,
|
|
),
|
|
)
|
|
|
|
pretrained.act_postprocess2 = nn.Sequential(
|
|
readout_oper[1],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[1],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
nn.ConvTranspose2d(
|
|
in_channels=features[1],
|
|
out_channels=features[1],
|
|
kernel_size=2,
|
|
stride=2,
|
|
padding=0,
|
|
bias=True,
|
|
dilation=1,
|
|
groups=1,
|
|
),
|
|
)
|
|
else:
|
|
pretrained.act_postprocess1 = nn.Sequential(
|
|
nn.Identity(), nn.Identity(), nn.Identity()
|
|
)
|
|
pretrained.act_postprocess2 = nn.Sequential(
|
|
nn.Identity(), nn.Identity(), nn.Identity()
|
|
)
|
|
|
|
pretrained.act_postprocess3 = nn.Sequential(
|
|
readout_oper[2],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[2],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
)
|
|
|
|
pretrained.act_postprocess4 = nn.Sequential(
|
|
readout_oper[3],
|
|
Transpose(1, 2),
|
|
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
|
|
nn.Conv2d(
|
|
in_channels=vit_features,
|
|
out_channels=features[3],
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
),
|
|
nn.Conv2d(
|
|
in_channels=features[3],
|
|
out_channels=features[3],
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
),
|
|
)
|
|
|
|
pretrained.model.start_index = start_index
|
|
pretrained.model.patch_size = [16, 16]
|
|
|
|
# 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 is 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,
|
|
)
|