250 lines
7.1 KiB
Python
Executable File
250 lines
7.1 KiB
Python
Executable File
import torch
|
|
|
|
import torch.nn as nn
|
|
|
|
|
|
class Slice(nn.Module):
|
|
def __init__(self, start_index=1):
|
|
super(Slice, self).__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(AddReadout, self).__init__()
|
|
self.start_index = start_index
|
|
|
|
def forward(self, x):
|
|
if self.start_index == 2:
|
|
readout = (x[:, 0] + x[:, 1]) / 2
|
|
else:
|
|
readout = x[:, 0]
|
|
return x[:, self.start_index:] + readout.unsqueeze(1)
|
|
|
|
|
|
class ProjectReadout(nn.Module):
|
|
def __init__(self, in_features, start_index=1):
|
|
super(ProjectReadout, self).__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(Transpose, self).__init__()
|
|
self.dim0 = dim0
|
|
self.dim1 = dim1
|
|
|
|
def forward(self, x):
|
|
x = x.transpose(self.dim0, self.dim1)
|
|
return x
|
|
|
|
|
|
activations = {}
|
|
|
|
|
|
def get_activation(name):
|
|
def hook(model, input, output):
|
|
activations[name] = output
|
|
|
|
return hook
|
|
|
|
|
|
def forward_default(pretrained, x, function_name="forward_features"):
|
|
exec(f"pretrained.model.{function_name}(x)")
|
|
|
|
layer_1 = pretrained.activations["1"]
|
|
layer_2 = pretrained.activations["2"]
|
|
layer_3 = pretrained.activations["3"]
|
|
layer_4 = pretrained.activations["4"]
|
|
|
|
if hasattr(pretrained, "act_postprocess1"):
|
|
layer_1 = pretrained.act_postprocess1(layer_1)
|
|
if hasattr(pretrained, "act_postprocess2"):
|
|
layer_2 = pretrained.act_postprocess2(layer_2)
|
|
if hasattr(pretrained, "act_postprocess3"):
|
|
layer_3 = pretrained.act_postprocess3(layer_3)
|
|
if hasattr(pretrained, "act_postprocess4"):
|
|
layer_4 = pretrained.act_postprocess4(layer_4)
|
|
|
|
return layer_1, layer_2, layer_3, layer_4
|
|
|
|
|
|
def forward_adapted_unflatten(pretrained, x, function_name="forward_features"):
|
|
b, c, h, w = x.shape
|
|
|
|
exec(f"glob = pretrained.model.{function_name}(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 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:
|
|
assert (
|
|
False
|
|
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
|
|
|
|
return readout_oper
|
|
|
|
|
|
def make_backbone_default(
|
|
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 = 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_readout)
|
|
|
|
# 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]
|
|
|
|
return pretrained
|